arXiv subjects

Sanjay Singh

Publications and source records attributed to Sanjay Singh.

At least 19 recordsLinked to original sources

arXiv2026

Magneto-Caloric effect and Multiple magnetic phases in Al doped Ni2MnSn0.75Al0.25 Heusler Alloys

Among Heusler compounds,Ni based alloys have been extensively investigated because they exhibit desirable properties such as high Curie temperatures, which are advantageous for advanced magnetic and spintronic devices.The effect of Al substitution on the magnetic ground state of Ni2MnSn was investigated using the Ni2MnSn0.75Al0.25 Heusler alloy.Temperature-dependent magnetisation measurements identify a second-order paramagnetic to ferromagnetic transition at TC is 734K,followed by a first-order martensitic transformation near 263K,demonstrating strong magnetostructural coupling.Curie Weiss analysis yields a positive Weiss temperature theta CW is 746.4K and an effective magnetic moment of 6.82muB,confirming the predominance of ferromagnetic exchange interactions. The bifurcation between the ZFC and FCW magnetization curves,together with non saturating hysteretic M vs H loops, indicates the coexistence of competing ferromagnetic and antiferromagnetic interactions.Further magnetic investigations establish the formation of an interacting reentrant cluster glass state accompanied by an exchange-bias effect.The observed magnetic behavior is attributed to the modification of Mn Mn exchange interactions induced by Al substitution and the associated atomic disorder,resulting in a complex magnetic ground state.

cond-mat.mtrl-sci↗

arXiv2026

Role of Disorder in Governing the Magnetic Properties of Cu2IrO3

Cu$_2$IrO$_3$ is a honeycomb iridate which has been studied recently as a candidate Kitaev quantum spin liquid. Its magnetic ground state however, has been reported to be quantum disordered, spin glassy, or magnetically ordered depending on synthesis details. We have prepared a Cu$_2$IrO$_3$ sample with large antisite disorder and studied in detail its structure (global and local), charge states, and thermodynamic properties to try to quantify and characterize the disorder and its connection to the magnetic ground state. X-ray diffraction, Extended x-ray absorption fine structure(EXAFS) and X-ray pair distribution function analysis revealed a large site disorder ($\sim$25\%), while XPS and XANES reveal mixed valence of Cu and Ir following Cu$^{1+}$ + Ir$^{4+}$ $\rightarrow$ Cu$^{2+}$ + Ir$^{3+}$. This combination of site disorder and charge redistribution generates competing antiferromagnetic interactions and magnetic frustration, resulting in dynamically fluctuating AFM clusters near 80K that freeze below 29K. These results demonstrate the crucial role of synthesis dependent disorder in determining the magnetic ground state of Cu$_2$IrO$_3$.

cond-mat.str-el↗

arXiv2025

Hard-Earned Lessons in Access Control at Scale: Enforcing Identity and Policy Across Trust Boundaries with Reverse Proxies and mTLS

In today's enterprise environment, traditional access methods such as Virtual Private Networks (VPNs) and application-specific Single Sign-On (SSO) often fall short when it comes to securely scaling access for a distributed and dynamic workforce. This paper presents our experience implementing a modern, Zero Trust-aligned architecture that leverages a reverse proxy integrated with Mutual TLS (mTLS) and centralized SSO, along with the key challenges we encountered and lessons learned during its deployment and scaling. This multidimensional solution involves both per-device and per-user authentication, centralized enforcement of security policies, and comprehensive observability, hence enabling organizations to deliver secure and seamless access to their internal applications.

cs.CR↗

arXiv2025

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction

Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these unstructured industrial settings. To address this gap, we present a novel Multimodal Industrial Activity Monitoring (MIAM) dataset that captures realistic assembly and disassembly tasks, facilitating the evaluation of key meta-tasks such as action localization, object interaction, and engagement prediction. The dataset comprises multi-view RGB, depth, and Inertial Measurement Unit (IMU) data collected from 22 sessions, amounting to 290 minutes of untrimmed video, annotated in detail for task performance and operator behavior. Its distinctiveness lies in the integration of multiple data modalities and its emphasis on real-world, untrimmed industrial workflows-key for advancing research in human-robot collaboration and operator monitoring. Additionally, we propose a multimodal network that fuses RGB frames, IMU data, and skeleton sequences to predict engagement levels during industrial tasks. Our approach improves the accuracy of recognizing engagement states, providing a robust solution for monitoring operator performance in dynamic industrial environments. The dataset and code can be accessed from https://github.com/navalkishoremehta95/MIAM/.

