Download or read book Compressive Sensing Based Algorithms for Electronic Defence written by Amit Kumar Mishra and published by Springer. This book was released on 2016-12-22 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book details some of the major developments in the implementation of compressive sensing in radio applications for electronic defense and warfare communication use. It provides a comprehensive background to the subject and at the same time describes some novel algorithms. It also investigates application value and performance-related parameters of compressive sensing in scenarios such as direction finding, spectrum monitoring, detection, and classification.

Download or read book Artificial Intelligence for Sustainable Energy written by Jimson Mathew and published by Springer Nature. This book was released on with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Compressed Sensing in Information Processing written by Gitta Kutyniok and published by Springer Nature. This book was released on 2022-10-20 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume showcases the most significant results obtained from the DFG Priority Program on Compressed Sensing in Information Processing. Topics considered revolve around timely aspects of compressed sensing with a special focus on applications, including compressed sensing-like approaches to deep learning; bilinear compressed sensing - efficiency, structure, and robustness; structured compressive sensing via neural network learning; compressed sensing for massive MIMO; and security of future communication and compressive sensing.

Download or read book Communications Signal Processing and Systems written by Qilian Liang and published by Springer Nature. This book was released on 2020-04-04 with total page 2720 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together papers from the 2019 International Conference on Communications, Signal Processing, and Systems, which was held in Urumqi, China, on July 20–22, 2019. Presenting the latest developments and discussing the interactions and links between these multidisciplinary fields, the book spans topics ranging from communications to signal processing and systems. It is chiefly intended for undergraduate and graduate students in electrical engineering, computer science and mathematics, researchers and engineers from academia and industry, as well as government employees.

Download or read book Compressive Sensing for Urban Radar written by Moeness Amin and published by CRC Press. This book was released on 2017-12-19 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the emergence of compressive sensing and sparse signal reconstruction, approaches to urban radar have shifted toward relaxed constraints on signal sampling schemes in time and space, and to effectively address logistic difficulties in data acquisition. Traditionally, these challenges have hindered high resolution imaging by restricting both bandwidth and aperture, and by imposing uniformity and bounds on sampling rates. Compressive Sensing for Urban Radar is the first book to focus on a hybrid of two key areas: compressive sensing and urban sensing. It explains how reliable imaging, tracking, and localization of indoor targets can be achieved using compressed observations that amount to a tiny percentage of the entire data volume. Capturing the latest and most important advances in the field, this state-of-the-art text: Covers both ground-based and airborne synthetic aperture radar (SAR) and uses different signal waveforms Demonstrates successful applications of compressive sensing for target detection and revealing building interiors Describes problems facing urban radar and highlights sparse reconstruction techniques applicable to urban environments Deals with both stationary and moving indoor targets in the presence of wall clutter and multipath exploitation Provides numerous supporting examples using real data and computational electromagnetic modeling Featuring 13 chapters written by leading researchers and experts, Compressive Sensing for Urban Radar is a useful and authoritative reference for radar engineers and defense contractors, as well as a seminal work for graduate students and academia.

Download or read book Compressed Sensing Algorithms for Electromagnetic Imaging Applications written by Richard Obermeier and published by . This book was released on 2016 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed Sensing (CS) theory is a novel signal processing paradigm, which states that sparse signals of interest can be accurately recovered from a small set of linear measurements using efficient L1-norm minimization techniques. CS theory has been successfully applied to many sensing applications; such has optical imaging, X-ray CT, and Magnetic Resonance Imaging (MRI). However, there are two critical deficiencies in how CS theory is applied to these practical sensing applications. First, the most common reconstruction algorithms ignore the constraints placed on the recovered variable by the laws of physics. Second, the measurement system must be constructed deterministically, and so it is not possible to utilize random matrix theory to assess the CS reconstruction capabilities of the sensing matrix. In this thesis, we propose solutions to these two deficiencies in the context of electromagnetic imaging applications, in which the unknown variables are related to the dielectric constant and conductivity of the scatterers. First, we introduce a set of novel Physicality Constrained Compressed Sensing (PCCS) optimization programs, which augment the standard CS optimization programs to force the resulting variables to obey the laws of physics. The PCCS problems are investigated from both theoretical and practical stand-points, as well as in the context of a hybrid Digital Breast Tomosynthesis (DBT) / Nearfield Radar Imaging (NRI) system for breast cancer detection. Our analysis shows how the PCCS problems provide enhanced recovery capabilities over the standard CS problems. We also describe three efficient algorithms for solving the PCCS optimization programs. Second, we present a novel numerical optimization method for designing so-called "compressive antennas" with enhanced CS recovery capabilities. In this method, the constitutive parameters of scatterers placed along a traditional antenna are designed in order to maximize the capacity of the sensing matrix. Through a theoretical analysis and a series of numerical examples, we demonstrate the ability of the optimization method to design antenna configurations with enhanced CS recovery capabilities. Finally, we briefly discuss an extension of the design method to Multiple Input Multiple Output (MIMO) communication systems.

