arXiv subjects

Abdul Wahab

Publications and source records attributed to Abdul Wahab.

At least 19 recordsLinked to original sources

arXiv2026

Model-Consistent Structured-Hankel Matrix Completion for 3D Sparse Multi-frequency Electromagnetic Source Reconstruction

Reconstruction of 3D electromagnetic currents from sparse, multi-frequency, far-field radiation data is severely ill-posed because sparse sampling masks specific current Fourier modes and the missing spectrum enlarges the null space of the forward operator. Although structured-Hankel completion can recover missing spectrum by exploiting the finite rate of innovations structure of a compactly supported geometrically sparse source, it treats current components independently without guaranteeing compatibility with the model. This often renders non-physical solutions that violate model consistency. To resolve this, we propose a training-free Maxwell-model-consistent 3D structured-Hankel framework (MC-Hankel) that alternates joint low-rank Hankel completion with an exact, closed-form projection onto the true Maxwell synthesis subspace while enforcing conjugate symmetry and noise-aware data fidelity. Across tests on two source models, three sparse sampling rates (30\%, 40\%, and 50\% of a Nyquist-sampled grid), and clean versus noisy (10 dB SNR additive white Gaussian noise) conditions, MC-Hankel consistently outperforms sub-sampled Fourier inversion, scale-normalized $\ell_1$-compressed sensing baseline, and the standard joint 3D annihilating filter-based low-rank Hankel matrix completion approach (ALOHA). Across all considered source-noise-sampling configurations, MC-Hankel improves mean peak SNR (PSNR) by 0.65-4.22 dB and tri-planer structural similarity (3D SSIM) by $0.0266$-$0.0703$, reduces relative full-volume $\ell_2$ reconstruction error by $7.4\%$-$38.9\%$, and brings non-physical model residuals down to machine precision over standard joint 3D ALOHA with merely 17\% computational overhead. Exact signed-rank tests are underpowered for five paired trials, so statistical conclusions are reported together with paired bootstrap intervals, effect sizes, and small-sample limitations.

math-ph↗

arXiv2026

Multi-frequency far-field data enrichment for electromagnetic source reconstruction

Reconstructing unknown electromagnetic sources from far-field radiation patterns is a fundamental inverse problem with broad applications in biomedical imaging, non-destructive testing, and telecommunications. In practical settings, however, collecting dense multi-frequency far-field measurements at the Nyquist sampling rate is often infeasible. Under-sampled or sparse data introduce non-radiating source components that sever the uniqueness of the solution, creating severe artifacts when standard inversion techniques are applied. To overcome this limitation, we present a two-stage reconstruction strategy exploiting the physical property that compactly supported, geometrically sparse sources exhibit a finite rate of innovations (FRI). In the first stage, we construct an associated wrap-around structured Hankel matrix. By leveraging the low-rank property of the matrix due to FRI of the unknown sources, we enrich the sub-sampled data. To that end, we convert missing multi-frequency far-field data recovery into a constrained matrix completion task solved via Annihilating Filter-based Low-rank Hankel Matrix Completion Approach (ALOHA). In the second stage, a Fourier inversion scheme reconstructs the current source density from the enriched dataset. Extensive numerical evaluations on electromagnetic source models show that our enrichment framework effectively eliminates under-sampling artifacts and resolves non-uniqueness challenges. The method delivers accurate and stable reconstructions under high sub-sampling rates (e.g., with $30$\% to $50$\% available samples) and strong noise conditions ($10$ dB SNR), outperforming standard $\ell_1$-compressed sensing baselines.

math-ph↗

arXiv2026

Panoptic Pairwise Distortion Graph

In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions. In contrast, existing methods focus on whole image analysis, while implicitly relying on region-level understanding. We extend the intra-image notion of a scene graph to inter-image, and propose a novel task of Distortion Graph (DG). DG treats paired images as a structured topology grounded in regions, and represents dense degradation information such as distortion type, severity, comparison and quality score in a compact interpretable graph structure. To realize the task of learning a distortion graph, we contribute (i) a region-level dataset, PandaSet, (ii) a benchmark suite, PandaBench, with varying region-level difficulty, and (iii) an efficient architecture, Panda, to generate distortion graphs. We demonstrate that PandaBench poses a significant challenge for state-of-the-art multimodal large language models (MLLMs) as they fail to understand region-level degradations even when fed with explicit region cues. We show that training on PandaSet or prompting with DG elicits region-wise distortion understanding, opening a new direction for fine-grained, structured pairwise image assessment.