cs.CV↗

arXiv2025

Optimizing Multitask Industrial Processes with Predictive Action Guidance

Monitoring complex assembly processes is critical for maintaining productivity and ensuring compliance with assembly standards. However, variability in human actions and subjective task preferences complicate accurate task anticipation and guidance. To address these challenges, we introduce the Multi-Modal Transformer Fusion and Recurrent Units (MMTFRU) Network for egocentric activity anticipation, utilizing multimodal fusion to improve prediction accuracy. Integrated with the Operator Action Monitoring Unit (OAMU), the system provides proactive operator guidance, preventing deviations in the assembly process. OAMU employs two strategies: (1) Top-5 MMTF-RU predictions, combined with a reference graph and an action dictionary, for next-step recommendations; and (2) Top-1 MMTF-RU predictions, integrated with a reference graph, for detecting sequence deviations and predicting anomaly scores via an entropy-informed confidence mechanism. We also introduce Time-Weighted Sequence Accuracy (TWSA) to evaluate operator efficiency and ensure timely task completion. Our approach is validated on the industrial Meccano dataset and the largescale EPIC-Kitchens-55 dataset, demonstrating its effectiveness in dynamic environments.

cs.CV↗

arXiv2024

Local Symmetry Breaking in Skyrmion-Hosting Centrosymmetric Hexagonal Compounds

Dzyaloshinskii-Moriya interaction (DMI) plays a crucial role in stabilizing the exotic topologically stable skyrmion spin textures in the noncentrosymmetric crystals. The recent discovery of biskyrmions and skyrmions in the globally centrosymmetric crystals has raised debate about the role of the DMI in causing the spin textures, since DMI vanishes in such crystal structures. Theoretical studies, on the other hand, suggest non-vanishing DMI even if there is local inversion symmetry breaking in an otherwise globally centrosymmetric crystal structure. Motivated by such theoretical predictions, we present here the results of a systematic crystal structure study of two skyrmion-hosting Ni2In-type centrosymmetric hexagonal compounds, MnNiGa and MnPtGa, using the atomic pair distribution function (PDF) technique. Our result provides information about structural correlations in the short-range (SR), medium-range (MR) and long-range (LR) regimes simultaneously. The analysis of the experimental PDFs, obtained from high flux, high energy, and high Q synchrotron x-ray powder diffraction patterns, reveals that the local SR structure of both MnNiGa and MnPtGa compounds corresponds to the noncentrosymmetric trigonal space group P3m1, while the structure in the MR+LR regimes remains hexagonal in the centrosymmetric P63/mmc space group. These findings are also supported by theoretical DFT calculations. Our results, in conjunction with the previous theoretical predictions, provide a rationale for the genesis of skyrmions in centrosymmetric materials in terms of non-vanishing DMI due to local inversion symmetry breaking. We believe that our findings would encourage a systematic search of skyrmionic textures and other topological phenomena in a vast family of centrosymmetric materials.

cond-mat.mtrl-sci↗

arXiv2024

Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime

The field of computational imaging has witnessed a promising paradigm shift with the emergence of untrained neural networks, offering novel solutions to inverse computational imaging problems. While existing techniques have demonstrated impressive results, they often operate either in the high-data regime, leveraging Generative Adversarial Networks (GANs) as image priors, or through untrained iterative reconstruction in a data-agnostic manner. This paper delves into lensless image reconstruction, a subset of computational imaging that replaces traditional lenses with computation, enabling the development of ultra-thin and lightweight imaging systems. To the best of our knowledge, we are the first to leverage implicit neural representations for lensless image deblurring, achieving reconstructions without the requirement of prior training. We perform prior-embedded untrained iterative optimization to enhance reconstruction performance and speed up convergence, effectively bridging the gap between the no-data and high-data regimes. Through a thorough comparative analysis encompassing various untrained and low-shot methods, including under-parameterized non-convolutional methods and domain-restricted low-shot methods, we showcase the superior performance of our approach by a significant margin.

