arXiv subjects

Zeeshan Ahmad

Publications and source records attributed to Zeeshan Ahmad.

At least 19 recordsLinked to original sources

arXiv2026

Stress Classification from ECG Signals Using Vision Transformer

Vision Transformers have shown tremendous success in numerous computer vision applications; however, they have not been exploited for stress assessment using physiological signals such as Electrocardiogram (ECG). In order to get the maximum benefit from the vision transformer for multilevel stress assessment, in this paper, we transform the raw ECG data into 2D spectrograms using short time Fourier transform (STFT). These spectrograms are divided into patches for feeding to the transformer encoder. We also perform experiments with 1D CNN and ResNet-18 (CNN model). We perform leave-onesubject-out cross validation (LOSOCV) experiments on WESAD and Ryerson Multimedia Lab (RML) dataset. One of the biggest challenges of LOSOCV based experiments is to tackle the problem of intersubject variability. In this research, we address the issue of intersubject variability and show our success using 2D spectrograms and the attention mechanism of transformer. Experiments show that vision transformer handles the effect of intersubject variability much better than CNN-based models and beats all previous state-of-the-art methods by a considerable margin. Moreover, our method is end-to-end, does not require handcrafted features, and can learn robust representations. The proposed method achieved 71.01% and 76.7% accuracies with RML dataset and WESAD dataset respectively for three class classification and 88.3% for binary classification on WESAD.

eess.SP↗

arXiv2026

Understanding halide segregation in metal halide perovskites through defect thermodynamics

Halide segregation in metal halide perovskites limits their bandgap tunability and hinders their adoption in tandem solar cells and light emitting diodes. Here, we reveal the thermodynamic driving force behind halide segregation in mixed halide (Br-I) perovskites. By performing first-principles calculations on slab models with varying bromide and iodide distributions, we demonstrate that bromide ions preferentially occupy surface sites over bulk sites. Our simulations show that the segregation tendency is higher in MAPb(Br$_x$I$_{1-x}$)$_3$ (MA=methylammonium) compared to FA$_{0.8}$Cs$_{0.2}$Pb(Br$_x$I$_{1-x}$)$_3$, highlighting the role of the A-site cation. To quantify this effect, we establish a descriptor for halide segregation: the difference in defect formation energies of Br antisite defects between the bulk and the surface, which confirms the role of the A-site cation at equimolar Br-I concentration. Furthermore, we identify the localization of photo-generated holes near iodide ions, which triggers their oxidation and accelerates the formation of iodide vacancies, thereby promoting segregation. Overall, this work establishes defect thermodynamics as a framework for understanding halide segregation and provides a structural basis for designing stable mixed halide perovskites.

cond-mat.mtrl-sci↗

arXiv2025

Performance Evaluation of Transfer Learning Based Medical Image Classification Techniques for Disease Detection

Medical image classification plays an increasingly vital role in identifying various diseases by classifying medical images, such as X-rays, MRIs and CT scans, into different categories based on their features. In recent years, deep learning techniques have attracted significant attention in medical image classification. However, it is usually infeasible to train an entire large deep learning model from scratch. To address this issue, one of the solutions is the transfer learning (TL) technique, where a pre-trained model is reused for a new task. In this paper, we present a comprehensive analysis of TL techniques for medical image classification using deep convolutional neural networks. We evaluate six pre-trained models (AlexNet, VGG16, ResNet18, ResNet34, ResNet50, and InceptionV3) on a custom chest X-ray dataset for disease detection. The experimental results demonstrate that InceptionV3 consistently outperforms other models across all the standard metrics. The ResNet family shows progressively better performance with increasing depth, whereas VGG16 and AlexNet perform reasonably well but with lower accuracy. In addition, we also conduct uncertainty analysis and runtime comparison to assess the robustness and computational efficiency of these models. Our findings reveal that TL is beneficial in most cases, especially with limited data, but the extent of improvement depends on several factors such as model architecture, dataset size, and domain similarity between source and target tasks. Moreover, we demonstrate that with a well-trained feature extractor, only a lightweight feedforward model is enough to provide efficient prediction. As such, this study contributes to the understanding of TL in medical image classification, and provides insights for selecting appropriate models based on specific requirements.

