Relevant bibliographies by topics / Sparse signal

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sparse signal'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Sparse signal"

1

Mingjiang Shi, Xiaoyan Zhuang, and He Zhang. "Signal Reconstruction for Frequency Sparse Sampling Signals." Journal of Convergence Information Technology 8, no. 9 (May 15, 2013): 1197–203. http://dx.doi.org/10.4156/jcit.vol8.issue9.147.

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Peng, Wei, Dong Wang, Changqing Shen, and Dongni Liu. "Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features." Shock and Vibration 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1835127.

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Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable. For bearing faults’ diagnosis, bearing faults signals collected from transducers are often overwhelmed by strong low-frequency periodic signals and heavy noises. In this paper, a joint signal processing method is proposed to extract sparse envelope coefficients, which are the sparse signal representations of bearing fault signals. Firstly, to enhance bearing fault signals, particle swarm optimization is introduced to tune the parameters of wavelet transform and the optimal wavelet transform is used for retaining one of the resonant frequency bands. Thus, sparse wavelet coefficients are obtained. Secondly, to reduce the in-band noises existing in the sparse wavelet coefficients, an adaptive morphological analysis with an iterative local maximum detection method is developed to extract sparse envelope coefficients. Simulated and real bearing fault signals are investigated to illustrate how the sparse envelope coefficients are extracted. The results show that the sparse envelope coefficients can be used to represent bearing fault features and identify different localized bearing faults.
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Wang, Tianjing, Hang Shen, Xiaomei Zhu, Guoqing Liu, and Hua Jiang. "An Adaptive Gradient Projection Algorithm for Piecewise Convex Optimization and Its Application in Compressed Spectrum Sensing." Mathematical Problems in Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/9547934.

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Signal sparse representation has attracted much attention in a wide range of application fields. A central aim of signal sparse representation is to find a sparse solution with the fewest nonzero entries from an underdetermined linear system, which leads to various optimization problems. In this paper, we propose an Adaptive Gradient Projection (AGP) algorithm to solve the piecewise convex optimization in signal sparse representation. To find a sparser solution, AGP provides an adaptive stepsize to move the iteration solution out of the attraction basin of a suboptimal sparse solution and enter the attraction basin of a sparser solution. Theoretical analyses are used to show its fast convergence property. The experimental results of real-world applications in compressed spectrum sensing show that AGP outperforms the traditional detection algorithms in low signal-to-noise-ratio environments.
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Vujović, Stefan, Andjela Draganić, Maja Lakičević Žarić, Irena Orović, Miloš Daković, Marko Beko, and Srdjan Stanković. "Sparse Analyzer Tool for Biomedical Signals." Sensors 20, no. 9 (May 2, 2020): 2602. http://dx.doi.org/10.3390/s20092602.

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The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals’ recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.
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Xuan Liu, Xuan Liu, and Jin U. Kang Jin U. Kang. "Iterative sparse reconstruction of spectral domain OCT signal." Chinese Optics Letters 12, no. 5 (2014): 051701–51704. http://dx.doi.org/10.3788/col201412.051701.

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Ye, Chen, Guan Gui, Shin-ya Matsushita, and Li Xu. "Block Sparse Signal Reconstruction Using Block-Sparse Adaptive Filtering Algorithms." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 7 (December 20, 2016): 1119–26. http://dx.doi.org/10.20965/jaciii.2016.p1119.

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Sparse signal reconstruction (SSR) problems based on compressive sensing (CS) arise in a broad range of application fields. Among these are the so-called “block-structured” or “block sparse” signals with nonzero atoms occurring in clusters that occur frequently in natural signals. To make block-structured sparsity use more explicit, many block-structure-based SSR algorithms, such as convex optimization and greedy pursuit, have been developed. Convex optimization algorithms usually pose a heavy computational burden, while greedy pursuit algorithms are overly sensitive to ambient interferences, so these two types of block-structure-based SSR algorithms may not be suited for solving large-scale problems in strong interference scenarios. Sparse adaptive filtering algorithms have recently been shown to solve large-scale CS problems effectively for conventional vector sparse signals. Encouraged by these facts, we propose two novel block-structure-based sparse adaptive filtering algorithms, i.e., the “block zero attracting least mean square” (BZA-LMS) algorithm and the “blockℓ0-norm LMS” (BL0-LMS) algorithm, to exploit their potential performance gain. Experimental results presented demonstrate the validity and applicability of these proposed algorithms.
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Selesnick, Ivan W., and Ilker Bayram. "Sparse Signal Estimation by Maximally Sparse Convex Optimization." IEEE Transactions on Signal Processing 62, no. 5 (March 2014): 1078–92. http://dx.doi.org/10.1109/tsp.2014.2298839.

