Prof Alex James

Biography: Alex James is the Dean External Linkages and Projects, and Full-professor of AI hardware at Digital University Kerala; and CTO of India Graphene and Engineering Innovation Centre (a section 8 company). James received his PhD from Queensland Micro and Nanotechnology Centre, Griffith University, Australia in a short 2 year duration. He heads the Maker Village, one of the largest electronic hardware incubators in India with over 80 electronics startups. He heads the Centre for excellence in Intelligent IoT Sensors, and India Innovation Centre for Graphene. He is the founding director board member of India’s first Digital Science Park. He has spun out multiple startup companies from his research group; published more than 200 papers. For the last two decades, he worked in the areas of board design, signal integrity and mixed signal design in Industry and in the area of AI hardware and systems in academia. He has taught more than 60 courses, in the areas of chip design and AI. He was an associate editor for IEEE TCAS1 (2018-2023), and IEEE OJCAS (2023).  He got the IEEE Kerala Section Best Researcher Award (2022), IEEE CASS Best Associate Editor for IEEE TCAS1 (2020-2021), Kerala State Higher Education Council Award 2022 - Kairali Gaveshana Puraskaram from the Kerala government, and 2024 IEEE Transactions on Circuits and Systems Guillemin-Cauer Best Paper Award.  First chair of IEEE CASS Kerala, which won the 2023 IEEE Circuits and Systems Regional Chapter-of-the-Year Award: Region 10 and 2024 IEEE Circuits and Systems Global Chapter-of-the-Year Award.  He is a member of SIG AgriFood and IEEE CASS Technical Committees on Neural Systems and Applications (NSA TC), Nonlinear Circuits and Systems Technical Committee (NCAS TC), Cellular Nanoscale Networks, and Memristor Array Computing (CNNMAC TC). He is Associate Editor in Chief of IEEE Open Journal of Circuits and Systems (2024-2025) and Associate Editor of IEEE Access, Frontiers in Neuroscience, IEEE Transactions on Biomedical Circuits and Systems and IEEE Transactions on Circuits and Systems for Artificial Intelligence. He is an IET, BCS, and HEA Senior Fellow.

Research group: www.aicas.info

https://orcid.org/0000-0001-5655-1213

IEEE Engagement (last 10 years)

• Founding chair for IEEE Kerala Section Circuits and Systems Society which was selected to be awarded the 2023 IEEE Circuits and Systems Regional Chapter-of-the-Year Award: Region 10, and in 2024 Best Chapter-of-the-Year Award

• IEEE CAS Best Associate Editor for IEEE TCAS1 (2020-2021); 

• IEEE Kerala Section, Best researcher award 2022

• Associate Editor for IEEE ACCESS, IEEE Transactions on Emerging Topics in Computational Intelligence (2017-18) (Guest associate editor),  IEEE Transactions on Circuits and Systems 1 (2018-2023) and IEEE Open Journal of Circuits and Systems (2022-present).

• Associate Editor in Chief for IEEE Open Journal of Circuits and Systems (2024-2026).

• IEEE CASS international outreach funding (2012, 2014, 2015, 2017)

• Organised IEEE Seasonal schools in 2017, 2021, 2022; Organising 6 outreach workshops in 2023

• Member of IEEE CASS Technical committee on Nonlinear Circuits and Systems, IEEE CASS Technical committee on Cellular Nanoscale Networks and Memristor Array Computing, IEEE Consumer Technology Society Technical Committee on Quantum in Consumer Technology (QCT),  Technical Committee on Machine learning, Deep learning and AI in CE (MDA)

• Technical committee member/reviewer in ICECS 2019-2023, AICAS 2019-2023, MWCAS 2019-2023, ISCAS 2019-2023; ISCAS 2024, ISICAS 2024. overall been a reviewer or session chairs or as program committee in more than 100 conference events in the career.