cs.CV↗

arXiv2025

Grain Boundaries in Ceramic Solid-State Lithium Metal Batteries: A Review

It is now widely accepted that grain boundaries play a critical role in the performance and reliability of solid-state batteries with lithium metal anodes. Understanding and controlling grain boundaries is essential for enabling safe, high-rate operation of solid-state batteries. This review explores the multifaceted influence of grain boundaries in ceramic solid electrolytes and metal anodes, including their impact on ionic and electronic transport, dendrite and void formation, connecting them to the failure mechanisms. We discuss the formation and structure of space charge layers at grain boundaries, their role in modulating local defect chemistry, and the conditions under which grain boundaries may serve as fast-ion pathways or as vulnerable sites for failure. We highlight key differences in the grain boundaries of different classes of solid electrolytes and advances in modeling, experimental characterization, and processing techniques to understand the complexity and engineer grain boundaries in solid electrolytes. Finally, we outline key open questions and opportunities for grain boundary engineering to stimulate further progress in the field.

cond-mat.mtrl-sci↗

arXiv2025

DPN-GAN: Inducing Periodic Activations in Generative Adversarial Networks for High-Fidelity Audio Synthesis

In recent years, generative adversarial networks (GANs) have made significant progress in generating audio sequences. However, these models typically rely on bandwidth-limited mel-spectrograms, which constrain the resolution of generated audio sequences, and lead to mode collapse during conditional generation. To address this issue, we propose Deformable Periodic Network based GAN (DPN-GAN), a novel GAN architecture that incorporates a kernel-based periodic ReLU activation function to induce periodic bias in audio generation. This innovative approach enhances the model's ability to capture and reproduce intricate audio patterns. In particular, our proposed model features a DPN module for multi-resolution generation utilizing deformable convolution operations, allowing for adaptive receptive fields that improve the quality and fidelity of the synthetic audio. Additionally, we enhance the discriminator network using deformable convolution to better distinguish between real and generated samples, further refining the audio quality. We trained two versions of the model: DPN-GAN small (38.67M parameters) and DPN-GAN large (124M parameters). For evaluation, we use five different datasets, covering both speech synthesis and music generation tasks, to demonstrate the efficiency of the DPN-GAN. The experimental results demonstrate that DPN-GAN delivers superior performance on both out-of-distribution and noisy data, showcasing its robustness and adaptability. Trained across various datasets, DPN-GAN outperforms state-of-the-art GAN architectures on standard evaluation metrics, and exhibits increased robustness in synthesized audio.

cs.SD↗

arXiv2025

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution

Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approaches struggle with systems exhibiting long-range interactions, charge transfer, and compositional heterogeneity. In this work, we develop a new equivariant MLIP incorporating long-range Coulomb interactions through explicit treatment of electronic degrees of freedom, specifically global charge distribution within the system. This is achieved using a charge equilibration scheme based on predicted atomic electronegativities. We systematically evaluate our model across a range of benchmark periodic and non-periodic datasets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions. Our approach enables more accurate and efficient simulations of systems with long-range interactions and charge heterogeneity, expanding the applicability of MLIPs in computational materials science.

physics.chem-ph↗

arXiv2025

Constriction and contact impedance of ceramic solid electrolytes

The development of solid-state batteries (SSBs) is hindered by degradation at solid-solid interfaces due to void formation and contact loss, resulting in increased impedance. Here, we systematically investigate the roles of real and unrecoverable interfacial contact areas at the electrode/Li$_6$PS$_5$Cl interface in driving the impedance rise. By controlling contact geometries and applied pressures, we identify their distinct contributions to the impedance spectra and quantify their influence on the interfacial resistance and transport. Experiments reveal that interfacial resistance varies strongly with recoverable contact area and applied pressure following power law scaling, with exponents of -1 and -0.5, respectively. Moreover, distributed contacts result in lower impedance due to smaller potential gradients and a more uniform potential distribution. Continuum simulations of the contact geometries predict interfacial resistances in agreement with experiments. Our work highlights the influence of unrecoverable and recoverable contact losses on SSB impedance while quantifying the effectiveness of mitigation strategies.

