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Synthetic Aperture Radar (SAR) Data Applications by Maciej Rysz (English) Hardco

Description: Synthetic Aperture Radar (SAR) Data Applications by Maciej Rysz, Arsenios Tsokas, Kathleen M. Dipple, Kaitlin L. Fair, Panos M. Pardalos Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. Publisher Description This carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are highlighted. Cutting edge methods, with particular emphasis on machine learning, are included. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. State-of-the-art neural network architectures in SAR-aided navigation are compared and discussed further. Advanced empirical and machine learning models in retrieving land and ocean information — wind, wave, soil conditions, among others, are also included. Author Biography Maciej Rysz is currently an assistant professor at the Department of Information Systems & Analytics at the Farmer School of Business within Miami University. Prior to joining Miami University, he was a research assistant professor at the Industrial & Systems Engineering Department at the University of Florida and served as a postdoctoral research associate under the National Research Council of the National Academies. He received his Ph.D. in Industrial Engineering with emphasis on operations research from the University of Iowa in 2014. His research areas of interest include mathematical programming, machine learning, network science and encryption. Arsenios Tsokas currently works as an analyst for Citibank, N.A. He received his B.Sc. in Mathematics from the Aristotle University of Thessaloniki in 2014. He received his Ph.D. degree in Industrial & Systems Engineering from the University of Florida in 2021. He has a diverse background including data science, optimization, and machine learning. He has worked on various topics, such as data analysis with applications in medicine and network science with applications on network robustness.Kathleen M Dipple is currently a research scientist with the US Air Force Research Lab (AFRL). A first-generation undergraduate student, she earned her bachelors degree in Chemistry from Appalachian State University and received her Ph.D. from the Nanoscale Science program at the University of North Carolina at Charlotte. She completed her post-doctoral work holding a provisional patent in the Nature-Inspired Section at AFRLs Munitions Directorate through the National Research Council Associateship Program. Kaitlin Fair is a Systems Development Engineer working on guidance, navigation, and control technologies at the Air Force Life Cycle Management Center. Dr. Fair served as a Research Engineer and Team Lead at the Air Force Research Lab for ten years prior to serving in her current role, during which she received the SMART Scholarship and completed her PhD in Electrical Engineering from the Georgia Institute of Technology in 2017. Her research interests include efficient signal processing and algorithm development on brain-inspired (neuromorphic) engineering architectures.Panos M. Pardalos is a distinguished Professor Emeritus of Industrial and Systems Engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor in Industrial & Systems Engineering. He is also an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the Director of the Center for Applied Optimization. Dr. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems,optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing. He has co-authored and co-edited more than 30 books, as well as publishing more than 600 journal articles and conference proceedings. Prof. Pardalos is a Fellow of AAAS (American Association for the Advancement of Science), Fellow of American Institute for Medical and Biological Engineering (AIMBE), and EUROPT. He is a Distinguished International Professor by the Chinese Minister of Education; Honorary Professor of Anhui University of Sciences and Technology, China; Elizabeth Wood Dunlevie Honors Term Professor; Honorary Doctor, V.M. Glushkov Institute of Cybernetics of The National Academy of Sciences of Ukraine; Foreign Associate Member of Reial Academia de Doctors, Spain; and Advisory board member of the Centre for Optimisation and Its Applications, Cardiff University, UK. He is also the recipient of UF 2009 International Educator Award; Medal (in recognition of broad contributions in science and engineering) of the University of Catani, Italy; EURO Gold Medal (EGM); Honorary Doctor of Science Degree, Wilfrid Laurier University, Canada; Senior Fulbright Specialist Award; University of Florida Research Foundation Professorship; and IBM Achievement Award. Details ISBN 303121224X ISBN-13 9783031212246 Title Synthetic Aperture Radar (SAR) Data Applications Author Maciej Rysz, Arsenios Tsokas, Kathleen M. Dipple, Kaitlin L. Fair, Panos M. Pardalos Format Hardcover Year 2023 Pages 278 Edition 1st Publisher Springer International Publishing AG GE_Item_ID:141671234; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. 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Synthetic Aperture Radar (SAR) Data Applications by Maciej Rysz (English) Hardco

Item Specifics

Restocking Fee: No

Return shipping will be paid by: Buyer

All returns accepted: Returns Accepted

Item must be returned within: 30 Days

Refund will be given as: Money Back

ISBN-13: 9783031212246

Book Title: Synthetic Aperture Radar (SAR) Data Applications

Number of Pages: X, 278 Pages

Language: English

Publication Name: Synthetic Aperture Radar (Sar) Data Applications

Publisher: Springer International Publishing A&G

Publication Year: 2023

Subject: Engineering (General), Intelligence (Ai) & Semantics, Probability & Statistics / General, Optimization

Item Weight: 21.2 Oz

Type: Textbook

Item Length: 9.3 in

Subject Area: Mathematics, Computers, Technology & Engineering

Author: Arsenios Tsokas

Item Width: 6.1 in

Series: Springer Optimization and Its Applications Ser.

Format: Hardcover

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