Electrical and Electronics Engineering publications abstract of: 01-2018 sorted by title, page: 4

» Characterization of High-Frequency Dielectric Laminates Using a Scanning-Probe Based on EBG Structure
Abstract:
In this paper, an electromagnetic bandgap inspired scanning-probe sensor and its measurement platform is presented for the characterization of high-frequency dielectric laminates. In the proposed sensing technique, the material under test (MUT) is placed between the electromagnetic bandgap structure and a substrate comprising of 50- microstrip lines. Transverse electric excitation is given from the microstrip line to the suspended electromagnetic bandgap structure via MUT, which allows the characterization of different types of damage and defect severities in the MUT. By tapping the material surface with the proposed sensor, a shift in the effective relative permittivity is calculated. The sensor structure consists of unit-cell elements. Each of the unit-cell structure has two asymmetrical H-shaped slots and a metal via. The sensor system is modeled and fabricated using Rogers isotropic thermoset microwave material of relative permittivity , and loss tangent . The dispersion analysis of the electromagnetic bandgap structure shows frequency bandgap between 1.93 to 3.18 GHz, which defines the operating range of the sensor. The fit effective permittivity values obtained from simulation and measured results for nondefective samples are in close agreement with the literature. A deviation in the effective permittivity is obtained for the defective material which indicates the severity of the defect. The proposed method can be effectively utilized for fault monitoring and testing of larger dielectric laminates.
Autors: Rahul Yadav;Piyush N. Patel;
Appeared in: IEEE Transactions on Instrumentation and Measurement
Publication date: Jan 2018, volume: 67, issue:1, pages: 107 - 115
Publisher: IEEE
 
» Chiller Plant Operation Optimization: Energy-Efficient Primary-Only and Primary–Secondary Systems
Abstract:
A chiller plant consists of chiller, cooling tower, and pump subsystems. Two major configurations, primary-only and primary–secondary systems, are often used. Given the high energy costs of a plant, chiller plant operation optimization is important to save energy. For both configurations, chilled/condenser water supply temperatures are critical in improving chiller efficiency and should be considered as decision variables. However, nonlinearity of the problem is increased since chiller power consumption is a highly nonlinear function of these temperatures. Additionally, the problem is combinatorial considering the number of active units (e.g., chillers). In this paper, primary-only systems with identical units in each subsystem and primary–secondary systems with units of two sizes are studied, and both supply temperatures are optimized for energy savings. To obtain near-optimal solutions efficiently, a recent decomposition and coordination approach with little multiplier zigzagging and fast reduction of coupling constraint violations combining with sequential quadratic programming (SQP) is used. Penalties for the constraints that are difficult to be satisfied (e.g., mass balance constraints between fixed-speed pumps and variable-speed chillers) are added. After decomposition, complexity and nonlinearity of a subproblem are reduced drastically as compared with the original problem so that SQP is used. Numerical testing demonstrates that our approach is efficient in obtaining near-optimal solutions, and major energy savings are achieved as compared with benchmark strategies. The approach is scalable and can be used for chiller plant optimization and beyond. Note to Practitioners—Traditionally, chiller plant operation is often based on rules. For example, the number of active chillers is the minimum number that satisfies cooling requirements, and chilled water supply temperature is constant. Chi- ler power consumption is a nonlinear function of chiller cooling load and chilled/condenser water supply temperatures. Energy is wasted since chiller efficiency is low with fixed supply temperatures, and sometimes energy consumption of two chillers is less than that of one chiller. To save energy, chiller plant optimization with good decision variables such as the number of active units and chilled/condenser water supply temperatures is studied. The problem is challenging with high nonlinearity caused by considering such temperatures as decision variables. Furthermore, with discrete variables (e.g., the number of active chillers), the problem is combinatorial. To efficiently solve the problem for high-quality solutions, a novel decomposition and coordination approach is developed. Complexity and nonlinearity of a subproblem are reduced drastically after decomposition as compared with the original problem so that appropriate nonlinear methods are used to solve the subproblems. The results show that the solutions are near-optimal with short computational time and the approach is scalable. Additionally, major energy savings are achieved as compared with benchmark strategies. The approach provides a new and powerful way to solve chiller plant optimization problems and beyond.
Autors: Danxu Zhang;Peter B. Luh;Junqiang Fan;Shalabh Gupta;
Appeared in: IEEE Transactions on Automation Science and Engineering
Publication date: Jan 2018, volume: 15, issue:1, pages: 341 - 355
Publisher: IEEE
 
» China promises the moon
Abstract:
Last July, when a Chinese Long March 5 rocket lifted off from the country’s newest spaceport, the Wenchang Space Launch Center on the island of Hainan, the vehicle's official mission was to place an experimental communications satellite into orbit. The launch, though, had a secondary purpose: It was to be a final test before the Long March 5, China's newest and largest rocket, was entrusted with the country's most ambitious science mission ever.
Autors: Jeff Foust;
Appeared in: IEEE Spectrum
Publication date: Jan 2018, volume: 55, issue:1, pages: 26 - 29
Publisher: IEEE
 
» Chordal Relaxation Based ACOPF for Unbalanced Distribution Systems With DERs and Voltage Regulation Devices
Abstract:
In emerging distribution systems with a proliferation of distributed energy resources (DERs) and flexible demand assets, operation characters of the unbalance network and voltage regulation devices need to be accurately addressed for ensuring the secure and economic operation. This paper focuses on the modeling and solution approach of AC optimal power flow (ACOPF) for unbalanced distribution systems with DERs and voltage regulation transformers (VRT). The ACOPF problem is formulated as a chordal relaxation based semidefinite programming (SDP) model, and a tighter convexification model of VRTs is proposed for mitigating solution inexactness. Analytical conditions are presented and proved to determine whether global optimal solution to the original ACOPF problem can be retrieved from solutions of the chordal relaxation based SDP model. Numerical studies on modified IEEE 34-bus and 8500-node systems show that the proposed approach presents a better computational performance as compared to rank relaxation based SDP approaches and general nonlinear solvers.
Autors: Yikui Liu;Jie Li;Lei Wu;Tom Ortmeyer;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 970 - 984
Publisher: IEEE
 
» Classification of Bent Monomials, Constructions of Bent Multinomials and Upper Bounds on the Nonlinearity of Vectorial Functions
Abstract:
The paper is composed of two main parts related to the nonlinearity of vectorial functions. The first part is devoted to maximally nonlinear (n,m)- functions (the so-called bent vectorial functions) which contribute to an optimal resistance to both linear and differential attacks on symmetric cryptosystems. They can be used in block ciphers at the cost of additional diffusion/compression/expansion layers, or as building blocks for the construction of substitution boxes (Sboxes) and they are also useful for constructing robust codes and algebraic manipulation detection codes. A main issue on bent vectorial functions is to characterize bent monomial functions Trnm (λxd) from F2n to F2m (where m is a divisor of n) leading to a classification of those bent monomials. We also treat the case of functions with multiple trace terms involving general results and explicit constructions. Furthermore, we investigate some open problems raised by Pasalic et al. and Muratović-Ribić et al. in a series of papers on vectorial functions. The second part is devoted to the nonlinearity of (n,m)-functions. No tight upper bound is known when n/2 < m < n. The covering radius bound is the only known upper bound in this range (the Sidelnikov-Chabaud-Vaudenay bound coincides with it when m = n − 1 and it has no sense when m < n − 1). Finding better bounds is an open problem since the 90’s. Moreover, no bound has been found during the last 23 years which improve upon the covering radius bound for a large part of (n;m)-functions. We derive such upper bounds for functions which are sufficiently unbalanced or which satisfy some conditions. These upper bounds imply some necessary conditions for vectorial functions to have large nonlinearity.
Autors: Yuwei Xu;Claude Carlet;Sihem Mesnager;Chuankun Wu;
Appeared in: IEEE Transactions on Information Theory
Publication date: Jan 2018, volume: 64, issue:1, pages: 367 - 383
Publisher: IEEE
 
» Classified Round Robin: A Simple Prioritized Arbitration to Equip Best Effort NoCs With Effective Hard QoS
Abstract:
Advances in semiconductor technology enable integrating tens of cores on a single chip. Providing quality-of-service (QoS) for communication flows in complex embedded applications is critical. In this paper, we present a new approach for designing guaranteed service (GS) networks-on-chip by introducing a new arbitration algorithm and differentiating high and low priority traffic flows in best-effort (BE) networks. An analytical model is provided to compute accurate performance bound parameters in the network with the new arbitration. When the flows have the same priorities in a switch, the new algorithm acts exactly the same as the basic round robin arbitration. It works as a superset of the basic algorithm, when the flows have different priorities. The proposed method helps designers to easily equip traditional BE networks with effective hard QoS, changing it to a GS network. This is done without the need to get involved in the designing complexity of traditional GS networks and still benefit from the superior properties of BE networks. We show substantial improvement in performance bounds for high priority flows (more than 40% in delay and 80% in bandwidth, on average) compared to the known approaches.
Autors: Dara Rahmati;Hamid Sarbazi-Azad;
Appeared in: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publication date: Jan 2018, volume: 37, issue:1, pages: 257 - 269
Publisher: IEEE
 
» Clean and Green India: Is Solar Energy the Answer?
Abstract:
Approximately 78% of the world's electricity demand is fulfilled by fossil-fuelbased power plants. Electricity generated from such power plants is costly and often leads to environmental pollution and subsequent health hazards. For example, pollutants from coal combustion cause a variety of respiratory ailments and impact cardiovascular health and the nervous system.
Autors: Abhinav Aggarwal;Aman Singhal;Sumit J. Darak;
Appeared in: IEEE Potentials
Publication date: Jan 2018, volume: 37, issue:1, pages: 40 - 46
Publisher: IEEE
 
» Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks
Abstract:
Complex networks require effective tools and visualizations for their analysis and comparison. Clique communities have been recognized as a powerful concept for describing cohesive structures in networks. We propose an approach that extends the computation of clique communities by considering persistent homology, a topological paradigm originally introduced to characterize and compare the global structure of shapes. Our persistence-based algorithm is able to detect clique communities and to keep track of their evolution according to different edge weight thresholds. We use this information to define comparison metrics and a new centrality measure, both reflecting the relevance of the clique communities inherent to the network. Moreover, we propose an interactive visualization tool based on nested graphs that is capable of compactly representing the evolving relationships between communities for different thresholds and clique degrees. We demonstrate the effectiveness of our approach on various network types.
Autors: Bastian Rieck;Ulderico Fugacci;Jonas Lukasczyk;Heike Leitte;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 822 - 831
Publisher: IEEE
 
» Clock Network Optimization With Multibit Flip-Flop Generation Considering Multicorner Multimode Timing Constraint
Abstract:
Clock network should be optimized to reduce clock power dissipation. The power efficient clock network can be constructed by multibit flip-flop generation and gated clock tree aware flip-flop clumping to pull flip-flops close to the same integrated clock gating cell. It is capable of providing an attractive solution to reduce clock power. This paper considers multicorner and multimode timing constraints for the two combined approach. This proposed method is applied to five industrial digital intellectual property blocks of state-of-the-art mobile system-on-a-chip fabricated in 14-nm CMOS process. Experimental results show that MBFF generation algorithm achieves 22% clock power reduction. Applying a gated clock tree aware flip-flop clumping on top of the MBFF generation further reduces the power to around 32%.
Autors: Taehee Lee;David Z. Pan;Joon-Sung Yang;
Appeared in: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publication date: Jan 2018, volume: 37, issue:1, pages: 245 - 256
Publisher: IEEE
 
» Closed-Form Optimization on Saliency-Guided Image Compression for HEVC-MSP
Abstract:
High efficiency video coding (HEVC) is the latest video coding standard, and it has the best performance among all the existing standards. HEVC main still picture profile (HEVC-MSP) also achieves top performance in image compr-ession. In this paper, we propose a closed-form bit allocation approach to optimize the saliency-guided PSNR (viewed as perceptual distortion) such that the coding efficiency of HEVC-based image compression can be significantly improved from a subjective perspective. Specifically, a bit allocation formulation is established to minimize perceptual distortion with a constraint on bit-rates. Then, this formulation is solved using the proposed recursive Taylor expansion method with a closed-form solution. On the basis of our solution, a bit allocation and re-allocation process is developed in our approach to minimize perceptual distortion, meanwhile accurately controlling bit-rates. In addition, we provide both theoretical and numerical analyses of the computational complexity, verifying the little extra time cost of our approach. The experimental results demonstrate the superior performance of our approach over the state-of-the-art HEVC-MSP, and the BD-rate savings are approximately 40% and 24% for face and generic images, respectively.
Autors: Shengxi Li;Mai Xu;Yun Ren;Zulin Wang;
Appeared in: IEEE Transactions on Multimedia
Publication date: Jan 2018, volume: 20, issue:1, pages: 155 - 170
Publisher: IEEE
 
» Clustering Trajectories by Relevant Parts for Air Traffic Analysis
Abstract:
Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.
Autors: Gennady Andrienko;Natalia Andrienko;Georg Fuchs;Jose Manuel Cordero Garcia;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 34 - 44
Publisher: IEEE
 
» Clustervision: Visual Supervision of Unsupervised Clustering
Abstract:
Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.
Autors: Bum Chul Kwon;Ben Eysenbach;Janu Verma;Kenney Ng;Christopher De Filippi;Walter F. Stewart;Adam Perer;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 142 - 151
Publisher: IEEE
 
» Co-Optimization of Transmission Expansion Planning and TCSC Placement Considering the Correlation Between Wind and Demand Scenarios
Abstract:
This paper combines two planning problems into a single planning problem and numerically shows the benefits of their combination. Specifically, we combine the optimal transmission expansion planning problem and the optimal placement of thyristor controlled series compensators (TCSCs) in a transmission network. The proposed single period math program for the combined problem minimizes the investment costs of the new lines and TCSCs and the expected costs of generation and load shed while multiple operation scenarios are considered. The model relies on the dc approximation of the power flow network and ties the maximum flow limit of each line to its length. The operation scenarios are developed by considering the load and wind power generation of a specific period using a proposed method for scenario reduction. The general linearization technique and the disjunctive model are used to linearize the polynomial constraints in the problem. The numerical results for solving the proposed optimization model for the Graver 6-bus and the IEEE 118-bus systems show the cost benefits and wind penetration of using the proposed combined planning model.
Autors: Omid Ziaee;Omid Alizadeh-Mousavi;F. Fred Choobineh;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 206 - 215
Publisher: IEEE
 
