Alex James

Nazarbayev University

Alex Pappachen James received his PhD degree from the Queensland Micro- and Nanotechnology Centre, Griffith University, Brisbane, QLD, Australia. He is internationally known for his contributions on memristive networks, neuromorphic computing and image processing. Currently, he is chairing the Electrical Engineering Department at Nazarbayev University. He is a mentor to several tech start-ups and co-founded companies in machine learning and computer vision hardware. Dr. James has been a founding chair for IEEE R10 Circuits and Systems Society Chapter and an executive board member of IET Vision and Imaging Network. He is also the founding chair of IEEE Kazakhstan subsection, R8, and a mentor to IEEE NU Student Branch. He was an editorial member of Information Fusion (2010–2015), Elsevier, and is an associate editor for HCIS (2015–present), Springer; IEEE Access (2017–present); IEEE Transactions on Emerging Topics in Computational Intelligence (2017–present); and IEEE Transactions on Circuits and Systems 1 (2018–present). Dr. James was a faculty senate chair of NU between 2016 and 2017 and he received an award from the president of Kazakhstan for his services in the field of education in Kazakhstan in 2017. He is also a senior member of IEEE, life member of ACM and senior fellow of HEA.

1books edited

3chapters authored

Latest work with IntechOpen by Alex James

This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications. The resistive switching property is an important aspect of the memristors, and there are several designs of this discussed in this book, such as in metal oxide/organic semiconductor nonvolatile memories, nanoscale switching and degradation of resistive random access memory and graphene oxide-based memristor. The modelling of the memristors is required to ensure that the devices can be put to use and improve emerging application. In this book, various memristor models are discussed, from a mathematical framework to implementations in SPICE and verilog, that will be useful for the practitioners and researchers to get a grounding on the topic. The applications of the memristor models in various neuromorphic networks are discussed covering various neural network models, implementations in A/D converter and hierarchical temporal memories.

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