cond-mat.mtrl-sci↗

arXiv2024

A unified and consistent electrical double layer model for treatment of core and space charge layer in solid electrolytes

The electrical double layer (EDL) is fundamental to the operation of devices for electrochemical energy storage and conversion. Existing models of EDL in solid electrolytes focus predominantly on the space charge layer and lack a complete treatment of the core layer which is an integral part of the EDL. The core layer exhibits significant variations in defect properties, such as defect formation energy (DFE), which influence the ionic charge carrier distribution in the space charge layer. In this work, we develop a general framework for treating both the core and space charge layer in solid electrolytes under dilute and concentrated regimes. We incorporate functional forms of the DFE variation in the core layer and defect-defect interactions that are consistent with previous first-principles simulations of solid electrolytes at surfaces and interfaces. Our simulations reveal that the core layer significantly impacts the potential distribution and defect concentrations in the solid electrolyte. In addition, the core contributes substantially to the conductivity when the surface DFE is much lower than the bulk DFE. Our model paves the way for accurate calculations of properties such as capacitance and ionic conductivity of solid-solid interfaces required for enhancing and optimizing the performance of solid-state electrochemical devices.

cond-mat.mtrl-sci↗

arXiv2024

Machine learning approaches for automatic defect detection in photovoltaic systems

Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation which reduces their power conversion efficiency. This diminishes their positive environmental impact over the lifecycle. Continuous monitoring of PV modules during operation via unmanned aerial vehicles is essential to ensure that defective panels are promptly replaced or repaired to maintain high power conversion efficiencies. Computer vision provides an automatic, non-destructive and cost-effective tool for monitoring defects in large-scale PV plants. We review the current landscape of deep learning-based computer vision techniques used for detecting defects in solar modules. We compare and evaluate the existing approaches at different levels, namely the type of images used, data collection and processing method, deep learning architectures employed, and model interpretability. Most approaches use convolutional neural networks together with data augmentation or generative adversarial network-based techniques. We evaluate the deep learning approaches by performing interpretability analysis on classification tasks. This analysis reveals that the model focuses on the darker regions of the image to perform the classification. We find clear gaps in the existing approaches while also laying out the groundwork for mitigating these challenges when building new models. We conclude with the relevant research gaps that need to be addressed and approaches for progress in this field: integrating geometric deep learning with existing approaches for building more robust and reliable models, leveraging physics-based neural networks that combine domain expertise of physical laws to build more domain-aware deep learning models, and incorporating interpretability as a factor for building models that can be trusted. The review points towards a clear roadmap for making this technology commercially relevant.

cs.CV↗

arXiv2024

Impact of finite volume on kaon, antikaon, and $ϕ$ meson masses and decay width in asymmetric strange hadronic matter

In the present work, we investigate the impact of finite volume on the in-medium properties of kaons ($K^+$, $K^0$) and antikaons ($K^-$, $\bar{K^0}$), and $ϕ$ mesons in the isospin asymmetric strange hadronic medium at finite density and temperature. We use the chiral SU(3) hadronic mean-field model, which accounts for the interactions between baryons through the exchange of scalar ($σ, ζ, δ$) and vector ($ω$, $ρ$, $ϕ$) fields. To investigate the effects of finite volume, we apply the multiple reflection expansion (MRE) technique for calculations of the density of states. The non-strange scalar field $σ$ shows significant variation in an asymmetric medium, while the strange scalar field $ζ$ shows good dependency in the strange medium. We use the medium-modified masses of kaons and antikaons calculated using the chiral SU(3) model to obtain the masses and decay width of $ϕ$ mesons in finite volume hadronic medium. To obtain the masses and decay widths of $ϕ$ mesons, an effective Lagrangian approach with $ϕ$$K$$\bar{K}$ interactions at one-loop level is used in the present work. We obtain the effective masses and decay widths in the finite volume matter, for the spherical geometry of the medium with Neumann and Dirichlet boundary conditions as well as for the cubic geometry. The finite volume effects are found to be appreciable at high baryon densities.