eess.IV↗

arXiv2024

Effect of antisite disorder on the magnetic and transport properties of a quaternary Heusler alloy

Spin gapless semiconductors based Heusler alloys are the special class of materials due to their unique band structure, high spin polarization and high Curie temperature. These materials exhibit a distinct electronic structure: a nonzero band gap in one spin channel while the other spin channel remains gapless, making them highly suitable for tunable spintronics. In this study, a comprehensive analysis of structural, magnetic, thermoelectric, and transport properties of the quaternary Heusler alloy CoFeMnSn is conducted. X-ray diffraction and Neutron diffraction analyses confirm a well ordered structure with partial antisite disorder between Co, Fe and Mn, Sn atoms. Magnetic studies show that the material exhibits room-temperature ferromagnetism, with a Curie temperature of around 660 K. Notably, we observe an anomalous Hall effect linked to intrinsic mechanisms driven by Berry curvature, underscoring the intricate relationship between structural disorder and electronic behavior. Transport measurements also highlight the impact of antisite disorder on the systems, with resistivity decreasing as temperature increases. These insights position CoFeMnSn as a promising material for future spintronic devices and advanced technological applications.

cond-mat.mtrl-sci↗

arXiv2024

Supporting Assessment of Novelty of Design Problems Using Concept of Problem SAPPhIRE

This paper proposes a framework for assessing the novelty of design problems using the SAPPhIRE model of causality. The novelty of a problem is measured as its minimum distance from the problems in a reference problem database. The distance is calculated by comparing the current problem and each reference past problem at the various levels of abstraction in the SAPPhIRE ontology. The basis for comparison is textual similarity. To demonstrate the applicability of the proposed framework, The current set of problems associated with an artifact, as collected from its stakeholders, were compared with the past set of problems, as collected from patents and other web sources, to assess the novelty of the current set. This approach is aimed at providing a better understanding of the degree of novelty of any given set of current problems by comparing them to similar problems available from historical records. Since manual assessment, the current mode of such assessments as reported in the literature, is a tedious process, to reduce time complexity and to afford better applicability for larger sets of problem statements, an automated assessment is proposed and used in this paper.

cs.CL↗

arXiv2024

Towards Physics-informed Cyclic Adversarial Multi-PSF Lensless Imaging

Lensless imaging has emerged as a promising field within inverse imaging, offering compact, cost-effective solutions with the potential to revolutionize the computational camera market. By circumventing traditional optical components like lenses and mirrors, novel approaches like mask-based lensless imaging eliminate the need for conventional hardware. However, advancements in lensless image reconstruction, particularly those leveraging Generative Adversarial Networks (GANs), are hindered by the reliance on data-driven training processes, resulting in network specificity to the Point Spread Function (PSF) of the imaging system. This necessitates a complete retraining for minor PSF changes, limiting adaptability and generalizability across diverse imaging scenarios. In this paper, we introduce a novel approach to multi-PSF lensless imaging, employing a dual discriminator cyclic adversarial framework. We propose a unique generator architecture with a sparse convolutional PSF-aware auxiliary branch, coupled with a forward model integrated into the training loop to facilitate physics-informed learning to handle the substantial domain gap between lensless and lensed images. Comprehensive performance evaluation and ablation studies underscore the effectiveness of our model, offering robust and adaptable lensless image reconstruction capabilities. Our method achieves comparable performance to existing PSF-agnostic generative methods for single PSF cases and demonstrates resilience to PSF changes without the need for retraining.

eess.IV↗

arXiv2024

Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning

Planetary exploration requires traversal in environments with rugged terrains. In addition, Mars rovers and other planetary exploration robots often carry sensitive scientific experiments and components onboard, which must be protected from mechanical harm. This paper deals with an active suspension system focused on chassis stabilisation and an efficient traversal method while encountering unavoidable obstacles. Soft Actor-Critic (SAC) was applied along with Proportional Integral Derivative (PID) control to stabilise the chassis and traverse large obstacles at low speeds. The model uses the rover's distance from surrounding obstacles, the height of the obstacle, and the chassis' orientation to actuate the control links of the suspension accurately. Simulations carried out in the Gazebo environment are used to validate the proposed active system.

