LEADER 03837nam 2200697Ia 450 001 9910971138703321 005 20200520144314.0 010 $a9781523117154 010 $a152311715X 010 $a9781580536875 010 $a1580536875 035 $a(CKB)1000000000239589 035 $a(EBL)227675 035 $a(OCoLC)123129434 035 $a(SSID)ssj0000151622 035 $a(PQKBManifestationID)11137138 035 $a(PQKBTitleCode)TC0000151622 035 $a(PQKBWorkID)10319484 035 $a(PQKB)11433366 035 $a(Au-PeEL)EBL227675 035 $a(CaPaEBR)ebr10082047 035 $a(OCoLC)56985803 035 $a(CaBNVSL)mat09100226 035 $a(IEEE)9100226 035 $a(MiAaPQ)EBC227675 035 $a(Perlego)4668266 035 $a(EXLCZ)991000000000239589 100 $a20040514d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEW 102 $ea second course in electronic warfare /$fDavid L. Adamy 205 $a1st ed. 210 $aBoston $cArtech House$dc2004 215 $a1 online resource (290 p.) 225 1 $aArtech House radar library 300 $aDescription based upon print version of record. 311 08$a9781580536868 311 08$a1580536867 320 $aIncludes bibliographical references (p. 259-262) and index. 327 $aTable of contents; Preface xv; 1 Introduction 1; 1.1 Generalities About EW 3; 1.2 Information Warfare 5; 1.3 How to Understand Electronic Warfare 6; 2 Threats 9; 2.1 Some Definitions 9; 2.2 Frequency Ranges 13; 2.3 Threat Guidance Approaches 15; 2.4 Scan Characteristics of Threat Radars 17; 2.5 Modulation Characteristics of Threat Radars 22; 2.6 Communication Signal Threats 26; 3 Radar Characteristics 33; 3.1 The Radar Function 33; 3.2 Radar Range Equation 36; 3.3 Detection Range Versus Detectability Range 42; 3.4 Radar Modulation 48; 3.5 Pulse Modulation 48. 327 $a3.6 CW and Pulse Doppler Radars 543.7 Moving Target Indicator Radars 58; 3.8 Synthetic Aperture Radars 63; 3.9 Low Probability of Intercept Radars 67; 4 Infrared and Electro-Optical Considerations in Electronic Warfare 77; 4.1 The Electromagnetic Spectrum 77; 4.2 IR Guided Missiles 82; 4.3 IR Line Scanners 87; 4.4 Infrared Imagery 90; 4.5 Night-Vision Devices 94; 4.6 Laser Target Designation 98; 4.7 Infrared Countermeasures 101; 5 EW. 330 8 $aAnnotation Serving as a continuation of the bestselling book EW 101: A First Course in Electronic Warfare, this new volume is a second installment of popular tutorials featured in the Journal of Electronic Defense. Without delving into complex mathematics, this book gives engineers, defense contractors, managers, and government procurers a basic working knowledge of the technologies deployed in today's electronic warfare (EW) systems. Organized into chapters with new introductory and supplementary material from the author, this unique book includes tutorials on radar characteristics, infrared and electro-optical systems, signal jamming, spectrum spreading, satellite communications, and emitter location systems. A thorough and challenging problem set for each class of EW technology covered in the book, complete with solutions, helps readers to evaluate EW systems and their applications. 410 0$aArtech House radar library. 517 3 $aElectronic warfare 102 606 $aElectronics in military engineering 606 $aInformation warfare 615 0$aElectronics in military engineering. 615 0$aInformation warfare. 676 $a623.043 676 $a623.043 700 $aAdamy$b David$0536436 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910971138703321 996 $aEW 102$94358175 997 $aUNINA LEADER 04047nam 22007455 450 001 9910683352603321 005 20251009075045.0 010 $a9789811988141$b(electronic bk.) 010 $z9789811988134 024 7 $a10.1007/978-981-19-8814-1 035 $a(MiAaPQ)EBC7219051 035 $a(Au-PeEL)EBL7219051 035 $a(OCoLC)1373984434 035 $a(DE-He213)978-981-19-8814-1 035 $a(PPN)269094717 035 $a(CKB)26309460200041 035 $a(EXLCZ)9926309460200041 100 $a20230323d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConvolutional Neural Networks for Medical Applications /$fby Teik Toe Teoh 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (103 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$aPrint version: Teoh, Teik Toe Convolutional Neural Networks for Medical Applications Singapore : Springer Singapore Pte. Limited,c2023 9789811988134 320 $aIncludes bibliographical references and index. 327 $a1) Introduction -- 2) CNN for Brain Tumor classification -- 3) CNN for Pneumonia image classification -- 4) CNN for White Blood Cell classification -- 5) CNN for Skin Cancer classification -- 6) CNN for Diabetic Retinopathy detection. 330 $aConvolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a research scholar to identify some futuristic problem areas. The topics covered include brain tumor classification, pneumonia image classification, white blood cell classification, skin cancer classification and diabetic retinopathy detection. The first chapter will begin by introducing various topics used in training CNNs to help readers with common concepts covered across the book. Each chapter begins by providing information about the disease, its implications to the affected and how the use of CNNs can help to tackle issues faced in healthcare. Readers would be exposed to various performance enhancement techniques, which have been tried and tested successfully, such as specific data augmentations and image processing techniques utilized to improve the accuracy of the models. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aComputer vision 606 $aMedical sciences 606 $aArtificial intelligence 606 $aMachine learning 606 $aImage processing 606 $aArtificial intelligence$xData processing 606 $aComputer Vision 606 $aHealth Sciences 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aImage Processing 606 $aData Science 615 0$aComputer vision. 615 0$aMedical sciences. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aImage processing. 615 0$aArtificial intelligence$xData processing. 615 14$aComputer Vision. 615 24$aHealth Sciences. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aImage Processing. 615 24$aData Science. 676 $a616.0754 700 $aTeoh$b Teik Toe$01216205 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910683352603321 996 $aConvolutional Neural Networks for Medical Applications$93087038 997 $aUNINA