https://dspace.iiti.ac.in:8080/jspui/handle/123456789/3642 2026-07-28T23:47:05Z 2026-07-28T23:47:05Z Babu, Prem https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18842 2026-07-27T06:30:11Z 2026-01-01T00:00:00Z

Title: PSHE-enhanced temperature sensor based on nonlinear magneto-optics and plasmonics Authors: Babu, Prem Abstract: A highly sensitive optical temperature sensor based on photonic spin Hall effect (PSHE)-induced beam shifts in a magneto-plasmonic multilayer configuration is theoretically proposed. The structure consists of an Au–TiO2–graphene anisotropic thin-film stack operating in the Kretschmann geometry with a GaP prism and with ethanol as the temperature-dependent sensing medium. Under surface plasmon resonance excitation in the mid-infrared wavelength regime, the spin-dependent in-plane and out-of-plane spatial beam shifts exhibit strong dependence on temperature. The sensor response is further analyzed through external magnetic field modulation and electrostatic gating of graphene, enabling controlled manipulation of magneto-optic and carrier-induced effects. Owing to the enhancement, the proposed scheme achieves a remarkably high temperature sensitivity of 3.49cm/K. The presented study highlights the potential of magneto-plasmonically assisted PSHE-based platforms for tunable and high-resolution temperature sensing, with prospective applications in nanophotonics, integrated optical sensing, and multifunctional plasmonic devices. © 2026 World Scientific Publishing Company.

2026-01-01T00:00:00Z Singh, Sharad Kumar Dewangan, Lokesh Kumar https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18847 2026-07-27T06:30:11Z 2026-01-01T00:00:00Z

Title: A data-driven geographic information system and machine learning based multi-criteria framework for strategic wind power plant siting Authors: Singh, Sharad Kumar; Dewangan, Lokesh Kumar Abstract: Wind energy site selection requires robust frameworks that simultaneously address expert uncertainty, objective efficiency screening, and predictive capability beyond sampled locations. This study presents an integrated framework for strategic wind farm site selection in Ethiopia’s Amhara Region by combining Fuzzy Analytic Hierarchy Process (FAHP), efficiency analysis, and predictive modelling to overcome the limitations of static GIS approaches. The FAHP stage incorporates expert judgment through fuzzy triangular numbers to weight six variables such as wind speed, slope, elevation, distance to transmission lines, distance to roads, and land-use/land-cover to achieve a consistency ratio of 0.0175 with wind speed emerging as the dominant factor of 0.4211 weights in order to generate a spatial suitability surface and extract candidate high-potential areas. High-potential zones identified by FAHP are then evaluated as decision-making units using input-oriented Data Envelopment Analysis (DEA) models. DEA efficiency screening identifies North Shewa as frontier-efficient zone (CCR = BCC = SBM = 1.0), which contributes 32.25% of regional suitable land. Finally, machine learning (ML) models (Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boost (XGBoost)) are trained on 1.5 million sampled pixels from the 31-million-cell feature space to learn generalizable suitability functions. Random Forest achieved optimal performance with RMSE of 0.2981 and R2 of 0.8145 in regression and F1-score of 0.9207 and accuracy of 0.9237 in classification to delineates 1,698 km2 of high-priority corridors within North Shewa for immediate wind farm deployment. Independent validation through five different sources that includes 250 MW of Debre Birhan under Ethiopian Ministry of Finance private public project pipeline to confirm North Shewa as the highest-potential development corridor. This framework advances wind energy planning by integrating subjective weighting, objective efficiency analysis, and predictive modelling into a unified strategic decision-support system applicable for wind energy planning in data-scarce region. © The Author(s) 2026.

2026-01-01T00:00:00Z Kumar, Yalla Ananda Singh, Sharad Kumar https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18836 2026-07-27T06:30:10Z 2026-01-01T00:00:00Z

Title: Battery Management for Lifelong Operation of Warehouse Robots Under Dynamic Demand Authors: Kumar, Yalla Ananda; Singh, Sharad Kumar Abstract: Effective battery management is crucial for sustaining continuous operations in warehouses employing fleets of battery-powered robots. This paper addresses the dual challenge of optimizing robot availability while managing energy constraints through a two-pronged optimization framework. We first formulate a Mixed-Integer Linear Programming (MILP) model with goal programming that simultaneously optimizes battery charging schedules and dynamic demand fulfillment, preventing overcharging while ensuring operational efficiency. Second, recognizing MILP's computational limitations for large fleets, we develop a modified rule-based algorithm with adaptive thresholds and time-slot scheduling for scalable deployment. The framework includes analytical derivations of minimum charging station requirements based on battery dynamics - bridging infrastructure planning with operational optimization - and implements a receding horizon strategy that enables real-time adaptation while maintaining collision-free navigation through integration with lifelong Multi-Agent Path Finding (MAPF). Comprehensive simulations under fluctuating demand scenarios demonstrate that both approaches outperform traditional threshold-based methods and recent optimization techniques, offering a practical spectrum from optimal to near-optimal solutions for warehouse automation systems. Note to Practitioners - This paper was motivated by the need for efficient energy management in autonomous warehouse systems, where fleets of battery-powered robots perform logistics tasks. In practice, robot downtime caused by suboptimal charging policies can disrupt operations and increase costs. Existing solutions often rely on fixed schedules or simplistic heuristics that fail to adapt to real-time demand or battery conditions. This work presents a flexible framework that balances operational efficiency and computational scalability. The optimization-based method is well-suited for high-accuracy planning in environments with sufficient computational resources, while the adaptive rule-based approach offers a responsive and easily deployable alternative for large-scale or time-sensitive operations. Practitioners can leverage these strategies to decide how many charging stations to deploy, when individual robots should recharge, and how to sustain continuous task fulfillment. Although the proposed methods are validated in simulation, future work will explore real-time deployment and robustness under fluctuating demand and environmental uncertainties in live warehouse settings. © 2004-2012 IEEE.

2026-01-01T00:00:00Z Maurya, Rahul Chauhan, Puspraj Singh Bhatia, Vimal https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18794 2026-07-20T17:05:48Z 2026-01-01T00:00:00Z

Title: On NOMA Short Packet THz Communications with Misalignment Error and Hardware Impairment Authors: Maurya, Rahul; Chauhan, Puspraj Singh; Bhatia, Vimal Abstract: Short packet communication (SPC) has gained increasing attention due to the demand for ultra-reliable and lowlatency applications such as the Internet-of-Things, Industry 4.0, and unmanned aerial vehicles. Unlike long-packet systems, SPC must transmit small data payloads quickly while ensuring high reliability, making it suitable for time-critical services. In this context, misalignment errors (ME) and transceiver hardware impairments (THI) have a stronger impact, as short blocklengths offer limited error correction capability. Ignoring these effects can significantly degrade system reliability and latency. This paper analyzes SPC performance in non-orthogonal multiple access systems over THz channels under diverse fading conditions while accounting for ME and THI. Novel probability density and cumulative distribution function expressions are derived using the Fox's H-function. Key performance metrics, including block error rate and delay outage rate, are evaluated for fixed and optimized antenna array configurations. The results show that antenna array optimization provides noticeable performance gains, and the analytical findings are validated through extensive Monte Carlo simulations. © 2026 IEEE.

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