<![CDATA[ Signal Processing Letters, IEEE - new TOC ]]>
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TOC Alert for Publication# 97 2015November 26<![CDATA[Speech Enhancement with Nonstationary Acoustic Noise Detection in Time Domain]]>231610800<![CDATA[An Effective Video Synopsis Approach with Seam Carving]]>2311114583<![CDATA[An Optimal Dimensionality Sampling Scheme on the Sphere with Accurate and Efficient Spherical Harmonic Transform for Diffusion MRI]]>2311519737<![CDATA[Channel Capacity Analysis of the Multiple Orthogonal Sequence Spread Spectrum Watermarking in Audio Signals]]>2312024984<![CDATA[Consistent Basis Pursuit for Signal and Matrix Estimates in Quantized Compressed Sensing]]> uniformly quantized compressive observations. Among such signals, we consider 1-D sparse vectors, low-rank matrices, or compressible signals that are well approximated by one of these two models. In this context, we prove the estimation efficiency of a variant of Basis Pursuit Denoise, called Consistent Basis Pursuit (CoBP), enforcing consistency between the observations and the re-observed estimate, while promoting its low-complexity nature. We show that the reconstruction error of CoBP decays like when all parameters but are fixed. Our proof is connected to recent bounds on the proximity of vectors or matrices when (i) those belong to a set of small intrinsic “dimension”, as measured by the Gaussian mean width, and (ii) they share the same quantized (dithered) random projections. By solving CoBP with a proximal algorithm, we provide some extensive numerical observations that confirm the theoretical bound as is increased, displaying even faster error decay than predicted. The same phenomenon is observed in the special, yet important case of 1-bit CS.]]>23125291239<![CDATA[An Improved Soft-Input Soft-Output Detector for Generalized Spatial Modulation]]>23130341089<![CDATA[Importance Sampling-Based Maximum Likelihood Estimation for Multidimensional Harmonic Retrieval]]>23135391220<![CDATA[Patch-based Scale Calculation for Real-time Visual Tracking]]>23140441373<![CDATA[Detection of Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction]]> and .]]>2314549958<![CDATA[Wireless Networks with Energy Harvesting and Power Transfer: Joint Power and Time Allocation]]>23150541075<![CDATA[Affine-Transformation Parameters Regression for Face Alignment]]>23155591189<![CDATA[Robustness Analysis of Structured Matrix Factorization via Self-Dictionary Mixed-Norm Optimization]]>23160641136<![CDATA[Decision Fusion for Image Quality Assessment using an Optimization Approach]]>2316569682<![CDATA[Integer 2-D Discrete Fourier Transform Pairs and Eigenvectors using Ramanujan’s Sum]]>2317074983<![CDATA[Fast Matrix Inversion Updates for Massive MIMO Detection and Precoding]]>2317579697<![CDATA[On the Properties of Cubic Metric for OFDM Signals]]>2318083978<![CDATA[Similarity Learning with Top-heavy Ranking Loss for Person Re-identification]]>2318488862<![CDATA[A Path-following Algorithm for Robust Point Matching]]>23189931211