In this paper, following the Compressed Sensing (CS) paradigm, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present ...
This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlati...
Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde...
Compressed sensing is applied to multiview image sets and interimage disparity compensation is incorporated into image reconstruction in order to take advantage of the high degree...
Maria Trocan, Thomas Maugey, Eric W. Tramel, James...
—Various sensor types, e.g., temperature, humidity, and acoustic, sense physical phenomena in different ways, and thus, are expected to have different sensing models. Even for th...
— In this paper, based on a passivity framework, admittance-type and hybrid-type delay-compensated communication channel models are introduced, which warrant different bilateral ...
Arash Aziminejad, Mahdi Tavakoli, Rajnikant V. Pat...