We introduce a new combinatorial structure: superselectors. We show that superselectors subsume several important combinatorial structures used in the past few years to solve problems in group testing, compressed sensing, multi-channel conflict resolution and data security. We prove close upper and lower bounds on the size of superselectors and we provide efficient algorithms for their construc...

In this paper we introduce a new image representation for texture classification. Our work is motivated by recent developments in the field of local patch based features, compressive sensing and descriptor encoding methods. Novel features called Compressed Random Pixel Difference (CRPD) are proposed. These features are low in dimensionality, highly discriminative, and easy to compute. Combined ...

Compressed1 sensing (sparse signal recovery) has been a popular and important research topic in recent years. By observing that natural signals are often nonnegative, we propose a new framework for nonnegative signal recovery using Compressed Counting (CC). CC is a technique built on maximally-skewed α-stable random projections originally developed for data stream computations. Our recovery pro...

Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be compa...

Wireless tomography is a technique for inferring a physical environment within a monitored region by analyzing RF signals traversed across the region. In this paper, we consider wireless tomography in a two and higher dimensionally structured monitored region, and propose a multi-dimensional wireless tomography scheme based on compressed sensing to estimate a spatial distribution of shadowing l...

In this paper we address a series of open questions about the construction of spatially coupled measurement matrices in compressed sensing. For hardware implementations one is forced to depart from the limiting regime of parameters in which the proofs of the so-called threshold saturation work. We investigate quantitatively the behavior under finite coupling range, the dependence on the shape o...

The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable humanlevel dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, power consumption, and data processing. To help address these cha...

We propose an alternative volumetric data modeling and reduction approach via compressive sensing theory. We provide evidence that with a small set of randomly chosen Fourier samples of a dataset, it is possible to recover the dataset accurately. Our experiments demonstrate that the number of samples necessary for an accurate reconstruction is linearly proportional to the number of features, as...

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