» Co-Planning of Investments in Transmission and Merchant Energy Storage
Abstract:
Suitably located energy storage systems are able to collect significant revenue through spatiotemporal arbitrage in congested transmission networks. However, transmission capacity expansion can significantly reduce or eliminate this source of revenue. Investment decisions by merchant storage operators must, therefore, account for the consequences of potential investments in transmission capacity by central planners. This paper presents a tri-level model to co-optimize merchant electrochemical storage siting and sizing with centralized transmission expansion planning. The upper level takes the merchant storage owner's perspective and aims to maximize the lifetime profits of the storage, while ensuring a given rate of return on investments. The middle level optimizes centralized decisions about transmission expansion. The lower level simulates market clearing. The proposed model is recast as a bi-level equivalent, which is solved using the column-and-constraint generation technique. A case study based on a 240-bus, 448-line testbed of the Western Electricity Coordinating Council interconnection demonstrates the usefulness of the proposed tri-level model.
Autors: Yury Dvorkin;Ricardo Fernández-Blanco;Yishen Wang;Bolun Xu;Daniel S. Kirschen;Hrvoje Pandžić;Jean-Paul Watson;Cesar A. Silva-Monroy;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 245 - 256
Publisher: IEEE
 
» CoCloud: Enabling Efficient Cross-Cloud File Collaboration Based on Inefficient Web APIs
Abstract:
Cloud storage services such as Dropbox have been widely used for file collaboration among multiple users. However, this desirable functionality is yet restricted to the “walled-garden” of each service. At present, the only feasible approach to cross-cloud file collaboration seems to be using web APIs, whose performance is known to be highly unstable and unpredictable. Now that using inefficient web APIs is inevitable, in this paper we attempt to achieve sound user-perceived performance for cross-cloud file collaboration. This attempt is enabled by two key observations from real-world measurements. First, for each cloud, we are always able to deploy one or several nearby (client) proxies which can efficiently access the web APIs. Second, during file collaboration, significant similarity exists among different versions of a file. This can be exploited to substantially reduce inter-proxy traffic and thus shorten the data sync time. Guided by the observations, we design and implement an open-source prototype system called CoCloud. Currently, it supports file collaboration among four popular cloud storage services in the US and China. Its performance is well acceptable to users under representative workloads, even approaching or exceeding that of intra-cloud collaboration in many cases.
Autors: Jinlong E;Yong Cui;Peng Wang;Zhenhua Li;Chaokun Zhang;
Appeared in: IEEE Transactions on Parallel and Distributed Systems
Publication date: Jan 2018, volume: 29, issue:1, pages: 56 - 69
Publisher: IEEE
 
» CODE: Coherence Based Decision Boundaries for Feature Correspondence
Abstract:
A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model. Once computed, the model can filter the entire set of nearest neighbor matches (which typically contains over 90 percent false matches) for true matches. We integrate our technique into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines where previous techniques provide few or no matches.
Autors: Wen-Yan Lin;Fan Wang;Ming-Ming Cheng;Sai-Kit Yeung;Philip H.S. Torr;Minh N. Do;Jiangbo Lu;
Appeared in: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publication date: Jan 2018, volume: 40, issue:1, pages: 34 - 47
Publisher: IEEE
 
» Coded Caching Under Arbitrary Popularity Distributions
Abstract:
Caching plays an important role in reducing the backbone traffic when serving high-volume multimedia content. Recently, a new class of coded caching schemes have received significant interest, because they can exploit coded multi-cast opportunities to further reduce backbone traffic. Without considering file popularity, prior works have characterized the fundamental performance limits of coded caching through a deterministic worst-case analysis. However, when heterogeneous file popularity is considered, there remain open questions regarding the fundamental limits of coded caching performance. In this paper, for an arbitrary popularity distribution, we first derive a new information-theoretic lower bound on the expected transmission rate of any coded caching schemes. We then show that a simple coded-caching scheme attains an expected transmission rate that is at most a constant factor away from the lower bound. Unlike other existing studies, the constant factor that we derived is independent of the popularity distribution.
Autors: Jinbei Zhang;Xiaojun Lin;Xinbing Wang;
Appeared in: IEEE Transactions on Information Theory
Publication date: Jan 2018, volume: 64, issue:1, pages: 349 - 366
Publisher: IEEE
 
» Codesign of Compact III–V Millimeter-Wave Wavelet Transmitters With On-Chip Antennas
Abstract:
Monolithically integrated millimeter-wave wavelet transmitters are presented and analyzed in this paper. Two designs that include compact dielectric resonator antennas with either inductive or self-resonant input characteristics are compared. The efficient transmitter front-end core consists of a resonant tunneling diode oscillator that is quenchable by a transistor switch. A physical diode model is used to formulate small-signal matching criteria and study codesign conditions. Fabricated transmitters provide up to 11-dBm equivalent isotropically radiated power in down to 80-ps wideband wavelets at -band carrier frequencies. Analyses of the radiated waveform envelope and time-frequency characteristics give insight in the circuit operation.
Autors: Lars Ohlsson;Daniel Sjöberg;Lars-Erik Wernersson;
Appeared in: IEEE Transactions on Microwave Theory and Techniques
Publication date: Jan 2018, volume: 66, issue:1, pages: 273 - 279
Publisher: IEEE
 
» Coding and Bounds for Channel Estimation in Visible Light Communications and Positioning
Abstract:
In visible light communications (VLC) and visible light positioning (VLP), it is essential to obtain accurate estimates of the channel gains between receiver and multiple light sources. When there are multiple transmitters, time-division multiple access (TDMA) is typically used in the channel estimation phase of radio frequency systems. However, the estimation performance of TDMA-based schemes in VLC and VLP systems is substantially impacted by the maximum power constraint and desired average power constraint that are unique to visible light systems. Under these constraints, this paper explores coding schemes for the simultaneous channel gain estimations of multiple light sources such that the total and maximum noise variances of the channel estimates by the receiver are minimized. Although the minimization problem is non-convex, criteria for optimal codes are found by using majorization theory. Coding scheme satisfying these criteria is proposed that helps to characterize the fundamental tradeoff between noise variance and codeword length.
Autors: Siu-Wai Ho;Abdullah A. Saed;Lifeng Lai;Chi Wan Sung;
Appeared in: IEEE Journal on Selected Areas in Communications
Publication date: Jan 2018, volume: 36, issue:1, pages: 34 - 44
Publisher: IEEE
 
» Collaboration Empowers Innovation [President's Message]
Abstract:
Presents the President’s message for this issue of the publication.
Autors: Rabab Ward;
Appeared in: IEEE Signal Processing Magazine
Publication date: Jan 2018, volume: 35, issue:1, pages: 5 - 6
Publisher: IEEE
 
» Collision Detection and Signal Recovery for UHF RFID Systems
Abstract:
In this paper, we present a novel high-throughput anti-collision algorithm for passive ultrahigh-frequency (UHF) radio-frequency identification (RFID) systems. Our algorithm utilizes signal recovery techniques of collided tag signals to both recover tag communications and obtain an accurate count of all tags in the field. Passive UHF RFID systems are used for long-range passive communications for applications including supply chain management and electronic tolling. Anti-collision algorithms are used to ensure successful RFID tag communications due to the likelihood of multiple tags being in the field and attempting to communicate simultaneously. The limited on-tag functionality necessitates the use of simple anti-collision algorithms such as a dynamic frame slotted Aloha (DFSA) algorithm. With our novel collision detection and signal recovery anti-collision algorithm, the RFID reader can retrieve multiple valid communications from each collided slot in a DFSA-based anti-collision protocol, while our algorithm allocates an optimal number of slots resulting in more collided but recoverable slots and fewer empty slots. Our algorithm achieves a nearly 100% throughput improvement with an expected throughput of 0.85 compared with an expected throughput of 0.426 for a standard DFSA algorithm. The reader receiver with the proposed algorithm is implemented in a field-programmable gate array and the whole reader system is verified using the communication tests with commercial tags. According to the synthesized results in an SMIC 0.13- CMOS technology, the collision detection and signal recovery module consumes about 135k GE.

Note to Practitioners—Signal recovery methods are capable of extracting information from collided signals. In this paper, we introduce and analyze a signal recovery method based on the voltage histogram of t- e received signal. Both the original signals and the number of collided signals are recovered. The information of tags is used by our novel algorithm to increase the system throughput in DFSA-based anti-collision algorithms. The recovered signals allow our algorithm to directly obtain the tag identifier. By determining the number of collided signals, our algorithm is able to calculate an accurate estimate of the total number of tags in the reader’s field. With signal recovery and an accurate tag count, our algorithm is able to reduce the total number of slots needed to identify all tags, thereby increasing the throughput. We present a novel frame length optimization method that increases throughout by shrinking the number of slots, thereby intentionally causing collisions and reducing the total number of empty slots. In addition, we present a hardware implementation of our novel method that is suitable for integration into RFID readers. When the signal strengths of the tags in collision in the in-phase or quadrature path are not close to each other, the performance of our method is much better than that of the traditional method in which the collision signal is treated as ineffective and ignored.

Autors: Xi Tan;He Wang;Lingzhi Fu;Junyu Wang;Hao Min;Daniel W. Engels;
Appeared in: IEEE Transactions on Automation Science and Engineering
Publication date: Jan 2018, volume: 15, issue:1, pages: 239 - 250
Publisher: IEEE
 
» Collision Monitoring of Foreign Object on CFRP Laminate Utilizing Electrostatic Induction
Abstract:
In this paper, a collision monitoring method utilizing electrostatic induction is proposed, designed, tested, and validated. When a foreign object, which is regarded as a charged body because any object is charged to a greater or lesser extent, approaches and collides with a grounded electric conductive material, an electric current caused by electrostatic induction flows to ground. The electric current is used for collision monitoring in this paper. The principle of the proposed collision monitoring method can be used for any conductive plate, which is in the air or a vacuum. However, the method is particularly aimed at collision monitoring on carbon fiber reinforced polymer (CFRP) structures for reducing the inspection cost by identifying the area where detailed inspection, such as ultrasonic inspection, needs to be conducted. The sensors are composed of the monitored object of a large conductive plate, such as a CFRP laminate, and strip conductors, such as copper tape arrayed in grid. When a foreign object collides with the monitored object, electric currents flowing through the strip conductors are measured and analyzed, and the collision point, angle, and speed of the foreign object are identified using an optimization method. As a result of several experiments, the collision point, angle, and speed are identified with high accuracy (within 5% error except for a specific case).
Autors: Masahiro Suzuki;Yoshiro Suzuki;Akira Todoroki;Yoshihiro Mizutani;
Appeared in: IEEE Sensors Journal
Publication date: Jan 2018, volume: 18, issue:2, pages: 613 - 622
Publisher: IEEE
 
» Colorless WRC-FPLDs Subject to Gain-Saturated RSOA Feedback for WDM-PONs
Abstract:
A directly modulated, colorless, weak-resonant-cavity Fabry–Perot laser diode (WRC-FPLD)-based transmitter is, for the first time, proposed and experimentally demonstrated for wavelength-division multiplexed passive optical networks (WDM-PONs). The transmitter utilizes optical gain-saturated reflective semiconductor optical amplifier (RSOA)-induced feedback and is free from external optical injection. Experimental investigations are undertaken of the impact of RSOA-feedback dynamic mechanisms on directly modulated output optical signals and corresponding colorless transmission performances for various system architectures. It is shown that the coherent-like optical source produced by the proposed transmitter architecture offers an output wavelength tunable range as large as 65 nm with side-mode suppression ratios (SMSRs) of >37 dB. In simple 25-km single-mode fiber intensity modulation and direct detection WDM-PON systems, the colorless transmission of directly modulated 7.5-Gb/s optical orthogonal frequency-division multiplexed signals is experimentally achieved over a wavelength tunable range of 35 nm, during which <1 dB differences of received optical powers at bit error rates of are also experimentally obtainable.
Autors: M. L. Deng;R. P. Giddings;C.-T. Tsai;G.-R. Lin;J. M. Tang;
Appeared in: IEEE Photonics Technology Letters
Publication date: Jan 2018, volume: 30, issue:1, pages: 43 - 46
Publisher: IEEE
 
» Combined MRAC for Unknown MIMO LTI Systems With Parameter Convergence
Abstract:
This work proposes a novel combined model reference adaptive controller (MRAC) for unknown multi input multi output (MIMO) LTI systems with guaranteed parameter convergence. An online plant-parameter identification method is developed in conjunction with a direct control-parameter update law to ensure exponential convergence (after a tunable finite time) of tracking error as well as plant and control-parameter estimation errors to zero. Unlike the restrictive persistence of excitation (PE) condition required in classical MRAC approaches, this method guarantees parameter convergence by imposing a significantly milder initial excitation condition on the relevant signals. The introduction of a low-pass filter obviates the need for state derivative knowledge, whereas, the novel inclusion of an integral-like update in the plant-parameter estimation law overcomes the requirement of the restrictive PE condition. As far as the authors are aware, this is the first work on MRAC for MIMO linear time invariant (LTI) systems, which guarantees exponential convergence of the error dynamics without requiring the PE condition as well as the structural knowledge of the system matrices.
Autors: Sayan Basu Roy;Shubhendu Bhasin;Indra Narayan Kar;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Jan 2018, volume: 63, issue:1, pages: 283 - 290
Publisher: IEEE
 
» Communication Enabled—Fast Acting Imbalance Reserve (CE-FAIR)
Abstract:
This letter presents a new frequency control strategy that takes advantage of communications and fast responding resources such as photovoltaic generation, energy storage, wind generation, and demand response, termed collectively as converter interfaced generators (CIGs). The proposed approach uses an active monitoring of power imbalances to rapidly redispatch CIGs. This approach differs from previously proposed frequency control schemes in that it employs feed-forward control based on a measured power imbalance rather than relying on a frequency measurement. Time-domain simulations of the full Western Electricity Coordinating Council system are conducted to demonstrate the effectiveness of the proposed method, showing improved performance.
Autors: Felipe Wilches-Bernal;Ricky Concepcion;Jason C. Neely;Raymond H. Byrne;Abraham Ellis;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 1101 - 1103
Publisher: IEEE
 
