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| Author | : IEEE Signal Processing Society Staff |
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| Total Pages | : |
| Release | : 2002 |
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| Author | : IEEE Signal Processing Society Staff |
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| Release | : 2000 |
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| Author | : Mats Viberg |
| Publisher | : Academic Press |
| Total Pages | : 1013 |
| Release | : 2013-08-31 |
| Genre | : Technology & Engineering |
| ISBN | : 0124116213 |
This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved Quick tutorial reviews of important and emerging topics of research in array and statistical signal processing Presents core principles and shows their application Reference content on core principles, technologies, algorithms and applications Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic
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| Total Pages | : 124 |
| Release | : 2000 |
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This year marks the eighth annual ASAP workshop, which is sponsored jointly by the DARPA Advanced and Tactical Technology Offices, the Navy AEGIS and E2C Program Offices, the Office of Naval Research and the NAVSEA Advanced Systems and Technology Office. This year, the first IEEE Sensor Array and Multichannel (SAM) Signal Processing Workshop in nearby Cambridge, Massachusetts will follow the ASAP workshop on 16-17 March 2000. This unique pairing of ASAP with SAM will provide an unparalleled opportunity for the adaptive sensor array processing community, combining ASAP's focus on state-of-the-art signal processing advances for military systems with the broader international scope and strong academic participation of the IEEE SAM workshop. As this workshop has evolved over its eight-year history, a common theme has been to highlight the similarity of adaptive sensor processing techniques for various disciplines. The ASAP forum has provided researchers from academia, government, and industry the opportunity to discuss common problems and develop ideas and solutions related to how these diverse technologies can be applied to national defense interests. This year's workshop will continue to stress the strong coupling between government, industry, and academia. A key theme will be the cross-fertilization of ideas between the ASAP and SAM participants to provide new areas of exploration and accelerate technological advances.
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| Release | : 1999 |
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| ISBN | : 9780780350410 |
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| Publisher | : Elsevier |
| Total Pages | : 676 |
| Release | : 2016-08-13 |
| Genre | : Computers |
| ISBN | : 0444636447 |
Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Superresolution Imaging offers a comprehensive look into the most important models and frameworks essential to resolving the spectral unmixing problem—from multivariate curve resolution and multi-way analysis to Bayesian positive source separation and nonlinear unmixing. Unravelling total spectral data into the contributions from individual unknown components with limited prior information is a complex problem that has attracted continuous interest for almost four decades. Spectral unmixing is a topic of interest in statistics, chemometrics, signal processing, and image analysis. For decades, researchers from these fields were often unaware of the work in other disciplines due to their different scientific and technical backgrounds and interest in different objects or samples. This led to the development of quite different approaches to solving the same problem. This multi-authored book will bridge the gap between disciplines with contributions from a number of well-known and strongly active chemometric and signal processing research groups. Among chemists, multivariate curve resolution methods are preferred to extract information about the nature, amount, and location in time (process) and space (imaging and microscopy) of chemical constituents in complex samples. In signal processing, assumptions are usually around statistical independence of the extracted components. However, the chapters include the complexity of the spectral data to be unmixed as well as dimensionality and size of the data sets. Advanced spectroscopy is the key thread linking the different chapters. Applications cover a large part of the electromagnetic spectrum. Time-resolution ranges from femtosecond to second in process spectroscopy and spatial resolution covers the submicronic to macroscopic scale in hyperspectral imaging. Demonstrates how and why data analysis, signal processing, and chemometrics are essential to the spectral unmixing problem Guides the reader through the fundamentals and details of the different methods Presents extensive plots, graphical representations, and illustrations to help readers understand the features of different techniques and to interpret results Bridges the gap between disciplines with contributions from a number of well-known and highly active chemometric and signal processing research groups
| Author | : Carlo Sansone |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 382 |
| Release | : 2011-06-14 |
| Genre | : Computers |
| ISBN | : 3642215564 |
This book constitutes the refereed proceedings of the 10th International Workshop on Multiple Classifier Systems, MCS 2011, held in Naples, Italy, in June 2011. The 36 revised papers presented together with two invited papers were carefully reviewed and selected from more than 50 submissions. The contributions are organized into sessions dealing with classifier ensembles; trees and forests; one-class classifiers; multiple kernels; classifier selection; sequential combination; ECOC; diversity; clustering; biometrics; and computer security.
| Author | : Pierre Comon |
| Publisher | : Academic Press |
| Total Pages | : 856 |
| Release | : 2010-02-17 |
| Genre | : Technology & Engineering |
| ISBN | : 0080884946 |
Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications
| Author | : Sai Zhang |
| Publisher | : Springer Nature |
| Total Pages | : 76 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 303101684X |
The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.