cs.RO↗

arXiv2024

Gaze-Vector Estimation in the Dark with Temporally Encoded Event-driven Neural Networks

In this paper, we address the intricate challenge of gaze vector prediction, a pivotal task with applications ranging from human-computer interaction to driver monitoring systems. Our innovative approach is designed for the demanding setting of extremely low-light conditions, leveraging a novel temporal event encoding scheme, and a dedicated neural network architecture. The temporal encoding method seamlessly integrates Dynamic Vision Sensor (DVS) events with grayscale guide frames, generating consecutively encoded images for input into our neural network. This unique solution not only captures diverse gaze responses from participants within the active age group but also introduces a curated dataset tailored for low-light conditions. The encoded temporal frames paired with our network showcase impressive spatial localization and reliable gaze direction in their predictions. Achieving a remarkable 100-pixel accuracy of 100%, our research underscores the potency of our neural network to work with temporally consecutive encoded images for precise gaze vector predictions in challenging low-light videos, contributing to the advancement of gaze prediction technologies.

cs.CV↗

arXiv2023

Evidence for Conical Magnetic Structure in M-type BaFe12O19 Hexaferrite: A Combined Single-Crystal XMCD and Neutron Diffraction Study

The magnetic ground state of BaFe12O19 (BFO) was investigated using X-ray absorption at 1.2 K and 1.5 K, respectively. The XMCD measurements on single-crystals of BFO in grazing incidence geometry reveal the canting of the spins away from the c-axis of the hexagonal unit cell. Single-crystal neutron diffraction studies reveal magnetic satellite peaks along the 00l reciprocal lattice row around the forbidden l = 2n +/- 1 positions confirming conical-type magnetic structure in the ground state of BFO. The observation of the conical magnetic structure of BFO opens the possibility of type-II multiferroicity in undoped BFO also.

cond-mat.mtrl-sci↗

arXiv2023

Oscillatory large-scale circulation in liquid-metal thermal convection and its structural unit

In Rayleigh-Bénard convection (RBC), the size of a flow domain and its aspect ratio $\varGamma$ (a ratio between the spatial length and height of the domain) affect the shape of the large-scale circulation (LSC). For some aspect ratios, the flow dynamics include a three-dimensional oscillatory mode known as a jump-rope vortex (JRV), however, the effects of varying aspect ratios on this mode are not well investigated. In this paper, we study these aspect-ratio effects in liquid metals, for a low Prandtl number $Pr=0.03$. Direct numerical simulations and experiments are carried out for a Rayleigh number range $2.9 \times 10^4 \leq Ra \leq 1.6 \times 10^6$ and square cuboid domains with $\varGamma=2$, $2.5$, $3$ and $5$. Our study demonstrates that a repeating pattern of a JRV encountered at an aspect ratio $\varGamma \approx 2.5$ is the basic structural unit that builds up to a lattice of interlaced JRVs at the largest aspect ratio. The size of the domain determines how many structural units are self-organized within the domain; the number of the realized units is expected to scale as $\varGamma^2$ with sufficiently large and growing $\varGamma$. We find the oscillatory modes for all investigated $\varGamma$, however, they are more pronounced for $\varGamma=2.5$ and $\varGamma=5$. Future studies for large-aspect ratio domains of different shapes would enhance our understanding of how the JRVs adjust and reorganize at such scaled-up geometries, and answer the question of whether they are indeed the smallest superstructure units.

physics.flu-dyn↗

arXiv2023

Colossal anomalous Hall and Nernst effect from the breaking of nodal-line symmetry in Cu2CoSn Weyl semimetal: A first-principles study