cs.CV↗

arXiv2026

Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning

Optimistic value estimates provide one mechanism for directed exploration in reinforcement learning (RL). The agent acts greedily with respect to an estimate of the value plus what can be seen as a value bonus. The value bonus can be learned by estimating a value function on reward bonuses, propagating local uncertainties around rewards. However, this approach only increases the value bonus for an action retroactively, after seeing a higher reward bonus from that state and action. Such an approach does not encourage the agent to visit a state and action for the first time. In this work, we introduce an algorithm for exploration called Value Bonuses with Ensemble errors (VBE), that maintains an ensemble of random action-value functions (RQFs). VBE uses the errors in the estimation of these RQFs to design value bonuses that provide first-visit optimism and deep exploration. The key idea is to design the rewards for these RQFs in such a way that the value bonus can decrease to zero. We show that VBE outperforms Bootstrap DQN and two reward bonus approaches (RND and ACB) on several classic environments used to test exploration and provide demonstrative experiments that it can scale easily to more complex environments like Atari.

cs.LG↗

arXiv2026

Non-Hermitian Second-Order Topological Phases and Bipolar Skin Effect in Photonic Kagome Crystals

Non-Hermitian photonics provides a fertile platform for exploring phenomena with no Hermitian counterparts, including the non-Hermitian skin effect and exceptional points, with direct relevance for integrated photonic technologies. In this work, we investigate the properties of non-Hermitian second-order topological phases by constructing a photonic kagome crystal with balanced gain and loss, and reveal the interplay between higher-order topology and the non-Hermitian skin effect. We demonstrate that non-Hermiticity not only lifts the degeneracy of the topological corner modes but also drives bulk states to accumulate at corners, giving rise to bipolar non-Hermitian skin effect. By defining the point-gap topology, we uncover the fundamental topological origin of the non-Hermitian skin effect. More interestingly, the non-Hermitian skin effect induces a fundamental breakdown of the conventional bulk-boundary correspondence based on the Bloch band theory. Our findings establish a general framework for non-Hermitian higher-order photonic systems and open avenues toward tailorable topological photonic devices exploiting non-Hermitian enhanced localization.

physics.optics↗

arXiv2024

Tunable optical amplification and group delay in cavity magnomechanics

In this work, we theoretically investigate the controllable output probe transmission and group delay in a hybrid cavity magnomechanics (CMM) system. The setup comprises a gain (active) cavity and a passive (loss) cavity, which incorporates an optical parametric amplifier (OPA) and two yttrium iron garnet spheres to facilitate magnon-photon coupling. Unlike the single transparency window typically resulting from magnon-photon interactions, we also observe magnomechanically induced transparency due to nonlinear magnon-phonon interactions. Additionally, two absorption dips on either side of the central absorption dip can be asymmetrically modulated into amplification and absorption by varying different system parameters. A PT-symmetric to broken-PT-symmetric phase transition is observed in both balanced and unbalanced gain-to-loss scenarios. Notably, replacing the second passive cavity with an active one mitigates high absorption and introduces effective gain into the system. Our findings reveal that the group delay of the probe light can be adjusted between positive and negative values by modifying various system parameters. This study provides a robust platform for controlling light propagation in CMM systems, highlighting potential applications in optical communication and signal processing.

physics.optics↗

arXiv2024

Enhanced second-order sideband generation and slow-fast light via coupled opto- and magnomechanical microspheres

