Angshul Majumdar

Compressed Sensing for Engineers (eBook, PDF)

• Format: PDF

Produktbeschreibung

The first portion of the book is on the algorithms. The second portion of the book is about applications, covering some of the major areas like computational imaging, medical imaging, biomedical signal processing and machine learning. The concentration of this book is on algorithms and applications.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 292
• Erscheinungstermin: 7. Dezember 2018
• Englisch
• ISBN-13: 9781351261357
• Artikelnr.: 55170904

Autorenporträt

Angshul Majumdar is currently an assistant professor in Electronics and Communications Engineering at the Indraprastha Institute of Information Technology, Delhi (IIIT-D). He completed his PhD in 2012 from the University of British Columbia, Canada. His main contribution is in reducing acquisition time for Magnetic Resonance Imaging acquisition. He has around 25 papers on this topic published in top-tier journals and conferences. Angshul also works in other areas of biomedical imaging and signal processing. Previously, he was interested in the problem of classification and has published several papers on robust classification techniques with applications in face recognition, fingerprint recognition and optical character recognition. In all, Angshul has published over 50 papers in top-tier journals and conferences in the last 5 years. Before Angshul started in the academia, he worked in business consulting at the Pricewaterhouse Coopers.

Inhaltsangabe

Introduction. Greedy Algorithms. Sparse Recovery. Co-sparse Recovery. Group
Sparsity. Joint Sparsity. Low-rank Matrix Recovery. Combined Sparse and
Low-rank Recovery. Dictionary Learning. Medical Imaging. Biomedical Signal
Reconstruction. Regression. Classification. Computational Imaging.
Denoising.

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Introduction. Greedy Algorithms. Sparse Recovery. Co-sparse Recovery. Group Sparsity. Joint Sparsity. Low-rank Matrix Recovery. Combined Sparse and Low-rank Recovery. Dictionary Learning. Medical Imaging. Biomedical Signal Reconstruction. Regression. Classification. Computational Imaging. Denoising.