» Communication Is Key: How to Discuss Energy and Environmental Issues with Consumers
Abstract:
Scientists are now more certain than ever that humans are responsible for climate change through the combustion of fossil fuels. A recent Global Energy Assessment report, compiled by the International Institute for Applied Systems Analysis, indicates that, globally, domestic energy consumption accounts for about a quarter of global greenhouse gas emissions. A fundamental shift in energy consumption is needed, moving away from the use of fossil fuels to meet emission reduction targets.
Autors: Wokje Abrahamse;Sarah Darby;Katherine McComas;
Appeared in: IEEE Power and Energy Magazine
Publication date: Jan 2018, volume: 16, issue:1, pages: 29 - 34
Publisher: IEEE
 
» Compact 60-GHz On-Chip Bandpass Filter With Low Insertion Loss
Abstract:
In this letter, a compact 60-GHz on-chip bandpass filter is presented using an integrated passive devices technology on a GaAs substrate. An E-shaped dual-mode resonator and stepped impedance resonator were adopted in this design for a compact size and sharp cut-off response. Two controllable transmission zeros are obtained on the both sides of the passband achieving a good frequency selectivity. The main size of the filter is only mm2. The presented filter has a center frequency of 60 GHz, with a 3 dB fractional bandwidth of 14.8 GHz from 52.6 to 67.4 GHz. The insertion loss of the filter is only 1.2 dB, and the return loss is lower than −10 dB within the passband. Good agreement is achieved between the measured results and the simulated ones.
Autors: Lin-Pu Li;Wei Shen;Jin-Yi Ding;Xiao-Wei Sun;
Appeared in: IEEE Electron Device Letters
Publication date: Jan 2018, volume: 39, issue:1, pages: 12 - 14
Publisher: IEEE
 
» Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study
Abstract:
Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling.
Autors: Jürgen Bernard;Marco Hutter;Matthias Zeppelzauer;Dieter Fellner;Michael Sedlmair;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 298 - 308
Publisher: IEEE
 
» Compartmental Modeling of Opportunistic Signals for Energy Efficient Optimal Clustering in WSN
Abstract:
This letter proposes the compartmental model-based cluster size optimization using opportunistic signals in wireless sensor networks. The opportunistic signals, namely, Wi-Fi, acoustic, and/or visible light, can be utilized depending upon good availability of the signals in the given area of interest. The compartmental model, which is an attenuation model, describes the variation of opportunistic signal power with propagation distance. In order to minimize the energy consumption, the optimal number of clusters is computed for different orders of the Taylor series expansion of the compartmental model. We illustrate that the second-order Taylor series expansion approximates well enough the exact performance of the compartmental model. A theoretical analysis of the compartmental model performance has been conducted with parameters derived from experimental measurements.
Autors: Sudhir Kumar;
Appeared in: IEEE Communications Letters
Publication date: Jan 2018, volume: 22, issue:1, pages: 173 - 176
Publisher: IEEE
 
» Competitive Linearity for Envelope Tracking: Dual-Band Crest Factor Reduction and 2D-Vector-Switched Digital Predistortion
Abstract:
As wireless communication standards evolve to support ever-higher data rates, the required linearity and bandwidth must increase, which leads to higher energy consumption [1]. The problem of high energy consumption has become more serious due to the wide-spread adoption of orthogonal frequency-division multiplexing (OFDM), which is used in long-term evolution (LTE) and Wi-Fi applications [2]. The low energy efficiency of OFDM-based systems results from the statistical distribution of OFDM signals, combined with the efficiency characteristics of power amplifiers (PAs).
Autors: Harald Enzinger;Karl Freiberger;Christian Vogel;
Appeared in: IEEE Microwave Magazine
Publication date: Jan 2018, volume: 19, issue:1, pages: 69 - 77
Publisher: IEEE
 
» Complex Variational Mode Decomposition for Slop-Preserving Denoising
Abstract:
We have introduced a new decomposition method for seismic data, termed complex variational mode decomposition (VMD), and we have also designed a new filtering technique for random noise attenuation in seismic data by applying the VMD on constant-frequency slices in the frequency–offset (–) domain. The motivation behind this paper is to overcome the potential low performance of empirical mode decomposition (EMD) for energy preservation of the steeply dipping events when used for noise attenuation, and low resolution when used for signal decomposition. The VMD is proposed to decompose a signal into an ensemble of band-limited modes. For seismic data consisting of linear events, the constant-frequency slices of its – spectrum are exactly band-limited. The noise attenuation algorithm is summarized as follows. First, the Fourier transform is applied on the time axis of the 2-D seismic data. Next, the VMD is applied on each frequency slice of the – spectrum and the decomposed modes are combined to obtain the filtered frequency slice. Finally, an inverse Fourier transform is applied on the frequency axis of the – spectrum to obtain the denoised result. The resulting VMD-based noise attenuation method is equivalent to applying a Wiener filter on each decomposed mode, which is achieve- during the decomposition progress. We also applied 2-D VMD on 3-D seismic data for denoising. Numerical results show that the proposed VMD-based method achieves a higher denoising quality than both the – deconvolution method and the EMD-based denoising method, especially for preserving the steep slopes.
Autors: Siwei Yu;Jianwei Ma;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: Jan 2018, volume: 56, issue:1, pages: 586 - 597
Publisher: IEEE
 
» Complexity of Infimal Observable Superlanguages
Abstract:
The infimal prefix-closed, controllable, and observable superlanguage plays an essential role in the relationship between controllability, observability, and co-observability—the central notions of supervisory control theory. Existing algorithms for its computation are exponential and it is not known whether a polynomial algorithm exists. We answer the question by studying the state complexity of this language. State complexity of a language is the number of states of its minimal deterministic finite automaton (DFA). For a language with state complexity , we show that the upper bound state complexity on the infimal prefix-closed and observable superlanguage is and that this bound is asymptotically tight. Hence, there is no algorithm computing a DFA of the infimal prefix-closed and observable superlanguage in polynomial time. Our construction shows that such a DFA can be computed in time . The construction involves nondeterministic finite automata (NFAs) and a computation of the supremal prefix-closed sublanguage. We study the computation of supremal prefix-closed sublanguages and show that there is no polynomial-time algorithm computing an NFA of the supremal prefix-closed sublanguage of a language given as an NFA even if the language is unary.
Autors: Tomáš Masopust;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Jan 2018, volume: 63, issue:1, pages: 249 - 254
Publisher: IEEE
 
» Composition Check Codes
Abstract:
We present composition check codes for noisy storage and transmission channels with unknown gain and/or offset. In the proposed composition check code, like in systematic error correcting codes, the encoding of the main data into a constant composition code is completely avoided. To the main data, a coded label is appended that carries information regarding the composition vector of the main data. Slepian’s optimal detection technique of codewords that are taken from a constant composition code is applied for detection. A first Slepian detector detects the label and subsequently restores the composition vector of the main data. The composition vector, in turn, is used by a second Slepian detector to optimally detect the main data. We compute the redundancy and error performance of the new method, and results of computer simulations are presented.
Autors: Kees A. Schouhamer Immink;Kui Cai;
Appeared in: IEEE Transactions on Information Theory
Publication date: Jan 2018, volume: 64, issue:1, pages: 249 - 256
Publisher: IEEE
 
» Comprehensive electrical and thermal analysis of the stress grading system of a large hydro generator
Abstract:
In the slot portion of a generator core the installed stator bars are covered with a semi-conductive coating to prevent partial discharge (PD) activity between the main insulation surface and the grounded laminated sheets. Typically, this outer corona protection (OCP) layer ends a few centimeters outside the slot portion. To suppress PD-activity at the end of the OCP, a field grading is mandatory for machines with a rated voltage greater than approximately 6 kV. Figure 1 shows the effect of a stress grading layer on stator bars energized to high voltage. It suppresses potential creepage discharges at the OCP ends caused by field enhancement.
Autors: Christian Staubach;Thomas Hildinger;Axel Staubach;
Appeared in: IEEE Electrical Insulation Magazine
Publication date: Jan 2018, volume: 34, issue:1, pages: 37 - 49
Publisher: IEEE
 
» Compressed Sensing for the Detection and Positioning of Dielectric Objects Inside Metal Enclosures by Means of Microwave Measurements
Abstract:
Based on compressed sensing and microwave measurements, we present a procedure for the detection and positioning of dielectric objects inside a metal enclosure, where the number of objects is unknown but assumed to be limited. The formulation features a convex quadratic optimization problem with 1-norm regularization, which allows for rapid detection and positioning given a precomputed dictionary. The dictionary consists of the scattering parameters computed from a single scattering object placed at the grid points of a structured grid that covers the entire measurement region. We test our method experimentally in a microwave measurement system that features a measurement region with a diameter of 11.6 cm. The measurement region is encircled by six aperture antennas, where each aperture is the end-opening of a rectangular waveguide operated from 2.7 to 4.2 GHz. We use acrylic-glass cylinders of radius 5.2 mm as scatterers and find that the compressed sensing method can correctly detect at least up to five scatterers with an average positioning accuracy of 3 mm. In addition, we investigate the performance of the method with respect to scarcity of data, where we omit scattering parameters or frequency points.
Autors: Johan Winges;Livia Cerullo;Thomas Rylander;Tomas McKelvey;Mats Viberg;
Appeared in: IEEE Transactions on Microwave Theory and Techniques
Publication date: Jan 2018, volume: 66, issue:1, pages: 462 - 476
Publisher: IEEE
 
» Computation and Memory Efficient Image Segmentation
Abstract:
In this paper, we address the segmentation problem under limited computation and memory resources. Given a segmentation algorithm, we propose a framework that can reduce its computation time and memory requirement simultaneously, while preserving its accuracy. The proposed framework uses standard pixel-domain downsampling and includes two main steps. Coarse segmentation is first performed on the downsampled image. Refinement is then applied to the coarse segmentation results. We make two novel contributions to enable competitive accuracy using this simple framework. First, we rigorously examine the effect of downsampling on segmentation using a signal processing analysis. The analysis helps to determine the uncertain regions, which are small image regions where pixel labels are uncertain after the coarse segmentation. Second, we propose an efficient minimum spanning tree-based algorithm to propagate the labels into the uncertain regions. We perform extensive experiments using several standard data sets. The experimental results show that our segmentation accuracy is comparable to state-of-the-art methods, while requiring much less computation time and memory than those methods.
Autors: Yiren Zhou;Thanh-Toan Do;Haitian Zheng;Ngai-Man Cheung;Lu Fang;
Appeared in: IEEE Transactions on Circuits and Systems for Video Technology
Publication date: Jan 2018, volume: 28, issue:1, pages: 46 - 61
Publisher: IEEE
 
» Computing Crowd Consensus with Partial Agreement
Abstract:
Crowdsourcing has been widely established as a means to enable human computation at large-scale, in particular for tasks that require manual labelling of large sets of data items. Answers obtained from heterogeneous crowd workers are aggregated to obtain a robust result. However, existing methods for answer aggregation are designed for discrete tasks, where answers are given as a single label per item. In this paper, we consider partial-agreement tasks that are common in many applications such as image tagging and document annotation, where items are assigned sets of labels. Common approaches for the aggregation of partial-agreement answers either (i) reduce the problem to several instances of an aggregation problem for discrete tasks or (ii) consider each label independently. Going beyond the state-of-the-art, we propose a novel Bayesian nonparametric model to aggregate the partial-agreement answers in a generic way. This model enables us to compute the consensus of partially-sound and partially-complete worker answers, while taking into account mutual relationships in labels and different answer sets. We also show how this model is instantiated for incremental learning, incorporating new answers from crowd workers as they arrive. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of precision, recall, and robustness against faulty workers and data sparsity.
Autors: Nguyen Quoc Viet Hung;Huynh Huu Viet;Nguyen Thanh Tam;Matthias Weidlich;Hongzhi Yin;Xiaofang Zhou;
Appeared in: IEEE Transactions on Knowledge and Data Engineering
Publication date: Jan 2018, volume: 30, issue:1, pages: 1 - 14
Publisher: IEEE
 
» Computing Maximized Effectiveness Distance for Recall-Based Metrics
Abstract:
Given an effectiveness metric , two ordered document rankings and generated by a score-based information retrieval activity, and relevance labels in regard to some subset (possibly empty) of the documents appearing in the two rankings, Tan and Clarke's Maximized Effectiveness Distance (MED) computes the greatest difference in metric score that can be achieved that is consistent with all provided information, crystallized via a set of relevance assignments to the unlabeled documents such that is maximized. The closer the maximized effectiveness distance is to zero, the more similar and can be considered to be from the point of view of the metric . Here, we consider issues that arise when Tan and Clarke's definitions are applied to recall-based metrics, notably normalized discounted cumulative gain (NDCG- , and average precision (AP). In particular, we show that MED can be applied to NDCG without requiring an a priori assumption in regard to the total number of relevant documents; we also show that making such an assumption leads to different outcomes for both NDCG and average precision (AP) compared to when no such assumption is made.
Autors: Alistair Moffat;
Appeared in: IEEE Transactions on Knowledge and Data Engineering
Publication date: Jan 2018, volume: 30, issue:1, pages: 198 - 203
Publisher: IEEE
 
» Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support
Abstract:
We explore how to rigorously evaluate multidimensional visualizations for their ability to support decision making. We first define multi-attribute choice tasks, a type of decision task commonly performed with such visualizations. We then identify which of the existing multidimensional visualizations are compatible with such tasks, and set out to evaluate three elementary visualizations: parallel coordinates, scatterplot matrices and tabular visualizations. Our method consists in first giving participants low-level analytic tasks, in order to ensure that they properly understood the visualizations and their interactions. Participants are then given multi-attribute choice tasks consisting of choosing holiday packages. We assess decision support through multiple objective and subjective metrics, including a decision accuracy metric based on the consistency between the choice made and self-reported preferences for attributes. We found the three visualizations to be comparable on most metrics, with a slight advantage for tabular visualizations. In particular, tabular visualizations allow participants to reach decisions faster. Thus, although decision time is typically not central in assessing decision support, it can be used as a tie-breaker when visualizations achieve similar decision accuracy. Our results also suggest that indirect methods for assessing choice confidence may allow to better distinguish between visualizations than direct ones. We finally discuss the limitations of our methods and directions for future work, such as the need for more sensitive metrics of decision support.
Autors: Evanthia Dimara;Anastasia Bezerianos;Pierre Dragicevic;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 749 - 759
Publisher: IEEE
 
» ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding
Abstract:
Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.
Autors: Deokgun Park;Seungyeon Kim;Jurim Lee;Jaegul Choo;Nicholas Diakopoulos;Niklas Elmqvist;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 361 - 370
Publisher: IEEE
 
» Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction
Abstract:
Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, deep neural networks (DNNs) recently have been shown to excel at a wide range of computer vision problems due to their ability to automatically learn rich feature representations from data, as opposed to traditional handcrafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modeling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarize future research directions.
Autors: Anurag Arnab;Shuai Zheng;Sadeep Jayasumana;Bernardino Romera-Paredes;Mans Larsson;Alexander Kirillov;Bogdan Savchynskyy;Carsten Rother;Fredrik Kahl;Philip H.S. Torr;
Appeared in: IEEE Signal Processing Magazine
Publication date: Jan 2018, volume: 35, issue:1, pages: 37 - 52
Publisher: IEEE
 
» Considerations for Visualizing Comparison
Abstract:
Supporting comparison is a common and diverse challenge in visualization. Such support is difficult to design because solutions must address both the specifics of their scenario as well as the general issues of comparison. This paper aids designers by providing a strategy for considering those general issues. It presents four considerations that abstract comparison. These considerations identify issues and categorize solutions in a domain independent manner. The first considers how the common elements of comparison—a target set of items that are related and an action the user wants to perform on that relationship—are present in an analysis problem. The second considers why these elements lead to challenges because of their scale, in number of items, complexity of items, or complexity of relationship. The third considers what strategies address the identified scaling challenges, grouping solutions into three broad categories. The fourth considers which visual designs map to these strategies to provide solutions for a comparison analysis problem. In sequence, these considerations provide a process for developers to consider support for comparison in the design of visualization tools. Case studies show how these considerations can help in the design and evaluation of visualization solutions for comparison problems.
Autors: Michael Gleicher;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 413 - 423
Publisher: IEEE
 
» Constructing Demand Curves in Forward Capacity Market
Abstract:
This paper presents an economic framework for designing demand curves in Forward Capacity Market (FCM). Capacity demand curves have been recognized as a way to reduce the price volatility inherited from fixed capacity requirements. However, due to the lack of direct demand bidding in FCM, obtaining demand curves that appropriately reflect load's willingness to pay for reliability is challenging. The proposed framework measures the value of reliability by the avoided cost of unserved energy (CUE), i.e., Expected Unserved Energy (EUE) multiplied by Value of Lost Load (VOLL). The total cost of capacity and CUE are then minimized, allowing economic tradeoffs between different reliability levels. EUE, a multivariate function of the total system capacity and its distribution among capacity zones, is decomposed into single-variable functions, which form the base for system and zonal demand curves. VOLL is implied from the Net Cost of New Entry (Net-CONE) based on long-term market equilibrium properties. The proposed framework is applied to a multizone ISO New England system to demonstrate its effectiveness.
Autors: Feng Zhao;Tongxin Zheng;Eugene Litvinov;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 525 - 535
Publisher: IEEE
 
» Construction and Mitigation of User-Behavior-Based Covert Channels on Smartphones
Abstract:
To protect user privacy, many smartphone systems adopt the permission-based mechanism in which a user can evaluate the risk of requests for private information from a mobile app before installing it. However, recent studies show that the permission based mechanism is vulnerable to application collusion attacks because two apps, which appear to be harmless individually, can establish a covert channel and use it to leak confidential information. Consequently, people have designed some covert channel detection schemes, by checking abnormal status of the phone. In this paper, we point out that existing covert channel detection schemes may fail to detect a new type of collusion attacks referred as user-behavior-based covert channels. We implement three covert channels on Android smartphones. Our work sets a new alarm for the security issue of using smartphones. We then study the countermeasures to this new type of covert channels. Instead of trying to directly detect the proposed new type of covert channels, we propose two mitigation solutions to reduce the effectiveness of such covert channels. The mitigation solutions are also valid to other existing sensor-based side channels and/or covert channels on the phone.
Autors: Wen Qi;Wanfu Ding;Xinyu Wang;Yonghang Jiang;Yichen Xu;Jianping Wang;Kejie Lu;
Appeared in: IEEE Transactions on Mobile Computing
Publication date: Jan 2018, volume: 17, issue:1, pages: 44 - 57
Publisher: IEEE
 
» Construction of $n$ -Variable ( $nequiv 2 bmod 4$ ) Balanced Boolean Functions With Maximum Absolute Value in Autocorrelation Spectra $< 2^{frac {n}2}$
Abstract:
In this paper, we consider the maximum absolute value in the autocorrelation spectrum (not considering the zero point) of a function . In an even number of variables , bent functions possess the highest nonlinearity with . The long standing open question (for two decades) in this area is to obtain a theoretical construction of balanced functions with . So far, there are only a few examples of such functions for , but no general construction technique is known. In this paper, we mathematically construct an infinite class of balanced Boolean functions on variables having absolute indicator strictly lesser than , nonlinearity strictly greater than and algebraic degree , where and . While the bound is required for proving the generic result, our construction starts from , and we could obtain balanced functions with and nonlinearity for , and 26.
Autors: Deng Tang;Subhamoy Maitra;
Appeared in: IEEE Transactions on Information Theory
Publication date: Jan 2018, volume: 64, issue:1, pages: 393 - 402
Publisher: IEEE
 
» Construction of Polar Codes for Arbitrary Discrete Memoryless Channels
Abstract:
It is known that polar codes can be efficiently constructed for binary-input channels. At the same time, existing algorithms for general input alphabets are less practical because of high complexity. We address the construction problem for the general case, and analyze an algorithm that is based on successive reduction of the output alphabet size of the subchannels in each recursion step. For this procedure, we estimate the approximation error as , where is the input alphabet size and is the “quantization parameter,” i.e., the maximum size of the subchannel output alphabet allowed by the algorithm. The complexity of the code construction scales as , where is the length of the code. We also show that if the polarizing operation relies on modulo- addition, it is possible to merge subsets of output symbols without any loss in subchannel capacity. Performing this procedure before each approximation step results in a further speed-up of the code construction, and the resulting codes have smaller gap to capacity. We also show that a similar acceleration can be attained for polar codes over finite field alphabets. Experimentation shows that the suggested construction algorithms can be used to construct long polar codes for alphabets of size and more with acceptable loss of the code rate for a variety of polarizing transforms.
Autors: Talha Cihad Gulcu;Min Ye;Alexander Barg;
Appeared in: IEEE Transactions on Information Theory
Publication date: Jan 2018, volume: 64, issue:1, pages: 309 - 321
Publisher: IEEE
 
» Consumer Behavior: Why Engineers Need to Read About It [Guest Editorial]
Abstract:
Energy involves everybody. Current changes in energy and power systems, including the distributed production of renewables, an increasing need for flexibility of operations, and energy storage and transmission, affect consumers in one way or another. Changes often require the active participation and support of consumers, who may become prosumers. All the new systems and technologies developed by electrical engineers may influence consumer behavior and trigger positive or negative responses. Hence, it is important for electrical engineers to understand how their work may affect consumers, which behavior changes their solutions involve, and which consumer needs and preferences must be considered when developing new technology. This issue encourages a conversation among electrical engineers and social scientists and facilitates the integration of their different expertise.
Autors: Geertje Schuitema;Linda Steg;Mark O'Malley;
Appeared in: IEEE Power and Energy Magazine
Publication date: Jan 2018, volume: 16, issue:1, pages: 14 - 18
Publisher: IEEE
 
» Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors
Abstract:
Objective: Fluctuations in response to levodopa in Parkinson's disease (PD) are difficult to treat as tools to monitor temporal patterns of symptoms are hampered by several challenges. The objective was to use wearable sensors to quantify the dose response of tremor, bradykinesia, and dyskinesia in individuals with PD. Methods: Thirteen individuals with PD and fluctuating motor benefit were instrumented with wrist and ankle motion sensors and recorded by video. Kinematic data were recorded as subjects completed a series of activities in a simulated home environment through transition from off to on medication. Subjects were evaluated using the unified Parkinson disease rating scale motor exam (UPDRS-III) at the start and end of data collection. Algorithms were applied to the kinematic data to score tremor, bradykinesia, and dyskinesia. A blinded clinician rated severity observed on video. Accuracy of algorithms was evaluated by comparing scores with clinician ratings using a receiver operating characteristic (ROC) analysis. Results: Algorithm scores for tremor, bradykinesia, and dyskinesia agreed with clinician ratings of video recordings (ROC area > 0.8). Summary metrics extracted from time intervals before and after taking medication provided quantitative measures of therapeutic response (p < 0.01). Radar charts provided intuitive visualization, with graphical features correlated with UPDRS-III scores (R = 0.81). Conclusion: A system with wrist and ankle motion sensors can provide accurate measures of tremor, bradykinesia, and dyskinesia as patients complete routine activities. Significance: This technology could provide insight on motor fluctuations in the context of daily life to guide clinical management and aid in development of new therapies.
Autors: Christopher L. Pulliam;Dustin A. Heldman;Elizabeth B. Brokaw;Thomas O. Mera;Zoltan K. Mari;Michelle A. Burack;
Appeared in: IEEE Transactions on Biomedical Engineering
Publication date: Jan 2018, volume: 65, issue:1, pages: 159 - 164
Publisher: IEEE
 
» Continuous Output Feedback TSM Control for Uncertain Systems With a DC–AC Inverter Example
Abstract:
This brief proposes a continuous output feedback terminal sliding mode (TSM) control method for the output tracking problem of nonlinear systems with mismatched uncertainties. This method is developed by two consecutive steps: 1) design a sliding mode observer to estimate the derivative and high-order derivatives of system output and 2) construct a continuous TSM controller based on system output and the estimations of its high-order derivatives. The proposed controller not only guarantees system output converges to its reference in finite time but also keeps the continuity of control action. Experiments on single-phase dc–ac inverter circuits are also carried out to show the effectiveness of the proposed controller.
Autors: Zhenhua Zhao;Jun Yang;Shihua Li;Xinghuo Yu;Zuo Wang;
Appeared in: IEEE Transactions on Circuits and Systems II: Express Briefs
Publication date: Jan 2018, volume: 65, issue:1, pages: 71 - 75
Publisher: IEEE
 
» Continuous Tracking Control for a Compliant Actuator With Two-Stage Stiffness
Abstract:
Emerging applications of robots with direct physical interactions with humans have led to the development of a variety of series elastic actuators (SEAs) which are compliant, force controllable, and back drivable. The performance of current SEAs is mainly dependent on the specific stiffness of the spring. In our previous work, a compliant actuator with two-stage stiffness has been designed to overcome the performance limitations in current SEAs. The key novelty is that a low-stiffness spring and a high-stiffness spring are employed instead of a single spring in current SEAs, which has the advantages of high fidelity, low output impedance, and also large force range and bandwidth. In this paper, a tracking control scheme is proposed for the compliant actuator with two-stage stiffness. Although the overall stiffness is discontinuous, the proposed controller is continuous by integrating different control modes for two springs into a single one. The transition between control modes is smooth and embedded inside the controller, and it is also automatically realized by monitoring the output force of the actuator. The stability and convergence of the closed-loop system are analyzed, and experimental results are presented to demonstrate the effectiveness of the proposed control scheme.

Note to Practitioners—An SEA is developed by placing an elastic element into the actuator; this elasticity gives SEAs several unique properties including low mechanical output impedance, tolerance to impact loads, and passive mechanical energy storage, which makes it suitable for human–robot interaction. The performance of existing SEAs is highly dependent on the stiffness of a single spring. To overcome the limitations, a novel SEA with two-stage stiffness was proposed in our previous work. This paper suggests a continuous tracking control method for the proposed compliant actuator. Although the overall stiffness is di- continuous, the transition between different control modes for two springs is smooth and automatically realized. Experimental results show that the output force of the actuator is bounded. In future research, uncertainties in actuator dynamics will be considered, such that system identification or calibration is not required.