In this research, we investigate second-order sideband generation (SSG) and slow-fast light using a hybrid system comprised of two coupled opto- and magnomechanical microspheres, namely a YIG sphere and a silica sphere. The YIG sphere hosts a magnon mode and a vibration mode induced by magnetostriction, whereas the silica sphere has an optical whispering gallery mode and a mechanical mode coupled via optomechanical interaction. The mechanical modes of both spheres are close in frequency and are coherently coupled by the straightway physical contact between the two microspheres. We use a perturbation approach to solve the Heisenberg-Langevin equations, offering an analytical framework for transmission rate and SSG. Using experimentally feasible settings, we demonstrate that the transmission rate and SSG are strongly dependent on the magnomechanical, optomechanical, and mechanics mechanics coupling strengths (MMCS) between the two microspheres. The numerical results show that increasing the MMCS can enhance both the transmission rate and SSG efficiency, resulting in gain within our system. Our findings, in particular, reveal that the efficiency of the SSG can be effectively controlled by cavity detuning, decay rate, and pump power. Notably, our findings suggest that modifying the system parameters can alter the group delay, thereby regulating the transition between fast and slow light propagation, and vice versa. Our protocol provides guidelines for manipulating nonlinear optical properties and controlling light propagation, with applications including optical switching, information storage, and precise measurement of weak signals.

physics.optics↗

arXiv2024

Latent fingerprint enhancement for accurate minutiae detection

Identification of suspects based on partial and smudged fingerprints, commonly referred to as fingermarks or latent fingerprints, presents a significant challenge in the field of fingerprint recognition. Although fixed-length embeddings have shown effectiveness in recognising rolled and slap fingerprints, the methods for matching latent fingerprints have primarily centred around local minutiae-based embeddings, failing to fully exploit global representations for matching purposes. Consequently, enhancing latent fingerprints becomes critical to ensuring robust identification for forensic investigations. Current approaches often prioritise restoring ridge patterns, overlooking the fine-macroeconomic details crucial for accurate fingerprint recognition. To address this, we propose a novel approach that uses generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE) through a structured approach to fingerprint generation. By directly optimising the minutiae information during the generation process, the model produces enhanced latent fingerprints that exhibit exceptional fidelity to ground-truth instances. This leads to a significant improvement in identification performance. Our framework integrates minutiae locations and orientation fields, ensuring the preservation of both local and structural fingerprint features. Extensive evaluations conducted on two publicly available datasets demonstrate our method's dominance over existing state-of-the-art techniques, highlighting its potential to significantly enhance latent fingerprint recognition accuracy in forensic applications.

cs.CV↗

arXiv2024

Optical Properties and Thermal Geometries of Hot NUT-Kerr-Newman-Kasuya-AdS Spacetime

This paper is devoted to studying the optical and thermal geometrical properties of Hot, NUT-KerrNewman-Kasuya-AdS black hole (BH). This BH is characterized by the NUT charge and a parameter Q that comprises the electric and magnetic charge. We compute the image of the BH shadow in two types: 1) at infinity, 2) at specific limit by analytical approach. We also investigate the effect of Nut, spin, inclination angle, and cosmological constant on the shape of shadow. We analyze that for type 1, the shadow in increasing for higher values of NUT charge, the cosmological constant, rotation parameters, and inclination angle, while for type 2, by increasing these parameters, the circular symmetry of the image of the BH shadow variate. Moreover, we discuss well-known thermal geometries such as Weinhold, Ruppeiner, HPEM, and Quevedo case I & II spacetime. It is found that Ruppeiner , HPEM and Quevedo (II) formulations provide physical information about the microscopic structure as compared to Weinhold and Quevedo (I) geometries of Hot NUT-Kerr-Newman-Kasuya-AdS BH. Our findings provides distinctive characteristics in the shadow and thermal geometries of this BH as compare to other BH types.

