Recent Advances in Information, Communications and Signal Processing (River Publishers Series in Signal, Image and Speech Processing)

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Recent Advances in Information, Communications and Signal Processing (River Publishers Series in Signal, Image and Speech Processing)

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  • 製本 Hardcover:ハードカバー版/ページ数 200 p.
  • 言語 ENG
  • 商品コード 9788793609433
  • DDC分類 621.3822

Full Description

Research in information, communications and signal processing has brought about new services, applications and functions in a large number of fields which include consumer electronics, biomedical devices and defence. These applications play an important role in advancing technologies to enhance human life in general.

Recent Advances in Information, Communications and Signal Processing aims to give students, researchers, and engineers information pertaining to recent advances in these fields. In terms of research in signal processing topics, the two chapters included in this book have a strong emphasis on advances in algorithmic development in the biomedical, and human-computer interfaces domain areas. More specifically, the use of deep learning for placental maturity staging is discussed as well as the use of vibration analysis for localising impacts on surfaces for human-computer applications. In terms of communications signal processing, advances in new wireless communication such as NOMA (non-orthogonal multiple access) and millimetre-wave antenna design for 5G cellular mobile radio, as well as innovations in LDPC (low density parity check code) decoding and networking coding, are featured.

Contents

Non-orthogonal Multiple Access: Recent Developments and Future Trends; Beam Steering MIMO Antenna for Mobile Phone of 5G Cellular Communications Operating at MM-Wave Frequencies: Design; Random Linear Network Coding with Source Precoding for Multi-session networks; Decoding Scheduling for Low-Density Parity-Check Codes; Location Template Matching on Rigid Surfaces for Human-Computer Touch Interface Applications; Automatic Placental Maturity Grading via Deep Convolutional Networks

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