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Wand, Weidong, Qunfei Zhang, Wentao Shi, Juan SHI, Weijie Tan, and Xuhu Wang. "Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 1 (February 2020): 14–23. http://dx.doi.org/10.1051/jnwpu/20203810014.

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Aiming at the direction of arrival (DOA) estimation of coherent signals in vector hydrophone array, an iterative sparse covariance matrix fitting algorithm is proposed. Based on the fitting criterion of weighted covariance matrix, the objective function of sparse signal power is constructed, and the recursive formula of sparse signal power iteration updating is deduced by using the properties of Frobenius norm. The present algorithm uses the idea of iterative reconstruction to calculate the power of signals on discrete grids, so that the estimated power is more accurate, and thus more accurate DOA estimation can be obtained. The theoretical analysis shows that the power of the signal at the grid point solved by the present algorithm is preprocessed by a filter, which allows signals in specified directions to pass through and attenuate signals in other directions, and has low sensitivity to the correlation of signals. The simulation results show that the average error estimated by the present method is 39.4% of the multi-signal classification high resolution method and 73.7% of the iterative adaptive sparse signal representation method when the signal-to-noise ratio is 15 dB and the non-coherent signal. Moreover, the average error estimated by the present method is 12.9% of the iterative adaptive sparse signal representation method in the case of coherent signal. Therefore, the present algorithm effectively improves the accuracy of target DOA estimation when applying to DOA estimation with highly correlated targets.
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Zhao, Shengjie, Jianchen Zhu, and Di Wu. "Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery." Traitement du Signal 37, no. 5 (November 25, 2020): 723–32. http://dx.doi.org/10.18280/ts.370504.

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Compressive sensing (CS) is a novel paradigm to recover a sparse signal in compressed domain. In some overcomplete dictionaries, most practical signals are sparse rather than orthonormal. Signal space greedy method can derive the optimal or near-optimal projections, making it possible to identify a few most relevant dictionary atoms of an arbitrary signal. More practically, such projections can be processed by standard CS recovery algorithms. This paper proposes a signal space subspace pursuit (SSSP) method to compute spare signal representations with overcomplete dictionaries, whenever the sensing matrix satisfies the restricted isometry property adapted to dictionary (D-RIP). Specifically, theoretical guarantees were provided to recover the signals from their measurements with overwhelming probability, as long as the sensing matrix satisfies the D-RIP. In addition, a thorough analysis was performed to minimize the number of measurements required for such guarantees. Simulation results demonstrate the validity of our hypothetical theory, as well as the superiority of the proposed approach.
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Song, Heping, and Guoli Wang. "Sparse Signal Recovery via ECME Thresholding Pursuits." Mathematical Problems in Engineering 2012 (2012): 1–22. http://dx.doi.org/10.1155/2012/478931.

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The emerging theory of compressive sensing (CS) provides a new sparse signal processing paradigm for reconstructing sparse signals from the undersampled linear measurements. Recently, numerous algorithms have been developed to solve convex optimization problems for CS sparse signal recovery. However, in some certain circumstances, greedy algorithms exhibit superior performance than convex methods. This paper is a followup to the recent paper of Wang and Yin (2010), who refine BP reconstructions via iterative support detection (ISD). The heuristic idea of ISD was applied to greedy algorithms. We developed two approaches for accelerating the ECME iteration. The described algorithms, named ECME thresholding pursuits (EMTP), introduced two greedy strategies that each iteration detects a support setIby thresholding the result of the ECME iteration and estimates the reconstructed signal by solving a truncated least-squares problem on the support setI. Two effective support detection strategies are devised for the sparse signals with components having a fast decaying distribution of nonzero components. The experimental studies are presented to demonstrate that EMTP offers an appealing alternative to state-of-the-art algorithms for sparse signal recovery.
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Dissertations / Theses on the topic "Sparse signal"

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Tan, Xing. "Bayesian sparse signal recovery." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0041176.

