ScholarSphere: IITJ Research Insights Hub

The intrinsic cycle-to-cycle variability in resistive random access memory (RRAM) devices supports their suitability as seed sources in circuits requiring stochastic input. This article presents the fabrication, characterization, and analysis of FTO/HfO2/Ag, FTO/HfO2/Cu, and co-sputtering (CS) FTO/HfO2-SiO2/Ag RRAMs for hardware security and stochastic applications. The RRAM switching-layer thin film has been deposited by radio frequency (RF) sputtering, and silver (Ag) and copper (Cu) electrodes were used to tune the switching behavior. Statistical analysis of cycle-to-cycle variability shows a coefficient of variation (CV) of ∼0.32 for FTO/HfO2/Ag and ∼0.088 for FTO/HfO2/Cu devices. It supports the possible suitability of the Ag-based devices for high-variability applications and the ability to control RRAM variability through fabrication choices. These controllable characteristics suggest proposed nanofabricated devices as possible candidates for applications in hardware security primitives, secure memory elements in low-power hardware, and Internet of Things (IoT) platforms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.

This article presents a two-degree-of-freedom sliding mode control with event-triggered feedback to guarantee robust output tracking for an uncertain plant against matched disturbances. The proposed controller comprises an event-based feedback loop for the plant measurements and the analog feedthrough path for the tracking (reference) signal, which is generated by an exogenous system. Here, the event-triggering mechanism uses only the feedback signals, unlike the existing works, and thus, it retains the two-degree-of-freedom architecture in the controller implementation. The main advantage of the proposed event-based controller is that the event mechanism generates a sparse sampling sequence due to the use of only feedback signals. It is seen that the solvability of the tracking problem is reduced to that of the sliding mode regulator equations. Indeed, it is established under this assumption that for any bounded initial conditions, the proposed sliding mode controller guarantees robust output tracking for the closed-loop system with arbitrary accuracy. Finally, the simulation results are presented to demonstrate the output tracking by using the proposed methodology. © 1963-2012 IEEE.

In enhancing transportation, the Internet of Vehicles (IoV) facilitates real-time vehicle-infrastructure interactions, improving traffic management and safety, crucial for autonomous vehicle development. However, high-traffic IoV networks face challenges with traditional Batch Message Verification (BMV) methods, often leading to critical alerts being unverified and dropped due to a lack of prioritization. Addressing this, we introduce an enhanced verification model, 'DyBatch: Message Prioritization and Dynamic Batch Verification in Large-scale IoV Networks', designed for Edge-computing enabled IoV (e-IoV) networks. DyBatch effectively tackles message loss by classifying Basic Alert Messages (BAMs) based on urgency through a flag-based mechanism, coupled with a flexible batch size concept to overcome fixed-size limitations. The model employs Verification-Proxy Vehicles (VPVs), special vehicles that handle verification during Edge Node (EN) bottlenecks. DyBatch aims to minimize message loss and verification delays, enhancing real-time application suitability. Utilizing the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL) and Java 8 on Jetson Nano and Raspberry Pi 4, our analysis reveals a 90% reduction in verification delays, a 91% improvement in computation overhead, and a 67% increase in verified messages, significantly boosting efficiency in high-density IoV environments. © 2025 IEEE.

Quantum computing offers new ways to explore the theory of computation via the laws of quantum mechanics. Due to the rising demand for quantum computing resources, there is growing interest in developing cloud-based quantum resource sharing platforms that enable researchers to test and execute their algorithms on real quantum hardware. These cloud-based systems face a fundamental challenge in efficiently allocating quantum hardware resources to fulfill the growing computational demand of modern Internet of Things (IoT) applications. So far, attempts have been made in order to make efficient resource allocation, ranging from heuristic-based solutions to machine learning. In this work, we employ quantum reinforcement learning based on parameterized quantum circuits to address the resource allocation problem to support large IoT networks. We propose a python-based toolkit called QAISim for the simulation and modeling of Quantum Artificial Intelligence (QAI) models for designing resource management policies in quantum cloud environments. We have simulated policy gradient and Deep Q-Learning algorithms for reinforcement learning. QAISim exhibits a substantial reduction in model complexity compared to its classical counterparts with fewer trainable variables. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.

