arXiv subjects

Tauhidur Rahman

Publications and source records attributed to Tauhidur Rahman.

At least 19 recordsLinked to original sources

arXiv2026

Phase Switchable Photocatalytic Water Splitting via a Paraelectric-Ferroelectric Transition in Zr2Ge2S6 Monolayer: A Comprehensive Theoretical Insights

Photocatalytic water splitting (PWS) is a promising technology for addressing the global energy crisis and producing renewable and clean hydrogen fuel. Although numerous 2D materials have recently been proposed as potential photocatalysts, effective strategies for regulating photocatalytic reactions and improving energy conversion efficiency remain limited due to performance regulation challenges. Here, using first-principles calculations, we demonstrate that the photocatalytic activity and energy conversion efficiency of a Zr2Ge2S6 monolayer can be effectively tuned through a paraelectric-ferroelectric phase transition. The Zr2Ge2S6 monolayer exhibits excellent structural stability, favorable mechanical properties, a suitable band gap, optimal band edge positions, and broad-spectrum light absorption. Moreover, the Zr2Ge2S6 monolayer exhibits a higher oxidation potential and a stronger driving force for photogenerated holes to promote oxygen evolution reaction (OER) in the ferroelectric phase. In contrast, the paraelectric phase provides photogenerated electrons with a greater reduction potential and driving force for hydrogen evolution reaction (HER). The solar-to-hydrogen conversion efficiency is also strongly influenced by the phase transition, increasing from 7.71% in the paraelectric phase to 15.31% in the ferroelectric phase because of the improved carrier utilization. Our theoretical investigation not only highlights the crucial role of ferroelectric polarization in photocatalytic water splitting but also provides an effective strategy for tuning the photocatalytic properties of 2D ferroelectric materials through ferroelectric switching.

cond-mat.mtrl-sci↗

arXiv2026

Topological Electronic States and Phonon Mediated Superconductivity in Ru Based Ternary Pnictides: ZrRuAs and HfRuP

This study investigates the structural, electronic, mechanical, optical, thermodynamic, and superconducting properties of ZrRuAs and HfRuP. Electronic band structure calculations reveal topological semimetallic behavior in both compounds with several bands crossing the Fermi level. The inclusion of spin orbit coupling lifts band degeneracies in both compounds. The Fermi surface shows both electron and hole segments. The estimated Pugh ratio, Poisson ratio and machinability index indicate that both compounds are ductile in nature and exhibit excellent machinability. Optical results show metallic reflectivity and notable absorption in the ultraviolet region. Thermodynamic analysis indicates stable behavior. The estimated superconducting transition temperatures are 9.75 K for ZrRuAs and 9.03 K for HfRuP that indicates strong coupling conventional superconductivity. The results provide a detailed view of the physical and superconducting properties of ZrRuAs and HfRuP and help to close the existing research gaps in these topological superconducting materials.

cond-mat.mtrl-sci↗

arXiv2026

Electronic Structure, Optical Response, Thermal and Mechanical Behavior of B6X (X = S, Se) under Pressure: A Comprehensive Ab-initio Exploration

This study presents a comprehensive investigation of the pressure dependent structural, electronic, optical, mechanical, and bonding properties of orthorhombic boron rich chalcogenides B6S and B6Se. Calculations were performed using density functional theory across a wide range of hydrostatic pressures. The computed elastic constants, bulk, Young, shear moduli, and Poisson ratio, revealed mechanical robustness and strong resistance to deformation, even under significant compression. Electronic band structure and density of states analyses indicate that the materials exhibit indirect bandgap semiconducting behavior. Optical results reveal clear pressure induced spectral shifts, particularly in the visible and ultraviolet regions, suggesting modified light matter interaction under compression. Phonon dispersion curves verified the dynamical stability of both materials within the investigated pressure range. Hardness estimations, combined with elastic parameters, and melting temperatures, further indicate that B6S and B6Se possess significant mechanical strength suitable for applications under harsh environments. The thermal properties suggest that both these compounds possess features suitable to be used as excellent thermal barrier coating materials.