Autors: Xiang Li;Yongping Pan;Gong Chen;Haoyong Yu;
Appeared in: IEEE Transactions on Automation Science and Engineering
Publication date: Jan 2018, volume: 15, issue:1, pages: 57 - 66
Publisher: IEEE
 
» Control Strategy for Dispatchable Distributed Energy Resources in Islanded Microgrids
Abstract:
This paper proposes a multi-input muti-output control strategy for dispatchable power electronic based distributed energy resources (DERs) in islanded mode operation of microgrids. The controller is designed based on the model of the DER unit. Cascade, feedforward, and internal model control principle, incorporating the theory of integral control and repetitive controller, are used. The proposed controller regulates the voltage at the output of DERs under any load (balanced, unbalanced, and harmonic) variation and/or fault condition. The stability of the closed-loop system has been investigated. Enhanced phase-locked loop (EPLL) is used to determine the system variables in the frame under unbalanced (and balanced) operating conditions. The structure of the EPLL is further modified to regulate the microgrid frequency and a stability analysis of proposed frequency control loop is presented. The performance of the proposed controller is verified through test case studies, carried out on an autonomous microgrid with single and multiple DER units.
Autors: Sasan Gholami;Mohammad Aldeen;Sajeeb Saha;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 141 - 152
Publisher: IEEE
 
» Controlling Photonic Nanojets: From the Standpoint of Eigenmodes
Abstract:
In this letter, we explain how to quickly engineer and optimize photonic nanojets (PNJs) using eigenmode expansion and a transmission matrix theory. We systematically investigate how to realize an ultra-narrow beam waist and how to steer and shift the PNJ hot spot position using different linear combinations of eigenmodes. The ability to design complex beam shapes makes the technique attractive in numerous applications, including inspecting nanoparticles located deep inside a cell, super-resolution imaging, and optical lithography.
Autors: Jinlong Zhu;Lynford L. Goddard;
Appeared in: IEEE Photonics Technology Letters
Publication date: Jan 2018, volume: 30, issue:1, pages: 75 - 78
Publisher: IEEE
 
» Convergence of the Z-Bus Method for Three-Phase Distribution Load-Flow with ZIP Loads
Abstract:
This paper derives a set of sufficient conditions guaranteeing that the load-flow problem in unbalanced three-phase distribution networks with wye and delta constant-power, constant-current, and constant-impedance loads (ZIP loads) has a unique solution over a region that can be explicitly calculated from the network parameters. It is also proved that the well-known Z-Bus iterative method is a contraction over the defined region, and hence converges to the unique solution.
Autors: Mohammadhafez Bazrafshan;Nikolaos Gatsis;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 153 - 165
Publisher: IEEE
 
» Convex Hull of the Quadratic Branch AC Power Flow Equations and Its Application in Radial Distribution Networks
Abstract:
A branch flow model (BFM) is used to formulate the AC power flow in general networks. For each branch/line, the BFM contains a nonconvex quadratic equality. A mathematical formulation of its convex hull is proposed, which is the tightest convex relaxation of this quadratic equation. The convex hull formulation consists of a second-order cone inequality and a linear inequality within the physical bounds of power flows. The convex hull formulation is analytically proved and geometrically validated. An optimal scheduling problem of distributed energy storage (DES) in radial distribution systems with high penetration of photovoltaic resources is investigated in this paper. To capture the performance of both the battery and converter, a second-order DES model is proposed. Following the convex hull of the quadratic branch flow equation, the convex hull formulation of the nonconvex constraint in the DES model is also derived. The proposed convex hull models are used to generate a tight convex relaxation of the DES optimal scheduling problem. The proposed approach is tested on several radial systems. A discussion on the extension to meshed networks is provided.
Autors: Qifeng Li;Vijay Vittal;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 839 - 850
Publisher: IEEE
 
» Convoluted Arc With Flux Concentrator for Current Interruption
Abstract:
Further considerations are given to the use of an electromagnetic flux concentrator for arc plasma control in a rotary arc current interrupter. Such flux concentrators have been previously proposed for plasma fusion and other plasma applications. The possible extension of the proposed method for enhancing the interruption of direct currents with a rotary arc interrupter is discussed with the aid of theoretical modeling of the concentrator geometry and with its possible enhancement of ablation from the arc containing cylinders.
Autors: Leonid M. Shpanin;Gordon R. Jones;Joseph W. Spencer;
Appeared in: IEEE Transactions on Plasma Science
Publication date: Jan 2018, volume: 46, issue:1, pages: 175 - 179
Publisher: IEEE
 
» Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming
Abstract:
Multiview video streaming continues to gain popularity due to the great viewing experience it offers, as well as its availability that has been enabled by increased network throughput and other recent technical developments. User demand for interactive multiview video streaming that provides seamless view switching upon request is also increasing. However, it is a highly challenging task to stream stable and high quality videos that allow real-time scene navigation within the bandwidth constraint. In this paper, a convolutional neural network (ConvNet)-assisted seamless multiview video streaming system is proposed to tackle the challenge. The proposed method solves the problem from two perspectives. First, a ConvNet-assisted multiview representation method is proposed, which provides flexible interactivity without compromising on multiview video compression efficiency. Second, a bit allocation mechanism guided by a navigation model is developed to provide seamless navigation and adapt to network bandwidth fluctuations at the same time. These two blocks work closely to provide an optimized viewing experience to users. They can be integrated into any existing multiview video streaming framework to enhance overall performance. Experimental results demonstrate the effectiveness of the proposed method for seamless multiview streaming.
Autors: Li Yu;Tammam Tillo;Jimin Xiao;Marco Grangetto;
Appeared in: IEEE Transactions on Multimedia
Publication date: Jan 2018, volume: 20, issue:1, pages: 15 - 28
Publisher: IEEE
 
» Cooperative Bargaining Game-Based Multiuser Bandwidth Allocation for Dynamic Adaptive Streaming Over HTTP
Abstract:
Dynamic adaptive streaming over HTTP (DASH) has emerged as an efficient technology for video streaming. For a DASH system, a most common case is that a limited server bandwidth is competed by multiusers. In order to improve user quality of experience (QoE) and guarantee fairness, we propose to use the game theory in a proxy server to allocate the bandwidth collaboratively for multiusers. By taking user buffer length, received video bit rates, video qualities, etc., into account, the bandwidth allocation problem is formulated as a cooperative bargaining problem and the Nash bargaining solution (NBS) is obtained by convex optimization. The requested bit rate of users will be rewritten as the proxy calculated bit rate (i.e., NBS) when the user requested bit rate is larger. Experimental results demonstrate that user QoE and fairness can be improved significantly, i.e., the delay frequency and duration are smaller, and the received video qualities are higher and more stable, when comparing the proposed method with existing methods.
Autors: Hui Yuan;Xuekai Wei;Fuzheng Yang;Jimin Xiao;Sam Kwong;
Appeared in: IEEE Transactions on Multimedia
Publication date: Jan 2018, volume: 20, issue:1, pages: 183 - 197
Publisher: IEEE
 
» Cooperative Device-to-Device Communication With Network Coding for Machine Type Communication Devices
Abstract:
With the rapid development of the Internet of Things, it is pressing to improve wireless transmission efficiency, especially for machine type communications, due to the limited wireless spectrum. In this paper, we propose a downlink transmission scheme leveraging cooperative device-to-device (D2D) communications and network coding, which can largely reduce the cellular resource consumption and the total energy consumption. In the proposed scheme, the base station generates and broadcasts linear combinations based on the packets requested by different user equipments (UEs) until at least one mature UE can recover all the original packets. Then, a selected mature UE broadcasts new linear combinations based on the recovered original packets to neighbors via D2D until all UEs can decode their packets. A feasible and backward-compatible system design including the necessary revisions on the protocol stack based on the current cellular system architecture is also provided. Then, the closed-form probability mass functions of transmission times for both cellular and D2D transmissions are derived, where the error rates in both cellular and D2D transmissions have been considered. The feedback load is also analyzed. Simulation results with different block error rate (BLER) settings are given, which can be used as references for the cellular network to decide the target BLER and adapt the modulation and coding.
Autors: Yue Li;Kai Sun;Lin Cai;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: Jan 2018, volume: 17, issue:1, pages: 296 - 309
Publisher: IEEE
 
» Coordinated Protection and Control Based on Synchrophasor Data Processing in Smart Distribution Networks
Abstract:
Modern distribution grids are shifting toward resilient and sustainable energy networks through the use of unprecedented number of distributed generation (DG) units and renewable energy sources. However, unlike conventional grids, they are also highly integrative in the sense that, centralized control applications must support critical protective actions to reliably provide power to consumers. To achieve this goal, this paper presents a novel mechanism for coordinated protection and control of DG systems under permanent line faults in distribution networks. First, a centralized fault detector employs voltage phasors and frequency data to identify and isolate the fault within a tolerance time. Upon fault detection, a secondary control algorithm retrieves archived synchrophasor datasets to calculate real and reactive power disturbances caused by the fault isolation. In this mechanism, the secondary controller facilitates voltage/frequency recovery by adapting reference points of local controllers to the postfault conditions. Coordination is carried out based on the knowledge of the response time of protective devices and communication delays in control links. The proposed approach is a promising paradigm for reliable networked protection and improved situational awareness in smart distribution networks.
Autors: Younes Seyedi;Houshang Karimi;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 634 - 645
Publisher: IEEE
 
» Corrections to “Rule Minimization for Traffic Evolution in Software-Defined Networks”
Abstract:
In [1], the text explanation of Fig. 2 was mislabeled as Fig. 3. Fig. 2 and its full text discussion are provided here.
Autors: Usman Ashraf;
Appeared in: IEEE Communications Letters
Publication date: Jan 2018, volume: 22, issue:1, pages: 217 - 217
Publisher: IEEE
 
» Correlative Magnetic Imaging of Heat-Assisted Magnetic Recording Media in Cross Section Using Lorentz TEM and MFM
Abstract:
In order to increase the storage density of hard disk drives, a detailed understanding of the magnetic structure of the granular magnetic layer is essential. Here, we demonstrate an experimental procedure of imaging recorded bits on heat-assisted magnetic recording (HAMR) media in cross section using Lorentz transmission electron microscopy (TEM). With magnetic force microscopy and focused ion beam (FIB), we successfully targeted a single track to prepare cross-sectional TEM specimens. Then, we characterized the magnetic structure of bits with their precise location and orientation using Fresnel mode of Lorentz TEM. This method can promote understanding of the correlation between bits and their material structure in HAMR media to design better the magnetic layer.
Autors: Taeho Roy Kim;Charudatta Phatak;Amanda K. Petford-Long;Yunzhi Liu;Chad Taylor;Bing Zhang;Sharon Myers;Andrea Greene;Tomoko Seki;Michael Alex;Gerardo A. Bertero;Robert Sinclair;
Appeared in: IEEE Transactions on Magnetics
Publication date: Jan 2018, volume: 54, issue:1, pages: 1 - 5
Publisher: IEEE
 
» Cost Causation Based Allocations of Costs for Market Integration of Renewable Energy
Abstract:
An important problem in modern electrical power systems is the inherent variability and uncertainty of renewable energy resources. Allocating the costs generated by the variable resources in a just and reasonable manner is crucial for economic efficiency. In this paper, we develop an axiomatic framework for allocating the deviation costs generated by variable resources using the cost causation based principle. The cost causation principle has a long historical tradition in electrical energy systems and has been proved to provide economic efficiency. We apply the new cost causation based framework to allocate the cost in the production deviation of a group of renewable energy producers and thereby help promote the market integration of renewable energy. The resulting cost allocation framework is then applied to five wind power producers bidding in the Iberian electricity market in Europe, using real data on wind speeds and market prices.
Autors: Pratyush Chakraborty;Enrique Baeyens;Pramod P. Khargonekar;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 70 - 83
Publisher: IEEE
 
» Covert Verb Reading Contributes to Signal Classification of Motor Imagery in BCI
Abstract:
Motor imagery is widely used in the brain–computer interface (BCI) systems that can help people actively control devices to directly communicate with the external world, but its training and performance effect is usually poor for normal people. To improve operators’ BCI performances, here we proposed a novel paradigm, which combined the covert verb reading in the traditional motor imagery paradigm. In our proposed paradigm, participants were asked to covertly read the presented verbs during imagining right hand or foot movements referred by those verbs. EEG signals were recorded with both our proposed paradigm and the traditional paradigm. By the common spatial pattern method, we, respectively, decomposed these signals into spatial patterns and extracted their features used in the following classification of support vector machine. Compared with the traditional paradigm, our proposed paradigm could generate clearer spatial patterns following a somatotopic distribution, which led to more distinguishable features and higher classification accuracies than those in the traditional paradigm. These results suggested that semantic processing of verbs can influence the brain activity of motor imagery and enhance the mu event-related desynchronisation. The combination of semantic processing with motor imagery is therefore a promising method for the improvement of operators’ BCI performances.
Autors: Hong Zhang;Yaoru Sun;Jie Li;Fang Wang;Zijian Wang;
Appeared in: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publication date: Jan 2018, volume: 26, issue:1, pages: 45 - 50
Publisher: IEEE
 
» Creatinine-Iron Complex and Its Use in Electrochemical Measurement of Urine Creatinine
Abstract:
A non-enzymatic electrochemical technique for creatinine sensing is presented, exploiting iron binding property of creatinine. Disposable carbon printed electrodes layered with FeCl3 coated cotton fiber membranes are used to sense creatinine from 10 to 245 mg/dl, on clinical urine samples. The energy-dispersive X-ray spectroscopy analysis confirms the presence of Fe(III) dry chemistry on cotton membrane. Creatinine binding with Fe(III) is verified with UV analysis, with a corresponding decrease in Fe(III) reduction current in cyclic voltammetry. The disposable test strips are interfaced with multi-potentiostat point of care (POC) hand-held device, working in amperometry mode. The results obtained on POC biosensors demonstrate good correlation () with Jaffe method laboratory gold standard. The intra-assay variability is less than 7.1%. The statistical bias as revealed from the Bland–Altman analysis indicates that the POC results are within 95% confidence interval. This POC device does not require any sample preparation step and provides sample to result in less than a minute. FeCl3 sensing chemistry is robust against urine albumin interference, which is especially significant for accurate estimation of albumin to creatinine ratio. The non-enzymatic nature of disposable test strips results in highly stable and robust operation of the POC device over a large range of temperature variations.
Autors: Vinay Kumar;Suraj Hebbar;Rahila Kalam;Sachin Panwar;Sujay Prasad;S. S. Srikanta;P. R. Krishnaswamy;Navakanta Bhat;
Appeared in: IEEE Sensors Journal
Publication date: Jan 2018, volume: 18, issue:2, pages: 830 - 836
Publisher: IEEE
 
» Cross-Layer Optimization for Ultra-Reliable and Low-Latency Radio Access Networks
Abstract:
In this paper, we propose a framework for cross-layer optimization to ensure ultra-high reliability and ultra-low latency in radio access networks, where both transmission delay and queueing delay are considered. With short transmission time, the blocklength of channel codes is finite, and the Shannon capacity cannot be used to characterize the maximal achievable rate with given transmission error probability. With randomly arrived packets, some packets may violate the queueing delay. Moreover, since the queueing delay is shorter than the channel coherence time in typical scenarios, the required transmit power to guarantee the queueing delay and transmission error probability will become unbounded even with spatial diversity. To ensure the required quality-of-service (QoS) with finite transmit power, a proactive packet dropping mechanism is introduced. Then, the overall packet loss probability includes transmission error probability, queueing delay violation probability, and packet dropping probability. We optimize the packet dropping policy, power allocation policy, and bandwidth allocation policy to minimize the transmit power under the QoS constraint. The optimal solution is obtained, which depends on both channel and queue state information. Simulation and numerical results validate our analysis, and show that setting the three packet loss probabilities as equal causes marginal power loss.
Autors: Changyang She;Chenyang Yang;Tony Q. S. Quek;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: Jan 2018, volume: 17, issue:1, pages: 127 - 141
Publisher: IEEE
 
