arXiv subjects

Hemant Singh

Publications and source records attributed to Hemant Singh.

5 recordsLinked to original sources

arXiv2026

Optimization of magneto-electric properties in Lead-free (x)Co1.2Ti0.2Fe1.6O4 - (100-x)BaTiO3 based composites

This work presents a systematic study of lead-free multiferroic composites of (x)Co1.2Ti0.2Fe1.6O4 - (100-x)BaTiO3 (x = 10, 20, 30), which were synthesized by a solid-state reaction method to investigate the effects of composition and sintering temperature on their structural , electrical, magnetic, and magnetoelectric (ME) properties. X-ray diffraction along with Rietveld refinement confirms the coexistence of tetragonal BaTiO3 (BTO) and cubic spinel Co1.2Ti0.2Fe1.6O4 (CTFO) phases. Microstructural analysis shows that densification and grain growth are better at higher sintering temperatures, leading to better coupling between the two phases. Dielectric and ferroelectric studies indicate lossy polarization-electric field (P-E) behaviour due to leakage from the conductive phase, while magnetic properties show increased magnetization with increasing ferrite content. All composites exhibit ME coefficients, which depend on the composition and sintering conditions; the highest ME coefficient (~1.28 mV/cm.Oe) was observed for the 30CTFO - 70BTO composite sintered at 1200 {\deg}C. This improvement is due to the optimal balance between magnetostrictive and piezoelectric responses and improved interfacial strain transfer. These results demonstrate that simultaneous optimization of dopant-modified composition and sintering conditions is essential for achieving improved magnetoelectric coupling in bulk multiferroic composites. Moreover, the results demonstrate the potential of lead-free composites for multifunctional device applications in next-generation, low-power technologies, including high-density non-volatile memory (e.g. FeRAM/MRAM), magnetic field sensors, spintronic devices, and actuators.

cond-mat.mtrl-sci↗

arXiv2025

Bayesian Optimisation: Which Constraints Matter?

Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the popular Knowledge Gradient acquisition functions for problems with \emph{decoupled} black-box constraints, in which subsets of the objective and constraint functions may be evaluated independently. In particular, our methods aim to take into account that often only a handful of the constraints may be binding at the optimum, and hence we should evaluate only relevant constraints when trying to optimise a function. We empirically benchmark these methods against existing methods and demonstrate their superiority over the state-of-the-art.

cs.LG↗

arXiv2024

QMViT: A Mushroom is worth 16x16 Words

Consuming poisonous mushrooms can have severe health consequences, even resulting in fatality and accurately distinguishing edible from toxic mushroom varieties remains a significant challenge in ensuring food safety. So, it's crucial to distinguish between edible and poisonous mushrooms within the existing species. This is essential due to the significant demand for mushrooms in people's daily meals and their potential contributions to medical science. This work presents a novel Quantum Vision Transformer architecture that leverages quantum computing to enhance mushroom classification performance. By implementing specialized quantum self-attention mechanisms using Variational Quantum Circuits, the proposed architecture achieved 92.33% and 99.24% accuracy based on their category and their edibility respectively. This demonstrates the success of the proposed architecture in reducing false negatives for toxic mushrooms, thus ensuring food safety. Our research highlights the potential of QMViT for improving mushroom classification as a whole.

cs.CV↗

arXiv2023

Planning-Assisted Context-Sensitive Autonomous Shepherding of Dispersed Robotic Swarms in Obstacle-Cluttered Environments

Robotic shepherding is a bio-inspired approach to autonomously guiding a swarm of agents towards a desired location. The research area has earned increasing research interest recently due to the efficacy of controlling a large number of agents in a swarm (sheep) using a smaller number of actuators (sheepdogs). However, shepherding a highly dispersed swarm in an obstacle-cluttered environment remains challenging for existing methods. To improve the efficacy of shepherding in complex environments with obstacles and dispersed sheep, this paper proposes a planning-assisted context-sensitive autonomous shepherding framework with collision avoidance abilities. The proposed approach models the swarm shepherding problem as a single Travelling Salesperson Problem (TSP), with two sheepdogs\textquoteright\ modes: no-interaction and interaction. An adaptive switching approach is integrated into the framework to guide real-time path planning for avoiding collisions with static and dynamic obstacles; the latter representing moving sheep swarms. We then propose an overarching hierarchical mission planning system, which is made of three sub-systems: a clustering approach to group and distinguish sheep sub-swarms, an Ant Colony Optimisation algorithm as a TSP solver for determining the optimal herding sequence of the sub-swarms, and an online path planner for calculating optimal paths for both sheepdogs and sheep. The experiments on various environments, both with and without obstacles, objectively demonstrate the effectiveness of the proposed shepherding framework and planning approaches.

cs.RO↗

arXiv2020

Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution

Shepherding involves herding a swarm of agents (\emph{sheep}) by another a control agent (\emph{sheepdog}) towards a goal. Multiple approaches have been documented in the literature to model this behaviour. In this paper, we present a modification to a well-known shepherding approach, and show, via simulation, that this modification improves shepherding efficacy. We then argue that given complexity arising from obstacles laden environments, path planning approaches could further enhance this model. To validate this hypothesis, we present a 2-stage evolutionary-based path planning algorithm for shepherding a swarm of agents in 2D environments. In the first stage, the algorithm attempts to find the best path for the sheepdog to move from its initial location to a strategic driving location behind the sheep. In the second stage, it calculates and optimises a path for the sheep. It does so by using \emph{way points} on that path as the sequential sub-goals for the sheepdog to aim towards. The proposed algorithm is evaluated in obstacle laden environments via simulation with further improvements achieved.

cs.RO↗