STM Journals Peer-reviewed research from Consortium e-Learning Network

15 publications

  • Published Subscription Review Article

    Photosynthetic Living Plant–Integrated Cybernetic Sensors for Autonomous Environmental Intelligence

    By Bibhu Prasad Ganthia, Subash Ranjan Kabat

    Abstract: The increasing demand for sustainable and autonomous environmental monitoring systems has motivated the development of biologically integrated sensing technologies that combine living organisms with advanced cybernetic intelligence. This study proposes a novel framework of Photosynthetic Living Plant–Integrated Cybernetic Sensors for Autonomous Environmental Intelligence, where living plants act as self-sustaining sensing platforms capable of continuously monitoring environmental conditions without external energy sources. By integrating bioelectrical signal acquisition modules, flexible nanomaterial electrodes, …

    Published in Recent Trends in Sensor Research & Technology · Vol. 13, Issue 2, 2026 Read article

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    Holographic Beam Switching and Intelligent Routing for Terahertz Space-Air-Ground Integrated Networks

    By Bibhu Prasad Ganthia, Subash Ranjan Kabat

    Abstract: The rapid evolution of sixth-generation (6G) and beyond communication technologies necessitates highly adaptive, ultra-high-capacity, and low-latency networking frameworks capable of supporting global connectivity across terrestrial and non-terrestrial domains. This study proposes a novel holographic beam switching and intelligent routing framework for terahertz (THz) Space-Air-Ground Integrated Networks (SAGINs). The proposed architecture leverages holographic beamforming techniques to dynamically manipulate electromagnetic wavefronts, enabling precise beam steering, reduced interference, and enhanced spectral efficiency in …

    Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article

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    A Physics-Informed Graph Neural Network Framework for Real- Time Thermal-Aware Fault Prediction and Adaptive Power Optimization in Heterogeneous System-on-Chip Architectures

    By Bibhu Prasad Ganthia, Subash Ranjan Kabat

    Abstract: Heterogeneous System-on-Chip (SoC) architectures are increasingly adopted in edge computing, artificial intelligence, autonomous systems, and high-performance embedded platforms due to their superior computational efficiency and flexibility. However, increasing integration density and workload diversity introduce severe thermal hotspots, accelerated device degradation, and unexpected hardware faults that adversely affect system reliability and energy efficiency. This study proposes a Physics-Informed Graph Neural Network (PI-GNN) framework for real-time thermal- aware fault prediction and adaptive …

    Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 2, 2026 Read article

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    Fractional Calculus-Based Analysis of Magneto-Thermal Nanofluid Flow Over Stretching and Shrinking Surfaces

    By Bibhu Prasad Ganthia, Subash Ranjan Kabat

    Abstract: This study presents a comprehensive investigation of magneto-thermal hybrid nanofluid flow over stretching and shrinking surfaces using a fractional calculus framework to capture the memory and hereditary characteristics of complex fluid transport phenomena. The proposed model incorporates the effects of magnetic field intensity, thermal radiation, viscous dissipation, Brownian motion, and thermophoretic diffusion on the velocity and temperature distributions within the boundary layer region. A fractional-order derivative formulation is employed to …

    Published in Recent Trends in Fluid Mechanics · Vol. 13, Issue 2, 2026 Read article

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    Explainable GeoAI-Based Multi-Temporal Remote Sensing Framework for Early Detection of Climate-Induced Land Cover Transformation

    By Bibhu Prasad Ganthia, Subash Ranjan Kabat

    Abstract: Climate change has emerged as one of the primary drivers of rapid land cover transformation, affecting ecosystems, agricultural productivity, biodiversity, and regional sustainability. Traditional remote sensing approaches often face challenges in detecting subtle and early-stage land cover changes due to limitations in temporal analysis and model interpretability. This study proposes an Explainable GeoAI-based multi-temporal remote sensing framework for the early detection of climate-induced land cover transformation using multi-source satellite imagery …

    Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 2, 2026 Read article

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    Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications

    By Bibhu Prasad Ganthia

    Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …

    Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 Read article

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    Green AI-Enabled Opto-Electronic Communication Systems for Carbon-Neutral Digital Networks

    By Bibhu Prasad Ganthia

    Abstract: The rapid expansion of digital communication infrastructure, driven by cloud computing, Internet of Things (IoT), 6G networks, and artificial intelligence applications, has significantly increased the energy consumption and carbon footprint of modern communication systems. Conventional optical communication networks often rely on static resource allocation and energy-intensive signal processing mechanisms, resulting in inefficient utilization of network resources and elevated operational costs. This study proposes a Green Artificial Intelligence (Green AI)-Enabled Opto-Electronic …

    Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 Read article

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    High-Gain Microstrip Patch Antenna for Satellite and Radar Applications

    By Bibhu Prasad Ganthia, Rosalin Pradhan

    Abstract: The growing demand for high-speed wireless communication, satellite connectivity, and advanced radar systems has increased the necessity for compact and high-performance antenna designs. This study presents the design and analysis of a high-gain microstrip patch antenna intended for satellite and radar applications operating in the microwave frequency range. The proposed antenna structure is developed using a low-loss dielectric substrate to enhance radiation efficiency, bandwidth, and gain characteristics while maintaining a …

    Published in Journal of Microwave Engineering and Technologies · Vol. 13, Issue 2, 2026 Read article

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    Thermally Adaptive Bio-Inspired VLSI Interconnect Model for Next-Generation Embedded Systems

    By Bibhu Prasad Ganthia, Rosalin Pradhan

    Abstract: The increasing complexity of next-generation embedded systems has intensified the challenges associated with power dissipation, thermal instability, signal integrity, and interconnect reliability in Very Large- Scale Integration (VLSI) architectures. This research proposes a thermally adaptive bio-inspired VLSI interconnect model designed to enhance communication efficiency and thermal resilience in advanced embedded platforms. The proposed model integrates bio-inspired adaptive routing principles with dynamic thermal-aware interconnect management to optimize data transmission under varying …

    Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 2, 2026 Read article

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    Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices

    By Rosalin Pradhan, Bibhu Prasad Ganthia

    Abstract: The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. …

    Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article

  • Published Subscription Review Article

    Photonic-Assisted Spintronic Solid-State Switching Model for High-Speed Memory Devices

    By Bibhu Prasad Ganthia, Rosalin Pradhan

    Abstract: The rapid advancement of high-speed computing and data-centric applications has intensified the demand for energy-efficient and ultra-fast memory technologies. This paper proposes a Photonic-Assisted Spintronic Solid-State Switching Model for next-generation high-speed memory devices. The proposed framework integrates photonic excitation mechanisms with spintronic switching dynamics to enhance data transfer speed, minimize switching delay, and reduce power dissipation in solid-state memory architectures. By combining optical pulse-assisted spin polarization with magnetic tunnel junction-based …

    Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article

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    Machine Learning Assisted Timing Violation Prediction in Sub-7nm VLSI Physical Design

    By Rosalin Pradhan, Bibhu Prasad Ganthia

    Abstract: The continuous scaling of semiconductor technology into the sub-7nm regime has introduced significant challenges in timing closure due to process variability, interconnect delay, power density, and manufacturing uncertainties. Conventional static timing analysis techniques often require extensive computational resources and iterative optimization cycles, resulting in increased design complexity and longer turnaround time. This research proposes a Machine Learning Assisted Timing Violation Prediction framework for sub-7nm VLSI physical design to improve early-stage …

    Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article

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    Spintronic Logic Device Modeling and Energy Optimization for Beyond-CMOS Computing Systems

    By Bibhu Prasad Ganthia, Rosalin Pradhan

    Abstract: The continuous scaling limitations of conventional CMOS technology have accelerated the exploration of alternative computing paradigms for next-generation low-power and high-performance systems. Spintronic logic devices have emerged as a promising solution due to their non-volatility, ultra-low switching energy, high integration density, and compatibility with beyond-CMOS architectures. This research presents a comprehensive modeling and energy optimization framework for spintronic logic devices applied in beyond- CMOS computing systems. The proposed work investigates …

    Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article

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    Graphene–Perovskite Hybrid Opto-Electronic Modulators for Ultra-Low Power Optical Communication

    By Omkar Tripathy, Rosalin Pradhan, Bibhu Prasad Ganthia

    Abstract: This paper proposes a novel self-adaptive neuromorphic opto-electronic transceiver architecture designed to enhance the intelligence, adaptability, and efficiency of next-generation optical communication networks. The proposed system integrates neuromorphic computing principles with photonic signal processing to enable real-time learning, dynamic resource allocation, and autonomous compensation of channel impairments such as dispersion, nonlinearities, and noise. Unlike conventional transceivers, the developed model employs spiking neural networks embedded within opto-electronic circuits to mimic biological …

    Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 1, 2026 · pp. 41–52 Read article

  • Published Subscription Review Article

    Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems

    By Bibhu Prasad Ganthia, Rosalin Pradhan

    Abstract: This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The …

    Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article

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