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Skretting, Karl. "Sparse Signal Representation using Overlapping Frames." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-102.

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Signal expansions using frames may be considered as generalizations of signal representations based on transforms and filter banks. Frames for sparse signal representations may be designed using an iterative method with two main steps: (1) Frame vector selection and expansion coefficient determination for signals in a training set, – selected to be representative of the signals for which compact representations are desired, using the frame designed in the previous iteration. (2) Update of frame vectors with the objective of improving the representation of step (1). In this thesis we solve step (2) of the general frame design problem using the compact notation of linear algebra.

This makes the solution both conceptually and computationally easy, especially for the non-block-oriented frames, – for short overlapping frames, that may be viewed as generalizations of critically sampled filter banks. Also, the solution is more general than those presented earlier, facilitating the imposition of constraints, such as symmetry, on the designed frame vectors. We also take a closer look at step (1) in the design method. Some of the available vector selection algorithms are reviewed, and adaptations to some of these are given. These adaptations make the algorithms better suited for both the frame design method and the sparse representation of signals problem, both for block-oriented and overlapping frames.

The performances of the improved frame design method are shown in extensive experiments. The sparse representation capabilities are illustrated both for one-dimensional and two-dimensional signals, and in both cases the new possibilities in frame design give better results.

Also a new method for texture classification, denoted Frame Texture Classification Method (FTCM), is presented. The main idea is that a frame trained for making sparse representations of a certain class of signals is a model for this signal class. The FTCM is applied to nine test images, yielding excellent overall performance, for many test images the number of wrongly classified pixels is more than halved, in comparison to state of the art texture classification methods presented in [59].

Finally, frames are analyzed from a practical viewpoint, rather than in a mathematical theoretic perspective. As a result of this, some new frame properties are suggested. So far, the new insight this has given has been moderate, but we think that this approach may be useful in frame analysis in the future.

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ABBASI, MUHAMMAD MOHSIN. "Solving Sudoku by Sparse Signal Processing." Thesis, KTH, Signalbehandling, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-160908.

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Sudoku is a discrete constraints satisfaction problem which is modeled as an underdetermined linear system. This report focuses on applying some new signal processing approaches to solve sudoku and comparisons to some of the existing approaches are implemented. As our goal is not meant for sudoku only in the long term, we applied approximate solvers using optimization theory methods. A Semi Definite Relaxation (SDR) convex optimization approach was developed for solving sudoku. The idea of Iterative Adaptive Algorithm for Amplitude and Phase Estimation (IAA-APES) from array processing is also being used for sudoku to utilize the sparsity of the sudoku solution as is the case in sensing applications. LIKES and SPICE were also tested on sudoku and their results are compared with l1-norm minimization, weighted l1-norm, and sinkhorn balancing. SPICE and l1-norm are equivalent in terms of accuracy, while SPICE is slower than l1-norm. LIKES and weighted l1-norm are equivalent and better than SPICE and l1-norm in accuracy. SDR proved to be best when the sudoku solutions are unique; however the computational complexity is worst for SDR. The accuracy for IAA-APES is somewhere between SPICE and LIKES and its computation speed is faster than both.
Sudoku är ett diskret bivillkorsproblem som kan modelleras som ett underbestämt ekvationssystem. Denna rapport fokuserar på att tillämpa ett antal nya signalbehandlingsmetoder för att lösa sudoku och att jämföra resultaten med några existerande metoder. Eftersom målet inte enbart är att lösa sudoku, implementerades approximativa lösare baserade på optimeringsteori. En positiv-definit konvex relaxeringsmetod (SDR) för att lösa sudoku utvecklades. Iterativ-adaptiv-metoden för amplitud- och fasskattning (IAA-APES) från gruppantennsignalbehandling användes också för sudoku för att utnyttja glesheten i sudokulösningen på liknande sätt som i mättillämpningen. LIKES och SPICE testades också för sudokuproblemet och resultaten jämfördes med l1-norm-minimiering, viktad l1- norm, och sinkhorn-balancering. SPICE och l1-norm är ekvivalenta i termer av prestanda men SPICE är långsammare. LIKES och viktad l1-norm är ekvivalenta och har bättre noggrannhet än SPICE och l1- norm. SDR visade sig ha bäst prestanda för sudoku med unika lösningar, men SDR är också den metod med beräkningsmässigt högst komplexitet. Prestandan för IAA-APES ligger någonstans mellan SPICE och LIKES men är snabbare än bägge dessa.
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Berinde, Radu. "Advances in sparse signal recovery methods." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/61274.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 96-101).
The general problem of obtaining a useful succinct representation (sketch) of some piece of data is ubiquitous; it has applications in signal acquisition, data compression, sub-linear space algorithms, etc. In this thesis we focus on sparse recovery, where the goal is to recover sparse vectors exactly, and to approximately recover nearly-sparse vectors. More precisely, from the short representation of a vector x, we want to recover a vector x* such that the approximation error ... is comparable to the "tail" min[subscript x'] ... where x' ranges over all vectors with at most k terms. The sparse recovery problem has been subject to extensive research over the last few years, notably in areas such as data stream computing and compressed sensing. We consider two types of sketches: linear and non-linear. For the linear sketching case, where the compressed representation of x is Ax for a measurement matrix A, we introduce a class of binary sparse matrices as valid measurement matrices. We show that they can be used with the popular geometric " 1 minimization" recovery procedure. We also present two iterative recovery algorithms, Sparse Matching Pursuit and Sequential Sparse Matching Pursuit, that can be used with the same matrices. Thanks to the sparsity of the matrices, the resulting algorithms are much more efficient than the ones previously known, while maintaining high quality of recovery. We also show experiments which establish the practicality of these algorithms. For the non-linear case, we present a better analysis of a class of counter algorithms which process large streams of items and maintain enough data to approximately recover the item frequencies. The class includes the popular FREQUENT and SPACESAVING algorithms. We show that the errors in the approximations generated by these algorithms do not grow with the frequencies of the most frequent elements, but only depend on the remaining "tail" of the frequency vector. Therefore, they provide a non-linear sparse recovery scheme, achieving compression rates that are an order of magnitude better than their linear counterparts.
by Radu Berinde.
M.Eng.
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Perelli, Alessandro <1985&gt. "Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6321/.

