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6790 results in Communications and signal processing

Page 6 of 340

  • 1 - Introduction

  • Richard E. Blahut, University of Illinois, Urbana-Champaign
  • Book:
    Theory of Image Formation
    Published online:
    17 April 2025
    Print publication:
    24 April 2025, pp 1-29
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Theory of Image Formation
  • 2nd edition
  • Richard E. Blahut
  • Published online:
    17 April 2025
    Print publication:
    24 April 2025
  • Fully revised and updated, the second edition of this classic text is the definitive guide to the mathematical models underlying imaging from sensed data. Building on fundamental principles derived from the two- and three-dimensional Fourier transform, and other key mathematical concepts, it introduces a broad range of imaging modalities within a unified framework, emphasising universal theoretical concepts over specific physical aspects. This expanded edition presents new coverage of optical-coherence microscopy, electron-beam microscopy, near-field microscopy, and medical imaging modalities including MRI, CAT, ultrasound, and the imaging of viruses, and introduces additional end-of-chapter problems to support reader understanding. Encapsulating the author's fifty years of experience in the field, this is the ideal introduction for senior undergraduate and graduate students, academic researchers, and professional engineers across engineering and the physical sciences.

Fundamentals of Digital Communication Systems
  • Tolga M. Duman
  • Published online:
    10 March 2025
    Print publication:
    23 January 2025
  • This dynamic textbook provides students with a concise and accessible introduction to the fundamentals of modern digital communications systems. Building from first principles, its comprehensive approach equips students with all of the mathematical tools, theoretical knowledge, and practical understanding they need to excel. It equips students with a strong mathematical foundation spanning signals and systems, probability, random variables, and random processes, and introduces students to key concepts in digital information sources, analog-to-digital conversion, digital modulation, power spectra, multi-carrier modulation, and channel coding. It includes over 85 illustrative examples, and more than 270 theoretical and computational end-of-chapter problems, allowing students to connect theory to practice, and is accompanied by downloadable Matlab code, and a digital solutions manual for instructors. Suitable for a single-semester course, this succinct textbook is an ideal introduction to the field of digital communications for senior undergraduate students in electrical engineering.

Channel Codes
  • Classical and Modern
  • 2nd edition
  • William E. Ryan, Shu Lin, Stephen G. Wilson
  • Published online:
    06 March 2025
    Print publication:
    21 November 2024
  • Channel coding lies at the heart of digital communication and data storage. Fully updated, including a new chapter on polar codes, this detailed introduction describes the core theory of channel coding, decoding algorithms, implementation details, and performance analyses. This new edition includes over 50 new end-of-chapter problems and new figures and worked examples throughout. The authors emphasize the practical approach and present clear information on modern channel codes, including turbo and low-density parity-check (LDPC) codes, detailed coverage of BCH codes, Reed-Solomon codes, convolutional codes, finite geometry codes, product codes as well as polar codes for error correction and detection, providing a one-stop resource for classical and modern coding techniques. Assuming no prior knowledge in the field of channel coding, the opening chapters begin with basic theory to introduce newcomers to the subject. Later chapters then extend to advanced topics such as code ensemble performance analyses and algebraic code design.

Machine Learning with Python
  • Principles and Practical Techniques
  • Parteek Bhatia
  • Published online:
    22 February 2025
    Print publication:
    30 November 2025
  • Machine learning has become a dominant problem-solving technique in the modern world, with applications ranging from search engines and social media to self-driving cars and artificial intelligence. This lucid textbook presents the theoretical foundations of machine learning algorithms, and then illustrates each concept with its detailed implementation in Python to allow beginners to effectively implement the principles in real-world applications. All major techniques, such as regression, classification, clustering, deep learning, and association mining, have been illustrated using step-by-step coding instructions to help inculcate a 'learning by doing' approach. The book has no prerequisites, and covers the subject from the ground up, including a detailed introductory chapter on the Python language. As such, it is going to be a valuable resource not only for students of computer science, but also for anyone looking for a foundation in the subject, as well as professionals looking for a ready reckoner.

Page 6 of 340