cond-mat.mtrl-sci↗

arXiv2026

First-Principles Study of Novel Lead-Free Double Perovskite \b{eta}2SnGeX6 (\b{eta} = K, Rb; X = Cl, Br, I) for thermomechanical, optoelectronic and outstanding thermoelectric applications

In this study, the structural, mechanical, electronic, optical, and thermoelectric properties of the novel lead-free halide double perovskite series beta2SnGeX6 (beta = K, Rb; X = Cl, Br, I) are systematically investigated using density functional theory (DFT). Calculated formation energies, Tolerance factors, and octahedral factors confirm that all six compounds exhibit robust thermodynamic stability within a highly symmetric cubic geometry. Mechanical analysis derived from elastic parameters characterizes the entire series as fundamentally ductile, ensuring high processing elasticity and resistance to micro-cracking during device manufacturing. Electronic band structures reveal direct bandgaps showing exceptional composition-dependent tunability from 1.44 eV down to 0.64 eV via progressive halogen substitution. The wide gap chloride variations are optimized for single-junction photovoltaic absorbers, while the narrower-gap bromide and iodide analogs show immense promise for tandem solar architectures and near-infrared photodetectors. Thermoelectrically, heavy constituent atoms introduce strong lattice anharmonicity and intense high-temperature Umklapp phonon scattering, significantly suppressing lattice thermal conductivity. Combined with low carrier effective masses that optimize electrical transport, the iodide compounds achieve higher power factors and outstanding dimensionless figures of merit (ZT = 2.4 for K2SnGeI6 at 1000 K). Ultimately, these lead-free double perovskite family emerges as an environmentally benign and versatile platform for next-generation green optoelectronics and solid-state waste-heat recovery.

cond-mat.mtrl-sci↗

arXiv2026

Ambient and Pressure Dependent Superconductivity with Hydrogen Storage Potential in Quaternary Hydride LiMgZr2H12: A Comprehensive First-principles Insights

Molecular hydrides have attracted relatively less attention in the search for high Tc superconductors because their hydrogen quasi-molecular units tend to be electronically inactive for superconductivity. In contrast, hydrogen rich compounds under high pressure have been widely considered strong candidates for achieving room-temperature superconductivity. However, their dependence on extreme pressure conditions significantly constrains their practical applicability. This work investigates hydrogen-rich superconducting materials that may be stable under ambient pressure conditions. Motivated by recent studies on the MgZrH2n family, a LiMgZr2H12 structure with Pmmm symmetry was designed. The mechanical, thermodynamic, and dynamical stability of the compound, together with its electronic and optical properties, were systematically investigated using first-principles calculations. Li doping in LiMgZr2H12 significantly increases the hydrogen derived contribution near the Fermi level (EF) and strengthens the electron-phonon coupling constant ({\lambda}) compared with MgZrH6. LiMgZr2H12 exhibits a critical temperature of 72.76 K at ambient pressure, which is further enhanced by applying pressure. At 10 GPa the critical temperature increases to 77.3 K. Elastic property analysis shows that the material remains mechanically stable over the pressure range studied (0-10 GPa). It also behaves like a ductile material suitable for current carrying applications. The material has a high machinability index, which is much higher than that of stainless steels. In addition, LiMgZr2H12 exhibits a gravimetric hydrogen storage capacity of 5.36 wt%, indicating its potential as a promising candidate for hybrid hydrogen storage technologies. This work offers a new direction for designing high-Tc hydrides at ambient conditions.