• Technical Program Chair – IEEE APCCAS 2023 

• Track chair – IEEE MWSCAS 2023

• IEEE ISICAS 2024, Plenary talk chair

• IEEE ISCAS 2024, International Advisory Member

• Nomcom Member, IEEE CASS Kerala 2022, 2023, 2024

• Nomcom Member, IEEE CASS 2024

• Chair of IEEE CASS Charles A. Desoer Technical Achievement Award Committee 2024

Recent fellowship and other recognitions

• Fellow of British Computer Society (FBCS)

• Fellow of IET (FIET)

• Fellow of RSA

• Senior Fellow of HEA, UK

• 2024 IEEE Guillemin-Cauer Best Paper Award

• Best Associate Editor Award of IEEE TCAS1 for 2020-21

• Kairali Research Award 2021, Government of Kerala

• Australian Research Council Fellowship

• Chief investigator establishing the Centre for Excellence on IIOT Sensors (Rs 42 crores ~USD 5 million)

• Chief investigator establishing the India Innovation Centre for Graphene (Rs 86 crores ~USD 10 million)

• Chief investigator, Graphene Aurora Program (Rs 95 crores ~USD 11 million)

• Chief Investigator, AI Chip Centre (Rs 86 lakhs)

• Advisory Board Member, Digital Science Park

• Member of BCS’ Fellows Technical Advisory Group (F-TAG)

• Associate Editor of Frontiers in Neuroscience

• Associate Editor in Chief of IEEE Open Journal of Circuits and Systems

• Associate Editor of  IEEE Transactions on Circuits and Systems for Artificial Intelligence

• Associate Editor of  IEEE Transactions on Biomedical Circuits and Systems 

AI Chip initiatives (White Paper link)

• Kairali open sources chip - from digital university kerala, developed with students. The project mainly uses open sources VLSI tools to build a simple MAC processing unit, that can be used for multiple neural networks. A digital and analog version of the MAC processing unit along with RSICV capabilities are implemented. This supported as part of the efabless program. The work is carried out at digital university kerala.

• As part of the Kairali Award, a project on development of analog AI processor is underway. This project largely inspires from biological neural networks such as spiking neural nets. This is supported as part of the Govt of Kerala research award. The work is carried out at digital university kerala.

• As part of the Chip to Start-up program, the a scalable AI processor development is underway. In this NOC architecture with multiple co-processor capability is build. This is supported as part of the MEITY C2S program. The work is carried out at digital university kerala.

Research Areas and Applications

Polymath with expertise in:

• Analog Circuits, Mixed-signal Circuits, Signal Integrity

• Neuromorphic VLSI, Neuro-memristive Systems

• Cognitive Hardware Architectures, Brain inspired circuits

• Quantum Image and Neural Systems

• Image processing and recognition

• Neural based NLP, Face and Speech recognition

• Drones, IoT Sensors, Robotics

• Graphene and 2D material Applications

• Education, Arts and Philosophy

Publications

Journals (Selected)

[1] S. Pallathuvalappil, R. Kottappuzhackal, and A. James, "Explainable Model Prediction of Memristor," IEEE Open Journal of the Industrial Electronics Society, 2024, Aug. 8.

• Summary: This paper introduces a method for providing interpretability to the prediction models of memristors, with a focus on making AI-driven predictions more explainable. A novel approach combining model-agnostic and embedded methods is used, allowing better diagnosis of memristor behaviors.

• Highlights: Emphasizes the importance of explainable AI for physical electronic devices like memristors.

[2] V. V. Nair, E. George, and A. James, "Real-time Tumor Detection Using Electromagnetic Signals With Memristive Echo State Networks," IEEE Internet of Things Journal, 2024, Jul. 22.

• Summary: This paper discusses the use of memristive echo state networks for real-time tumor detection utilizing electromagnetic signals. The results demonstrate a significant increase in detection accuracy and efficiency compared to traditional methods.

• Highlights: Combines memristors with IoT technologies for medical applications.

[3] S. K. Vohra, M. Sakare, A. P. James, and D. M. Das, "SpiMAM: CMOS Implementation of Bio-Inspired Spiking Multidirectional Associative Memory Featuring In-Situ Learning," IEEE Transactions on Circuits and Systems I: Regular Papers, 2024, Jul. 22.

• Summary: The paper proposes a CMOS-compatible implementation of a spiking associative memory capable of in-situ learning inspired by biological principles. This architecture is targeted for neuromorphic applications.