gr-qc↗

arXiv2023

Constant-roll Warm Inflation within Rastall Gravity

This research paper used a newly proposed strategy for finding the exact inflationary solutions to the Friedman equations in the context of Rastall theory of gravity (RTG), which is known as constant-roll warm inflation (CRWI). The dissipative effects produced during WI are studied by introducing a dissipation factor $Q=\fracΓ{3H}$, where $Γ$ is the coefficient of dissipation. We establish the model to evaluate the inflaton field, effective potential requires to produce inflation, and entropy density. These physical quantities lead to developing the important inflationary observables like scalar/tensor power spectrum, scalar spectral index, tensor-to-scalar ratio, and running of spectral-index for two choices of obtained potential that are $V_0=0$ and $V_0\neq0$. In this study, we focus on the effects of the theory parameter $λ$, CR parameter $β$, and dissipation factor $Q$ (under a high dissipative regime for which $Q=$constant) on inflation and are constrained to observe the compatibility of our model with Planck TT+lowP (2013), Planck TT, TE, EE+lowP (2015), Planck 2018 and BICEP/Keck 2021 bounds. The results are feasible and interesting up to the $2σ$ confidence level. Finally, we conclude that the CR technique produces significant changes in the early universe.

gr-qc↗

arXiv2023

Multipolar Acoustic Source Reconstruction from Sparse Far-Field Data using ALOHA

The reconstruction of multipolar acoustic or electromagnetic sources from their far-field signature plays a crucial role in numerous applications. Most of the existing techniques require dense multi-frequency data at the Nyquist sampling rate. The availability of a sub-sampled grid contributes to the null space of the inverse source-to-data operator, which causes significant imaging artifacts. For this purpose, additional knowledge about the source or regularization is required. In this letter, we propose a novel two-stage strategy for multipolar source reconstruction from sub-sampled sparse data that takes advantage of the sparsity of the sources in the physical domain. The data at the Nyquist sampling rate is recovered from sub-sampled data and then a conventional inversion algorithm is used to reconstruct sources. The data recovery problem is linked to a spectrum recovery problem for the signal with the \textit{finite rate of innovations} (FIR) that is solved using an annihilating filter-based structured Hankel matrix completion approach (ALOHA). For an accurate reconstruction, a Fourier inversion algorithm is used. The suitability of the approach is supported by experiments.

cs.IT↗

arXiv2022

Acoustic scattering from a wave-bearing cavity with flexible inlet and outlet

In this article, we substantiate the appositeness of the \emph{mode-matching technique} to study the scattering response of bridging elastic plates connecting two flexible duct regions of different heights. We present two different solution schemes to analyze the structure-borne and fluid-borne radiations in the elastic plate-bounded waveguide. The first scheme supplements the mode-matching technique with the so-called \emph{tailored-Galerkin approach} which uses a solution ansatz with homogeneous and integral parts corresponding to the vibrations of the bridging elastic plate and the cavity, respectively. In the second scheme, we supplement the mode-matching technique with the \emph{modal approach} wherein the displacement of the bridging elastic plate is projected onto the eigenmodes of the cavity. To handle the non-orthogonality of the eigenfunctions, we invoke generalized orthogonality relations. An advantage of the proposed mode-matching schemes is that they provide a convenient way of incorporating a variety of edge conditions on the joints of the plates, including clamped, pin-joint, or restraint connections. The numerical analysis of the waveguide scattering problems substantiates that edge connections on the joints have a significant impact on the scattering energies and transmission loss.

physics.class-ph↗

arXiv2022

Anticancer Peptides Classification using Kernel Sparse Representation Classifier

Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. \emph{Anticancer peptides} (ACPs) are the most promising treatment option, but their large-scale identification and synthesis require reliable prediction methods, which is still a problem. In this paper, we present an intuitive classification strategy that differs from the traditional \emph{black box} method and is based on the well-known statistical theory of \emph{sparse-representation classification} (SRC). Specifically, we create over-complete dictionary matrices by embedding the \emph{composition of the K-spaced amino acid pairs} (CKSAAP). Unlike the traditional SRC frameworks, we use an efficient \emph{matching pursuit} solver instead of the computationally expensive \emph{basis pursuit} solver in this strategy. Furthermore, the \emph{kernel principal component analysis} (KPCA) is employed to cope with non-linearity and dimension reduction of the feature space whereas the \emph{synthetic minority oversampling technique} (SMOTE) is used to balance the dictionary. The proposed method is evaluated on two benchmark datasets for well-known statistical parameters and is found to outperform the existing methods. The results show the highest sensitivity with the most balanced accuracy, which might be beneficial in understanding structural and chemical aspects and developing new ACPs. The Google-Colab implementation of the proposed method is available at the author's GitHub page (\href{https://github.com/ehtisham-Fazal/ACP-Kernel-SRC}{https://github.com/ehtisham-fazal/ACP-Kernel-SRC}).