Download or read book Compressed Sensing for Privacy preserving Data Processing written by Matteo Testa and published by . This book was released on 2019 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this book is to provide the reader with a comprehensive survey of the topic compressed sensing in information retrieval and signal detection with privacy preserving functionality without compromising the performance of the embedding in terms of accuracy or computational efficiency. The reader is guided in exploring the topic by first establishing a shared knowledge about compressed sensing and how it is used nowadays. Then, clear models and definitions for its use as a cryptosystem and a privacy-preserving embedding are laid down, before tackling state-of-the-art results for both applications. The reader will conclude the book having learned that the current results in terms of security of compressed techniques allow it to be a very promising solution to many practical problems of interest. The book caters to a broad audience among researchers, scientists, or engineers with very diverse backgrounds, having interests in security, cryptography and privacy in information retrieval systems. Accompanying software is made available on the authors’ website to reproduce the experiments and techniques presented in the book. The only background required to the reader is a good knowledge of linear algebra, probability and information theory.

Download or read book New Theory and Algorithms for Compressive Sensing written by and published by . This book was released on 2009 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this project we expanded the field of compressive sensing in both theoretical and practical ways. We first demonstrated the information scalability of CS. We applied CS principles to analog-to-digital conversion, showing ADC can be accomplished on structured high rate signals with sub-Nyquist sampling. We introduced a smashed filter to perform statistical classification problems with a rate of measurements that corresponds to the problem structure, rather than bandwidth. Second, we improved on previous work in distributed compressive sensing. We used graphical models to derive performance bounds on multi-sensor settings. Finally, we created a CS-based radar framework and applied it to both 1-D ranging and 2-D synthetic aperture problems.

Download or read book Secure Compressive Sensing in Multimedia Data Cloud Computing and IoT written by Yushu Zhang and published by Springer. This book was released on 2018-09-01 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a comprehensive and systematic review of secure compressive sensing (CS) for applications in various fields such as image processing, pattern recognition, Internet of things (IoT), and cloud computing. It will help readers grasp the knowledge of secure CS and its applications, and stimulate more readers to work on the research and development of secure CS. It discusses how CS becomes a cryptosystem, followed by the corresponding designs and analyses. The application of CS in multimedia data encryption is presented, in which the general design framework is given together with several particular frameworks including parallel CS, involvement of image processing techniques, and double protection mechanism. It also describes the applications of CS in cloud computing security and IoT security, i.e., privacy-preserving reconstruction in cloud computing and secure low-cost sampling in IoT, respectively.

Download or read book Fast and Robust Algorithms for Compressive Sensing and Other Applications written by Yi Yang and published by . This book was released on 2014 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Efficiency and robustness are often the main concerns in model design and algorithm development. Nowadays a lot of algorithms have been proposed with emphasis on one or the other. This thesis provides several algorithms together with their applications to address these two needs. The first part of the thesis discusses the efficiency concern in video compression and reconstruction. With the increasing demand in real-time data transmission and storage, these two problems are attracting more and more attention. In terms of video compression, classic models often use a fixed temporal compression rate, while there are many potential gains in developing systems and procedures incorporating adaptive temporal compression rate. In Chapter 2, an algorithm based on local patches and polynomial fitting is proposed to adaptively predicts the temporal compression rate given the behavior of a few previous compressed frames. As for the inverse model, Chapter 3 presents a fast total variation based method for reconstructing video compressive sensing data. The regularization in the model is imposed on both the spatial and temporal components, which provides a more consistent approximation of the connection between neighboring frames with little to no increase in model complexity. The second part of the thesis covers a new technique named adaptive outlier pursuit for handling sparsely corrupted data. In many real world applications, noise is often unavoidable during data acquisition and transmission. Some noise can damage part of the data seriously and make it contain no useful information at all. Algorithms robust to this type of noise are strongly needed. The technique adaptive outlier pursuit is introduced to deal with outliers in the acquired measurements. Instead of detecting and removing the outliers before applying classic algorithms, it alternates between the outlier detection and the signal reconstruction task, hence iteratively approaches the true signal in a more accurate way. It is applied to robust 1-bit compressive sensing and exact matrix completion in Chapter 5 and Chapter 6 respectively.