cond-mat.supr-con↗

arXiv2026

Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics

Physiological time series signals reflect complex, multi-scale dynamical processes of the human body. Existing modeling studies focus on static tasks such as classification, event forecasting, or short-horizon next step prediction, while long-horizon signal-level forecasting and predictive nature of physiological signals remain underexplored. We introduce NormWear-2, a world model that encodes both multivariate physiological signals and clinical intervention variables into a shared latent space and models their joint temporal evolution as a dynamical system. Our approach combines inference from prior pre-trained knowledge (intuition) with instant non-parametric latent state transition adaptation (insight), enabling coherent forecasting across multiple temporal scales, conditioned on heterogeneous clinical interventions. During the pretraining phase, we find that chaos-theoretic balancing of dynamical regime diversity yields more robust representations, with a smaller balanced corpus outperforming one twice its size and capturing bifurcation regimes. We evaluate the world model performance across diverse real-world physiological datasets spanning heterogeneous temporal resolutions and intervention regimes, covering daily life, point-of-care, and clinical settings, including fitness planning, hemodialysis, diabetes management, and surgical monitoring. These evaluation datasets comprise records from 8,026 subjects, spanning study durations from 3.2 hours for high-resolution signal data to 2.3 years for longitudinal clinical biomarker tracking. NormWear-2 achieves the best overall forecasting performance across time, frequency, and latent representation domains, with significant improvements over state-of-the-art time series foundation models, while maintaining competitive downstream representation quality, providing a step toward general-purpose world models for physiological signals.

cs.LG↗

arXiv2026

HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind

Theory of mind (ToM) enables AI systems to infer agents' hidden goals and mental states, but existing approaches focus mainly on small human understandable gridworld spaces. We introduce HiVAE, a hierarchical variational architecture that scales ToM reasoning to realistic spatiotemporal domains. Inspired by the belief-desire-intention structure of human cognition, our three-level VAE hierarchy achieves substantial performance improvements on a 3,185-node campus navigation task. However, we identify a critical limitation: while our hierarchical structure improves prediction, learned latent representations lack explicit grounding to actual mental states. We propose self-supervised alignment strategies and present this work to solicit community feedback on grounding approaches.

cs.LG↗

arXiv2026

Pressure dependent topological, superconducting, optoelectronic and thermophysical properties of Ta2Se chalcogenide: Theoretical insights

Tetragonal Ta2Se is a layered, Ta-rich chalcogenide that departs from conventional MX2 transition-metal dichalcogenides by hosting dense Ta-Ta networks capped by Se square-net layers. Here, we present a unified first-principles investigation of hydrostatic-pressure tuning in Ta2Se from 0 to 10 GPa, connecting the structural response, mechanical stability, thermophysical indicators, bonding evolution, electronic and optical behavior, lattice dynamics and superconductivity within a single framework. The derived thermophysical descriptors corroborate a pressure-stiffened lattice: density increases, Debye temperature rises, melting temperature is elevated and minimum thermal conductivity increases, whereas the Grüneisen parameter remains within a narrow window, suggesting no anomalous anharmonic softening. Bond population metrics and electron-density-difference analysis revealed a mixed metallic-covalent bonding picture dominated by a robust Ta-Ta metallic backbone, accompanied by pressure-strengthened Ta-Se hybridization. Electronic-structure calculations show persistent metallicity under compression; pressure broadens bands, reduces density of states at the Fermi level, reshapes the Fermi surface and points to a possible Lifshitz-type reconstruction without symmetry breaking. The optical response remained metallic with Drude-like low-energy behavior and pressure-tunable spectral features. The phonon dispersions exhibit no imaginary modes, confirming dynamical stability. Electron-phonon coupling calculations classify Ta2Se as a weak-coupling, phonon-mediated superconductor with Tc around 3.9 K, consistent with available experiments and establish pressure as a practical control knob for stability and superconductivity-relevant descriptors in this metal-rich layered platform.