q-bio.QM↗

arXiv2021

Performance Analysis of Fractional Learning Algorithms

Fractional learning algorithms are trending in signal processing and adaptive filtering recently. However, it is unclear whether the proclaimed superiority over conventional algorithms is well-grounded or is a myth as their performance has never been extensively analyzed. In this article, a rigorous analysis of fractional variants of the least mean squares and steepest descent algorithms is performed. Some critical schematic kinks in fractional learning algorithms are identified. Their origins and consequences on the performance of the learning algorithms are discussed and swift ready-witted remedies are proposed. Apposite numerical experiments are conducted to discuss the convergence and efficiency of the fractional learning algorithms in stochastic environments.

cs.LG↗

arXiv2021

AFP-SRC: Identification of Antifreeze Proteins Using Sparse Representation Classifier

Species living in the extreme cold environment fight against the harsh conditions using antifreeze proteins (AFPs), that manipulates the freezing mechanism of water in more than one way. This amazing nature of AFP turns out to be extremely useful in several industrial and medical applications. The lack of similarity in their structure and sequence makes their prediction an arduous task and identifying them experimentally in the wet-lab is time-consuming and expensive. In this research, we propose a computational framework for the prediction of AFPs which is essentially based on a sample-specific classification method using the sparse reconstruction. A linear model and an over-complete dictionary matrix of known AFPs are used to predict a sparse class-label vector that provides a sample-association score. Delta-rule is applied for the reconstruction of two pseudo-samples using lower and upper parts of the sample-association vector and based on the minimum recovery score, class labels are assigned. We compare our approach with contemporary methods on a standard dataset and the proposed method is found to outperform in terms of Balanced accuracy and Youden's index. The MATLAB implementation of the proposed method is available at the author's GitHub page (\{https://github.com/Shujaat123/AFP-SRC}{https://github.com/Shujaat123/AFP-SRC}).

cs.CV↗

arXiv2021

Comments on "Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model"

The purpose of this article is to discuss some aspects of the convergence analysis performed in the paper [Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model, Eur. Phys. J. Plus (2019) \textbf{134}: 407]. It is highlighted that the way the authors prove convergence suffers a lack of correct and valid mathematical justifications.

math.OC↗

arXiv2020

Three-dimensional Elastic Scattering Coefficients and Enhancement of the Elastic Near Cloaking

This paper is concerned with the elastic near cloaking for the Lamé system in three-dimensions using the notion of elastic scattering coefficients (ESC). Accordingly, the ESC of arbitrary three-dimensional objects are designed and some of their properties are discussed using elements of the elastic layer potential theory. Then, near-cloaking structures, coined as ESC-vanishing-structures, are constructed for the elastic cloaking at a fixed frequency or a band of frequencies. These multi-layered structures are designed so that their first few significant ESC vanish ahead of transformation-elastodynamics. The invisibility effect is achieved as the arbitrary elastic object inside the cloak has a near-zero scattering cross-section for a band of frequencies. The cloaking effect for the Lamé system is significantly enhanced by the proposed near-cloaking structures.

math-ph↗

arXiv2020

Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets

Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency. Electricity demand profiles can vary drastically from one region to another on diurnal, seasonal and yearly scale. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, specially when the training data is limited, is a big challenge. This paper presents a deep learning architecture for short-term load forecasting based on bidirectional sequential models in conjunction with feature engineering that extracts the hand-crafted derived features in order to aid the model for better learning and predictions. In the proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), the raw input and hand-crafted features are trained at separate levels and then their respective outputs are combined to make the final prediction. The efficacy of the proposed methodology is evaluated on datasets from five countries with completely different patterns. The results demonstrate that the proposed technique is superior to the existing state of the art.

cs.LG↗