365 research outputs found
Operational Rate-Distortion Performance of Single-source and Distributed Compressed Sensing
We consider correlated and distributed sources without cooperation at the
encoder. For these sources, we derive the best achievable performance in the
rate-distortion sense of any distributed compressed sensing scheme, under the
constraint of high--rate quantization. Moreover, under this model we derive a
closed--form expression of the rate gain achieved by taking into account the
correlation of the sources at the receiver and a closed--form expression of the
average performance of the oracle receiver for independent and joint
reconstruction. Finally, we show experimentally that the exploitation of the
correlation between the sources performs close to optimal and that the only
penalty is due to the missing knowledge of the sparsity support as in (non
distributed) compressed sensing. Even if the derivation is performed in the
large system regime, where signal and system parameters tend to infinity,
numerical results show that the equations match simulations for parameter
values of practical interest.Comment: To appear in IEEE Transactions on Communication
Exact Performance Analysis of the Oracle Receiver for Compressed Sensing Reconstruction
A sparse or compressible signal can be recovered from a certain number of
noisy random projections, smaller than what dictated by classic Shannon/Nyquist
theory. In this paper, we derive the closed-form expression of the mean square
error performance of the oracle receiver, knowing the sparsity pattern of the
signal. With respect to existing bounds, our result is exact and does not
depend on a particular realization of the sensing matrix. Moreover, our result
holds irrespective of whether the noise affecting the measurements is white or
correlated. Numerical results show a perfect match between equations and
simulations, confirming the validity of the result.Comment: To be published in ICASSP 2014 proceeding
Graded quantization for multiple description coding of compressive measurements
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed
representations of a sparse signal. Its low complexity is appealing for
resource-constrained scenarios like sensor networks. However, such scenarios
are often coupled with unreliable communication channels and providing robust
transmission of the acquired data to a receiver is an issue. Multiple
description coding (MDC) effectively combats channel losses for systems without
feedback, thus raising the interest in developing MDC methods explicitly
designed for the CS framework, and exploiting its properties. We propose a
method called Graded Quantization (CS-GQ) that leverages the democratic
property of compressive measurements to effectively implement MDC, and we
provide methods to optimize its performance. A novel decoding algorithm based
on the alternating directions method of multipliers is derived to reconstruct
signals from a limited number of received descriptions. Simulations are
performed to assess the performance of CS-GQ against other methods in presence
of packet losses. The proposed method is successful at providing robust coding
of CS measurements and outperforms other schemes for the considered test
metrics
Joint recovery algorithms using difference of innovations for distributed compressed sensing
Distributed compressed sensing is concerned with representing an ensemble of
jointly sparse signals using as few linear measurements as possible. Two novel
joint reconstruction algorithms for distributed compressed sensing are
presented in this paper. These algorithms are based on the idea of using one of
the signals as side information; this allows to exploit joint sparsity in a
more effective way with respect to existing schemes. They provide gains in
reconstruction quality, especially when the nodes acquire few measurements, so
that the system is able to operate with fewer measurements than is required by
other existing schemes. We show that the algorithms achieve better performance
with respect to the state-of-the-art.Comment: Conference Record of the Forty Seventh Asilomar Conference on
Signals, Systems and Computers (ASILOMAR), 201
Compressive Hyperspectral Imaging Using Progressive Total Variation
Compressed Sensing (CS) is suitable for remote acquisition of hyperspectral
images for earth observation, since it could exploit the strong spatial and
spectral correlations, llowing to simplify the architecture of the onboard
sensors. Solutions proposed so far tend to decouple spatial and spectral
dimensions to reduce the complexity of the reconstruction, not taking into
account that onboard sensors progressively acquire spectral rows rather than
acquiring spectral channels. For this reason, we propose a novel progressive CS
architecture based on separate sensing of spectral rows and joint
reconstruction employing Total Variation. Experimental results run on raw
AVIRIS and AIRS images confirm the validity of the proposed system.Comment: To be published on ICASSP 2014 proceeding
Graded quantization: democracy for multiple descriptions in compressed sensing
The compressed sensing paradigm allows to efficiently represent sparse
signals by means of their linear measurements. However, the problem of
transmitting these measurements to a receiver over a channel potentially prone
to packet losses has received little attention so far. In this paper, we
propose novel methods to generate multiple descriptions from compressed sensing
measurements to increase the robustness over unreliable channels. In
particular, we exploit the democracy property of compressive measurements to
generate descriptions in a simple manner by partitioning the measurement vector
and properly allocating bit-rate, outperforming classical methods like the
multiple description scalar quantizer. In addition, we propose a modified
version of the Basis Pursuit Denoising recovery procedure that is specifically
tailored to the proposed methods. Experimental results show significant
performance gains with respect to existing methods
Optimum Receiver Design for MIMO Fading Channels
This thesis describes the analytical design and the performance analysis of optimum receivers for Multiple Input - Multiple Output (MIMO) fading channels. In particular, a novel Optimum Receiver for separately-correlated MIMO channels is proposed. This novel pilot-aided receiver is able to process jointly the pilot symbols, transmitted within each time frame as a preamble, and the information symbols and to decode the transmitted data in a single step, avoiding the explicit estimation of the channel matrix. The optimum receiver is designed for the following two scenarios, corresponding to different transmission schemes and channel models: 1) Narrowband Rician fading MIMO channel with spatial separate correlation; 2) MIMO-OFDM Rician fading channel with space and frequency separate correlation. For each system the performance of the optimum receiver is studied in detail under different channel conditions. The optimum receiver is compared with: - the ideal Genie Receiver, knowing perfectly the Channel State Information (CSI) at no cost; - the standard Mismatched Receiver, estimating the CSI in a first step, then using this imperfect estimate in the ideal channel metric. Since the optimum receiver requires the knowledge of the channel parameters for the decoding process, an estimation algorithm is proposed and tested. Moreover, a complexity analysis is carried out and methods for complexity reduction are proposed. Furthermore, the narrowband receiver is tested in realistic conditions using measured channel samples. Finally, a blind version of the receiver is propose
Graded quantization for multiple description coding of compressive measurements
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics
ToothPic: camera-based image retrieval on large scales
Being able to reliably link a picture to the device that shot it is of paramount importance to give credit or assign responsibility to the author of the picture itself. However, this task needs to be performed at large scales due to the recent explosion in the number of photos taken and shared. Existing methods cannot satisfy those requirements. Methods based on the Photo Response Non-Uniformity (PRNU) of digital sensors are able to link a photo to the device that shot it and have already been used as proof in the Court of Law. Such methods are reliable but so far, they can be only used for small-scale forensic tasks involving few cameras and pictures. ToothPic, an acronym for "Who Took This Picture?", is a novel image retrieval engine that allows to find all the pictures in a large-scale database shot by a given query camera
Compressed Fingerprint Matching and Camera Identification via Random Projections
Sensor imperfections in the form of photo-response nonuniformity (PRNU) patterns are a well-established fingerprinting technique to link pictures to the camera sensors that acquired them. The noise-like characteristics of the PRNU pattern make it a difficult object to compress, thus hindering many interesting applications that would require storage of a large number of fingerprints or transmission over a bandlimited channel for real-time camera matching. In this paper, we propose to use realvalued or binary random projections to effectively compress the fingerprints at a small cost in terms of matching accuracy. The performance of randomly projected fingerprints is analyzed from a theoretical standpoint and experimentally verified on databases of real photographs. Practical issues concerning the complexity of implementing random projections are also addressed by using circulant matrices
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