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Onebit Compressed Sensing: Provable Support and Vector Recovery
"... In this paper, we study the problem of onebit compressed sensing (1bit CS), where the goal is to design a measurement matrix A and a recovery algorithm such that a ksparse unit vector x ∗ can be efficiently recovered from the sign of its linear measurements, i.e., b = sign(Ax ∗). This is an import ..."
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Cited by 5 (0 self)
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In this paper, we study the problem of onebit compressed sensing (1bit CS), where the goal is to design a measurement matrix A and a recovery algorithm such that a ksparse unit vector x ∗ can be efficiently recovered from the sign of its linear measurements, i.e., b = sign
Praneeth Netrapalli Onebit Compressed Sensing: Provable Support and Vector Recovery 1bit CS Support Approx. Summary Quantization
, 2013
"... Goal: Reconstruct a sparse signal using very few linear measurements Tremendous amount of work in the last decade O (k log n) measurements to reconstruct ksparse signals in Rn ..."
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Goal: Reconstruct a sparse signal using very few linear measurements Tremendous amount of work in the last decade O (k log n) measurements to reconstruct ksparse signals in Rn
Efficient Algorithms for Robust Onebit Compressive Sensing
"... While the conventional compressive sensing assumes measurements of infinite precision, onebit compressive sensing considers an extreme setting where each measurement is quantized to just a single bit. In this paper, we study the vector recovery problem from noisy onebit measurements, and develop ..."
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Cited by 2 (2 self)
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While the conventional compressive sensing assumes measurements of infinite precision, onebit compressive sensing considers an extreme setting where each measurement is quantized to just a single bit. In this paper, we study the vector recovery problem from noisy onebit measurements
Compressive sampling
, 2006
"... Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired res ..."
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Cited by 1427 (15 self)
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resolution of the image, i.e. the number of pixels in the image. This paper surveys an emerging theory which goes by the name of “compressive sampling” or “compressed sensing,” and which says that this conventional wisdom is inaccurate. Perhaps surprisingly, it is possible to reconstruct images or signals
Onebit compressed sensing by linear programming
, 2011
"... We give the first computationally tractable and almost optimal solution to the problem of onebit compressed sensing, showing how to accurately recover an ssparse vector x ∈ R n from the signs of O(s log² (n/s)) random linear measurements of x. The recovery is achieved by a simple linear program. ..."
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Cited by 58 (5 self)
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We give the first computationally tractable and almost optimal solution to the problem of onebit compressed sensing, showing how to accurately recover an ssparse vector x ∈ R n from the signs of O(s log² (n/s)) random linear measurements of x. The recovery is achieved by a simple linear program
Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding
 IEEE TRANS. ON INFORMATION THEORY
, 1999
"... We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing informationembedding rate, mini ..."
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Cited by 495 (15 self)
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We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing informationembedding rate
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a powerlaw (or if the coefficient sequence of f in a fixed basis decays like a power
The JPEG still picture compression standard
 Communications of the ACM
, 1991
"... This paper is a revised version of an article by the same title and author which appeared in the April 1991 issue of Communications of the ACM. For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international c ..."
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Cited by 1128 (0 self)
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compression standard for continuoustone still images, both grayscale and color. JPEG’s proposed standard aims to be generic, to support a wide variety of applications for continuoustone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each
Onebit compressed sensing with nongaussian measurements
, 2013
"... Abstract. In onebit compressed sensing, previous results state that sparse signals may be robustly recovered when the measurements are taken using Gaussian random vectors. In contrast to standard compressed sensing, these results are not extendable to natural nonGaussian distributions without furt ..."
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Cited by 14 (3 self)
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Abstract. In onebit compressed sensing, previous results state that sparse signals may be robustly recovered when the measurements are taken using Gaussian random vectors. In contrast to standard compressed sensing, these results are not extendable to natural nonGaussian distributions without
SEAD: Secure Efficient Distance Vector Routing for Mobile Wireless Ad Hoc Networks
, 2003
"... An ad hoc network is a collection of wireless computers (nodes), communicating among themselves over possibly multihop paths, without the help of any infrastructure such as base stations or access points. Although many previous ad hoc network routing protocols have been based in part on distance vec ..."
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Cited by 522 (8 self)
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. In order to support use with nodes of limited CPU processing capability, and to guard against DenialofService attacks in which an attacker attempts to cause other nodes to consume excess network bandwidth or processing time, we use efficient oneway hash functions and do not use asymmetric cryptographic
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