» CrowdGIS: Updating Digital Maps via Mobile Crowdsensing
Abstract:
Accurate digital maps play a crucial role in various location-based services and applications. However, store information is usually missing or outdated in current maps. In this paper, we propose CrowdGIS, an automatic store self-updating system for digital maps that leverages street views and sensing data crowdsourced from mobile users. We first develop a new weighted artificial neural network to learn the underlying relationship between estimated positions and real positions to localize user’s shooting positions. Then, a novel text detection method is designed by considering two valuable features, including the color and texture information of letters. In this way, we can recognize complete store name instead of individual letters as in the previous study. Furthermore, we transfer the shooting position to the location of recognized stores in the map. Finally, CrowdGIS considers three updating categories (replacing, adding, and deleting) to update changed stores in the map based on the kernel density estimate model. We implement CrowdGIS and conduct extensive experiments in a real outdoor region for 1 month. The evaluation results demonstrate that CrowdGIS effectively accommodates store variations and updates stores to maintain an up-to-date map with high accuracy. Note to Practitioners—This paper was motivated by the problem of automatically updating digital maps in a manner of mobile crowdsensing. Existing approaches can update stores in maps through a manual survey or update roads automatically from mobile crowdsensing data. Since the store information is a crucial component in digital map, this paper suggests a novel approach to automatically update stores in digital maps through mobile crowdsensing. This is necessary, in general, because the accuracy of digital map will directly affect the quality of various location-based services. Therefore, the system proposed in this paper is useful for engineers and - evelopers to obtain precise digital maps for localization, navigation, automatic drive, etc.
Autors: Zhe Peng;Shang Gao;Bin Xiao;Songtao Guo;Yuanyuan Yang;
Appeared in: IEEE Transactions on Automation Science and Engineering
Publication date: Jan 2018, volume: 15, issue:1, pages: 369 - 380
Publisher: IEEE
 
» Cryo-CMOS Circuits and Systems for Quantum Computing Applications
Abstract:
A fault-tolerant quantum computer with millions of quantum bits (qubits) requires massive yet very precise control electronics for the manipulation and readout of individual qubits. CMOS operating at cryogenic temperatures down to 4 K (cryo-CMOS) allows for closer system integration, thus promising a scalable solution to enable future quantum computers. In this paper, a cryogenic control system is proposed, along with the required specifications, for the interface of the classical electronics with the quantum processor. To prove the advantages of such a system, the functionality of key circuit blocks is experimentally demonstrated. The characteristic properties of cryo-CMOS are exploited to design a noise-canceling low-noise amplifier for spin-qubit RF-reflectometry readout and a class-F2,3 digitally controlled oscillator required to manipulate the state of qubits.
Autors: Bishnu Patra;Rosario M. Incandela;Jeroen P. G. van Dijk;Harald A. R. Homulle;Lin Song;Mina Shahmohammadi;Robert Bogdan Staszewski;Andrei Vladimirescu;Masoud Babaie;Fabio Sebastiano;Edoardo Charbon;
Appeared in: IEEE Journal of Solid-State Circuits
Publication date: Jan 2018, volume: 53, issue:1, pages: 309 - 321
Publisher: IEEE
 
» CTU-Level Complexity Control for High Efficiency Video Coding
Abstract:
Among the existing video-related applications, a large proportion have requirements for the scalability of the video coding complexity, such as live video chatting and video coding on power-limited mobile devices. Hence, the complexity control algorithms, which aim to make an effective and flexible tradeoff between coding complexity and rate-distortion (RD) performance, have a great practical value. In this paper, a novel complexity control scheme for high efficiency video coding (HEVC) is proposed by dynamically adjusting the depth range for each coding tree unit (CTU). To control the complexity accurately, a statistical model is proposed to estimate the coding complexity of each CTU. Then the complexity budget is allocated to each CTU proportionally to its estimated complexity. At last, the depth range is optimized for each CTU based on the allocated complexity and the probability that contains the actual maximum depth. Our method works well even if the ratio of target complexity to full complexity drops to 40%. The experimental results show that our proposed method outperforms other four state-of-the-art methods in terms of the RD performance, and has superior complexity control accuracy and complexity control stability compared with other one-pass complexity control strategies.
Autors: Jia Zhang;Sam Kwong;Tiesong Zhao;Zhaoqing Pan;
Appeared in: IEEE Transactions on Multimedia
Publication date: Jan 2018, volume: 20, issue:1, pages: 29 - 44
Publisher: IEEE
 
» Curved Reflection Symmetric Axes on Free-Form Surfaces and Their Extraction
Abstract:
Feature detection on smooth free-form surfaces is much more difficult than that on shapes with sharp features. In this paper, we extract the curved reflection axes (CRAs) of an arbitrary free-form surface as features if they exist. The extraction result is robust to boundary noises and strongly sensitive to extrinsic properties of the surface such as projected normals and curvatures. Compared with the general reflection symmetry, curved reflection symmetry is defined to be a reflection symmetry along a smooth 3D embedded curve instead of a plane, where any point on the curve is a local reflection center for some surface points. The properties of the curved reflection symmetric axis are analyzed, and a novel computational model for detecting and extracting CRAs on free-form surfaces is presented. The experimental results are then compared with both the medial axis and the intrinsic symmetric axis, which are two popular feature representations of 3D shapes, and the advantages and uniqueness of the proposed method are convincingly demonstrated. An application of the proposed method in sweep scanning is also presented. Other applications of the proposed method include feature extraction, shape symmetrization, segmentation, and registration.

Note to Practitioners—This paper was originally motivated by the stylus path planning problem in sweep scanning on curved surfaces (which is a new surface inspection technology), though it is also applicable to shape registration and matching. Traditional coordinate measuring machine inspection of curved surfaces depends heavily on manual intervention, resulting in a very slow and tedious process and the acquired data are prone to inconsistency due to the discontinuous nature of the operation. In contrast, sweep scanning adopts a configuration of a deflectable stylus mounted on a motorized articulating head with two rotary axes inside, which enables the stylus tip to follow - continuous path without having to leave the surface. The head itself is mounted on an xyz table. During a sweep scanning, the head slowly moves on the xyz table (which is heavy and has stringent dynamic constraints due to its large mass) along a nominal path, while the stylus (which is extremely light) quickly oscillates in a direction transverse to the path of the head. The proposed curved reflection axis offers to be a powerful algebraic model for generating a nominal head path for sweep scanning. The reported work is preliminary though, especially in terms of covering the entire surface and also considering the specific length of the stylus, which will be our future work.

Autors: Lulin Quan;Yang Zhang;Kai Tang;
Appeared in: IEEE Transactions on Automation Science and Engineering
Publication date: Jan 2018, volume: 15, issue:1, pages: 111 - 126
Publisher: IEEE
 
» Cyclic Voltammetry Peaks Due to Deep Level Traps in Si Nanowire Array Electrodes
Abstract:
When metal-assisted chemical etching (MACE) is used to increase the effective surface area of Si electrodes for electrochemical capacitors, it is often found that the cyclic voltammetry characteristics contain anodic and cathodic peaks. We link these peaks to the charging–discharging dynamics of deep level traps within the nanowire system. The trap levels are associated with the use of Ag in the MACE process that can leave minute amounts of Ag residue within the nanowire system to interact with the H2O layer surrounding the nanowires in a room temperature ionic liquid. The influence of the traps can be removed by shifting the Fermi level away from the trap levels via spin-on doping. These results in lower capacitance values but improved charge–discharge cycling behavior. Low-frequency noise measurements proof the presence or absence of these deep level traps.
Autors: Abdurrahman Shougee;Foivia Konstantinou;Tim Albrecht;Kristel Fobelets;
Appeared in: IEEE Transactions on Nanotechnology
Publication date: Jan 2018, volume: 17, issue:1, pages: 154 - 160
Publisher: IEEE
 
» CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis
Abstract:
Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work, we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation.
Autors: Thomas Höllt;Nicola Pezzotti;Vincent van Unen;Frits Koning;Boudewijn P.F. Lelieveldt;Anna Vilanova;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 739 - 748
Publisher: IEEE
 
» Data Drainage: A Novel Load Balancing Strategy for Wireless Sensor Networks
Abstract:
Load balance is a vital goal for battery-powered wireless sensor networks (WSNs). In this letter, we propose a novel load balancing strategy for data transmission of WSNs, namely, super links-based data drainage, which makes full use of the advantages of super nodes with more powerful hardware and greater communication capacity to realize data traffic redistribution. Being different from conventional passive late-remedy approaches, this is a positive and early-intervention strategy. Specifically, an evaluation function is designed to select appropriate start points and end points of super links, and the core idea is to transfer data from locations relatively far from the sink with a jump of data traffic to those near the sink with little data traffic. Extensive simulations are conducted to validate the effectiveness and advantages of the new strategy.
Autors: Xuxun Liu;Peiyu Zhang;
Appeared in: IEEE Communications Letters
Publication date: Jan 2018, volume: 22, issue:1, pages: 125 - 128
Publisher: IEEE
 
» Data Through Others' Eyes: The Impact of Visualizing Others' Expectations on Visualization Interpretation
Abstract:
In addition to visualizing input data, interactive visualizations have the potential to be social artifacts that reveal other people's perspectives on the data. However, how such social information embedded in a visualization impacts a viewer's interpretation of the data remains unknown. Inspired by recent interactive visualizations that display people's expectations of data against the data, we conducted a controlled experiment to evaluate the effect of showing social information in the form of other people's expectations on people's ability to recall the data, the degree to which they adjust their expectations to align with the data, and their trust in the accuracy of the data. We found that social information that exhibits a high degree of consensus lead participants to recall the data more accurately relative to participants who were exposed to the data alone. Additionally, participants trusted the accuracy of the data less and were more likely to maintain their initial expectations when other people's expectations aligned with their own initial expectations but not with the data. We conclude by characterizing the design space for visualizing others' expectations alongside data.
Autors: Yea-Seul Kim;Katharina Reinecke;Jessica Hullman;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 760 - 769
Publisher: IEEE
 
» Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations
Abstract:
Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.
Autors: Laura E. Matzen;Michael J. Haass;Kristin M. Divis;Zhiyuan Wang;Andrew T. Wilson;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 563 - 573
Publisher: IEEE
 
» Data-Cell-Variation-Tolerant Dual-Mode Sensing Scheme for Deep Submicrometer STT-RAM
Abstract:
In the spin-transfer-torque random access memory design, the sensing scheme has become a bottleneck from the viewpoints of performance and read energy, because the required read current and time are too large to satisfy a target read yield. When the target read yield is greater than the fundamental read-yield limit determined by bit-to-bit data-cell variation, the conventional data-cell-variation-tolerant (DCVT) sensing scheme cannot satisfy the target read yield without requiring impractically high performance and energy overhead. To resolve this problem, this paper proposes a DCVT dual-mode sensing scheme (DMSS) that operates mostly in normal mode when correct sensing is assured, and infrequently in exception mode when correct sensing is uncertain. Using the dual-mode strategy, the DMSS can achieve the target read yield, while significantly mitigating the performance and energy overhead. Monte–Carlo HSPICE simulation results, using industry-compatible 45-nm model parameters, show that the proposed DMSS achieves a read yield of 6.1 sigma with a faster read time and lower read energy than the destructive self-reference sensing scheme.
Autors: Taehui Na;Byungkyu Song;Jung Pill Kim;Seung H. Kang;Seong-Ook Jung;
Appeared in: IEEE Transactions on Circuits and Systems I: Regular Papers
Publication date: Jan 2018, volume: 65, issue:1, pages: 163 - 174
Publisher: IEEE
 
» Data-Defect Inspection With Kernel-Neighbor-Density-Change Outlier Factor
Abstract:
Data-defect would affect the data quality and the analysis results of data mining. This paper presents a data-defect inspection method with kernel-neighbor-density-change outlier factor (KNDCOF). The definition of kernel neighbor density is proposed to represent the density of each object in database, and the ascending distance series (ADS) of each object is calculated based on the kernel distance between the object and its neighbors. Then, the average density fluctuation (ADF) of the object is established according to the weighted sum of the square of density difference between the object and others in ADS. Finally, the KNDCOF of the object is equal to the ratios of the ADF of the object and the average ADF of neighbors of the object. The degree of the object being an outlier is indicated by the KNDCOF value. The experiments are performed on three real data sets to evaluate the effectiveness of the proposed method. The experimental results verify that the proposed method has higher quality of data-defect inspection and does not increase the time complexity.

Note to Practitioners–Data-defect inspection is an important procedure of data preprocessing for a real industrial process. This paper presents a data-defect inspection method with kernel-neighbor-density-change outlier factor to identify the outliers, and addresses the challenges associated with the strong correlation and the nonlinearity of the industrial data. The proposed method calculates the outlier factor for each object, which quantifies how outlying it is. The outlier factor is based on the density difference between the object and its neighbors. The larger the outlier factor of an object is, the higher the outlierness of the object is. The proposed method could be wildly used in an industrial complex data set with different density regions. In the industrial field, engineers can deal with the objects with high outlier factor values based on the actual requirem- nts.

