Content Gazette - October 2019 - 4

A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z. Li, W. Shi, and M. Yan

4494

Fully Orthogonal 2-D Lattice Structures for Quarter-Plane and Asymmetric Half-Plane Autoregressive Modeling of
Random Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. H. Kayran and E. Camcioglu

4507

Modeling the Active Neuron Separation in the Compressed Sensing and Finite Rate of Innovation Framework . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Schnier, C. Bockelmann, and A. Dekorsy

4521

A New Approach to ARMAX Signals Smoothing: Application to Variable-Q ARMA Filter Design . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Kheirati Roonizi

4535

Convolutional Recurrent Predictor: Implicit Representation for Multi-Target Filtering and Tracking . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Emambakhsh, A. Bay, and E. Vazquez

4545

Sketched Clustering via Hybrid Approximate Message Passing . . . . E. Byrne, A. Chatalic, R. Gribonval, and P. Schniter

4556

Joint Rate and Power Optimization for Multimedia Streaming in Wireless Fading Channels via Parametric Policy Gradient
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Chai and V. K. N. Lau

4570

Joint Network Topology and Dynamics Recovery From Perturbed Stationary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H.-T. Wai, A. Scaglione, B. Barzel, and A. Leshem

4582

MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Simon, J. Sulam, Y. Romano, Y. M. Lu, and M. Elad

4597

Noise Benefits in Combined Nonlinear Bayesian Estimators . . . . . F. Duan, Y. Pan, F. Chapeau-Blondeau, and D. Abbott

4611

A CFAR Detector for Mismatched Eigenvalues of Training Sample Covariance Matrix . . . . . . . . . . . . . . . . R. S. Raghavan

4624

On the Algorithmic Computability of the Secret Key and Authentication Capacity Under Channel, Storage, and Privacy
Leakage Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Boche, R. F. Schaefer, S. Baur, and H. V. Poor

4636

SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing Based on Sparse-Graph Codes . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Lee, K. Chandrasekher, R. Pedarsani, and K. Ramchandran

EDICS-Editors' Information Classification Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

www.signalprocessingsociety.org

[4]

OCTOBER 2019

4649
Content Gazette - October 2019

Table of Contents for the Digital Edition of Content Gazette - October 2019
