IMPACT-2026

Prof. Ranjan K. Mallik

Professor, Department of Electrical Engineering
Indian Institute of Technology (IIT) Delhi

Talk Abstract

Two partially noncoherent low-complexity receivers, namely, the energy detection (ED) receiver and the weighted envelope detection (WENVD) receiver, employing receive diversity, operating in flat Rayleigh fading with multi-level amplitude-shift keying (ASK), and utilizing only channel magnitude information, that is, knowledge of the magnitudes of the fading gains of the receive diversity branches, are considered. The error performance of the ED receiver is analyzed, resulting in a closed form expression for the symbol error probability (SEP). Closed form expressions for the approximate SEPs of the ED and the WENVD receivers for high signal-to-noise ratio (SNR) are derived. The ED receiver is found to have the same diversity order as that of coherent receivers and to perform much better than fully noncoherent detection, but is also found to maintain the low complexity structure of fully noncoherent detectors. It is numerically shown that the SEP performance of the ED receiver is much closer to that of a coherent receiver than to that of a fully noncoherent one. Furthermore, we also consider optimization of the transmit symbol amplitude levels to minimize the SEP subject to a total energy constraint. Analytical results on the optimization for high SNR and large number of receive diversity branches are presented. The performance improvement obtained by using these optimal levels over using symbol amplitude levels with equal spacing, that is, in arithmetic progression, for both the receivers is shown through numerical results.

Brief Bio

Ranjan K. Mallik is a Professor in the Department of Electrical Engineering, Indian Institute of Technology (IIT) Delhi and a J. C. Bose National Fellow. He received the B.Tech. degree from IIT Kanpur and the M.S. and Ph.D. degrees from the University of Southern California, Los Angeles, all in electrical engineering. He has worked as a scientist in the Defence Electronics Research Laboratory, Hyderabad, India, and as a faculty member in IIT Kharagpur and IIT Guwahati. His research interests are in diversity combining and channel modeling for wireless communications, space-time systems, cooperative communications, multiple-access systems, power line communications, molecular communications, and terahertz communications. He is a recipient of the Shanti Swarup Bhatnagar Prize, the Dr. Vikram Sarabhai Research Award, the Khosla National Award (by IIT Roorkee), the Prof. P. C. P. Bhatt Faculty Research Award (by IIT Delhi), the IEI-IEEE Award for Engineering Excellence, and the J. C. Bose Fellowship. He is a member of Eta Kappa Nu, and a fellow of IEEE; TWAS; the Indian National Academies INAE, INSA, NASI, and IASc; and Asia-Pacific Artificial Intelligence Association.

Prof. Sujay Deb

Institute Chair Professor
Department of Electronics & Communication Engineering and Computer Science & Engineering
IIIT Delhi

Talk Abstract

Machine learning based AI algorithms are computationally intensive and need to deal with a large amount of data. This along with slowing of Moore's law imposes various challenges for the hardware implementation of these algorithms. To meet the high computing demand, designs with many-accelerators are proposed. These designs revolve around achieving high parallelization, efficient memory hierarchy, addressing communication challenges etc. In this talk we will:

• Highlight the steps and strategies that need to be adopted for designing the optimized RISC V SoC hardware platform
• Get an overview about how to design accelerator rich systems and optimally use the shared resources
• Address the on-chip communication bottlenecks

Brief Bio

Sujay Deb is an Institute Chair Professor in Dept. of Electronics and Communication Engineering and Dept. of Computer Science and Engineering at Indraprastha Institute of Information Technology, Delhi (IIIT-D). He received PhD from the School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA on May 2012. Before his current position, he worked as an intern at Intel Labs, Hillsboro, OR. His major awards and achievements include DST INSPIRE Faculty award in 2012; Outstanding PhD student award in Computer Engineering, WSU, 2011; Winner of India-US Grand Challenge Initiative for Affordable Blood Pressure measurement technologies in 2014, won best poster award at ASP-DAC 2021, won best paper award at iNIS 2017, best tutorial award at VLSID 2025. He is currently serving as IEEE CASS CSS Delhi section Vice Chairman, Associate Editor of IET Computer & Digital Techniques. He is a senior member of IEEE.

Research Interests

His research Interests are broadly in the areas of power and performance efficient and reliable System-on-Chip Design, Network-on-Chip (NoC) communication fabrics, Heterogeneous System Architectures (HSA), hardware for deep learning, low-cost bio-sensors for preventive healthcare. More specifically, topics of interest include: RISC V based systems, intra and inter-chip wireless interconnection, efficient and reliable routing schemes, scalable coherence protocols for many-core systems, hardware support for on-chip broadcast and multi-cast traffic, cache architectures for HSA, single chip bio potential acquisition system for preventive health-care. He also works on application of technology to chiplet architectures, healthcare, hardware security, mobile sensing etc.