cond-mat.mtrl-sci↗

arXiv2026

Automated Dysphagia Screening Using Noninvasive Neck Acoustic Sensing

Pharyngeal health plays a vital role in essential human functions such as breathing, swallowing, and vocalization. Early detection of swallowing abnormalities, also known as dysphagia, is crucial for timely intervention. However, current diagnostic methods often rely on radiographic imaging or invasive procedures. In this study, we propose an automated framework for detecting dysphagia using portable and noninvasive acoustic sensing coupled with applied machine learning. By capturing subtle acoustic signals from the neck during swallowing tasks, we aim to identify patterns associated with abnormal physiological conditions. Our approach achieves promising test-time abnormality detection performance, with an AUC-ROC of 0.904 under 5 independent train-test splits. This work demonstrates the feasibility of using noninvasive acoustic sensing as a practical and scalable tool for pharyngeal health monitoring.

cs.LG↗

arXiv2025

Forecasting Occupational Survivability of Rickshaw Pullers in a Changing Climate with Wearable Data

Cycle rickshaw pullers are highly vulnerable to extreme heat, yet little is known about how their physiological biomarkers respond under such conditions. This study collected real-time weather and physiological data using wearable sensors from 100 rickshaw pullers in Dhaka, Bangladesh. In addition, interviews with 12 pullers explored their knowledge, perceptions, and experiences related to climate change. We developed a Linear Gaussian Bayesian Network (LGBN) regression model to predict key physiological biomarkers based on activity, weather, and demographic features. The model achieved normalized mean absolute error values of 0.82, 0.47, 0.65, and 0.67 for skin temperature, relative cardiac cost, skin conductance response, and skin conductance level, respectively. Using projections from 18 CMIP6 climate models, we layered the LGBN on future climate forecasts to analyze survivability for current (2023-2025) and future years (2026-2100). Based on thresholds of WBGT above 31.1°C and skin temperature above 35°C, 32% of rickshaw pullers already face high heat exposure risk. By 2026-2030, this percentage may rise to 37% with average exposure lasting nearly 12 minutes, or about two-thirds of the trip duration. A thematic analysis of interviews complements these findings, showing that rickshaw pullers recognize their increasing climate vulnerability and express concern about its effects on health and occupational survivability.

cs.CY↗

arXiv2025

AeroSafe: Mobile Indoor Air Purification using Aerosol Residence Time Analysis and Robotic Cough Emulator Testbed

Indoor air quality plays an essential role in the safety and well-being of occupants, especially in the context of airborne diseases. This paper introduces AeroSafe, a novel approach aimed at enhancing the efficacy of indoor air purification systems through a robotic cough emulator testbed and a digital-twins-based aerosol residence time analysis. Current portable air filters often overlook the concentrations of respiratory aerosols generated by coughs, posing a risk, particularly in high-exposure environments like healthcare facilities and public spaces. To address this gap, we present a robotic dual-agent physical emulator comprising a maneuverable mannequin simulating cough events and a portable air purifier autonomously responding to aerosols. The generated data from this emulator trains a digital twins model, combining a physics-based compartment model with a machine learning approach, using Long Short-Term Memory (LSTM) networks and graph convolution layers. Experimental results demonstrate the model's ability to predict aerosol concentration dynamics with a mean residence time prediction error within 35 seconds. The proposed system's real-time intervention strategies outperform static air filter placement, showcasing its potential in mitigating airborne pathogen risks.

cs.RO↗

arXiv2025

Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals

Time-series foundation models excel at tasks like forecasting across diverse data types by leveraging informative waveform representations. Wearable sensing data, however, pose unique challenges due to their variability in patterns and frequency bands, especially for healthcare-related outcomes. The main obstacle lies in crafting generalizable representations that adapt efficiently across heterogeneous sensing configurations and applications. To address this, we propose NormWear, the first multi-modal and ubiquitous foundation model designed to extract generalized and informative representations from wearable sensing data. Specifically, we design a channel-aware attention mechanism with a shared special liaison [CLS] token to detect signal patterns in both intra-sensor and inter-sensors. This helps the model to extract more meaningful information considering both time series themselves and the relationships between input sensors. This helps the model to be widely compatible with various sensors settings. NormWear is pretrained on a diverse set of physiological signals, including PPG, ECG, EEG, GSR, and IMU, from various public datasets. Our model shows exceptional generalizability across 11 public wearable sensing datasets, spanning 18 applications in mental health, body state inference, vital sign estimation, and disease risk evaluation. It consistently outperforms competitive baselines under zero-shot, partial-shot, and full-shot settings, indicating broad applicability in real-world health applications.

