Deep Learning Networks

Dr. Jayakumar Singaram is a veteran in semiconductor electronics, and a Strategic consultant to companies, such as Mistral Solutions, Bangalore, Bilva infra, Bangalore, SunPlus Software, Bangalore, and Apollo Tyre's, Chennai, among others. He drives deployment ready solutions using AI in IoT Edge/Node. He began his career working in Hindustan Aeronautics Bangalore, followed by Cranes InfoTech and Mistral Solution before launching a start-up world by steering brands like Epigon Media Technologies, Rinanu Semiconductor, and many more. He had been one of the core team members and also led the design and development of `Networked Karaoke Machine'' (which enabled TAITO Corp. Japan to become No. 2 in the world of Networked Karaoke). Another major milestone has been his contribution to the development of Satellite Radio Receiver going by the brand name of 'WorldSpace'. He is working on Deep Learning applications by using IBM Watson services. He has worked on key projects such as design anddevelopment of the Karaoke Machine (a project in collaboration with Analog Devices and MIT Media Lab) for TAITO Corp. He also worked on Satellite Radio Receiver for WorldSpace broadcast Satellites (and DAB Radio Receivers), by using low-cost Digital Signal Processors from Analog Devices. He has a B.Tech in Aeronautical engineering from Madras Institute of Technology and also holds an undergraduate degree in Mathematics from Madras University. He  has Masters and Doctorate degree  in Systems and Control Engineering from Indian Institute of Technology, Bombay and a Research Fellow at KU Leuven University.  As a Doctoral Thesis problem, he had worked on " Simultaneous Stabilization of Feedback Systems". He had worked as a Board Member and Dean of School of Computing Science and Engineering at Periyar Maniammai Institute of Science and Technology, Thanjavur, India, from 2007 to 2014. He was trustee of School of Music and Dance, Indiranagar-Bangalore, during 2009 to 2016. Link   www.jkuse.com provides more details about him. He would like to thank  the founders of Mistral Solutions,   Anees Ahmed and  Rajeev Ramachandra. And also he likes to thank  his family members, Dr Suneetha Rao, Dr Vaidhehi and Niranjan Kumar for their support while writing this early version of book.

Dr. S.S. Iyengar is currently the Distinguished University Professor, Founding Director of the Discovery Lab and Director of the US Army funded Center of Excellence in Digital Forensics at Florida International University, Miami. He is also the Distinguished Chaired Professor (Hon.) at National Forensics Sciences University, Gandhinagar, India. He has been involved with research and education in high-performance intelligent systems, Data Science and Machine Learning Algorithms, Sensor Fusion, Data Mining, and Intelligent Systems. Since receiving his Ph.D. degree in 1974 from MSU, USA, he has directed over 65 Ph.D. students, 100 Master's students, and many undergraduate students who are now faculty at Major Universities worldwide or Scientists or Engineers at National Labs/Industries around the world. He has published more than 600 research papers, has authored/co-authored and edited 26 books. His h-index is 65 with over 20,100 citations and is among the list of top 2% cited scholars of Stanford study this year. His books are published by MIT Press, John Wiley and Sons, CRC Press, Prentice Hall, Springer Verlag, IEEE Computer Society Press, etc. One of his books titled "Introduction to Parallel Algorithms" has been translated into Chinese. During the last thirty years Dr. Iyengar has brought in over 65 million dollars for research and education. More recently in Spring 2021, Dr. Iyengar in collaboration with HBCUs were awarded a $2.25 M funding for setting up a Digital Forensics Center of Excellence over a period of 5 years (2021-2026). He has been awarded the Lifetime Achievement Award for his contribution to the fie

Introduction.- Deep Learning.- Brief survey on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).- Tool Set for Deep Learning Applications.- Data-Set Design and Data Labeling.- DL Model: Design and Development.- Training and Testing of DL Model.- Deploying DL in Jetson Nano.- Deploying DL in Android Phone.- Deploying DL in Ultra96-V2 Field Programmable Gate Array (FPGA).- Conclusion.