Download or read book The Future of Hyperspectral Imaging written by Stefano Selci and published by MDPI. This book was released on 2019-11-20 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes some very recent applications and the newest emerging trends of hyper-spectral imaging (HSI). HSI is a very recent and strange beast, a sort of a melting pot of previous techniques and scientific interests, merging and concentrating the efforts of physicists, chemists, botanists, biologists, and physicians, to mention just a few, as well as experts in data crunching and statistical elaboration. For almost a century, scientific observation, from looking to planets and stars down to our own cells and below, could be divided into two main categories: analyzing objects on the basis of their physical dimension (recording size, position, weight, etc. and their variations) or on how the object emits, reflects, or absorbs part of the electromagnetic spectrum, i.e., spectroscopy. While the two aspects have been obviously entangled, instruments and skills have always been clearly distinct from each other. With HSI now available, this is no longer the case. This instrument can return specimen dimensionalities and spectroscopic properties to any single pixel of your specimen, in a single set of data. HSI modality is ubiquitous and scale-invariant enough to be used to mark terrestrial resources on the basis of a land map obtained from satellite observation (actually, the oldest application of this type) or to understand if the cell you are looking at is cancerous or perfectly healthy. For all these reasons, HSI represents one of the most exciting methodologies of the new millennium.

Download or read book Compressed Sensing in Radar Signal Processing written by Antonio De Maio and published by Cambridge University Press. This book was released on 2019-10-17 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn about the most recent theoretical and practical advances in radar signal processing using tools and techniques from compressive sensing. Providing a broad perspective that fully demonstrates the impact of these tools, the accessible and tutorial-like chapters cover topics such as clutter rejection, CFAR detection, adaptive beamforming, random arrays for radar, space-time adaptive processing, and MIMO radar. Each chapter includes coverage of theoretical principles, a detailed review of current knowledge, and discussion of key applications, and also highlights the potential benefits of using compressed sensing algorithms. A unified notation and numerous cross-references between chapters make it easy to explore different topics side by side. Written by leading experts from both academia and industry, this is the ideal text for researchers, graduate students and industry professionals working in signal processing and radar.

Download or read book Algorithms for discrete Compressed Sensing a Communications Engineering Perspective written by Susanne Sparrer and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Compressive Sensing Using Lp Optimization written by Jeevan Kumar Pant and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Three problems in compressive sensing, namely, recovery of sparse signals from noise-free measurements, recovery of sparse signals from noisy measurements, and recovery of so called block-sparse signals from noisy measurements, are investigated. In Chapter 2, the reconstruction of sparse signals from noise-free measurements is investigated and three algorithms are developed. The first and second algorithms minimize the approximate L0 and Lp pseudonorms, respectively, in the null space of the measurement matrix using a sequential quasi-Newton algorithm. An efficient line search based on Banach's fixed-point theorem is developed and applied in the second algorithm. The third algorithm minimizes the approximate Lp pseudonorm in the null space by using a sequential conjugate-gradient (CG) algorithm. Simulation results are presented which demonstrate that the proposed algorithms yield improved signal reconstruction performance relative to that of the iterative reweighted (IR), smoothed L0 (SL0), and L1-minimization based algorithms. They also require a reduced amount of computations relative to the IR and L1-minimization based algorithms. The Lp-minimization based algorithms require less computation than the SL0 algorithm. In Chapter 3, the reconstruction of sparse signals and images from noisy measurements is investigated. First, two algorithms for the reconstruction of signals are developed by minimizing an Lp-pseudonorm regularized squared error as the objective function using the sequential optimization procedure developed in Chapter 2. The first algorithm minimizes the objective function by taking steps along descent directions that are computed in the null space of the measurement matrix and its complement space. The second algorithm minimizes the objective function in the time domain by using a CG algorithm. Second, the well known total variation (TV) norm has been extended to a nonconvex version called the TVp pseudonorm and an algorithm for the reconstruction of images is developed that involves minimizing a TVp-pseudonorm regularized squared error using a sequential Fletcher-Reeves' CG algorithm. Simulation results are presented which demonstrate that the first two algorithms yield improved signal reconstruction performance relative to the IR, SL0, and L1-minimization based algorithms and require a reduced amount of computation relative to the IR and L1-minimization based algorithms. The TVp-minimization based algorithm yields improved image reconstruction performance and a reduced amount of computation relative to Romberg's algorithm. In Chapter 4, the reconstruction of so-called block-sparse signals is investigated. The L2/1 norm is extended to a nonconvex version, called the L2/p pseudonorm, and an algorithm based on the minimization of an L2/p-pseudonorm regularized squared error is developed. The minimization is carried out using a sequential Fletcher-Reeves' CG algorithm and the line search described in Chapter 2. A reweighting technique for the reduction of amount of computation and a method to use prior information about the locations of nonzero blocks for the improvement in signal reconstruction performance are also proposed. Simulation results are presented which demonstrate that the proposed algorithm yields improved reconstruction performance and requires a reduced amount of computation relative to the L2/1-minimization based, block orthogonal matching pursuit, IR, and L1-minimization based algorithms.