Prof. Muhamad Abdul Awal

Professor
New York Institute of Technology & CUNY, USA

Talk Abstract

Digital Signal Processing (DSP) has fundamentally reshaped modern technology by enabling efficient manipulation, analysis, and interpretation of real-world signals in digital form. At its core, DSP transforms raw data—such as audio, video, and sensor outputs—into usable information, unlocking capabilities that were once impractical or impossible with analog systems. One of the most visible impacts has been in communications technology, DSP plays a key role in dramatically increasing data rates, spectral efficiency, and reliability. This has accelerated global connectivity, enabling services like high-definition streaming, low-latency gaming, and massive IoT deployments. DSP also drives advancements in autonomous systems and sensor technologies. Self-driving cars, drones, and robotics use DSP for real-time interpretation of radar, LiDAR, and camera data, enabling accurate object detection, tracking, and decision-making. In healthcare, DSP improves medical imaging (MRI, CT) and wearable sensors, yielding clearer diagnostic images and more reliable physiological monitoring. Emerging fields like machine learning and edge computing increasingly integrate DSP to preprocess data, reduce noise, and extract features before inference, enhancing performance with lower power consumption. In summary, DSP acts as a cornerstone of digital innovation, enabling smarter connectivity, richer multimedia, and intelligent sensing across industries—driving both incremental improvements and paradigm shifts in how technology interacts with the physical world.

Brief Bio

Educated in Germany and USA, M Abdul Awal has 17+ years of Industrial and R&D experience at AT&T Bell Laboratories (NJ), and 20+ years of Academic Teaching experience during his 35+ years of professional career in a very broad and diverse national and international environment (mostly USA, UK, Middle East and South Asia). Currently, Dr. Awal is the Professor at the New York Institute of Technology and the NYC College of Technology, CUNY. His major past research contributions are in the area of optical and wireless communications, opto-electronic IC, Ultra-thin opto-electronic materials growth and device fabrication and characterization, system engineering and concurrent engineering, commercialization of technologies, system analysis, High Tech Manufacturing, optimization of global supply chain network, Technology Economic modeling, Management of technology and innovations, business and network performance modeling, and current interest in the area of technologies/innovations, Nano Technology, Voice over LTE, academy-industry-government liaison, and next generation wireless technology driven services and products (IoT) involving 5G technology.

Dr. Musfira Jilani

Assistant Professor
School of Enterprise Computing and Digital Transformation
Technological University Dublin, Ireland

Talk Abstract

Human-Centric Artificial Intelligence (HCAI) is a research paradigm that emphasizes the systematic integration of human values, cognitive capacities, and social contexts into the design, evaluation, and deployment of AI systems. This is achieved by prioritising properties such as interpretability, adaptability, fairness, privacy preservation, inclusivity, and trustworthiness, particularly in high-stakes and socially embedded domains. This keynote presents a set of applied research contributions that operationalize HCAI principles across diverse technical contexts. These include adaptive and explainable seismic prediction models that support expert decision-making under uncertainty; methods for measuring and analyzing bias in multi-agent AI systems; privacy-aware approaches for hospital bed allocation; and structured frameworks for explainable AI that align model behavior with human understanding. The talk also discusses the design of inclusive conversational agents, illustrated through patient onboarding chatbots that account for inclusive design. The second part of the keynote focuses on trust in human-robot interaction (HRI), approached as a dynamic, data-driven construct rather than a static user attribute. Trust is examined as an emergent property inferred from longitudinal interaction data, including behavioral signals, task performance, system transparency, and interaction context.

Brief Bio

Dr Musfira Jilani is an Assistant Professor at the School of Enterprise Computing and Digital Transformation, Technological University Dublin, Ireland where she also serves as Programme Director for the MSc in Human-Centric AI programme. Her research focuses on the design, development, and evaluation of Human-Centric Artificial Intelligence systems, with particular emphasis on explainability, fairness, privacy-aware decision-making, inclusive AI design, and trust in human-robot interactions. Dr Jilani is currently a task lead on the Pan-European Network for Responsible Artificial Intelligence (PANORAMA) EU-funded project. She is affiliated with the Digital Futures Hub and the CSInc research groups at TUDublin. Dr Jilani holds a BTech in Electronics Engineering from Aligarh Muslim University, India, a PhD in Computer Science from University College Dublin, and also completed postdoctoral research at the University of Galway.

Prof. Mohd Rihan

Director General
National Institute of Solar Energy
Ministry of New and Renewable Energy, Government of India

Talk Abstract

The transition to green energy is to a large extent a data-driven transformation. Apart from advances in generation technologies, seamless integration of renewable energy at scale depends on multimedia signal processing and reliable communication technologies. This talk intends to share how signals, sensors, and intelligent communication networks not only forms the backbone but are really enablers of this transition. In the new largely distributed systems the monitoring, control, and optimisation can only be achieved through use of these technologies. Some real world examples highlighting recent developments and applications are included in the talk.

Brief Bio

Prof. Mohammad Rihan is serving as Director General of National Institute of Solar Energy, Ministry of New and Renewable Energy, Government of India. In this position he is leading the research and development, and capacity building initiatives in the area of solar energy in line with the national requirements. He is serving as Chairperson of sectional committee on Solar PV of Bureau of Indian Standards. Before joining as DG, NISE on deputation Dr. Mohammad Rihan worked as a Professor of Electrical Engineering at AMU for about 22 years with significant contributions in the areas of smart grid and Solar Energy. Dr. Rihan has led the integration of a 6.5MWp solar PV plant into a campus grid—one of the largest in any academic institution in India. He has authored a Cambridge-published textbook, completed major research projects, and provided technical consultancy on smart cities, power transmission, and sustainable energy initiatives. He received the IEEE PES Award for Excellence in Solar Photovoltaic Systems (2025) and a Gold Medal in B.Tech. He is a Fellow of IET (UK), IE (India), IETE, a Senior Member of IEEE, and currently serves as Chair-Elect of IEEE UP Section and Vice Chair of the IET Delhi Local Network.