<![CDATA[ IEEE Signal Processing Letters - new TOC ]]>
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TOC Alert for Publication# 97 2019April 18<![CDATA[Single-Image De-Raining With Feature-Supervised Generative Adversarial Network]]>2656506542068<![CDATA[Article Awards for the IEEE Signal Processing Letters]]>265ii22<![CDATA[Precise Performance Analysis of the Box-Elastic Net Under Matrix Uncertainties]]>265655659326<![CDATA[Time-Frequency Filtering Based on Model Fitting in the Time-Frequency Plane]]>265660664919<![CDATA[<inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula>-Channel Critically Sampled Spectral Graph Filter Banks With Symmetric Structure]]>2656656691161<![CDATA[Optimal Leak Factor Selection for the Output-Constrained Leaky Filtered-Input Least Mean Square Algorithm]]>2656706741531<![CDATA[Distributed Rate-Constrained LCMV Beamforming]]>265675679379<![CDATA[Kalman Filter-Based Channel Estimation for Mobile-to-Mobile and Relay Networks]]>265680684297<![CDATA[New Approximate Distributions for the Generalized Likelihood Ratio Test Detection in Passive Radar]]>265685689808<![CDATA[Strategy for Accelerating Multiway Greedy Compressive Sensing Reconstruction]]>265690694696<![CDATA[Hash Code Reconstruction for Fast Similarity Search]]>265695699996<![CDATA[Low-Rank Structured Covariance Matrix Estimation]]>265700704243<![CDATA[On the Security of Secret Sharing Over a Ring and the Fast Implementation]]>$O(Nlog _2 N)$ Boolean operations per secret bit, which improves the prior result $O(8^{log ^* N}Nlog _2 N)$ Boolean operations per secret bit. The simulation shows that the proposed scheme is in average four times faster than the conventional approach.]]>265705709527<![CDATA[Adversarial Deep Learning in EEG Biometrics]]>265710714516<![CDATA[A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis]]>new user. In contrast, the subject-independent scenario, where a well trained model can be directly applied to new users without precalibration, is particularly desired. Considering this critical gap, the focus in this letter is developing an effective EEG signal analysis adaptively applied to subject-independent settings. We present a convolutional recurrent attention model (CRAM) that utilizes a convolutional neural network to encode the high-level representation of EEG signals and a recurrent attention mechanism to explore the temporal dynamics of the EEG signals as well as to focus on the most discriminative temporal periods. Extensive experiments on a benchmark multiclass EEG dataset containing four movement intentions indicate that the proposed model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches by at least eight percentage points. The implementation code is made publicly available.^{1}

https://github.com/dalinzhang/CRAM.

]]>2657157191323<![CDATA[Watersheds for Semi-Supervised Classification]]>morphMedian, resulting in the maximum margin principle. It is then shown that watersheds form a particular class of morphMedian classifiers. Using the ensemble technique, watersheds are also extended to ensemble watersheds. These techniques are compared with relevant methods from the literature and it is shown that watersheds perform better than support vector machines on some datasets, and ensemble watersheds usually outperform random forest classifiers.]]>265720724706<![CDATA[Direction of Arrival Estimation for Complex Sources Through <inline-formula><tex-math notation="LaTeX">$ell _1$</tex-math></inline-formula> Norm Sparse Bayesian Learning]]>2657657691515<![CDATA[Speech Enhancement Using a Two-Stage Network for an Efficient Boosting Strategy]]>265770774688<![CDATA[A Fast Forward Full-Duplex Cooperative Relay Scheme for Securing Wireless Communications]]>265775779402<![CDATA[An Improved Data-Aided Linear Estimator of Modulation Index for Binary CPM Signals]]>265780784416