• Highlights: Innovative hybrid CMOS and memristor design for associative learning tasks.

[4] V. Alimisis, C. Aletraris, N. P. Eleftheriou, E. A. Serlis, P. P. Sotiriadis, and A. James, "Low-Power Analog Integrated Architecture of the Voting Classification Algorithm for Diabetes Disease Prediction," IEEE Transactions on Biomedical Circuits and Systems, 2024, Jul. 2.

• Summary: This work introduces an analog integrated circuit that implements a voting classification algorithm for diabetes prediction. The architecture significantly reduces the power consumption associated with predictive analytics in health monitoring.

• Highlights: Demonstrates the capability of analog circuits for healthcare predictive models with minimal power requirements.

[5] R. R. Das, T. R. Rajalekshmi, S. Pallathuvalappil, and A. James, "FETs for Analog Neural MACs," IEEE Access, 2024, Apr. 10.

• Summary: This paper discusses the feasibility of using Field-Effect Transistors (FETs) for analog Multiply-Accumulate (MAC) operations in neural network hardware. The study provides a comprehensive evaluation of different FET technologies in terms of performance.

• Highlights: Investigates analog computing as an alternative to digital neural networks for improved efficiency.

[6] R. R. Das, T. R. Rajalekshmi, and A. James, "FinFET to GAA MBCFET: A Review and Insights," IEEE Access, 2024, Apr. 2.

• Summary: The paper reviews the evolution of transistor technology from FinFET to Gate-All-Around Multi-Bridge-Channel FET (GAA MBCFET). It highlights the advancements that these new transistor types bring to scaling and power efficiency.

• Highlights: Provides a detailed technical comparison between two advanced transistor technologies.

[7] A. Radhakrishnan, A. Gopi, C. Reghuvaran, and A. James, "Variability-Aware Memristive Crossbars With ImageSplit Neural Architecture," IEEE Transactions on Nanotechnology, 2024, Mar. 8.

• Summary: This paper presents a variability-aware design for memristive crossbar circuits using a novel ImageSplit architecture for neural networks. The approach helps mitigate the issues of hardware variability and enhances the robustness of memristive computing.

• Highlights: Proposes an architectural improvement addressing the reliability issues of memristive hardware.

[8] A. Radhakrishnan, J. Palliyalil, S. Babu, A. Dorzhigulov, and A. James, "PyMem: A Graphical User Interface Tool for Neuro-Memristive Hardware-Software Co-design," IEEE Open Journal of the Industrial Electronics Society, 2024, Feb. 6.

• Summary: PyMem is introduced as a GUI-based tool that facilitates the co-design of hardware and software for neuro-memristive applications. The tool is aimed at simplifying the development process of complex neuro-computing systems.

• Highlights: Provides an open-source solution for integrating hardware-software workflows in neuromorphic engineering.

[9] V. Nair, A. Radhakrishnan, R. Chithra, and A. James, "Memristive Pixel-CNN Loop Generate for CNN Generalisations," IEEE Transactions on Nanotechnology, 2023, Feb. 23, vol. 22, pp. 120-125.

• Summary: This work discusses using memristors for Pixel-CNN loop generation to improve convolutional neural network (CNN) generalizations. The design enables more efficient and rapid processing suitable for AI hardware accelerators.

• Highlights: Utilizes memristive circuits to optimize machine learning algorithms.

[10] G. Sivapalan, K. K. Nundy, A. James, B. Cardiff, and D. John, "Interpretable Rule Mining for Real-Time ECG Anomaly Detection in IoT Edge Sensors," IEEE Internet of Things Journal, 2023, Mar. 22.

• Summary: This paper presents a rule-mining algorithm for interpretable real-time ECG anomaly detection in IoT edge sensors. The focus is on enhancing interpretability while maintaining high accuracy, which is crucial for medical applications.

• Highlights: Integrates edge computing and interpretable AI to detect ECG anomalies.

[11] V. S. Mallan, A. Gopi, C. Reghuvaran, A. A. Radhakrishnan, and A. James, "Rapid Prototyping Mixed-Signal Development Kit for Tactile Neural Computing," Frontiers in Neuroscience, 2023, Feb. 7, vol. 17, art. no. 1118615.

