15,241 research outputs found
A System for Compressive Sensing Signal Reconstruction
An architecture for hardware realization of a system for sparse signal
reconstruction is presented. The threshold based reconstruction method is
considered, which is further modified in this paper to reduce the system
complexity in order to provide easier hardware realization. Instead of using
the partial random Fourier transform matrix, the minimization problem is
reformulated using only the triangular R matrix from the QR decomposition. The
triangular R matrix can be efficiently implemented in hardware without
calculating the orthogonal Q matrix. A flexible and scalable realization of
matrix R is proposed, such that the size of R changes with the number of
available samples and sparsity level.Comment: 6 page
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A Decentralized Bayesian Algorithm for Distributed Compressive Sensing in Networked Sensing Systems
Compressive sensing (CS), as a new sensing/sampling paradigm, facilitates signal acquisition by reducing the number of samples required for reconstruction of the original signal, and thus appears to be a promising technique for applications where the sampling cost is high, e.g., the Nyquist rate exceeds the current capabilities of analog-to-digital converters (ADCs). Conventional CS, although effective for dealing with one signal, only leverages the intra-signal correlation for reconstruction. This paper develops a decentralized Bayesian reconstruction algorithm for networked sensing systems to jointly reconstruct multiple signals based on the distributed compressive sensing (DCS) model that exploits both intra- and inter-signal correlations. The proposed approach is able to address networked sensing system applications with privacy concerns and/or for a fusion-centre-free scenario, where centralized approaches fail. Simulation results demonstrate that the proposed decentralized approaches have good recovery performance and converge reasonably quicklyThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TWC.2015.248798
Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing
The random demodulator is a recent compressive sensing architecture providing
efficient sub-Nyquist sampling of sparse band-limited signals. The compressive
sensing paradigm requires an accurate model of the analog front-end to enable
correct signal reconstruction in the digital domain. In practice, hardware
devices such as filters deviate from their desired design behavior due to
component variations. Existing reconstruction algorithms are sensitive to such
deviations, which fall into the more general category of measurement matrix
perturbations. This paper proposes a model-based technique that aims to
calibrate filter model mismatches to facilitate improved signal reconstruction
quality. The mismatch is considered to be an additive error in the discretized
impulse response. We identify the error by sampling a known calibrating signal,
enabling least-squares estimation of the impulse response error. The error
estimate and the known system model are used to calibrate the measurement
matrix. Numerical analysis demonstrates the effectiveness of the calibration
method even for highly deviating low-pass filter responses. The proposed method
performance is also compared to a state of the art method based on discrete
Fourier transform trigonometric interpolation.Comment: 10 pages, 8 figures, submitted to IEEE Transactions on Signal
Processin
Extracting Signals and Graphical Models from Compressed Measurements
The thesis is to give an integrated approach to efficiently learn the interdependency relation among high dimensional signal components and reconstruct signals from observations collected in a linear sensing system, Broadly speaking, the research topics consists of three parts: (i) interdependency relation learning; (ii) sensing system design; and (iii) signal reconstruction. In the interdependency relation learning part, we considered both the parametric and non-parametric methods to learn the graphical structure under the noisy indirect measurements. In the sensing system design part, we introduced a density evolution framework to design sensing systems for compressive sensing for the first time. In the signal reconstruction part, we focused on the signal reconstruction with a given sensing system, which consists of three parts: signal reconstruction with inexact knowledge of the sensing system; signal reconstruction with the signal being contaminated by undesired noise; signal reconstruction with the signal belonging to a union of convex sets.Ph.D
Bayesian compressive sensing framework for spectrum reconstruction in Rayleigh fading channels
Compressive sensing (CS) is a novel digital signal processing technique that has found great interest in
many applications including communication theory and wireless communications. In wireless communications, CS
is particularly suitable for its application in the area of spectrum sensing for cognitive radios, where the complete
spectrum under observation, with many spectral holes, can be modeled as a sparse wide-band signal in the frequency
domain. Considering the initial works performed to exploit the benefits of Bayesian CS in spectrum sensing, the fading
characteristic of wireless communications has not been considered yet to a great extent, although it is an inherent feature
for all sorts of wireless communications and it must be considered for the design of any practically viable wireless system.
In this paper, we extend the Bayesian CS framework for the recovery of a sparse signal, whose nonzero coefficients follow
a Rayleigh distribution. It is then demonstrated via simulations that mean square error significantly improves when
appropriate prior distribution is used for the faded signal coefficients and thus, in turns, the spectrum reconstruction
improves. Different parameters of the system model, e.g., sparsity level and number of measurements, are then varied
to show the consistency of the results for different cases
A Computational Study Of The Role Of Spatial Receptive Field Structure In Processing Natural And Non-Natural Scenes
The center-surround receptive field structure, ubiquitous in the visual system, is hypothesized to be evolutionarily advantageous in image processing tasks. We address the potential functional benefits and shortcomings of spatial localization and center-surround antagonism in the context of an integrate-and-fire neuronal network model with image-based forcing. Utilizing the sparsity of natural scenes, we derive a compressive-sensing framework for input image reconstruction utilizing evoked neuronal firing rates. We investigate how the accuracy of input encoding depends on the receptive field architecture, and demonstrate that spatial localization in visual stimulus sampling facilitates marked improvements in natural scene processing beyond uniformly-random excitatory connectivity. However, for specific classes of images, we show that spatial localization inherent in physiological receptive fields combined with information loss through nonlinear neuronal network dynamics may underlie common optical illusions, giving a novel explanation for their manifestation. In the context of signal processing, we expect this work may suggest new sampling protocols useful for extending conventional compressive sensing theory
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
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