hep-ph↗

arXiv2024

Modulation of Point Defect Properties Near Surfaces in Metal Halide Perovskites

It is now widely recognized that surface and interfacial defects exhibit distinct behavior compared to bulk defects in metal halide perovskites. However, the transition from bulk to surface behavior and the spatial extent of the surface's influence are not well understood. To address this, we conducted first-principles calculations on iodine vacancies and interstitial defects in methylammonium lead iodide and cesium lead iodide at various depths from the surface, enabling us to map out depth-dependent behavior. We find that the defect formation energy follows a saturating exponential curve as the defect moves away from the surface to the bulk. Using first-principles calculated defect formation energies, we quantify the extent of the surface's influence by calculating the decay length associated with each defect. The difference between the surface and bulk defect formation energy is found to be as high as 1.12 eV for the negatively charged iodine vacancy in methylammonium lead iodide, leading to the enrichment of the surface with defects. Through analysis of defective structures, we find that the differences in the bulk and surface defect properties are a consequence of different bond lengths and in some cases, even changes in bonding and coordination environments. Finally, we determine how the defect transition levels change as a function of the layer index, which could contribute to increased non-radiative recombination. Our findings pave the way for a systematic treatment of non-radiative losses in perovskite solar cells that incorporate spatially dependent defect densities and transition levels.

cond-mat.mtrl-sci↗

arXiv2024

Heterogeneity in Point Defect Distribution and Mobility in Solid Ion Conductors

Alkali metal anodes paired with solid ion conductors offer promising avenues for enhancing battery energy density and safety. To facilitate rapid ion transport crucial for fast charging and discharging, it is essential to understand point defects within these conductors. In this study, we investigate the heterogeneity of defect distribution in Li$_3$OCl solid ion conductor, quantifying the defect formation energy (DFE) of lithium vacancies and interstitials as a function of distance from the surface through first-principles simulations. Our results reveal that the surface DFE is consistently lower than bulk except for one surface termination, indicating significant defect aggregation at surfaces. This difference can cause the defect density to be up to 14 orders of magnitude higher at surfaces compared to the bulk. Moreover, we unveil the transition in DFE when moving from the surface to the bulk through the DFE function, which exhibits an exponentially decaying relationship. Incorporating this exponential trend, we develop a revised model for the average behavior of defects that offers a more accurate description of the influence of grain size. Surface effects dominate for grain sizes $\lesssim$ 1 $μ$m, highlighting the importance of surface defect engineering and the DFE function for accurately capturing ion transport in devices. We further explore the kinetics of defect redistribution by calculating the migration barriers for defect movement between bulk and surfaces. We find a highly asymmetric energy landscape for the lithium vacancies, exhibiting lower migration barriers for movement towards the surface compared to the bulk, while interstitial defects exhibit comparable kinetics between surface and bulk regions. These insights underscore the importance of considering both thermodynamic and kinetic factors in the design of solid ion conductors.

cond-mat.mtrl-sci↗

arXiv2023

Transverse-electric surface plasmon polaritons in periodically modulated graphene

Transverse-electric (TE) surface plasmon polaritons are unique eigenmodes of a homogeneous graphene layer that are tunable with the chemical potential and temperature. However, as their dispersion curve spectrally lies just below the light line, they cannot be resonantly excited by an externally incident wave. Here, we propose a way of exciting the TE modes and tuning their peaks in the transmission by introducing a one-dimensional graphene grating. Using the scattering-matrix formalism, we show that periodic modulations of graphene make the transmission more pronounced, potentially allowing for experimental observation of the TE modes. Furthermore, we propose the use of turbostratic graphene to enhance the role of the surface plasmon polaritons in optical spectra.