• Summary: This paper introduces a mixed-signal development kit designed for the rapid prototyping of tactile neural computing solutions. The kit facilitates the evaluation of new sensorimotor integration concepts for robotic and prosthetic applications.

• Highlights: Enables fast prototyping for neuromorphic tactile computing research.

[12] A. Radhakrishnan, D. Mahapatra, and A. James, "Consumer Document Analytical Accelerator Hardware," IEEE Access, 2023, Jan. 16, vol. 11, pp. 5161-5167.

• Summary: The authors propose a hardware accelerator specifically designed for analyzing consumer documents efficiently. It leverages neuromorphic hardware to significantly reduce processing time while maintaining high accuracy.

• Highlights: Targets efficient document analysis with reduced latency and power consumption.

[13] A. R. Aswani, P. Sruthi, B. Choubay, and A. P. James, "Bridge Memristor Super-resolution Crossbars," IEEE Journal of Emerging Topics in Circuits and Systems, 2022.

• Summary: This paper introduces a bridge memristor-based crossbar for achieving super-resolution in neural networks. The architecture addresses the limitations in resolution and enhances image processing performance.

• Highlights: Focuses on overcoming resolution limitations with novel memristive crossbar designs.

[14] A. P. James and L. O. Chua, "Variability-Aware Memristive Crossbars—A Tutorial," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 6, pp. 2570-2574, June 2022.

• Summary: This tutorial provides insights into the design of variability-aware memristive crossbars. The work focuses on addressing the key challenges posed by process and environmental variability in memristive systems.

• Highlights: In-depth explanation and methods for managing variability in memristive devices.

[15] A. James, Y. Toleubay, O. Krestinskaya, and C. Reghuvaran, "Inference Dropouts in Binary Weighted Analog Memristive Crossbar," IEEE Transactions on Nanotechnology, vol. 21, pp. 271-277, 2022.

• Summary: The paper introduces a dropout technique applied in binary-weighted analog memristive crossbars to prevent overfitting during inference. Results show enhanced generalization capabilities for memristive neural networks.

• Highlights: Applies dropout methods typically used in digital networks to analog memristive systems for improved robustness.

[16] N. Raj, R. K. Ranjan, and A. James, "Chua’s Oscillator With OTA Based Memcapacitor Emulator," IEEE Transactions on Nanotechnology, vol. 21, pp. 213-218, 2022.

• Summary: This paper presents a novel design for Chua's oscillator using an Operational Transconductance Amplifier (OTA) based memcapacitor emulator. The design achieves improved chaotic behaviors suitable for neuromorphic computing.

• Highlights: Proposes an OTA-based emulator to enhance memristive circuit applications in chaos theory.

[17] TMS-Crossbars with Tactile Sensing, IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, (Accepted).

• Summary: This paper focuses on TMS crossbars with tactile sensing, aimed at improving tactile information processing. The crossbars facilitate the development of hardware architectures optimized for robotic touch sensing.

• Highlights: Explores tactile sensing hardware design using TMS crossbar technology.

[18] "Edge to Quantum: Hybrid Quantum-Spiking Neural Network Image Classifier," Neuromorphic Computing and Engineering, 2021, (Accepted).

• Summary: This paper proposes a hybrid quantum-spiking neural network classifier to enhance image classification performance. The hybrid approach leverages both quantum computation and neuromorphic spiking networks to improve recognition accuracy.

• Highlights: Combines quantum computing with spiking neural networks for enhanced image classification.

[19] "Generalised Analog LSTMs Recurrent Modules for Neural Computing," Frontiers in Computational Neuroscience, 2021, (Accepted).

• Summary: This paper introduces generalized analog Long Short-Term Memory (LSTM) modules for neural computing, aimed at improving the efficiency of recurrent neural networks in analog hardware. The proposed modules are shown to provide better power efficiency compared to digital implementations.

• Highlights: Presents an analog approach to LSTM networks, targeting improved efficiency in neural computing tasks.