Download or read book Sparse Representations for Radar with MATLAB Examples written by Peter Knee and published by Springer Nature. This book was released on 2022-05-31 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the field of sparse representations is relatively new, research activities in academic and industrial research labs are already producing encouraging results. The sparse signal or parameter model motivated several researchers and practitioners to explore high complexity/wide bandwidth applications such as Digital TV, MRI processing, and certain defense applications. The potential signal processing advancements in this area may influence radar technologies. This book presents the basic mathematical concepts along with a number of useful MATLAB® examples to emphasize the practical implementations both inside and outside the radar field. Table of Contents: Radar Systems: A Signal Processing Perspective / Introduction to Sparse Representations / Dimensionality Reduction / Radar Signal Processing Fundamentals / Sparse Representations in Radar

Download or read book Through the Wall Radar Imaging written by Moeness G. Amin and published by CRC Press. This book was released on 2017-12-19 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through-the-wall radar imaging (TWRI) allows police, fire and rescue personnel, first responders, and defense forces to detect, identify, classify, and track the whereabouts of humans and moving objects. Electromagnetic waves are considered the most effective at achieving this objective, yet advances in this multi-faceted and multi-disciplinary technology require taking phenomenological issues into consideration and must be based on a solid understanding of the intricacies of EM wave interactions with interior and exterior objects and structures. Providing a broad overview of the myriad factors involved, namely size, weight, mobility, acquisition time, aperture distribution, power, bandwidth, standoff distance, and, most importantly, reliable performance and delivery of accurate information, Through-the-Wall Radar Imaging examines this technology from the algorithmic, modeling, experimentation, and system design perspectives. It begins with coverage of the electromagnetic properties of walls and building materials, and discusses techniques in the design of antenna elements and array configurations, beamforming concepts and issues, and the use of antenna array with collocated and distributed apertures. Detailed chapters discuss several suitable waveforms inverse scattering approaches and revolve around the relevance of physical-based model approaches in TWRI along with theoretical and experimental research in 3D building tomography using microwave remote sensing, high-frequency asymptotic modeling methods, synthetic aperture radar (SAR) techniques, impulse radars, airborne radar imaging of multi-floor buildings strategies for target detection, and detection of concealed targets. The book concludes with a discussion of how the Doppler principle can be used to measure motion at a very fine level of detail. The book provides a deep understanding of the challenges of TWRI, stressing its multidisciplinary and phenomenological nature. The breadth and depth of topics covered presents a highly detailed treatment of this potentially life-saving technology.

Download or read book Compressive Sensing for DoD Sensor Systems cMichael Gregg et Al written by Michael Gregg and published by . This book was released on 2012 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: During its 2012 Summer Study, JASON was asked by ASDR&E (Assistant Secretary of Defense for Research and Engineering) to consider how compressed sensing may be applied to Department of Defense systems, emphasizing radar because installations on small platforms can have duty cycles limited by average transmit power.