 Abstract — This paper addresses the image representation problem in visual sensor networks. We propose a new image representation method for visual sensor networks based on compressive sensing (CS). CS is a new sampling method for sparse signals, which is able to compress the input data in the sampling process. Combining both signal sampling and data compression, CS is more capable of image r...

In recent years compressive sampling (CS) has appeared in the signal processing literature as a legitimate contender for processing of sparse signals. Natural signals such as speech, image and video are compressible. In most signal processing systems dealing with these signals the signal is first sampled and later on compressed. The philosophy of CS however is to sample and compress the signal ...

INTRODUCTION Compressed Sensing (CS) is of significant interest for fast MR imaging because it can be used with k-space sub-sampling and parallel imaging [1,2]. Although implementations were made to combine CS with parallel imaging, the incoherent sampling requirement is a bottleneck for implementation because most k-space sampling in parallel imaging is coherent. Thus, a direct plug-in of CS t...

Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. Block-based CS is a lightweight CS approach that is mostly suitable for processing very high-dimensional images and videos: it operates on local patches, employs a low-complexity reconstruction operator and requires s...

Misrepresenting private information is often costly, for technological, legal, or psychological reasons. I develop a model of strategic information transmission based on Crawford and Sobel (1982) (CS), but with a convex cost of lying or misreporting. There are three main results. First, I prove that a sequence of monotone equilibria converges to a CS equilibrium as the cost of misreporting shri...

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The copyright to this Article is held by the Econometric Society. It may be downloaded, printed and reproduced only for educational or research purposes, including use in course packs. No downloading or copying may be done for any commercial purpose without the explicit permission of the Econometric Society. For such commercial purposes contact the Office of the Econometric Society (contact inf...

Introduction: Flexible radial imaging allows multiple image sets, each having a different spatiotemporal balance, to be retrospectively reconstructed from the same dataset [1,2]. One of the applications that may benefit from this flexibility is dynamic contrast-enhanced breast imaging, in which the optimal spatiotemporal balance for image diagnosis is unknown. Images from radial undersampling h...

Introductory Computer Science students often do not clearly understand the concepts of operator precedence and associativity, and make mistakes when they write and evaluate expressions. In order to help them learn better by solving problems, we have developed an applet that generates problems (henceforth called problet) and corrects the learner’s answers. In this paper, we will describe the des...