arXiv subjects

Md Rashidul Islam

Publications and source records attributed to Md Rashidul Islam.

5 recordsLinked to original sources

arXiv2026

Cross-frequency amplification of perturbations in a laminar separation bubble using resolvent analysis

A large-eddy simulation (LES) of a laminar separation bubble (LSB) induced by an adverse pressure gradient over a flat plate is performed at an inflow displacement-thickness-based Reynolds number of 410 and a free-stream Mach number of 0.25. With a mean peak reverse flow of 21.4%, the bubble sustains self-excited vortex shedding through a local region of absolute instability, in the absence of any external forcing. Spectral proper orthogonal decomposition (SPOD) applied to the LES data identifies three dominant coherent structures within the LSB: two-dimensional and oblique Kelvin--Helmholtz (KH) waves in the separated shear layer at the vortex-shedding frequency, and stationary spanwise-periodic streaks near reattachment at near-zero frequency. Classical resolvent analysis of the mean flow identifies strong convective amplification of the KH waves over a range of spanwise wavenumbers, but predicts only weak amplification in the low-frequency, streak-forming region, where the leading gain is orders of magnitude smaller and no dominant rank-one mechanism is present. This discrepancy with the SPOD energy indicates that the streaks are not sustained by same-frequency linear amplification, but are instead energized by the intrinsic forcing, which the classical framework treats as an unexplained input. Harmonic resolvent analysis of the time-periodic base flow reveals the underlying mechanism: the base-flow unsteadiness couples the oblique KH wave at the shedding frequency to the stationary streak through cross-frequency amplification, yielding a gain far larger than that of the direct same-frequency amplification. This cross-frequency route provides a likely explanation for how the stationary streaks observed near reattachment are energized.

physics.flu-dyn↗

arXiv2025

A Hybrid Deep Learning Framework with Explainable AI for Lung Cancer Classification with DenseNet169 and SVM

Lung cancer is a very deadly disease worldwide, and its early diagnosis is crucial for increasing patient survival rates. Computed tomography (CT) scans are widely used for lung cancer diagnosis as they can give detailed lung structures. However, manual interpretation is time-consuming and prone to human error. To surmount this challenge, the study proposes a deep learning-based automatic lung cancer classification system to enhance detection accuracy and interpretability. The IQOTHNCCD lung cancer dataset is utilized, which is a public CT scan dataset consisting of cases categorized into Normal, Benign, and Malignant and used DenseNet169, which includes Squeezeand-Excitation blocks for attention-based feature extraction, Focal Loss for handling class imbalance, and a Feature Pyramid Network (FPN) for multi-scale feature fusion. In addition, an SVM model was developed using MobileNetV2 for feature extraction, improving its classification performance. For model interpretability enhancement, the study integrated Grad-CAM for the visualization of decision-making regions in CT scans and SHAP (Shapley Additive Explanations) for explanation of feature contributions within the SVM model. Intensive evaluation was performed, and it was found that both DenseNet169 and SVM models achieved 98% accuracy, suggesting their robustness for real-world medical practice. These results open up the potential for deep learning to improve the diagnosis of lung cancer by a higher level of accuracy, transparency, and robustness.

cs.CV↗

arXiv2025

Explainable Multi-Modal Deep Learning for Automatic Detection of Lung Diseases from Respiratory Audio Signals