Autors: Hui Cao;Rui Ma;Hongliang Ren;Shuzhi Sam Ge;
Appeared in: IEEE Transactions on Automation Science and Engineering
Publication date: Jan 2018, volume: 15, issue:1, pages: 225 - 238
Publisher: IEEE
 
» Decentralized Stochastic Control of Distributed Energy Resources
Abstract:
We consider the decentralized control of radial distribution systems with controllable photovoltaic inverters and energy storage resources. For such systems, we investigate the problem of designing fully decentralized controllers that minimize the expected cost of balancing demand, while guaranteeing the satisfaction of individual resource and distribution system voltage constraints. Employing a linear approximation of the branch flow model, we formulate this problem as the design of a decentralized disturbance-feedback controller that minimizes the expected value of a convex quadratic cost function, subject to robust convex quadratic constraints on the system state and input. As such problems are, in general, computationally intractable, we derive a tractable inner approximation to this decentralized control problem, which enables the efficient computation of an affine control policy via the solution of a finite-dimensional conic program. As affine policies are, in general, suboptimal for the family of systems considered, we provide an efficient method to bound their suboptimality via the optimal solution of another finite-dimensional conic program. A case study of a 12 kV radial distribution system demonstrates that decentralized affine controllers can perform close to optimal.
Autors: Weixuan Lin;Eilyan Bitar;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 888 - 900
Publisher: IEEE
 
» Decimal Full Adders Specially Designed for Quantum-Dot Cellular Automata
Abstract:
New emerging technologies, with low area/ power/latency properties, are gaining momentum as replacements for CMOS. In particular, for quantum-dot cellular automata (QCA) realization, many arithmetic circuits have been redesigned. The basic QCA elements are a three-way majority gate and an inverter. The trivial mapping of logical circuits to their QCA equivalents via direct replacement of AND and OR gates with partially utilized majority (PUM) gates (i.e., with a “0” and “1” input, respectively) leads to exploitation of QCA basic components. Regarding the revitalized decimal arithmetic units in digital processors, researchers have begun to design decimal arithmetic circuits on QCA. For example, several QCA decimal full adders are trivially designed via the aforementioned direct mapping. However, only one proposition in the literature tries to make better use of majority gates, but yet replaces many AND and OR gates by PUMs. In this brief, we propose a QCA decimal full adder that is mostly composed of fully utilized majority gates (i.e., with no constant inputs), and rarely includes PUMs. The proposed circuit has been designed and tested by QCADesigner, and compared with relevant previous works, where the cell count, area, and delay, show 39%, 78%, 12% improvement, respectively.
Autors: Dariush Abedi;Ghassem Jaberipur;
Appeared in: IEEE Transactions on Circuits and Systems II: Express Briefs
Publication date: Jan 2018, volume: 65, issue:1, pages: 106 - 110
Publisher: IEEE
 
» Decision Graph Embedding for High-Resolution Manometry Diagnosis
Abstract:
High-resolution manometry is an imaging modality which enables the categorization of esophageal motility disorders. Spatio-temporal pressure data along the esophagus is acquired using a tubular device and multiple test swallows are performed by the patient. Current approaches visualize these swallows as individual instances, despite the fact that aggregated metrics are relevant in the diagnostic process. Based on the current Chicago Classification, which serves as the gold standard in this area, we introduce a visualization supporting an efficient and correct diagnosis. To reach this goal, we propose a novel decision graph representing the Chicago Classification with workflow optimization in mind. Based on this graph, we are further able to prioritize the different metrics used during diagnosis and can exploit this prioritization in the actual data visualization. Thus, different disorders and their related parameters are directly represented and intuitively influence the appearance of our visualization. Within this paper, we introduce our novel visualization, justify the design decisions, and provide the results of a user study we performed with medical students as well as a domain expert. On top of the presented visualization, we further discuss how to derive a visual signature for individual patients that allows us for the first time to perform an intuitive comparison between subjects, in the form of small multiples.
Autors: Julian Kreiser;Alexander Hann;Eugen Zizer;Timo Ropinski;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 873 - 882
Publisher: IEEE
 
» Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data
Abstract:
With increasing capabilities of energy efficient systems, computational technology can be deployed, virtually everywhere. Machine learning has proven a valuable tool for extracting meaningful information from measured data and forms one of the basic building blocks of ubiquitous computing. In high-throughput applications, measurements are rapidly taken to monitor physical processes. This brings modern communication technologies to its limits. Therefore, only a subset of measurements, the interesting ones, should be further processed and possibly communicated to other devices. In this paper, we investigate architectural characteristics of embedded systems for filtering high-volume sensor data before further processing. In particular, we investigate implementations of decision trees and random forests for the classical von-Neumann computing architecture and custom circuits by the means of field programmable gate arrays.
Autors: Sebastian Buschjäger;Katharina Morik;
Appeared in: IEEE Transactions on Circuits and Systems I: Regular Papers
Publication date: Jan 2018, volume: 65, issue:1, pages: 209 - 222
Publisher: IEEE
 
» Decision-Directed Retention-Failure Recovery With Channel Update for MLC NAND Flash Memory
Abstract:
To recover from the retention noise induced errors in nand flash memory, a retention-aware belief-propagation (RABP) decoding scheme for low-density parity-check codes is introduced. The RABP is a two-stage decoding scheme in which the memory cell’s charge-loss effect is systematically compensated. In RABP decoding, instead of read retries for data recovery, the probable victim cells are first determined with the help of read-back voltage signal and the decoded bit decisions. Then, for such suspected victim cells, their log-likelihood-ratio regions are modified in such a way as to absorb the effect of cell voltage downshift caused by retention noise, and then a second round of belief-propagation (BP) decoding is performed afresh, often with decoding failure recovery. Furthermore, leveraging on the RABP decoded bit-error pattern, an RABP assisted channel update (RABP-CU) algorithm is proposed which re-estimates the latest cell voltage distribution parameters without incurring new memory sensing operations. This is achieved by minimizing the mean squared error between the measured and predicted bit error/erasure values. Through simulations, it is shown that the RABP decoder increases the retention time limit by up to 70% compared with single round of BP decoding. The proposed RABP-CU algorithm further extends the data retention time.
Autors: Chaudhry Adnan Aslam;Yong Liang Guan;Kui Cai;
Appeared in: IEEE Transactions on Circuits and Systems I: Regular Papers
Publication date: Jan 2018, volume: 65, issue:1, pages: 353 - 365
Publisher: IEEE
 
» Decision-Making Framework for Automated Driving in Highway Environments
Abstract:
This paper presents a decision-making framework for automated driving in highway environments. The framework is capable of reliably, robustly assessing a given highway situation (with respect to the possibility of collision) and of automatically determining an appropriate maneuver for the situation. It consists of two main components: situation assessment and strategy decision. The situation assessment component utilizes multiple complementary “threat measures” and Bayesian networks in its calculations of “threat levels” at the car and lane level to evaluate the possibility of collisions for a given highway traffic situation. The strategy decision component, designed to generate goal-directed and collision-free behaviors, automatically determines an appropriate maneuver in a given highway situation via a hierarchical state machine—such a machine both reduces the complexity of and extends a strategy model. The types of maneuver determined by the component include both simple maneuvers, such as slowing down to avoid collision with a vehicle in front, and complex maneuvers, such as lane changes and overtaking. The presented decision-making framework is tested and evaluated—both on a closed high-speed test track in simulated traffic with various driving scenarios and on public highways in real traffic through in-vehicle testing—to verify that it can provide sufficiently reliable performance for automated driving in highway environments in terms of safety, reliability, and robustness.
Autors: Samyeul Noh;Kyounghwan An;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Jan 2018, volume: 19, issue:1, pages: 58 - 71
Publisher: IEEE
 
» Decoding Motor Unit Activity From Forearm Muscles: Perspectives for Myoelectric Control
Abstract:
We prove the feasibility of decomposing high density surface EMG signals from forearm muscles in non-isometric wrist motor tasks of normally limbed and limb-deficient individuals with the perspective of using the decoded neural information for prosthesis control. For this purpose, we recorded surface EMG signals during motions of three degrees of freedom of the wrist in seven normally limbed subjects and two patients with limb deficiency. The signals were decomposed into individual motor unit activity with a convolutive blind source separation algorithm. On average, for each subject, 16 ± 7 motor units were identified per motor task. The discharge timings of these motor units were estimated with an accuracy > 85%. Moreover, the activity of 6 ± 5 motor units per motor task was consistently detected in all repetitions of the same task. The joint angle at which motor units were first identified was 62.5 ± 26.4% of the range of motion, indicating a prevalence in the identification of high threshold motor units. These findings prove the feasibility of accurate identification of the neural drive to muscles in contractions relevant for myoelectric control, allowing the development of a new generation of myocontrol methods based on motor unit spike trains.
Autors: Tamás Kapelner;Francesco Negro;Oskar C. Aszmann;Dario Farina;
Appeared in: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publication date: Jan 2018, volume: 26, issue:1, pages: 244 - 251
Publisher: IEEE
 
» Decomposition of Settlement Residues Using Loss Compensated DC-OPF Model
Abstract:
This letter presents an approach to decompose the post-settlement residues (SR) into network loss and congestion related components in a loss-compensated (LC) direct current (DC) optimal power flow (OPF) model. The LC-DC-OPF model is presented based on quadratically constrained quadratic programming. The Karush-Kuhn-Tucker conditions of this LC-DC-OPF model are used to derive the decomposed components of SR. The numerical efficiency and accuracy of the model are demonstrated by simulations on five benchmark test systems.
Autors: Deep Kiran;Abhijit R. Abhyankar;Bijaya K. Panigrahi;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 1071 - 1073
Publisher: IEEE
 
» Deconvolved Conventional Beamforming for a Horizontal Line Array
Abstract:
Horizontal line arrays are often used in underwater environments to detect/separate a weak signal and estimate its direction of arrival from many loud interfering sources and ambient noise. Conventional beamforming is robust but suffers from fat beams and high-level sidelobes. High-resolution beamforming, based on the inverse of the signal covariance matrix, such as minimum-variance distortionless response (MVDR), yields narrow beamwidths and low sidelobe levels but is sensitive to signal mismatch and requires many snapshots of data. This paper applies a deconvolution algorithm used in image deblurring to the conventional beam power of a uniform line array (spaced at half-wavelength) to avoid the instability problems of common deconvolution methods. The deconvolved beam power yields narrow beams, and low sidelobe levels similar to or better than high-resolution beamforming and at the same time retains the robustness of conventional beamforming. It yields a higher output signal-to-noise ratio than conventional (and MVDR) beamforming for isotropic noise. Performance is evaluated with simulated and real data.
Autors: T. C. Yang;
Appeared in: IEEE Journal of Oceanic Engineering
Publication date: Jan 2018, volume: 43, issue:1, pages: 160 - 172
Publisher: IEEE
 
» Decorrelation-Based Concurrent Digital Predistortion With a Single Feedback Path
Abstract:
In this paper, a novel decorrelation-based concurrent digital predistortion (DPD) solution is proposed for dual-band transmitters (TXs) employing a single wideband power amplifier (PA), and utilizing only a single feedback receiver path. The proposed decorrelation-based parameter learning solution is both flexible and simple, and operates in a closed-loop manner, opposed to the widely applied indirect learning architecture. The proposed decorrelation-based learning and DPD processing can also be effectively applied to more ordinary single carrier/band transmissions, as well as generalized to more than two transmit bands. Through a comprehensive analysis covering both the DPD parameter learning and the main path processing, it is shown that the complexity of the proposed concurrent DPD is substantially lower compared with the other state-of-the-art concurrent DPD methods. Extensive set of simulation and RF measurement results are also presented, using base-station PAs as well as a commercial LTE-Advanced mobile PA, to evaluate and validate the effectiveness of the proposed DPD solution in various real world scenarios, incorporating both single-band and dual-band TX cases. The simulation and RF measurement results demonstrate excellent linearization performance of the proposed concurrent DPD, even outperforming current state-of-the-art methods, despite the significantly lower complexity.
Autors: Mahmoud Abdelaziz;Lauri Anttila;Adnan Kiayani;Mikko Valkama;
Appeared in: IEEE Transactions on Microwave Theory and Techniques
Publication date: Jan 2018, volume: 66, issue:1, pages: 280 - 293
Publisher: IEEE
 
» Decoupled Visual Servoing With Fuzzy Q-Learning
Abstract:
The objective of visual servoing aims to control an object's motion with visual feedbacks and becomes popular recently. Problems of complex modeling and instability always exist in visual servoing methods. Moreover, there are few research works on selection of the servoing gain in image-based visual servoing (IBVS) methods. This paper proposes an IBVS method with Q-Learning, where the learning rate is adjusted by a fuzzy system. Meanwhile, a synthetic preprocess is introduced to perform feature extraction. The extraction method is actually a combination of a color-based recognition algorithm and an improved contour-based recognition algorithm. For dealing with underactuated dynamics of the unmanned aerial vehicles (UAVs), a decoupled controller is designed, where the velocity and attitude are decoupled through attenuating the effects of underactuation in roll and pitch and two independent servoing gains, for linear and angular motion servoing, respectively, are designed in place of single servoing gain in traditional methods. For further improvement in convergence and stability, a reinforcement learning method, Q-Learning, is taken for adaptive servoing gain adjustment. The Q-Learning is composed of two independent learning agents for adjusting two serving gains, respectively. In order to improve the performance of the Q-Learning, a fuzzy-based method is proposed for tuning the learning rate. The results of simulations and experiments on control of UAVs demonstrate that the proposed method has better properties in stability and convergence than the competing methods.
Autors: Haobin Shi;Xuesi Li;Kao-Shing Hwang;Wei Pan;Genjiu Xu;
Appeared in: IEEE Transactions on Industrial Informatics
Publication date: Jan 2018, volume: 14, issue:1, pages: 241 - 252
Publisher: IEEE
 
» Deep Background Modeling Using Fully Convolutional Network
Abstract:
Background modeling plays an important role for video surveillance, object tracking, and object counting. In this paper, we propose a novel deep background modeling approach utilizing fully convolutional network. In the network block constructing the deep background model, three atrous convolution branches with different dilate are used to extract spatial information from different neighborhoods of pixels, which breaks the limitation that extracting spatial information of the pixel from fixed pixel neighborhood. Furthermore, we sample multiple frames from original sequential images with increasing interval, in order to capture more temporal information and reduce the computation. Compared with classical background modeling approaches, our approach outperforms the state-of-art approaches both in indoor and outdoor scenes.
Autors: Lu Yang;Jing Li;Yuansheng Luo;Yang Zhao;Hong Cheng;Jun Li;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Jan 2018, volume: 19, issue:1, pages: 254 - 262
Publisher: IEEE
 
» Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans
Abstract:
Recent developments in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on various object detection/recognition problems. This has been made possible due to the availability of large annotated data and a better understanding of the nonlinear mapping between images and class labels, as well as the affordability of powerful graphics processing units (GPUs). These developments in deep learning have also improved the capabilities of machines in understanding faces and automatically executing the tasks of face detection, pose estimation, landmark localization, and face recognition from unconstrained images and videos. In this article, we provide an overview of deep-learning methods used for face recognition. We discuss different modules involved in designing an automatic face recognition system and the role of deep learning for each of them. Some open issues regarding DCNNs for face recognition problems are then discussed. This article should prove valuable to scientists, engineers, and end users working in the fields of face recognition, security, visual surveillance, and biometrics.
Autors: Rajeev Ranjan;Swami Sankaranarayanan;Ankan Bansal;Navaneeth Bodla;Jun-Cheng Chen;Vishal M. Patel;Carlos D. Castillo;Rama Chellappa;
Appeared in: IEEE Signal Processing Magazine
Publication date: Jan 2018, volume: 35, issue:1, pages: 66 - 83
Publisher: IEEE
 