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Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In this thesis, the study and development of techniques for GW ultrasound signal analysis and compression in the context of non-destructive testing of structures will be presented. In guided wave inspections, it is necessary to address the problem of the dispersion compensation. A signal processing approach based on frequency warping was adopted. Such operator maps the frequencies axis through a function derived by the group velocity of the test material and it is used to remove the dependence on the travelled distance from the acquired signals. Such processing strategy was fruitfully applied for impact location and damage localization tasks in composite and aluminum panels. It has been shown that, basing on this processing tool, low power embedded system for GW structural monitoring can be implemented. Finally, a new procedure based on Compressive Sensing has been developed and applied for data reduction. Such procedure has also a beneficial effect in enhancing the accuracy of structural defects localization. This algorithm uses the convolutive model of the propagation of ultrasonic guided waves which takes advantage of a sparse signal representation in the warped frequency domain. The recovery from the compressed samples is based on an alternating minimization procedure which achieves both an accurate reconstruction of the ultrasonic signal and a precise estimation of waves time of flight. Such information is used to feed hyperbolic or elliptic localization procedures, for accurate impact or damage localization.
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Almshaal, Rashwan M. "Sparse Signal Processing Based Image Compression and Inpainting." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4286.

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In this thesis, we investigate the application of compressive sensing and sparse signal processing techniques to image compression and inpainting problems. Considering that many signals are sparse in certain transformation domain, a natural question to ask is: can an image be represented by as few coefficients as possible? In this thesis, we propose a new model for image compression/decompression based on sparse representation. We suggest constructing an overcomplete dictionary by combining two compression matrices, the discrete cosine transform (DCT) matrix and Hadamard-Walsh transform (HWT) matrix, instead of using only one transformation matrix that has been used by the common compression techniques such as JPEG and JPEG2000. We analyze the Structural Similarity Index (SSIM) versus the number of coefficients, measured by the Normalized Sparse Coefficient Rate (NSCR) for our approach. We observe that using the same NSCR, SSIM for images compressed using the proposed approach is between 4%-17% higher than when using JPEG. Several algorithms have been used for sparse coding. Based on experimental results, Orthogonal Matching Pursuit (OMP) is proved to be the most efficient algorithm in terms of computational time and the quality of the decompressed image. In addition, based on compressive sensing techniques, we propose an image inpainting approach, which could be used to fill missing pixels and reconstruct damaged images. In this approach, we use the Gradient Projection for Sparse Reconstruction (GPSR) algorithm and wavelet transformation with Daubechies filters to reconstruct the damaged images based on the information available in the original image. Experimental results show that our approach outperforms existing image inpainting techniques in terms of computational time with reasonably good image reconstruction performance.
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Lebed, Evgeniy. "Sparse signal recovery in a transform domain." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/4171.

