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Application Dual Tree Complex Wavelet by Klein Randall (17 results)
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Condition: As New. Unread book in perfect condition.
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Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
Language: English
Published by Creative Media Partners, LLC Mai 2025, 2025
ISBN 10: 102513785X ISBN 13: 9781025137858
Taschenbuch. Condition: Neu. Neuware.
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Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
Language: English
Published by Creative Media Partners, LLC Mai 2025, 2025
ISBN 10: 1025132785 ISBN 13: 9781025132785
Buch. Condition: Neu. Neuware - This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract and exploit RF waveform features ('fingerprints') that are inherently unique to specific devices and that may be used to provide hardware specific identification(manufacturer, model, and/or serial number). This is addressed by applying a Dual-Tree Complex Wavelet Transform (DT-CWT) to improve burst detection and RF fingerprint classification. A 'Denoised VT' technique is introduced to improve performance at lower SNRs, with denoising implemented using a DT-CWT decomposition prior to Traditional VT processing. A newly developed Wavelet Domain (WD) fingerprinting technique is presented using statistical WD fingerprints with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification.
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Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
Language: English
Published by Creative Media Partners, LLC, 2025
ISBN 10: 102513785X ISBN 13: 9781025137858
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
£ 15.90
£ 3.29 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Paperback. Condition: Like New. Like New. book.
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Condition: New. Print on Demand.
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Condition: New. Print on Demand.
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Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
Language: English
Published by Creative Media Partners, LLC, 2025
ISBN 10: 1025132785 ISBN 13: 9781025132785
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
£ 25.96
£ 4.16 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Condition: New. PRINT ON DEMAND.
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Condition: New. Print on Demand.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
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Paperback. Condition: new. Paperback. This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract and exploit RF waveform features ("fingerprints") that are inherently unique to specific devices and that may be used to provide hardware specific identification(manufacturer, model, and/or serial number). This is addressed by applying a Dual-Tree Complex Wavelet Transform (DT-CWT) to improve burst detection and RF fingerprint classification. A "Denoised VT" technique is introduced to improve performance at lower SNRs, with denoising implemented using a DT-CWT decomposition prior to Traditional VT processing. A newly developed Wavelet Domain (WD) fingerprinting technique is presented using statistical WD fingerprints with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Hardcover. Condition: new. Hardcover. This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract and exploit RF waveform features ("fingerprints") that are inherently unique to specific devices and that may be used to provide hardware specific identification(manufacturer, model, and/or serial number). This is addressed by applying a Dual-Tree Complex Wavelet Transform (DT-CWT) to improve burst detection and RF fingerprint classification. A "Denoised VT" technique is introduced to improve performance at lower SNRs, with denoising implemented using a DT-CWT decomposition prior to Traditional VT processing. A newly developed Wavelet Domain (WD) fingerprinting technique is presented using statistical WD fingerprints with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