cs.LG↗

arXiv2025

Anti-Sensing: Defense against Unauthorized Radar-based Human Vital Sign Sensing with Physically Realizable Wearable Oscillators

Recent advancements in Ultra-Wideband (UWB) radar technology have enabled contactless, non-line-of-sight vital sign monitoring, making it a valuable tool for healthcare. However, UWB radar's ability to capture sensitive physiological data, even through walls, raises significant privacy concerns, particularly in human-robot interactions and autonomous systems that rely on radar for sensing human presence and physiological functions. In this paper, we present Anti-Sensing, a novel defense mechanism designed to prevent unauthorized radar-based sensing. Our approach introduces physically realizable perturbations, such as oscillatory motion from wearable devices, to disrupt radar sensing by mimicking natural cardiac motion, thereby misleading heart rate (HR) estimations. We develop a gradient-based algorithm to optimize the frequency and spatial amplitude of these oscillations for maximal disruption while ensuring physiological plausibility. Through both simulations and real-world experiments with radar data and neural network-based HR sensing models, we demonstrate the effectiveness of Anti-Sensing in significantly degrading model accuracy, offering a practical solution for privacy preservation.

cs.HC↗

arXiv2025

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks

By replacing standard non-linearities with polynomial activations, Polynomial Neural Networks (PNNs) are pivotal for applications such as privacy-preserving inference via Homomorphic Encryption (HE). However, training PNNs effectively presents a significant challenge: low-degree polynomials can limit model expressivity, while higher-degree polynomials, crucial for capturing complex functions, often suffer from numerical instability and gradient explosion. We introduce a robust and versatile training framework featuring two synergistic innovations: 1) a novel Boundary Loss that exponentially penalizes activation inputs outside a predefined stable range, and 2) Selective Gradient Clipping that effectively tames gradient magnitudes while preserving essential Batch Normalization statistics. We demonstrate our framework's broad efficacy by training PNNs within deep architectures composed of HE-compatible layers (e.g., linear layers, average pooling, batch normalization, as used in ResNet variants) across diverse image, audio, and human activity recognition datasets. These models consistently achieve high accuracy with low-degree polynomial activations (such as degree 2) and, critically, exhibit stable training and strong performance with polynomial degrees up to 22, where standard methods typically fail or suffer severe degradation. Furthermore, the performance of these PNNs achieves a remarkable parity, closely approaching that of their original ReLU-based counterparts. Extensive ablation studies validate the contributions of our techniques and guide hyperparameter selection. We confirm the HE-compatibility of the trained models, advancing the practical deployment of accurate, stable, and secure deep learning inference.

cs.LG↗

arXiv2025

Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning

Frames Per Second (FPS) significantly affects the gaming experience. Providing players with accurate FPS estimates prior to purchase benefits both players and game developers. However, we have a limited understanding of how to predict a game's FPS performance on a specific device. In this paper, we first conduct a comprehensive analysis of a wide range of factors that may affect game FPS on a global-scale dataset to identify the determinants of FPS. This includes player-side and game-side characteristics, as well as country-level socio-economic statistics. Furthermore, recognizing that accurate FPS predictions require extensive user data, which raises privacy concerns, we propose a federated learning-based model to ensure user privacy. Each player and game is assigned a unique learnable knowledge kernel that gradually extracts latent features for improved accuracy. We also introduce a novel training and prediction scheme that allows these kernels to be dynamically plug-and-play, effectively addressing cold start issues. To train this model with minimal bias, we collected a large telemetry dataset from 224 countries and regions, 100,000 users, and 835 games. Our model achieved a mean Wasserstein distance of 0.469 between predicted and ground truth FPS distributions, outperforming all baseline methods.