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The ability to efficiently and sparsely represent seismic data is becoming an increasingly important problem in geophysics. Over the last thirty years many transforms such as wavelets, curvelets, contourlets, surfacelets, shearlets, and many other types of ‘x-lets’ have been developed. Such transform were leveraged to resolve this issue of sparse representations. In this work we compare the properties of four of these commonly used transforms, namely the shift-invariant wavelets, complex wavelets, curvelets and surfacelets. We also explore the performance of these transforms for the problem of recovering seismic wavefields from incomplete measurements.
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Charles, Adam Shabti. "Dynamics and correlations in sparse signal acquisition." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53592.

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One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth. One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited. While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference. Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1--9]. We also analyze sparse signals that are generated via a dynamical system (i.e. a series of correlated, temporally ordered, sparse signals). For this class of signals, we present a series of inference algorithms that leverage both dynamics and sparsity information, improving the potential for signal recovery in a host of applications~[10--19]. As an extension of dynamical filtering, we show how these dynamic filtering ideas can be expanded to the broader class of spatially correlated signals. Specifically, explore how sparsity and spatial correlations can improve inference of material distributions and spectral super-resolution in hyperspectral imagery~[20--25]. Finally, we analyze dynamical systems that perform optimization routines for sparsity-based inference. We analyze a networked system driven by a continuous-time differential equation and show that such a system is capable of recovering a large variety of different sparse signal classes~[26--30].
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Han, Puxiao. "Distributed sparse signal recovery in networked systems." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4630.

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In this dissertation, two classes of distributed algorithms are developed for sparse signal recovery in large sensor networks. All the proposed approaches consist of local computation (LC) and global computation (GC) steps carried out by a group of distributed local sensors, and do not require the local sensors to know the global sensing matrix. These algorithms are based on the original approximate message passing (AMP) and iterative hard thresholding (IHT) algorithms in the area of compressed sensing (CS), also known as sparse signal recovery. For distributed AMP (DiAMP), we develop a communication-efficient algorithm GCAMP. Numerical results demonstrate that it outperforms the modified thresholding algorithm (MTA), another popular GC algorithm for Top-K query from distributed large databases. For distributed IHT (DIHT), there is a step size $\mu$ which depends on the $\ell_2$ norm of the global sensing matrix A. The exact computation of $\|A\|_2$ is non-separable. We propose a new method, based on the random matrix theory (RMT), to give a very tight statistical upper bound of $\|A\|_2$, and the calculation of that upper bound is separable without any communication cost. In the GC step of DIHT, we develop another algorithm named GC.K, which is also communication-efficient and outperforms MTA. Then, by adjusting the metric of communication cost, which enables transmission of quantized data, and taking advantage of the correlation of data in adjacent iterations, we develop quantized adaptive GCAMP (Q-A-GCAMP) and quantized adaptive GC.K (Q-A-GC.K) algorithms, leading to a significant improvement on communication savings. Furthermore, we prove that state evolution (SE), a fundamental property of AMP that in high dimensionality limit, the output data are asymptotically Gaussian regardless of the distribution of input data, also holds for DiAMP. In addition, compared with the most recent theoretical results that SE holds for sensing matrices with independent subgaussian entries, we prove that the universality of SE can be extended to far more general sensing matrices. These two theoretical results provide strong guarantee of AMP's performance, and greatly broaden its potential applications.
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Zachariah, Dave. "Estimation for Sensor Fusion and Sparse Signal Processing." Doctoral thesis, KTH, Signalbehandling, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-121283.

