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Multidimensional Signal Processing: Handbook of Statistics, cartea 54

Arni S.R. Srinivasa Rao Kumar Vijay Mishra, Gonzalo R. Arce
en Limba Engleză Hardback – mai 2026
Multidimensional Signal Processing, Volume 54 in the Handbook of Statistics series is dedicated to presenting the latest developments and methodologies in multidimensional signal processing. The book aims to provide a comprehensive overview of the theories, models, and methods that form the foundation of this field. Chapters in this new release include Robust Parameter Estimation of Two Dimensional Chirp Model, Computability Theory for Multidimensional Signal Processing, Tensor signal processing, Spectral compressed sensing by structured matrix optimization methods, Space-time imaging, Hypercomplex Widely Linear Processing, and much more. The book's chapters are meticulously curated to offer detailed, educational content rather than conventional journal-style articles.

Other chapters cover Hypercomplex phase retrieval, Hypercomplex widely linear estimation, MIMO radar signal processing, Computational lidar, Signal processing applications of higher-dimensional graphs, Space-Time Radio Signal Processing by Photonic Upconversion, Computational imaging, and Topology identification and learning over graphs using multi-dimensional data.

  • Provides easy to understand concepts
  • Includes materials that are provided in an implementable way
  • Written by experts in the field
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Specificații

ISBN-13: 9780443414657
ISBN-10: 0443414653
Pagini: 420
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Handbook of Statistics


Cuprins

Preface
1. Robust Parameter Estimation of Two Dimensional Chirp Model
Debassis Kundu
2. Computability Theory for Multidimensional Signal Processing
Holger Boche, Volker Pohl and H. VIncent Poor
3. Tensor signal processing
David Hong
4. Spectral compressed sensing by structured matrix optimization methods
Jian-Feng Cai, Xunmeng Wu, Zai Yang and Juntao You
5. Space-time imaging
David Brady
6. Hypercomplex Widely Linear Processing
Sayed Pouria Talebi and Clive Cheong Took
7. Hypercomplex phase retrieval
Kumar Vijay Mishra, Henry Arguello and Brian M. Sadler
8. Hypercomplex widely linear estimation
Wenyuan Wang and Kutluyil Dogancay
9. MIMO radar signal processing
Sergiy A. Vorobyov and Visa Koivunen
10. Computational lidar
Gonzalo R. Arce
11. Signal processing applications of higher-dimensional graphs
Santiago Segarra
12. Space-Time Radio Signal Processing by Photonic Upconversion
Xiao-Feng Qi and Dennis W. Prather
13. Computational imaging
Ayush Bhandari
14. Topology identification and learning over graphs using multi-dimensional data
Gonzalo Mateos, Georgios Giannakis, Yanning Shen and Ananthram Swami