[20] "Analog Neural Computing with Super-resolution Memristor Crossbars," IEEE Transactions on Circuits and Systems 1: Regular Papers, 2021, accepted.

• Summary: This paper discusses the implementation of super-resolution memristor crossbars for analog neural computing, enhancing the precision of analog computations in neuromorphic circuits. The results indicate significant improvements in computational accuracy and speed.

• Highlights: Uses super-resolution techniques in memristor crossbars to boost analog neural computation capabilities.

[21] "The Why, What, and How of Artificial General Intelligence Chip Development," IEEE Transactions on Cognitive and Developmental Systems, 2021, accepted.

• Summary: This single-author paper provides an in-depth exploration of artificial general intelligence (AGI) chip development, discussing the motivations, challenges, and technical methods involved. It highlights the requirements for creating AGI-capable hardware systems.

• Highlights: Comprehensive discussion on AGI chip development from a single author.

[22] "Analog Self-timed Programming Circuits for Aging Memristors," IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, accepted.

• Summary: This paper discusses the development of self-timed programming circuits designed to counteract the aging effects in memristors, maintaining their performance over time. The proposed circuit is designed to be energy-efficient and adaptive to memristor aging.

• Highlights: Focus on combating aging effects in memristive devices with adaptive circuits.

[23] "Recursive Threshold Logic – A Bioinspired Reconfigurable Dynamic Logic System With Crossbar Arrays," IEEE Transactions on Biomedical Circuits and Systems, 2020/21, accepted.

Summary: This paper introduces a bioinspired recursive threshold logic system, utilizing crossbar arrays to achieve reconfigurable dynamic logic suitable for neuromorphic applications. The approach allows for the efficient execution of complex threshold logic tasks in hardware.

Highlights: Applies bioinspired concepts to hardware logic systems for enhanced reconfigurability.

[24] "Memristive GAN in Analog," Scientific Reports, vol. 10, no. 1, Article no. 8944, May 2020.

Summary: This paper presents an analog implementation of Generative Adversarial Networks (GANs) using memristive devices. The authors demonstrate that memristive analog circuits can efficiently perform GAN operations with reduced power consumption.

Highlight: This pioneering work showcases the potential of memristive technology in advancing analog AI hardware.

[25] Krestinskaya, O., B. Choubey, and A. P. James. "Memristive GAN in analog." Scientific reports 10, no. 1 (2020): 5838.

[26] Krestinskaya, O., and Alex P. James. "Analogue neuro-memristive convolutional dropout nets." Proceedings of the Royal Society A 476, no. 2242 (2020): 20200210.

[27] Krestinskaya, Olga, Alex James, and Leon Ong Chua. "Neuromemristive circuits for edge computing: A review." IEEE transactions on neural networks and learning systems 31, no. 1 (2019): 4-23. 

[28] Krestinskaya, Olga, Khaled N. Salama, and Alex P. James. "Automating analogue AI chip design with genetic search." Advanced Intelligent Systems 2, no. 8 (2020): 2000075. 

[29] Sadykova, Diana, Damira Pernebayeva, Mehdi Bagheri, and Alex James. "IN-YOLO: Real-time detection of outdoor high voltage insulators using UAV imaging." IEEE Transactions on Power Delivery 35, no. 3 (2019): 1599-1601. 

[30] Smagulova, Kamilya, Olga Krestinskaya, and Alex James. "Who is the winner? Memristive-CMOS hybrid modules: CNN-LSTM versus HTM." IEEE Transactions on Biomedical Circuits and Systems 14, no. 2 (2019): 164-172. 

[31] Zollanvari, Amin, Alex James, and Reza Sameni. "A theoretical analysis of the peaking phenomenon in classification." Journal of Classification 37 (2020): 421-434. 

[32] Aliakhmet, Kamilla, and Alex James. "Temporal G-neighbor filtering for Analog domain noise reduction in astronomical videos." IEEE Transactions on Circuits and Systems II: Express Briefs 66, no. 5 (2019): 868-872. 

[33]  James, Alex. "A hybrid memristor–CMOS chip for AI." Nature Electronics 2, no. 7 (2019): 268-269. 