The presence of topological band crossings near the Fermi energy is essential for the realization of large anomalous transport properties in the materials. The topological semimetals (TSMs) host such properties owing to their unique topological band structure such as Weyl points or nodal lines (NLs), that is protected by certain symmetries of the crystal. When the NLs break out in the system due to perturbation in Hamiltonian, a large Berry curvature arises in the surrounding area of the gapped NL. In the present work, we studied anomalous transport properties of Cu2CoSn compound, which has a cubic Heusler crystal structure (space group: Fm-3m). The Cu2CoSn full Heusler compound possesses three NLs in the absence of spin-orbit coupling close to the Fermi level. These NLs gap out with the consideration of the SOC and a large Berry curvature observed along the gapped NLs. The integral of Berry curvature gives the intrinsic anomalous Hall conductivity (AHC) about 1003 S/cm and the anomalous Nernst conductivity (ANC) of about 3.98 A/m-K at the Fermi level. These values of AHC and ANC are comparable to the largest reported values for the Co2MnGa Heusler compound. Therefore, Cu2CoSn becomes a newborn member of the family of full Heusler compounds, which possesses giant AHC and ANC that can be useful for the spintronics application.

cond-mat.mtrl-sci↗

arXiv2022

Convolutional Ensembling based Few-Shot Defect Detection Technique

Over the past few years, there has been a significant improvement in the domain of few-shot learning. This learning paradigm has shown promising results for the challenging problem of anomaly detection, where the general task is to deal with heavy class imbalance. Our paper presents a new approach to few-shot classification, where we employ the knowledge-base of multiple pre-trained convolutional models that act as the backbone for our proposed few-shot framework. Our framework uses a novel ensembling technique for boosting the accuracy while drastically decreasing the total parameter count, thus paving the way for real-time implementation. We perform an extensive hyperparameter search using a power-line defect detection dataset and obtain an accuracy of 92.30% for the 5-way 5-shot task. Without further tuning, we evaluate our model on competing standards with the existing state-of-the-art methods and outperform them.

cs.CV↗

arXiv2022

Anti-site disorder and Berry curvature driven anomalous Hall effect in spin gapless semiconducting Mn2CoAl Heusler compound

Spin gapless semiconductors exhibit a finite band gap for one spin channel and closed gap for other spin channel, emerged as a new state of magnetic materials with a great potential for spintronic applications. The first experimental evidence for the spin gapless semiconducting behavior was observed in an inverse Heusler compound Mn2CoAl. Here, we report a detailed investigation of the crystal structure and anomalous Hall effect in the Mn2CoAl using experimental and theoretical studies. The analysis of the high-resolution synchrotron x-ray diffraction data shows anti-site disorder between Mn and Al atoms within the inverse Heusler structure. The temperature-dependent resistivity shows semiconducting behavior and follows Mooijs criteria for disordered metal. Scaling behavior of the anomalous Hall resistivity suggests that the anomalous Hall effect in the Mn2CoAl is primarily governed by intrinsic mechanism due to the Berry curvature in momentum space. The experimental intrinsic anomalous Hall conductivity (AHC) is found to be 35 S/cm, which is considerably larger than the theoretically predicted value for ordered Mn2CoAl. Our first-principle calculations conclude that the anti-site disorder between Mn and Al atoms enhances the Berry curvature and hence the value of intrinsic AHC, which is in a very well agreement with the experiment.

cond-mat.mtrl-sci↗

arXiv2022

ParaColorizer: Realistic Image Colorization using Parallel Generative Networks

Grayscale image colorization is a fascinating application of AI for information restoration. The inherently ill-posed nature of the problem makes it even more challenging since the outputs could be multi-modal. The learning-based methods currently in use produce acceptable results for straightforward cases but usually fail to restore the contextual information in the absence of clear figure-ground separation. Also, the images suffer from color bleeding and desaturated backgrounds since a single model trained on full image features is insufficient for learning the diverse data modes. To address these issues, we present a parallel GAN-based colorization framework. In our approach, each separately tailored GAN pipeline colorizes the foreground (using object-level features) or the background (using full-image features). The foreground pipeline employs a Residual-UNet with self-attention as its generator trained using the full-image features and the corresponding object-level features from the COCO dataset. The background pipeline relies on full-image features and additional training examples from the Places dataset. We design a DenseFuse-based fusion network to obtain the final colorized image by feature-based fusion of the parallelly generated outputs. We show the shortcomings of the non-perceptual evaluation metrics commonly used to assess multi-modal problems like image colorization and perform extensive performance evaluation of our framework using multiple perceptual metrics. Our approach outperforms most of the existing learning-based methods and produces results comparable to the state-of-the-art. Further, we performed a runtime analysis and obtained an average inference time of 24ms per image.

cs.CV↗