Respiratory diseases remain major global health challenges, and traditional auscultation is often limited by subjectivity, environmental noise, and inter-clinician variability. This study presents an explainable multimodal deep learning framework for automatic lung-disease detection using respiratory audio signals. The proposed system integrates two complementary representations: a spectral-temporal encoder based on a CNN-BiLSTM Attention architecture, and a handcrafted acoustic-feature encoder capturing physiologically meaningful descriptors such as MFCCs, spectral centroid, spectral bandwidth, and zero-crossing rate. These branches are combined through late-stage fusion to leverage both data-driven learning and domain-informed acoustic cues. The model is trained and evaluated on the Asthma Detection Dataset Version 2 using rigorous preprocessing, including resampling, normalization, noise filtering, data augmentation, and patient-level stratified partitioning. The study achieved strong generalization with 91.21% accuracy, 0.899 macro F1-score, and 0.9866 macro ROC-AUC, outperforming all ablated variants. An ablation study confirms the importance of temporal modeling, attention mechanisms, and multimodal fusion. The framework incorporates Grad-CAM, Integrated Gradients, and SHAP, generating interpretable spectral, temporal, and feature-level explanations aligned with known acoustic biomarkers to build clinical transparency. The findings demonstrate the framework's potential for telemedicine, point-of-care diagnostics, and real-world respiratory screening.

cs.SD↗

arXiv2025

Effect of cavity-induced perturbation interactions on the transitional flow after the trailing edge

We investigate the modal and non-modal linear amplification mechanisms in the flow over a subsonic open cavity and their subsequent interactions to identify optimal flow perturbations that propagate downstream the cavity and trigger flow transitions. Using both the stationary and time-varying base flows from a Direct Numerical Simulation of a cavity flow at Mach 0.6, we employ classical and harmonic resolvent analyses to explain the role of the cavity-generated perturbations in destabilizing the flow downstream. Our analysis of perturbation amplification about the mean flow identifies structures that resemble Tollmien-Schlichting (T-S) waves at the Rossiter frequency in the attached boundary layer region after the cavity. A low-frequency centrifugal instability dominates inside the cavity. The mean flow also amplifies stationary streaks via a lift-up mechanism that extends throughout the boundary layer region downstream of the cavity. The harmonic resolvent analysis (HRA) reveals the amplification of additional perturbations by the unsteady Rossiter base flow. By restricting the input and output at the same frequency in the HRA, we find the amplification of the stationary perturbation to be the most dominant 3D instability mechanism. This amplification is driven by the interaction of the 3D streaks with the unsteady Rossiter base flow, which generates internal forcing in the form of oblique T-S waves, thereby further amplifying the streaks. The interaction between the centrifugal perturbation and the unsteady flow also generates streamwise elongated structures in the boundary layer region after the cavity. Together, the centrifugal-Rossiter and streak-Rossiter interactions synergistically amplify perturbations downstream of the cavity.

physics.flu-dyn↗

arXiv2024

Identification of cross-frequency interactions in compressible cavity flow using harmonic resolvent analysis

The resolvent analysis reveals the worst-case disturbances and the most amplified response in a fluid flow that can develop around a stationary base state. The recent work by Padovan et al.(2020) extended the classical resolvent analysis to the harmonic resolvent analysis framework by incorporating the time-varying nature of the base flow. The harmonic resolvent analysis can capture the triadic interactions between perturbations at two different frequencies through a base flow at a particular frequency. The singular values of the harmonic resolvent operator act as a gain between the spatio-temporal forcing and the response provided by the singular vectors. In the current study, we formulate the harmonic resolvent analysis framework for compressible flows based on the linearized Navier-Stokes equation (i.e., operator-based formulation). We validate our approach by applying the technique to the low-mach number flow past an airfoil. We further illustrate the application of this method to compressible cavity flows at Mach numbers of 0.6 and 0.8 with a length-to-depth ratio of $2$. For the cavity flow at Mach number of 0.6, the harmonic resolvent analysis reveals that the nonlinear cross-frequency interactions dominate the amplification of perturbations at frequencies that are harmonics of the leading Rossiter mode in the nonlinear flow. The findings demonstrate a physically consistent representation of an energy transfer from slow-evolving modes toward fast-evolving modes in the flow through cross-frequency interactions. For the cavity flow at Mach number of 0.8, the analysis also sheds light on the nature of cross-frequency interaction in a cavity flow with two coexisting resonances.

physics.flu-dyn↗