[34] James, Alex. "An overview of memristive cryptography." The European Physical Journal Special Topics 228, no. 10 (2019): 2301-2312. 

[35] Pernebayeva, Damira, Aidana Irmanova, Diana Sadykova, Mehdi Bagheri, and Alex James. "High voltage outdoor insulator surface condition evaluation using aerial insulator images." High Voltage 4, no. 3 (2019): 178-185. 

[36] Dastanova, Nazgul, Sultan Duisenbay, Olga Krestinskaya, and Alex James. "Bit-plane extracted moving-object detection using memristive crossbar-cam arrays for edge computing image devices." IEEE Access 6 (2018): 18954-18966.

[37] Irmanova, Aidana, Timur Ibrayev, and Alex James. "Discrete‐level memristive circuits for HTM‐based spatiotemporal data classification system." IET Cyber‐Physical Systems: Theory & Applications 3, no. 1 (2018): 34-43.

[38] James, A., K. Nabil Salama, Hai Li, Dalibor Biolek, Giacomo Indiveri, and Leon O. Chua. "Guest editorial: special issue on large-scale memristive systems and neurochips for computational intelligence." IEEE Transactions on Emerging Topics in Computational Intelligence 2, no. 5 (2018): 320-323.

[39] Krestinskaya, Olga, Irina Dolzhikova, and Alex James. "Hierarchical temporal memory using memristor networks: A survey." IEEE Transactions on Emerging Topics in Computational Intelligence 2, no. 5 (2018): 380-395.

[40] Krestinskaya, Olga, Khaled Nabil Salama, and Alex James. "Learning in memristive neural network architectures using analog backpropagation circuits." IEEE Transactions on Circuits and Systems I: Regular Papers 66, no. 2 (2018): 719-732.

[41] Lu, Maxim, Mehdi Bagheri, Alex P. James, and Toan Phung. "Wireless charging techniques for UAVs: A review, reconceptualization, and extension." IEEE access 6 (2018): 29865-29884.

[42] Mathew, Joshin John, Alex James, Chandrasekhar Kesavadas, and Joseph Suresh Paul. "Diffusion sensitivity enhancement filter for raw DWIs." IET Computer Vision 12, no. 7 (2018): 950-956.

[43] Mussard, Maxime, and Alex James. "Engineering the global university rankings: gold standards, limitations and implications." IEEE Access 6 (2018): 6765-6776.

[44] James, Alex, Irina Fedorova, Timur Ibrayev, and Dhireesha Kudithipudi. "HTM spatial pooler with memristor crossbar circuits for sparse biometric recognition." IEEE Transactions on Biomedical Circuits and Systems 11, no. 3 (2017): 640-651.

[45] Krestinskaya, Olga, Timur Ibrayev, and Alex James. "Hierarchical temporal memory features with memristor logic circuits for pattern recognition." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, no. 6 (2017): 1143-1156.

[46] Mathew, Joshin, Amin Zollanvari, and Alex James. "Edge-aware spatial denoising filtering based on a psychological model of stimulus similarity." IEEE Access 6 (2017): 3433-3447.

[47] Bakir, Daniyar, Alex James, and Amin Zollanvari. "An efficient method to estimate the optimum regularization parameter in RLDA." Bioinformatics 32, no. 22 (2016): 3461-3468.

[48] Maan, Akshay Kumar, Deepthi Anirudhan Jayadevi, and Alex James. "A survey of memristive threshold logic circuits." IEEE transactions on neural networks and learning systems 28, no. 8 (2016): 1734-1746.

[49] Davletcharova, Assel, Sherin Sugathan, Bibia Abraham, and Alex James. "Detection and analysis of emotion from speech signals." Procedia Computer Science 58 (2015): 91-96.

[50] James, Alex. "Heart rate monitoring using human speech spectral features." Human-centric Computing and Information Sciences 5 (2015): 1-12.

[51] James, Alex, Dinesh S. Kumar, and Arun Ajayan. "Threshold logic computing: Memristive-cmos circuits for fast fourier transform and vedic multiplication." IEEE transactions on very large scale integration (VLSI) systems 23, no. 11 (2015): 2690-2694.