cond-mat.mes-hall↗

arXiv2022

A Survey on Physiological Signal Based Emotion Recognition

Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data preprocessing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally we discuss key challenges and future directions in this field.

cs.HC↗

arXiv2022

Effect of disorder and doping on electronic structure and diffusion properties of Li$_{3}$V$_{2}$O$_{5}$

V$_{2}$O$_{5}$ in its $ω$ phase (Li$_{3}$V$_{2}$O$_{5}$) with excess lithium is a potential alternative to the graphite anode for lithium-ion batteries at low temperature and fast charging conditions due to its safer voltage (0.6 V vs Li$^{+}$/Li(s)) and high lithium transport rate. In-operando cationic disorder, as observed in most ordered materials, can produce significant changes in charge compensation mechanisms, anionic activity, lithium diffusion and operational voltages. In this work, we report the variation in structural distortion, electronic structure and migration barrier accompanied by disorder using first-principles calculations. Due to segregation of lithium atoms in the disordered state, we observe greater distortion, emergence of metallic behaviour and potential anionic activity from non-bonding oxygen states near the Fermi level. Redox capacity can be tuned by doping with 3d metals which can adjust the participating cationic states, and by fluorine substitution which can stabilize or suppress anionic states. Moreover, suppression of anionic activity is found to decrease structural distortion, crucial for mitigating voltage fade and hysteresis. Diffusion barrier calculations in the presence of disorder indicate the activation of the remaining 3D-paths for lithium hopping which are unavailable in the ordered configuration, explaining its fast-charging ability observed in experiments.

cond-mat.mtrl-sci↗

arXiv2022

Chemomechanics: friend or foe of the "AND problem" of solid-state batteries?

Solid electrolytes are widely considered as the enabler of lithium metal anodes for safe, durable, and high energy density rechargeable lithium-ion batteries. Despite the promise, failure mechanisms associated with solid-state batteries are not well-established, largely due to limited understanding of the chemomechanical factors governing them. We focus on the recent developments in understanding solid-state aspects including the effects of mechanical stresses, constitutive relations, fracture, and void formation, and outline the gaps in the literature. We also provide an overview of the manufacturing and processing of solid-state batteries in relation to chemomechanics. The gaps identified provide concrete directions towards the rational design and development of failure-resistant solid-state batteries.

physics.app-ph↗

arXiv2022

Understanding the Effect of Lead Iodide Excess on the Performance of Methylammonium Lead Iodide Perovskite Solar Cells

The presence of unreacted lead iodide in organic-inorganic lead halide perovskite solar cells is widely correlated with an increase in power conversion efficiency. We investigate the mechanism for this increase by identifying the role of surfaces and interfaces present between methylammonium lead iodide perovskite films and excess lead iodide. We show how type I and II band alignments arising under different conditions result in either passivation of surface defects or hole injection. Through first-principles simulations of solid-solid interfaces, we find that lead iodide captures holes from methylammonium lead iodide and modulates the formation of defects in the perovskite, affecting recombination. Using surface-sensitive optical spectroscopy techniques, such as transient reflectance and time-resolved photoluminescence, we show how excess lead iodide affects the diffusion and surface recombination velocity of charge carriers in methylammonium lead iodide films. Our coupled experimental and theoretical results elucidate the role of excess lead iodide in perovskite solar cells.

cond-mat.mtrl-sci↗

arXiv2021

A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System

Modern smart grid systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyberattacks. The occurrence of a cyberattack has increased in recent years resulting in substantial damage to power systems. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. Automated detection of cyberattacks with high accuracy is a challenge. To address this, we propose a two-layer hierarchical machine learning model having an accuracy of 95.44 % to improve the detection of cyberattacks. The first layer of the model is used to distinguish between the two modes of operation (normal state or cyberattack). The second layer is used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we compared its performance against other recent cyber attack detection models proposed in the literature.

cs.CR↗