Signal Processing and Biomedical Engineering Research: Applications of Machine Learning Based on Big Data Principles
暫譯: 信號處理與生物醫學工程研究：基於大數據原則的機器學習應用
Ahmed, Ammar, Picone, Joseph
- 出版商: Springer
- 出版日期: 2026-08-03
- 售價: $4,740
- 貴賓價: 9.5 折 $4,503
- 語言: 英文
- 頁數: 232
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3032185084
- ISBN-13: 9783032185082
-
相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
相關主題
商品描述
Signal Processing and Biomedical Engineering Research: Applications of Machine Learning Based on Big Data Principles contains expanded versions of selected contributions from the 2024 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB 2024) held at Temple University. The symposium covers a wide range of topics in the life sciences and promotes machine learning and big data applications in bioengineering. The topics covered include signal and image analysis (e.g., EEG, ECG, MRI), machine learning, data mining, and classification, big data resources and applications, applications of quantum computing, digital pathology, computational biology, and genomics, genetics, and proteomics. The book features detailed review articles, tutorials, and examples of successful applications that will appeal to professionals and researchers in signal processing, medicine, and biology. It also provides an easy-to-understand introduction to various bioengineering topics for students and professionals new to the field, and essential algorithmic details on valuable benchmarks for professionals active in the field.
商品描述(中文翻譯)
《信號處理與生物醫學工程研究：基於大數據原則的機器學習應用》包含了2024年在天普大學舉行的IEEE醫學與生物學信號處理研討會（IEEE SPMB 2024）中選定貢獻的擴展版本。該研討會涵蓋了生命科學中的廣泛主題，並促進機器學習和大數據在生物工程中的應用。所涵蓋的主題包括信號和影像分析（例如，腦電圖 EEG、心電圖 ECG、磁共振成像 MRI）、機器學習、資料探勘和分類、大數據資源和應用、量子計算的應用、數位病理學、計算生物學，以及基因組學、遺傳學和蛋白質組學。這本書包含詳細的綜述文章、教程和成功應用的範例，將吸引信號處理、醫學和生物學領域的專業人士和研究人員。它還為新進學生和專業人士提供了易於理解的各種生物工程主題介紹，以及對於活躍於該領域的專業人士來說，重要基準的必要算法細節。
作者簡介
Ammar Ahmed, Ph.D., is a radar signal processing engineer at Aptiv, Agoura Hills, CA. He earned his Ph.D. in Electrical Engineering from Temple University under the supervision of Dr. Daniel Zhang, Dr. Dennis Silage, and Dr. Joseph Picone. Dr. Ahmed received a B.Sc. degree in Electrical Engineering from the University of Engineering & Technology, Lahore, Pakistan, in 2009 and an M.S. in Systems Engineering from the Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan, in 2011. He has published over 40 technical papers and holds two patents. His research interests are in signal processing, optimization, and radar systems.
Joseph Picone, Ph.D., is a Professor of Electrical and Computer Engineering at Temple University, where he directs the Institute for Signal and Information Processing and is the Associate Director of the Neural Engineering Data Consortium. Dr. Joseph Picone received his Ph.D. in Electrical Engineering in 1983 from the Illinois Institute of Technology. He has spent significant portions of his career in academia (MS State), research (Texas Instruments, AT&T) and the government (NSA). His primary research interests currently are applications of machine learning in the health sciences. Dr. Picone's research funding sources over the years have included NSF, NIH, DoD, DARPA as well as the private sector. He has also been involved in several startup companies in healthcare. For over 40 years, his research groups have been known for producing many innovative open source materials for education and research, including the first state of the art public domain speech recognition system and the TUH EEG Corpus, which has over 8,000 subscribers (see www.isip.piconepress.com). Dr. Picone is a Senior Member of the IEEE, holds several patents in human language technology.
作者簡介(中文翻譯)
Ammar Ahmed, Ph.D. 是位於加州阿古拉山的 Aptiv 的雷達信號處理工程師。他在天普大學獲得電機工程博士學位，指導教授為 Dr. Daniel Zhang、Dr. Dennis Silage 和 Dr. Joseph Picone。Ahmed 博士於 2009 年在巴基斯坦拉合爾的工程與技術大學獲得電機工程學士學位，並於 2011 年在巴基斯坦伊斯蘭堡的巴基斯坦工程與應用科學研究所獲得系統工程碩士學位。他已發表超過 40 篇技術論文並持有兩項專利。他的研究興趣包括信號處理、優化和雷達系統。
Joseph Picone, Ph.D. 是天普大學電機與計算機工程系的教授，並負責信號與信息處理研究所，還擔任神經工程數據聯盟的副主任。Joseph Picone 博士於 1983 年在伊利諾伊理工學院獲得電機工程博士學位。他的職業生涯中有相當一部分時間是在學術界（密西西比州立大學）、研究機構（德州儀器、AT&T）和政府機構（NSA）工作。目前，他的主要研究興趣是機器學習在健康科學中的應用。多年間，Picone 博士的研究資金來源包括 NSF、NIH、國防部、DARPA 以及私營部門。他還參與了幾家醫療保健的初創公司。在過去的 40 年中，他的研究團隊以生產許多創新的開源教育和研究材料而聞名，包括第一個最先進的公共領域語音識別系統和 TUH EEG Corpus，該資料庫擁有超過 8,000 名訂閱者（請參見 www.isip.piconepress.com）。Picone 博士是 IEEE 的資深會員，並在自然語言技術方面擁有多項專利。