[52] Maan, Akshay Kumar, Alex James, and Sima Dimitrijev. "Memristor pattern recogniser: isolated speech word recognition." Electronics Letters 51, no. 17 (2015): 1370-1372.

[53] Mathew, Joshin John, and Alex James. "Spatial stimuli gradient sketch model." IEEE Signal Processing Letters 22, no. 9 (2015): 1336-1339.

[54] Sudhakaran, Swathikiran, and Alex James. "Sparse distributed localized gradient fused features of objects." Pattern Recognition 48, no. 4 (2015): 1538-1546.

[55] Ibrayev, Timur, Irina Fedorova, Akshay Kumar Maan, and Alex James. "Memristive operational amplifiers." Procedia Computer Science 41 (2014): 114-119.

[56] James, Alex, Linu Rose VJ Francis, and Dinesh S. Kumar. "Resistive threshold logic." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 22, no. 1 (2013): 190-195.

[57] James, Alex, and Belur V. Dasarathy. "Medical image fusion: A survey of the state of the art." Information fusion 19 (2014): 4-19.

[58] James, Alex, Sheshadri Thiruvenkadam, Joseph Suresh Paul, and Michael Braun. "Guest Editorial: Special issue on medical image computing and systems." Information Fusion 19 (2014): 2-3.

[59] Maan, Akshay Kumar, Dinesh Sasi Kumar, Sherin Sugathan, and Alex James. "Memristive threshold logic circuit design of fast moving object detection." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 23, no. 10 (2014): 2337-2341.

[60] James, Alex, and Sima Dimitrijev. "Nearest neighbor classifier based on nearest feature decisions." The Computer Journal 55, no. 9 (2012): 1072-1087.

[61] James, Alex, and Sima Dimitrijev. "Ranked selection of nearest discriminating features." Human-Centric Computing and Information Sciences 2 (2012): 1-14.

[62] James, Alex, and Akshay Kumar Maan. "Improving feature selection algorithms using normalised feature histograms." Electronics letters 47, no. 8 (2011): 490-491.

[63] James, Alex, and Sima Dimitrijev. "Cognitive memory network." Electronics Letters 46, no. 10 (2010): 677-678.

[64] James, Alex, and Sima Dimitrijev. "Inter-image outliers and their application to image classification." Pattern recognition 43, no. 12 (2010): 4101-4112.

[65] James, Alex Pappachen, and Sima Dimitrijev. "Face recognition using local binary decisions." IEEE Signal Processing Letters 15 (2008): 821-824.

Patents

• Alex James, Kishan Kartha, Intelligent IoT toys with integrated graphene surface sensor, IN, App no. 202341046054, 2023

• Alex James, Vasudev Mallan, Method of 3D printing of interlocking floors using 2D composites, IN, App no. 202341045941, 2023

• Alex James, Rajalekshmi TR, Shilpa Pavithran, Method for Inducing Light Emission and Strengthening Glass Sculptures By Incorporating Graphene Nanoplatelets, IN, App no. 202341007940, 2023

• Alex James, Rajalekshmi TR, Shilpa Pavithran, Composition of a Graphene Dispersion To Enhance properties of a Material and a Method thereof, IN, App no. 202341039692, 2023

• Alex James, Kishan Kartha, Autonomous Multi-Copter 3d Drone Printer, IN, App no. 202341033398, 2023

• Alex James, Vineeta Nair, System And Method For Classifying Real-Time Images Using A Quantum Echo State Network Unit, IN, App no. 202341043177, 2023

• Alex James, Chithra Raghuvaran, Aswani Radhakrishnan, Method Of Designing Secure Ai Hardware System Using Physical Unclonable Function, IN, App no. 202341034637, 2023

• Alex James, Aswani Radhakrishnan, Sruthi P, Data Processing Architecture For Designing Fully Reconfigurable Arithmetic Logic Unit, IN, App no. 202341044093, 2023

• Alex James, Rajalekshmi TR, Shilpa P, Graphene-Based Intelligent Solar Panel For Radiation Shielding In Space Vehicle Applications, IN, App no. 202341043178