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| Author | : Feibai Zhu |
| Publisher | : |
| Total Pages | : 159 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
| Author | : Radha Sankararajan |
| Publisher | : CRC Press |
| Total Pages | : 493 |
| Release | : 2022-09-01 |
| Genre | : Technology & Engineering |
| ISBN | : 1000794369 |
Compressed Sensing (CS) is a promising method that recovers the sparse and compressible signals from severely under-sampled measurements. CS can be applied to wireless communication to enhance its capabilities. As this technology is proliferating, it is possible to explore its need and benefits for emerging applicationsCompressive Sensing for Wireless Communication provides:• A clear insight into the basics of compressed sensing• A thorough exploration of applying CS to audio, image and computer vision• Different dimensions of applying CS in Cognitive radio networks• CS in wireless sensor network for spatial compression and projection• Real world problems/projects that can be implemented and tested• Efficient methods to sample and reconstruct the images in resource constrained WMSN environmentThis book provides the details of CS and its associated applications in a thorough manner. It lays a direction for students and new engineers and prepares them for developing new tasks within the field of CS. It is an indispensable companion for practicing engineers who wish to learn about the emerging areas of interest.
| Author | : Zhu Han |
| Publisher | : Cambridge University Press |
| Total Pages | : 308 |
| Release | : 2013-06-06 |
| Genre | : Computers |
| ISBN | : 1107018838 |
This comprehensive reference delivers the understanding and skills needed to take advantage of compressive sensing in wireless networks.
| Author | : Norma Aurea Rangel-Vázquez |
| Publisher | : River Publishers |
| Total Pages | : 184 |
| Release | : 2016-09-30 |
| Genre | : Science |
| ISBN | : 8793379854 |
Computational chemistry is a science that allows researchers to study, characterize and predict the structure and stability of chemical systems. In other words: studying energy differences between different states to explain spectroscopic properties and reaction mechanisms at the atomic level. This field is gaining in relevance and strength due to field applications from chemical engineering, electrical engineering, electronics, biomedicine, biology, materials science, to name but a few. Structural Analysis using Computational Chemistry arises from the need to present the progress of computational chemistry in various application areas. Technical topics discussed in the book include: Quantum mechanics and structural molecular study (AM1)Application of quantum models in molecular analysisMolecular analysis of insulin through controlled adsorption in hydrogels based on chitosanAnalysis and molecular characterization of organic materials for application in solar cellsDetermination of thermodynamic properties of ionic liquids through molecular simulation
| Author | : Markus Leinonen |
| Publisher | : |
| Total Pages | : 310 |
| Release | : 2019-11-29 |
| Genre | : Technology & Engineering |
| ISBN | : 9781680836462 |
This monograph reviews several recent compressed sensing advancements in wireless networks with an aim to improve the quality of signal reconstruction or detection while reducing the use of energy, radio, and computation resources.
| Author | : Ji Wu |
| Publisher | : |
| Total Pages | : |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. It provides a potential way to acquire the sparse data efficiently, or equivalently, highly accurate recovery of sparse data from undersampled measurements. Huffman coding and compressive sensing are adopted to compress real-world wind tunnel data. Both uniform and non-uniform Huffman coding are evaluated in terms of the number of quantization levels, mean square error, codeword length and compression ratio. The main drawback of Huffman coding is that it requires calculating the probability of each symbol before encoding. It means it may not be appropriate for real-time compression. We applied CS to wind tunnel data and compared its performance against the theoretical error bound.Due to limited energy and physical size of the sensor nodes, the conventional security mechanisms with high computation complexity are not feasible for wireless sensor networks (WSNs). A compressive sensing-based encryption is proposed for distributed WSNs, which provide both signal compression and encryption guarantees, without the additional computational cost of a separate encryption protocol. The computational and information-theoretical secrecy of the compressive sensing algorithm is also investigated. For the proposed distributed WSNs, if only a fraction of randomizer bits is stored by an eavesdropper, then the eavesdropper cannot obtain any information about the plaintext.We studied a compressive sensing-based Ultra-WideBand (UWB) wireless communication system. Compared with the conventional UWB system, it can jointly estimate the channel and compress the data. No information about the transmitted signal is required in advance as long as the channel follows the autoregressive model. The performance of compressive sensing-based data encryption scheme shows that the original data could never be reconstructed when the measurement matrix is not available. Hence, compressive sensing can be implemented as a data encryption scheme with good secrecy.
| Author | : Giulio Coluccia |
| Publisher | : Springer |
| Total Pages | : 104 |
| Release | : 2015-05-29 |
| Genre | : Technology & Engineering |
| ISBN | : 9812873902 |
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new. Remarkably, such applications are ideally suited to exploit all the benefits that compressed sensing can provide. The objective of this book is to provide the reader with a comprehensive survey of this topic, from the basic concepts to different classes of centralized and distributed reconstruction algorithms, as well as a comparison of these techniques. This book collects different contributions on these aspects. It presents the underlying theory in a complete and unified way for the first time, presenting various signal models and their use cases. It contains a theoretical part collecting latest results in rate-distortion analysis of distributed compressed sensing, as well as practical implementations of algorithms obtaining performance close to the theoretical bounds. It presents and discusses various distributed reconstruction algorithms, summarizing the theoretical reconstruction guarantees and providing a comparative analysis of their performance and complexity. In summary, this book will allow the reader to get started in the field of distributed compressed sensing from theory to practice. We believe that this book can find a broad audience among researchers, scientists, or engineers with very diverse backgrounds, having interests in mathematical optimization, network systems, graph theoretical methods, linear systems, stochastic systems, and randomized algorithms. To help the reader become familiar with the theory and algorithms presented, accompanying software is made available on the authors’ web site, implementing several of the algorithms described in the book. The only background required of the reader is a good knowledge of advanced calculus and linear algebra.
| Author | : Malek Benslama |
| Publisher | : Elsevier |
| Total Pages | : 257 |
| Release | : 2017-11-20 |
| Genre | : Technology & Engineering |
| ISBN | : 0081019688 |
Compressed Sensing in Li-Fi and Wi-Fi Networks features coverage of the first applications in optical telecommunications and wireless. After extensive development of basic theory, many techniques are presented, such as non-asymptotic analysis of random matrices, adaptive detection, greedy algorithms, and the use of graphical models. The book can be used as a comprehensive manual for teaching and research in courses covering advanced signal processing, efficient data processing algorithms, and telecommunications. After a thorough review of the basic theory of compressed sensing, many mathematical techniques are presented, including advanced signal modeling, Nyquist sub-sampling of analog signals, the non-asymptotic analysis of random matrices, adaptive detection, greedy algorithms, and the use of graphical models. Offers extensive development of basic theory behind telecommunications and wireless networks Contains broad coverage of treat compressed sensing, including electromagnetism signals Provides insights into the two key areas of telecommunications, WIFI and LIFI Includes information on advanced signal modeling, Nyquist sub-sampling of analog signals, the non-asymptotic analysis of random matrices, adaptive detection, greedy algorithms, and more
| Author | : Yonina C. Eldar |
| Publisher | : Cambridge University Press |
| Total Pages | : 557 |
| Release | : 2012-05-17 |
| Genre | : Mathematics |
| ISBN | : 1107005582 |
A detailed presentation of compressed sensing by leading researchers, covering the most significant theoretical and application-oriented advances.
| Author | : Simon Foucart |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 634 |
| Release | : 2013-08-13 |
| Genre | : Computers |
| ISBN | : 0817649484 |
At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. With only moderate prerequisites, it is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.