cs.HC↗

arXiv2024

Labits: Layered Bidirectional Time Surfaces Representation for Event Camera-based Continuous Dense Trajectory Estimation

Event cameras provide a compelling alternative to traditional frame-based sensors, capturing dynamic scenes with high temporal resolution and low latency. Moving objects trigger events with precise timestamps along their trajectory, enabling smooth continuous-time estimation. However, few works have attempted to optimize the information loss during event representation construction, imposing a ceiling on this task. Fully exploiting event cameras requires representations that simultaneously preserve fine-grained temporal information, stable and characteristic 2D visual features, and temporally consistent information density, an unmet challenge in existing representations. We introduce Labits: Layered Bidirectional Time Surfaces, a simple yet elegant representation designed to retain all these features. Additionally, we propose a dedicated module for extracting active pixel local optical flow (APLOF), significantly boosting the performance. Our approach achieves an impressive 49% reduction in trajectory end-point error (TEPE) compared to the previous state-of-the-art on the MultiFlow dataset. The code will be released upon acceptance.

cs.CV↗

arXiv2024

NeRF-enabled Analysis-Through-Synthesis for ISAR Imaging of Small Everyday Objects with Sparse and Noisy UWB Radar Data

Inverse Synthetic Aperture Radar (ISAR) imaging presents a formidable challenge when it comes to small everyday objects due to their limited Radar Cross-Section (RCS) and the inherent resolution constraints of radar systems. Existing ISAR reconstruction methods including backprojection (BP) often require complex setups and controlled environments, rendering them impractical for many real-world noisy scenarios. In this paper, we propose a novel Analysis-through-Synthesis (ATS) framework enabled by Neural Radiance Fields (NeRF) for high-resolution coherent ISAR imaging of small objects using sparse and noisy Ultra-Wideband (UWB) radar data with an inexpensive and portable setup. Our end-to-end framework integrates ultra-wideband radar wave propagation, reflection characteristics, and scene priors, enabling efficient 2D scene reconstruction without the need for costly anechoic chambers or complex measurement test beds. With qualitative and quantitative comparisons, we demonstrate that the proposed method outperforms traditional techniques and generates ISAR images of complex scenes with multiple targets and complex structures in Non-Line-of-Sight (NLOS) and noisy scenarios, particularly with limited number of views and sparse UWB radar scans. This work represents a significant step towards practical, cost-effective ISAR imaging of small everyday objects, with broad implications for robotics and mobile sensing applications.

cs.RO↗

arXiv2024

V2CE: Video to Continuous Events Simulator

Dynamic Vision Sensor (DVS)-based solutions have recently garnered significant interest across various computer vision tasks, offering notable benefits in terms of dynamic range, temporal resolution, and inference speed. However, as a relatively nascent vision sensor compared to Active Pixel Sensor (APS) devices such as RGB cameras, DVS suffers from a dearth of ample labeled datasets. Prior efforts to convert APS data into events often grapple with issues such as a considerable domain shift from real events, the absence of quantified validation, and layering problems within the time axis. In this paper, we present a novel method for video-to-events stream conversion from multiple perspectives, considering the specific characteristics of DVS. A series of carefully designed losses helps enhance the quality of generated event voxels significantly. We also propose a novel local dynamic-aware timestamp inference strategy to accurately recover event timestamps from event voxels in a continuous fashion and eliminate the temporal layering problem. Results from rigorous validation through quantified metrics at all stages of the pipeline establish our method unquestionably as the current state-of-the-art (SOTA).

cs.CV↗