عنوان

Compressed sensing & sparse filtering

شابک

9783642383984

کد کشور

IR

شماره

312611

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

کشور محل نشر

IR

عنوان اصلي

Compressed sensing & sparse filtering

نام عام مواد

[Electronic]

نام نخستين پديدآور

/ Avishy Y Carmi

نام ساير پديدآوران

; Simon J Godsill

محل نشرو پخش و غیره

Heidelberg

نام ناشر، پخش کننده و غيره

: Springer

تاریخ نشرو بخش و غیره

, 2014.

عنوان فروست

(Signals and communication technology)

متن يادداشت

Introduction to Compressed Sensing and Sparse Filtering --The Geometry of Compressed Sensing -- Sparse Signal Recovery with Exponential-Family Noise -- Nuclear Norm Optimization and its Application to Observation Model Specification -- Nonnegative Tensor Decomposition -- Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks -- Sparse Nonlinear MIMO Filtering and Identification -- Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation -- Compressive System Identification -- Distributed Approximation and Tracking using Selective Gossip -- Recursive Reconstruction of Sparse Signal Sequences -- Estimation of Time-Varying Sparse Signals in Sensor Networks -- Sparsity and Compressed Sensing in Mono-static and Multi-static Radar Imaging -- Structured Sparse Bayesian Modelling for Audio Restoration -- Sparse Representations for Speech Recognition.

متن يادداشت

This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems. This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing..

موضوع مستند نشده

Signal processing -- Digital techniques.

موضوع مستند نشده

TECHNOLOGY & ENGINEERING / Mechanical

شماره رده

مستند نام اشخاص تاييد نشده

Carmi, Avishy Y

مستند نام اشخاص تاييد نشده

Mihaylova, Lyudmila

مستند نام اشخاص تاييد نشده

Godsill, Simon J

کشور

ایران

سازمان

Central Library of the University of Kurdistan

تاريخ و ساعت مذاکره و دسترسي

C75F064D-4119-40DB-9318-965D322DDAE1.pdf

وضعیت فهرست نویسی

old catalog

فرمت انتشار

e

نوع ماده

BL

پیشوند ISBD اعمال شده است

1

سطح دسترسي

a

تكميل شده

Y