Neurodegenerative disorders such as Parkinson's (PD) and Alzheimer's diseases (AD) are linked with the assembly and accumulation of proteins into structured scaffold called amyloids. These diseases pose significant challenges due to their complex and multifaceted nature. While the primary focus has been on endogenous amyloids, recent evidence suggests that bacterial amyloids may contribute to the development and exacerbation of such disorders. The gut-brain axis is emerging as a communication pathway between bacterial and human amyloids. This review delves into the novel role and potential mechanism of bacterial amyloids in modulating human amyloid formation and the progression of AD and PD.

In the MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula Φ, and a positive integer k, and the goal is to find an assignment β with at most k variables set to true (also called a weight k-assignment) such that the number of clauses satisfied by β is maximized. Maximum Coverage can be seen as a special case of CC-MaxSat, where the formula Φ is monotone, i.e., does not contain any negative literals. CC-MaxSat and Maximum Coverage are extremely well-studied problems in the approximation algorithms as well as the parameterized complexity literature. Our first conceptual contribution is that CC-MaxSat and Maximum Coverage are equivalent to each other in the context of FPT-Approximation parameterized by k (here, the approximation is in terms of the number of clauses satisfied/elements covered). In particular, we give a randomized reduction from CC-MaxSat to Maximum Coverage running in time O(1/ϵ)k · (m + n)O(1) that preserves the approximation guarantee up to a factor of (1 − ϵ). Furthermore, this reduction also works in the presence of “fairness” constraints on the satisfied clauses, as well as matroid constraints on the set of variables that are assigned true. Here, the “fairness” constraints are modeled by partitioning the clauses of the formula Φ into r different colors, and the goal is to find an assignment that satisfies at least tj clauses of each color 1 ≤ j ≤ r. Armed with this reduction, we focus on designing FPT-Approximation schemes (FPT-ASes) for Maximum Coverage and its generalizations. Our algorithms are based on a novel combination of a variety of ideas, including a carefully designed probability distribution that exploits sparse coverage functions. These algorithms substantially generalize the results in Jain et al. [SODA 2023] for CC-MaxSat and Maximum Coverage for Kd,d-free set systems (i.e., no d sets share d elements), as well as a recent FPT-AS for Matroid Constrained Maximum Coverage by Sellier [ESA 2023] for frequency-d set systems.

The technique of micro-exfoliation has gained prominence as a highly effective and adaptable method for exploiting two-dimensional (2D) materials, such as graphene Transition metal dichalcogenides (TMDCs), Borophene, Molybdenum disulfide (MoS2), among others. This paper presents an analysis of optical images and the micro exfoliation technique, focusing on the application to MoS2 and graphene. Additionally, the study investigates the exfoliated sheet of graphene, MoS2, and their hybrid on a (111) crystal plane of silicon wafer. The micro-exfoliation technique employed for MoS2 involves a mechanical process that gently disentangles the layers of MoS2 from the larger crystal structure, resulting in the formation of ultrathin two-dimensional nanosheets. This paper comprehensively analyses the exfoliation processes' mechanisms, emphasizing the intricate relationship between van der Waals forces, interlayer bonding, and external forces. The micro-mechanical exfoliation, a fundamental technique, entails the utilization of adhesive scotch tape to remove monolayers from a large MoS2 crystal delicately. The integration of MoS2 into various applications such as electronics, optoelectronics, sensors, and energy storage devices has been driven by its exceptional properties, including its distinctive electronic, optical, and mechanical characteristics. Furthermore, the ability to adjust the bandgap of MoS2 has created novel opportunities for potential applications in the field of semiconductors. This paper provides a succinct summary of recent studies, that have concentrated on the optical characterization of MoS2 monolayers. Optical and Raman spectroscopy was employed to characterize the 2D sheets of MoS2 and its hybrid materials.