Radio Frequency Machine Learning: A Practical Deep Learning Perspective
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| Title: | Radio Frequency Machine Learning: A Practical Deep Learning Perspective |
|---|---|
| Description: | Radio Frequency Machine Learning: A Practical Deep Learning Perspective goes beyond general introductions to deep learning, offering a focused exploration of how modern deep learning techniques can be applied directly to radio frequency (RF) challenges. It covers a wide range of applications, including classification tasks where deep learning is used to label and categorize signals based on a labeled training dataset, as well as clustering tasks that group similar signals together without labels. Additionally, it expands into deep learning (generative AI) for waveform synthesis and how reinforcement learning can be used within the domain. This book also investigates advanced topics like RF sensor control, feedback mechanisms, and real-time system operations, offering a comprehensive understanding of how deep learning can be integrated into dynamic RF environments. This resource addresses the practical concerns of deploying machine learning in operational RF systems. It goes beyond applications and techniques, covering how to ensure the robustness of solutions, with insights into data sources, augmentation techniques, and strategies for integrating ML with existing RF infrastructure. The full development process is examined, from data collection to deployment, along with numerous case studies throughout. Looking to the future, the book explores emerging trends like edge computing and federated learning, offering a forward-looking perspective on the continued evolution of RF machine learning. Whether the reader is just beginning the journey into RF machine learning or is looking to refine skills, this book provides an essential resource for understanding the intersection of deep learning and RF technology. This is a must-have resource for anyone interested in the cutting edge of wireless technologies and their potential to shape the future of communication. |
| Authors: | Kuzdeba, Scott |
| Resource Type: | eBook. |
| Subjects: | Radio frequency, Deep learning (Machine learning), Data sets, Artificial intelligence |
| Categories: | TECHNOLOGY & ENGINEERING / Microwaves |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Radio Frequency Machine Learning: A Practical Deep Learning Perspective – Name: Abstract Label: Description Group: Ab Data: Radio Frequency Machine Learning: A Practical Deep Learning Perspective goes beyond general introductions to deep learning, offering a focused exploration of how modern deep learning techniques can be applied directly to radio frequency (RF) challenges. It covers a wide range of applications, including classification tasks where deep learning is used to label and categorize signals based on a labeled training dataset, as well as clustering tasks that group similar signals together without labels. Additionally, it expands into deep learning (generative AI) for waveform synthesis and how reinforcement learning can be used within the domain. This book also investigates advanced topics like RF sensor control, feedback mechanisms, and real-time system operations, offering a comprehensive understanding of how deep learning can be integrated into dynamic RF environments. This resource addresses the practical concerns of deploying machine learning in operational RF systems. It goes beyond applications and techniques, covering how to ensure the robustness of solutions, with insights into data sources, augmentation techniques, and strategies for integrating ML with existing RF infrastructure. The full development process is examined, from data collection to deployment, along with numerous case studies throughout. Looking to the future, the book explores emerging trends like edge computing and federated learning, offering a forward-looking perspective on the continued evolution of RF machine learning. Whether the reader is just beginning the journey into RF machine learning or is looking to refine skills, this book provides an essential resource for understanding the intersection of deep learning and RF technology. This is a must-have resource for anyone interested in the cutting edge of wireless technologies and their potential to shape the future of communication. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kuzdeba%2C+Scott%22">Kuzdeba, Scott</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Radio+frequency%22">Radio frequency</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning+%28Machine+learning%29%22">Deep learning (Machine learning)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+sets%22">Data sets</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Microwaves%22">TECHNOLOGY & ENGINEERING / Microwaves</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 006.31 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Radio frequency Type: general – SubjectFull: Deep learning (Machine learning) Type: general – SubjectFull: Data sets Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: Radio Frequency Machine Learning: A Practical Deep Learning Perspective Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kuzdeba, Scott – PersonEntity: Name: NameFull: Kuzdeba, Scott IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 – D: 28 M: 06 Type: profile Y: 2025 Identifiers: – Type: isbn-print Value: 9781685690335 – Type: isbn-electronic Value: 9781685690342 Titles: – TitleFull: Radio Frequency Machine Learning: A Practical Deep Learning Perspective Type: main |
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