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Progressive developments in computing and sensor technologies during the past decades have enabled the formulation of increasingly advanced problems in statistical inference and signal processing. The thesis is concerned with statistical estimation methods, and is divided into three parts with focus on two different areas: sensor fusion and sparse signal processing. The first part introduces the well-established Bayesian, Fisherian and least-squares estimation frameworks, and derives new estimators. Specifically, the Bayesian framework is applied in two different classes of estimation problems: scenarios in which (i) the signal covariances themselves are subject to uncertainties, and (ii) distance bounds are used as side information. Applications include localization, tracking and channel estimation. The second part is concerned with the extraction of useful information from multiple sensors by exploiting their joint properties. Two sensor configurations are considered here: (i) a monocular camera and an inertial measurement unit, and (ii) an array of passive receivers. New estimators are developed with applications that include inertial navigation, source localization and multiple waveform estimation. The third part is concerned with signals that have sparse representations. Two problems are considered: (i) spectral estimation of signals with power concentrated to a small number of frequencies,and (ii) estimation of sparse signals that are observed by few samples, including scenarios in which they are linearly underdetermined. New estimators are developed with applications that include spectral analysis, magnetic resonance imaging and array processing.

QC 20130426

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Books on the topic "Sparse signal"

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A wavelet tour of signal processing: The sparse way. 3rd ed. Amsterdam: Elsevier/Academic Press, 2009.

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Mächler, Patrick. VLSI architectures for compressive sensing and sparse signal recovery. Konstanz: Hartung-Gorre Verlag, 2013.

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Starck, J. L. Sparse image and signal processing: Wavelets, curvelets, morphological diversity. New York: Cambridge University Press, 2010.

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Starck, J. L. Sparse image and signal processing: Wavelets, curvelets, morphological diversity. Cambridge: Cambridge University Press, 2010.

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Elad, M. Sparse and redundant representations: From theory to applications in signal and image processing. New York: Springer, 2010.

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Xin hao xi shu biao shi li lun ji qi ying yong. Beijing: Ke xue chu ban she, 2013.

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Hlawatsch, Franz. Time-Frequency Analysis and Synthesis of Linear Signal Spaces. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2815-6.

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Brown, Susan. Police signal boxes in Glasgow: A survey of surroundings, structure and spare parts. [Glasgow]: [Glasgow Building Preservation Trust], 1993.

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Time-frequency analysis and synthesis of linear signal spaces: Time-frequency filters, signal detection and estimation, and range-Doppler estimation. Boston: Kluwer Academic Publishers, 1998.

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Hlawatsch, F. Time-Frequency Analysis and Synthesis of Linear Signal Spaces: Time-Frequency Filters, Signal Detection and Estimation, and Range-Doppler Estimation. Boston, MA: Springer US, 1998.

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Book chapters on the topic "Sparse signal"

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Azghani, Masoumeh, and Farokh Marvasti. "Sparse Signal Processing." In New Perspectives on Approximation and Sampling Theory, 189–213. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08801-3_8.

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Henniges, Marc, Gervasio Puertas, Jörg Bornschein, Julian Eggert, and Jörg Lücke. "Binary Sparse Coding." In Latent Variable Analysis and Signal Separation, 450–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15995-4_56.

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Exarchakis, Georgios, Marc Henniges, Julian Eggert, and Jörg Lücke. "Ternary Sparse Coding." In Latent Variable Analysis and Signal Separation, 204–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28551-6_26.

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De Mol, Christine. "Sparse Markowitz Portfolios." In Financial Signal Processing and Machine Learning, 11–22. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118745540.ch2.

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Elad, Michael. "Sparsity-Seeking Methods in Signal Processing." In Sparse and Redundant Representations, 169–84. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7011-4_9.

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Mowlaee, Pejman, Amirhossein Froghani, and Abolghasem Sayadiyan. "Sparse Sinusoidal Signal Representation for Speech and Music Signals." In Communications in Computer and Information Science, 469–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89985-3_58.

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Vaswani, Namrata, and Wei Lu. "Recursive Reconstruction of Sparse Signal Sequences." In Compressed Sensing & Sparse Filtering, 357–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38398-4_11.

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Georgiev, Pando, Danielle Nuzillard, and Anca Ralescu. "Sparse Deflations in Blind Signal Separation." In Independent Component Analysis and Blind Signal Separation, 807–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_100.

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Chen, Guangyi, Sridhar Krishnan, Weihua Liu, and Wenfang Xie. "Sparse Signal Analysis Using Ramanujan Sums." In Intelligent Computing Theories and Technology, 450–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39482-9_52.

