LEADER 05718nam 2200685 450 001 9910788133003321 005 20200520144314.0 035 $a(CKB)2670000000616288 035 $a(EBL)2055016 035 $a(SSID)ssj0001561660 035 $a(PQKBManifestationID)16203881 035 $a(PQKBTitleCode)TC0001561660 035 $a(PQKBWorkID)14832791 035 $a(PQKB)10853441 035 $a(Au-PeEL)EBL2055016 035 $a(CaPaEBR)ebr11055731 035 $a(CaONFJC)MIL784418 035 $a(OCoLC)910069665 035 $a(CaSebORM)9781782422648 035 $a(MiAaPQ)EBC2055016 035 $a(EXLCZ)992670000000616288 100 $a20150528h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aComposite magnetoelectrics $ematerials, structures, and applications /$fGopalan Srinivasan, Shashank Priya and Nian X. Sun 205 $a1st edition 210 1$aAmsterdam, [Netherlands] :$cWoodhead Publishing,$d2015. 210 4$dİ2015 215 $a1 online resource (381 p.) 225 1 $aWoodhead Publishing Series in Electronic and Optical Materials ;$vNumber 62 300 $aDescription based upon print version of record. 311 $a1-78242-264-1 311 $a1-78242-254-4 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aFront Cover; Related titles; Composite Magnetoelectrics: Materials, Structures, and Applications; Copyright; Contents; Woodhead Publishing Series in Electronic and Optical Materials; Part One - Introduction to magnetoelectric materials and phenomena; 1 - Theory of magnetoelectric phenomena in composites; 1.1 Introduction; 1.2 Low-frequency ME in composites; 1.3 Resonance ME effect in composites; 1.4 ME effect at magnetic resonance; 1.5 Conclusions; References; 2 - Magnetoelectric characterization techniques; 2.1 Introduction; 2.2 Direct-ME effects; 2.3 Converse ME effects 327 $a2.4 Scanning probe microscopy techniques for ME effects in nanocompositesReferences; 3 - Layered multiferroic composites; 3.1 Ferromagnetic-ferroelectric composites; 3.2 Direct magnetoelectric effects; 3.3 Converse ME effects; 3.4 Conclusions; References; 4 - Multiferroic nanostructures; 4.1 Introduction; 4.2 Magnetoelectric magnetic film/piezoelectric slab heterostructures; References; 5 - Epitaxial multiferroic heterostructures; 5.1 Introduction; 5.2 BiFeO3 systems-related multiferroics; 5.3 La-manganite-related multiferroics; 5.4 Ferrite-related multiferroics; 5.5 Summary and prospects 327 $aReferences6 - Recent advances in piezoelectric and magnetoelectric materials phenomena; 6.1 Introduction; 6.2 Magnetoelectric solid solution; 6.3 Magnetoelectric composite; 6.4 Recent advances in piezoelectric and magnetoelectric materials; 6.5 Recent advances in fabrication of magnetoelectric composites; 6.6 Recent advances in lead-free piezoelectric and magnetoelectric composites; 6.7 Conclusion; Acknowledgments; References; 7 - Magnetoelectric energy harvester; 7.1 Introduction; 7.2 Development of magnetoelectric energy harvester; 7.3 Magnetoelectric composite 327 $a7.4 Self-biased magnetoelectric energy harvester7.5 Multimode magnetoelectric energy harvester; 7.6 Low frequency and wideband magnetoelectric energy harvester; Acknowledgments; References; 8 - Magnetoelectric current sensor; 8.1 Introduction; 8.2 Development of magnetoelectric current sensors; 8.3 Conventional ME composites-based current sensors; 8.4 Self-biased ME composites-based current sensors; 8.5 ME transformer-based current sensors; 8.6 Magnetic noise and elimination; Acknowledgments; References; 9 - Microwave and millimeter-wave multiferroic devices; 9.1 Introduction 327 $a9.2 Converse ME effects at ferromagnetic resonance9.3 Hybrid spin-electromagnetic waves in composites; 9.4 Composites for high-frequency devices; 9.5 Multiferroic high-frequency devices; 9.6 Conclusion; References; 10 - Magnetoelectric composites for miniature antennas; 10.1 Introduction; 10.2 Effect of high permeability/permittivity ratio on antenna performance; 10.3 High permeability RF/microwave thick film materials; 10.4 Bulk composites; 10.5 Layered thin film systems; 10.6 Antenna design and characteristics; References; 11 - Magnetoelectric composites for medical application 327 $a11.1 Detailed background on wireless capsule endoscopy 330 $aComposite Magnetoelectrics: Materials, Structures, and Applications gives the reader a summary of the theory behind magnetoelectric phenomena, later introducing magnetoelectric materials and structures and the techniques used to fabricate and characterize them. Part two of the book looks at magnetoelectric devices. Applications include magnetic and current sensors, transducers for energy harvesting, microwave and millimeter wave devices, miniature antennas and medical imaging. The final chapter discusses progress towards magnetoelectric memory.Summarises clearly the theory behind magnetoelectr 410 0$aWoodhead Publishing series in electronic and optical materials ;$vNumber 62. 606 $aComposite materials$xMagnetic properties 606 $aComposite materials$xElectric properties 615 0$aComposite materials$xMagnetic properties. 615 0$aComposite materials$xElectric properties. 676 $a620.118 700 $aSrinivasan$b G$g(Gopalan),$01539984 702 $aPriya$b Shashank 702 $aSun$b Nian X. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788133003321 996 $aComposite magnetoelectrics$93791301 997 $aUNINA LEADER 03854nam 22006495 450 001 9910300747503321 005 20200703082830.0 010 $a9781484235911 010 $a1484235916 024 7 $a10.1007/978-1-4842-3591-1 035 $a(CKB)4100000003359121 035 $a(MiAaPQ)EBC5356209 035 $a(DE-He213)978-1-4842-3591-1 035 $a(CaSebORM)9781484235911 035 $a(PPN)226699471 035 $a(OCoLC)1037100034 035 $a(OCoLC)on1037100034 035 $a(EXLCZ)994100000003359121 100 $a20180423d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Belief Nets in C++ and CUDA C: Volume 1 $eRestricted Boltzmann Machines and Supervised Feedforward Networks /$fby Timothy Masters 205 $a1st ed. 2018. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018. 215 $a1 online resource (225 pages) $cillustrations 300 $aIncludes index. 311 08$a9781484235904 311 08$a1484235908 327 $a1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual. 330 $aDiscover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you?ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. You will: Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important. 517 3 $aRestricted Boltzmann machines and supervised feedforward networks 517 3 $aDeep Belief Nets in C plus plus and CUDA C 606 $aArtificial intelligence 606 $aProgramming languages (Electronic computers) 606 $aBig data 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 615 0$aArtificial intelligence. 615 0$aProgramming languages (Electronic computers) 615 0$aBig data. 615 14$aArtificial Intelligence. 615 24$aProgramming Languages, Compilers, Interpreters. 615 24$aBig Data. 615 24$aBig Data/Analytics. 676 $a006.32 700 $aMasters$b Timothy$4aut$4http://id.loc.gov/vocabulary/relators/aut$0105163 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910300747503321 996 $aDeep Belief Nets in C++ and CUDA C: Volume 1$92497770 997 $aUNINA