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TOC Alert for Publication# 6221036 2016June 27<![CDATA[Table of contents]]>467C11485388<![CDATA[IEEE Transactions on Cybernetics]]>467C2C2159<![CDATA[MIRACLE: Mobility Prediction Inside a Coverage Hole Using Stochastic Learning Weak Estimator]]>mobility prediction inside acoverage hole (MIRACLE). The objectives of MIRACLE are two fold. First, one should be able to correctly predict the mobility pattern of a target inside a coverage hole with low computational overhead. Second, if a target changes its mobility pattern inside the coverage hole, the proposed estimator should give some estimation about all possible transitions among the mobility models. We use the trajectory extrapolation and fusion techniques for exploring all possible transitions among the mobility models. We validate the results with simulated traces of mobile targets generated using network simulator NS-2. Simulation results show that MIRACLE estimates the mobility patterns inside coverage hole with an accuracy of more than 60% in WSNs.]]>467148614971220<![CDATA[Learning A Superpixel-Driven Speed Function for Level Set Tracking]]>467149815102788<![CDATA[Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System]]>467151115231968<![CDATA[Crowd Event Detection on Optical Flow Manifolds]]>467152415371168<![CDATA[Dynamic Neural Networks for Kinematic Redundancy Resolution of Parallel Stewart Platforms]]>467153815502161<![CDATA[High-Speed General Purpose Genetic Algorithm Processor]]> process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously.]]>467155115653532<![CDATA[Reaching Synchronization in Networked Harmonic Oscillators With Outdated Position Data]]>46715661578757<![CDATA[Synchronization of a Group of Mobile Agents With Variable Speeds Over Proximity Nets]]>46715791590557<![CDATA[Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multiagent Systems]]>46715911601949<![CDATA[View Transformation Model Incorporating Quality Measures for Cross-View Gait Recognition]]>467160216153188<![CDATA[High-Order Energies for Stereo Segmentation]]>467161616271736<![CDATA[<inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-Discrepancy for Efficient Sampling in Approximate Dynamic Programming]]> -discrepancy, a quantity that measures how closely a set of random points represents a probability distribution, and introduce an example of an algorithm based on such concept to automatically select point sets that are efficient with respect to the underlying Markovian process. An error analysis of the approximate solution is provided, showing how the proposed algorithm enables convergence under suitable regularity hypotheses. Then, simulation results are provided concerning an inventory forecasting test problem. The tests confirm in general the important role of -discrepancy, and show how the proposed algorithm is able to yield better results than uniform sampling, using sets even 50 times smaller.]]>46716281639556<![CDATA[Learning Stationary Correlated Equilibria in Constrained General-Sum Stochastic Games]]> -learning scheme (CNR) is presented to guarantee convergence to the set of stationary correlated equilibria of the game. Prior art addresses the unconstrained case only, is structured with nested control loops, and has no convergence result. CNR is cast as a single-loop three-timescale asynchronous stochastic approximation algorithm with set-valued update increments. A rigorous convergence analysis with differential inclusion arguments is given which draws on recent extensions of the theory of stochastic approximation to the case of asynchronous recursive inclusions with set-valued mean fields. Numerical results are given for the exemplary application of CNR to decentralized resource control in heterogeneous wireless networks.]]>467164016541772<![CDATA[Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection]]>46716551666768<![CDATA[Learning Hierarchical Spectral–Spatial Features for Hyperspectral Image Classification]]> ) coefficient of agreement, especially when the number of the training samples is small.]]>467166716782350<![CDATA[Identification-Based Closed-Loop NMES Limb Tracking With Amplitude-Modulated Control Input]]>467167916901273<![CDATA[Learning the Inverse Dynamics of Robotic Manipulators in Structured Reproducing Kernel Hilbert Space]]>467169117031084<![CDATA[Multi-Step Ahead Predictions for Critical Levels in Physiological Time Series]]>46717041714981<![CDATA[Introducing IEEE Collabratec]]>467171517152130<![CDATA[Member Get-A-Member (MGM) Program]]>467171617163461<![CDATA[IEEE Transcations on Cybernetics society information]]>467C3C3151<![CDATA[IEEE Transactions on Cybernetics information for author]]>467C4C4129