» Deep Learning for Visual Understanding: Part 2 [From the Guest Editors]
Abstract:
Visual perception is one of our most essential and fundamental abilities that enables us to make sense of what our eyes see and interpret the world that surrounds us. It allows us to function and, thus, our civilization to survive. No sensory loss is more debilitating than blindness as we are, above all, visual beings. Close your eyes for a moment after reading this sentence and try grabbing something in front of you, navigating your way in your environment, or just walking straight, reading a book, playing a game, or perhaps learning something new. Of course, please do not attempt to drive a vehicle. As you would realize again and appreciate profoundly, we owe so much to this amazing facility. It is no coincidence that most of the electrical activity in the human brain and most of its cerebral cortex is associated with visual understanding. Computer vision is the field of study that develops solutions for visual perception. In other words, it aims to make computers understand the seen data in the same way that human vision does. It incorporates several scientific disciplines such as signal processing, machine learning, applied mathematics, sensing, geometry, optimization, statistics, and data sciences to name a few.
Autors: Fatih Porikli;Shiguang Shan;Cees Snoek;Rahul Sukthankar;Xiaogang Wang;
Appeared in: IEEE Signal Processing Magazine
Publication date: Jan 2018, volume: 35, issue:1, pages: 17 - 19
Publisher: IEEE
 
» Deep Multiple Instance Learning-Based Spatial–Spectral Classification for PAN and MS Imagery
Abstract:
Panchromatic (PAN) and multispectral (MS) imagery classification is one of the hottest topics in the field of remote sensing. In recent years, deep learning techniques have been widely applied in many areas of image processing. In this paper, an end-to-end learning framework based on deep multiple instance learning (DMIL) is proposed for MS and PAN images’ classification using the joint spectral and spatial information based on feature fusion. There are two instances in the proposed framework: one instance is used to capture the spatial information of PAN and the other is used to describe the spectral information of MS. The features obtained by the two instances are concatenated directly, which can be treated as simple fusion features. To fully fuse the spatial–spectral information for further classification, the simple fusion features are fed into a fusion network with three fully connected layers to learn the high-level fusion features. Classification experiments carried out on four different airborne MS and PAN images indicate that the classifier provides feasible and efficient solution. It demonstrates that DMIL performs better than using a convolutional neural network and a stacked autoencoder network separately. In addition, this paper shows that the DMIL model can learn and fuse spectral and spatial information effectively, and has huge potential for MS and PAN imagery classification.
Autors: Xu Liu;Licheng Jiao;Jiaqi Zhao;Jin Zhao;Dan Zhang;Fang Liu;Shuyuan Yang;Xu Tang;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: Jan 2018, volume: 56, issue:1, pages: 461 - 473
Publisher: IEEE
 
» Deep Temporal Multimodal Fusion for Medical Procedure Monitoring Using Wearable Sensors
Abstract:
Process monitoring and verification have a wide range of uses in the medical and healthcare fields. Currently, such tasks are often carried out by a trained specialist, which makes them expensive, inefficient, and time-consuming. Recent advances in automated video- and multimodal-data-based action and activity recognition have made it possible to reduce the extent of manual intervention required to effectively carry out process supervision tasks. In this paper, we propose algorithms for automated egocentric human action and activity recognition from multimodal data, with a target application of monitoring and assisting a user perform a multistep medical procedure. We propose a supervised deep multimodal fusion framework that relies on concurrent processing of motion data acquired with wearable sensors and video data acquired with an egocentric or body-mounted camera. We demonstrate the effectiveness of the algorithm on a public multimodal dataset and conclude that automated process monitoring via the use of multiple heterogeneous sensors is a viable alternative to its manual counterpart. Furthermore, we demonstrate that the application of previously proposed adaptive sampling schemes to the video processing branch of the multimodal framework results in significant performance improvements.
Autors: Edgar A. Bernal;Xitong Yang;Qun Li;Jayant Kumar;Sriganesh Madhvanath;Palghat Ramesh;Raja Bala;
Appeared in: IEEE Transactions on Multimedia
Publication date: Jan 2018, volume: 20, issue:1, pages: 107 - 118
Publisher: IEEE
 
» DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks
Abstract:
Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.
Autors: Nicola Pezzotti;Thomas Höllt;Jan Van Gemert;Boudewijn P.F. Lelieveldt;Elmar Eisemann;Anna Vilanova;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: Jan 2018, volume: 24, issue:1, pages: 98 - 108
Publisher: IEEE
 
» Delay Effects on Consensus-Based Distributed Economic Dispatch Algorithm in Microgrid
Abstract:
For microgrids with connected but sparse communication networks, consensus-based approach can provide a distributed solution to the economic dispatch problem. However, as time delays in communication networks are nonnegligible, performances of consensus-based distributed economic dispatch algorithms are not well disclosed and investigated. Considering the effects of time delays, we first provide a novel consensus-based economic dispatch algorithm. The algorithm is fully distributed such that the optimal dispatch of energy resources in microgrid can be implemented in a distributed manner. The influence of time delays on distributed economic dispatch is strictly analyzed. The maximum allowable delay bounds are derived by applying the generalized Nyquist criterion. Several simulations are presented to verify the effectiveness of the algorithm and the correctness of the theoretical results.
Autors: Gang Chen;Zhongyuan Zhao;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 602 - 612
Publisher: IEEE
 
» Delay Monitoring System With Multiple Generic Monitors for Wide Voltage Range Operation
Abstract:
As the semiconductor process technology continuously scales down, circuit delay variations due to manufacturing and environmental variations become more and more serious. These delay variations are hardly predictable and thus require an additional design margin, which impedes the chance to reduce the area and power consumption of a chip. One of the best solutions to alleviate this problem is to measure circuit delays at run time and control the supply voltage accordingly through a closed-loop dynamic voltage and frequency scaling (DVFS) scheme. The key issue of this scheme is the delay mismatch between the monitoring circuit and the target block. A large delay mismatch might lose the advantage of the closed-loop DVFS. It becomes much worse as a circuit block operates in wider voltage range, from near-threshold voltage to super-overdrive voltage. This paper proposes novel delay monitoring systems with multiple generic monitors for wide voltage range operation, which provide a better delay correlation between the monitoring circuit and the target block compared to conventional monitoring approaches. The proposed approaches reduce the maximum error by up to 91% for a popular processor core in a 14-nm FinFET process technology, thereby bring a decrease of design margin, lower-power, and/or lower-cost design.
Autors: Jongho Kim;Kiyoung Choi;Yonghwan Kim;Wook Kim;Kyungtae Do;Jungyun Choi;
Appeared in: IEEE Transactions on Very Large Scale Integration Systems
Publication date: Jan 2018, volume: 26, issue:1, pages: 37 - 49
Publisher: IEEE
 
» Deliverable Robust Ramping Products in Real-Time Markets
Abstract:
The increasing penetration of variable energy resources has led to more uncertainties in power systems. Flexible Ramping Products (FRP) have been adopted by several electricity markets to manage the uncertainties. We reveal that neglected line congestion for FRP may not only cause infeasibility, but also result in a failure of cost recovery. To address the deliverability issues on FRP, this paper proposes a new concept, Deliverable Robust Ramping Products (DRRP), in real-time markets. The DRRP includes generation ramping reserve and generation capacity reserve. The DRRP is deliverable and immunized against any predefined uncertainty. It also fully addresses the bid cost recovery issue caused by the line congestion in existing FRPs. The prices of DRRP are derived within the Affine Adjustable Robust Optimization (AARO) framework. These prices can be used to identify valuable reserves among available reserves and quantify the values of flexible resources that provide reserves. This paper also proposes a general approach to obtaining the time-decoupled prices for DRRP and generation, which can be used for the market settlement of the first interval only in real-time markets. Simulations on a 3-bus system and the IEEE 118-bus system are performed to illustrate the concept of DRRP and the advantages of DRRP compared to existing FRP.
Autors: Hongxing Ye;Zuyi Li;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 5 - 18
Publisher: IEEE
 
» Demand Response of Ancillary Service From Industrial Loads Coordinated With Energy Storage
Abstract:
As one of the featured initiatives in smart grids, demand response is enabling active participation of electricity consumers in the supply/demand balancing process, thereby enhancing the power system's operational flexibility in a cost-effective way. Industrial load plays an important role in demand response because of its intense power consumption, already existing advanced monitoring, and control infrastructure, and its strong economic incentive due to the high energy costs. As typical industrial loads, cement plants are able to quickly adjust their power consumption rate by switching on/off the crushers. However, in the cement plant as well as other industrial loads, switching on/off the loading units only achieves discrete power changes, which restricts the load from offering valuable ancillary services such as regulation and load following, as continuous power changes are required for these services. In this paper, we overcome this restriction of poor granularity by proposing methods that enable these loads to provide regulation or load following with the support of an onsite energy storage system.
Autors: Xiao Zhang;Gabriela Hug;J. Zico Kolter;Iiro Harjunkoski;
Appeared in: IEEE Transactions on Power Systems
Publication date: Jan 2018, volume: 33, issue:1, pages: 951 - 961
Publisher: IEEE
 
» Demand-Side Management by Regulating Charging and Discharging of the EV, ESS, and Utilizing Renewable Energy
Abstract:
The evolution in microgrid technologies as well as the integration of electric vehicles (EVs), energy storage systems (ESSs), and renewable energy sources will all play a significant role in balancing the planned generation of electricity and its real-time use. We propose a real-time decentralized demand-side management (RDCDSM) to adjust the real-time residential load to follow a preplanned day-ahead energy generation by the microgrid, based on predicted customers’ aggregate load. A deviation from the predicted demand at the time of consumption is assumed to result in additional cost or penalty inflicted on the deviated customers. To develop our system, we formulate a game with mixed strategy which in the first phase (i.e., prediction phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flattened curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another game with mixed strategy to mitigate the deviation between the instantaneous real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and ESSs and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator to better deal with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. We evaluate the performance of our method against a centralized allocation and an existing decentralized EV charge control noncooperative game method both of which rely on a day ahead demand prediction without any refinement. We run simulations with various microgrid configurations, by varying the load and generated power, and compare the outcomes.
Autors: Mosaddek Hossain Kamal Tushar;Adel W. Zeineddine;Chadi Assi;
Appeared in: IEEE Transactions on Industrial Informatics
Publication date: Jan 2018, volume: 14, issue:1, pages: 117 - 126
Publisher: IEEE
 
» Demonstration of Constant 8 W/mm Power Density at 10, 30, and 94 GHz in State-of-the-Art Millimeter-Wave N-Polar GaN MISHEMTs
Abstract:
This paper reports on state-of-the-art millimeter-wave power performance of N-polar GaN-based metal–insulator–semiconductor high-electron-mobility transistors at 30 and 94 GHz. The performance is enabled by our N-polar deep recess structure, whereby a GaN cap layer is added in the access regions of the transistor to simultaneously enhance the access region conductivity while mitigating dc-to-RF dispersion. The impact of lateral scaling of the drain access region length is examined using the tradeoff between breakdown voltage and small-signal gain. Load-pull measurements are presented at 94 GHz, corresponding to the target device operating frequency in W-band, where the device demonstrated a peak power-added efficiency (PAE) of 28.8% at 16 V and record-high maximum output power density of 8 W/mm at 20 V. Additional load-pull measurements at 30 and 10 GHz demonstrate the viability of this device across a wide frequency range where the peak power remained constant at 8 W/mm and with peak PAEs of 56% and 58%, respectively.
Autors: Brian Romanczyk;Steven Wienecke;Matthew Guidry;Haoran Li;Elaheh Ahmadi;Xun Zheng;Stacia Keller;Umesh K. Mishra;
Appeared in: IEEE Transactions on Electron Devices
Publication date: Jan 2018, volume: 65, issue:1, pages: 45 - 50
Publisher: IEEE
 
» Density of Spherically Embedded Stiefel and Grassmann Codes
Abstract:
The density of a code is the fraction of the coding space covered by packing balls centered around the codewords. A high density indicates that a code performs well when used as a uniform point-wise discretization of an ambient space. This paper investigates the density of codes in the complex Stiefel and Grassmann manifolds equipped with the chordal distance arising from an Euclidean embedding, including the unitary group as a special case. The choice of distance enables the treatment of the manifolds as subspaces of Euclidean hyperspheres. In this geometry, the densest packings are not necessarily equivalent to maximum–minimum-distance codes. Computing a code’s density follows from computing: 1) the normalized volume of a metric ball and 2) the kissing radius, the radius of the largest balls one can pack around the codewords without overlapping. First, the normalized volume of a metric ball is evaluated by asymptotic approximations. The volume of a small ball can be well-approximated by the volume of a locally equivalent tangential ball. In order to properly normalize this approximation, the precise volumes of the manifolds induced by their spherical embedding are computed. For larger balls, a hyperspherical cap approximation is used, which is justified by a volume comparison theorem showing that the normalized volume of a ball in the Stiefel or Grassmann manifold is asymptotically equal to the normalized volume of a ball in its embedding sphere as the dimension grows to infinity. Then, bounds on the kissing radius are derived alongside corresponding bounds on the density. Unlike spherical codes or codes in flat spaces, the kissing radius of Grassmann or Stiefel codes cannot be exactly determined from its minimum distance. It is nonetheless possible to derive bounds on density as functions of the minimum distance. Stiefel and Grassmann codes have larger density than their image spherical codes when dimensions tend to infinity- Finally, the bounds on density lead to refinements of the standard Hamming bounds for Stiefel and Grassmann codes.
Autors: Renaud-Alexandre Pitaval;Lu Wei;Olav Tirkkonen;Camilla Hollanti;
Appeared in: IEEE Transactions on Information Theory
Publication date: Jan 2018, volume: 64, issue:1, pages: 225 - 248
Publisher: IEEE
 

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