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Yanhong, Zhang, Guo Jinku, and Wu Jinying. "Sparse Signal Representation with Dispersion Dictionary." In Lecture Notes in Electrical Engineering, 1023–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21747-0_133.

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Conference papers on the topic "Sparse signal"

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Pope, Graeme, and Helmut Bolcskei. "Sparse signal recovery in Hilbert spaces." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283506.

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Babaie-Zadeh, Massoud, Behzad Mehrdad, and Georgios B. Giannakis. "Weighted sparse signal decomposition." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288652.

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Giri, Ritwik, and Bhaskar D. Rao. "Bootstrapped sparse Bayesian learning for sparse signal recovery." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094748.

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Azghani, Masoumeh, and Farokh Marvasti. "Applications of sparse signal processing." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7906061.

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"Session TP3b: Sparse signal recovery." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094745.

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Wang, Linyu, Mingqi He, and Jianhong Xiang. "Sparse Signal Recovery via Improved Sparse Adaptive Matching Pursuit Algorithm." In the 2019 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3316551.3316553.

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Ji, Aiguo, Weiping Liu, and Zhiqiang Liu. "DOA Estimation of Quasi-Stationary Signals Using Sparse Signal Reconstruction." In 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, 2019. http://dx.doi.org/10.1109/smartiot.2019.00084.

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Qu, Dongdong, Jiuling Jia, and Jian Zhou. "Digital alias-free signal processing methodology for sparse multiband signals." In 2013 6th International Congress on Image and Signal Processing (CISP). IEEE, 2013. http://dx.doi.org/10.1109/cisp.2013.6743865.

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Motamedvaziri, Delaram, Mohammad H. Rohban, and Venkatesh Saligrama. "Sparse signal recovery under Poisson statistics." In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2013. http://dx.doi.org/10.1109/allerton.2013.6736698.

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Mixon, Dustin G., Waheed U. Bajwa, and Robert Calderbank. "Frame coherence and sparse signal processing." In 2011 IEEE International Symposium on Information Theory - ISIT. IEEE, 2011. http://dx.doi.org/10.1109/isit.2011.6034214.

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Reports on the topic "Sparse signal"

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Cevher, Volkan, Chinmay Hegde, Marco F. Duarte, and Richard G. Baraniuk. Sparse Signal Recovery Using Markov Random Fields. Fort Belvoir, VA: Defense Technical Information Center, December 2009. http://dx.doi.org/10.21236/ada520187.

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Reeves, Galen. Sparse Signal Sampling using Noisy Linear Projections. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada519085.

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Stiles, James M. Multi-Dimensional Signal Processing for Sparse Radar Arrays. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada419885.

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Mascarenas, David D., Rose Long, Metodi Iliev, Kiril Ianakiev, and Charles R. Farrar. Nonlinear Signal Processing for Removing Microphonic Noise from Nuclear Spectrometer Measurements: Sparse Linear Modeling via L1 Norm Regularization. Office of Scientific and Technical Information (OSTI), January 2013. http://dx.doi.org/10.2172/1060366.

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Goodman, Joel, Keith Forsythe, and Benjamin Miller. Efficient Reconstruction of Block-Sparse Signals. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada541046.

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Chinco, Alexander, Adam Clark-Joseph, and Mao Ye. Sparse Signals in the Cross-Section of Returns. Cambridge, MA: National Bureau of Economic Research, October 2017. http://dx.doi.org/10.3386/w23933.

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Cevher, Volkan, Piotr Indyk, Chinmay Hegde, and Richard G. Baraniuk. Recovery of Clustered Sparse Signals from Compressive Measurements. Fort Belvoir, VA: Defense Technical Information Center, December 2009. http://dx.doi.org/10.21236/ada520218.

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Ringo, John. Mixed-Signal Electronics Technology for Space (MSETS). Fort Belvoir, VA: Defense Technical Information Center, February 2006. http://dx.doi.org/10.21236/ada453348.

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Kuperman, William A., and Gerald D'Spain. Waveguide Invariants and Space-Frequency Time Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada425248.

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Fainman, Yeshaiahu, and Paul E. Shames. Optoelectronic Systems for Space-Variant Signal and Image Processing. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada358424.

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