arXiv subjects

Rajdeep Chatterjee

Publications and source records attributed to Rajdeep Chatterjee.

7 recordsLinked to original sources

arXiv2026

Spectroscopic factors as a probe of nuclear shape in $^{44}$S via one-neutron knockout reaction

Background: Neutron-rich nucleus $^{44}$S lies in the region where traditional $N=28$ shell closure weakens, leading to the emergence of shape coexistence and large-amplitude collective motion (LACM). Understanding the nature and degree of shape mixing in this nucleus remains an important and fascinating problem. Purpose: We investigate the manifestation of shape fluctuations in $^{44}$S and examine how the electric transitions and the spectroscopic factors from one-neutron knockout reactions can serve as probes of shapes mixing. Method: The antisymmetrized molecular dynamics combined with the generator coordinate method (AMD+GCM) is used to study the structure of $^{44}$S and $^{43}$S. Calculations are performed by using Gogny effective interactions with two different parameter sets, D1S and D1M, to explore the interaction dependence of shape mixing. Monopole and quadrupole transition strengths and spectroscopic factors are evaluated. The cross sections for the $^{44}$S$(p,pn)^{43}$S reaction are calculated within the distorted wave impulse approximation (DWIA). Results: The calculations reveal a strong interaction dependence of shape fluctuation in $^{44}$S. The structural differences obtained from D1S and D1M interactions produce distinct patterns of the electric transitions, the spectroscopic factors, and the cross sections for $^{44}$S$(p,pn)^{43}$S knockout reaction. Conclusion: The population of $3/2^-$ and $7/2^-$ states of $^{43}$S is particularly sensitive to the underlying shape fluctuation in $^{44}$S. Thus, the measurement of $^{44}$S$(p,pn)^{43}$S reaction can provide a direct experimental probe.

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arXiv2026

Soundscapes in Spectrograms: Pioneering Multilabel Classification for South Asian Sounds

Environmental sound classification is a field of growing importance for urban monitoring and cultural soundscape analysis, especially within the acoustically rich environments of South Asia. These regions present a unique challenge as multiple natural, human, and cultural sounds often overlap, straining traditional methods that frequently rely on Mel Frequency Cepstral Coefficients (MFCC). This study introduces a novel spectrogram-based methodology with a superior ability to capture these complex auditory patterns. A Convolutional Neural Network (CNN) architecture is implemented to solve a demanding multilabel, multiclass classification problem on the SAS-KIIT dataset. To demonstrate robustness and comparability, the approach is also validated using the renowned UrbanSound8K dataset. The results confirm that the proposed spectrogram-based method significantly outperforms existing MFCC-based techniques, achieving higher classification accuracy across both datasets. This improvement lays the groundwork for more robust and accurate audio classification systems in real-world applications.

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arXiv2026

Explainable Continuous-Time Mask Refinement with Local Self-Similarity Priors for Medical Image Segmentation

Accurate semantic segmentation of foot ulcers is essential for automated wound monitoring, yet boundary delineation remains challenging due to tissue heterogeneity and poor contrast with surrounding skin. To overcome the limitations of standard intensity-based networks, we present LSS-LTCNet:an ante-hoc explainable framework synergizing deterministic structural priors with continuous-time neural dynamics. Our architecture departs from traditional black-box models by employing a Local Self-Similarity (LSS) mechanism that extracts dense, illumination-invariant texture descriptors to explicitly disentangle necrotic tissue from background artifacts. To enforce topological precision, we introduce a Liquid Time-Constant (LTC) refinement module that treats boundary evolution as an ODEgoverned dynamic system, iteratively refining masks over continuous time-steps. Comprehensive evaluation on the MICCAI FUSeg dataset demonstrates that LSS-LTCNet achieves state-of-the-art boundary alignment, securing a peak Dice score of 86.96% and an exceptional 95th percentile Hausdorff Distance (HD95) of 8.91 pixels. Requiring merely 25.70M parameters, the model significantly outperforms heavier U-Net and transformer baselines in efficiency. By providing inherent visual audit trails alongside high-fidelity predictions, LSS-LTCNet offers a robust and transparent solution for computer-aided diagnosis in mobile healthcare (mHealth) settings.

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arXiv2025

AUDRON: A Deep Learning Framework with Fused Acoustic Signatures for Drone Type Recognition

Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used across diverse domains, including logistics, agriculture, surveillance, and defense. While these systems provide numerous benefits, their misuse raises safety and security concerns, making effective detection mechanisms essential. Acoustic sensing offers a low-cost and non-intrusive alternative to vision or radar-based detection, as drone propellers generate distinctive sound patterns. This study introduces AUDRON (AUdio-based Drone Recognition Network), a hybrid deep learning framework for drone sound detection, employing a combination of Mel-Frequency Cepstral Coefficients (MFCC), Short-Time Fourier Transform (STFT) spectrograms processed with convolutional neural networks (CNNs), recurrent layers for temporal modeling, and autoencoder-based representations. Feature-level fusion integrates complementary information before classification. Experimental evaluation demonstrates that AUDRON effectively differentiates drone acoustic signatures from background noise, achieving high accuracy while maintaining generalizability across varying conditions. AUDRON achieves 98.51 percent and 97.11 percent accuracy in binary and multiclass classification. The results highlight the advantage of combining multiple feature representations with deep learning for reliable acoustic drone detection, suggesting the framework's potential for deployment in security and surveillance applications where visual or radar sensing may be limited.

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arXiv2025

Explainable Transformer-CNN Fusion for Noise-Robust Speech Emotion Recognition

Speech Emotion Recognition (SER) systems often degrade in performance when exposed to the unpredictable acoustic interference found in real-world environments. Additionally, the opacity of deep learning models hinders their adoption in trust-sensitive applications. To bridge this gap, we propose a Hybrid Transformer-CNN framework that unifies the contextual modeling of Wav2Vec 2.0 with the spectral stability of 1D-Convolutional Neural Networks. Our dual-stream architecture processes raw waveforms to capture long-range temporal dependencies while simultaneously extracting noise-resistant spectral features (MFCC, ZCR, RMSE) via a custom Attentive Temporal Pooling mechanism. We conducted extensive validation across four diverse benchmark datasets: RAVDESS, TESS, SAVEE, and CREMA-D. To rigorously test robustness, we subjected the model to non-stationary acoustic interference using real-world noise profiles from the SAS-KIIT dataset. The proposed framework demonstrates superior generalization and state-of-the-art accuracy across all datasets, significantly outperforming single-branch baselines under realistic environmental interference. Furthermore, we address the ``black-box" problem by integrating SHAP and Score-CAM into the evaluation pipeline. These tools provide granular visual explanations, revealing how the model strategically shifts attention between temporal and spectral cues to maintain reliability in the presence of complex environmental noise.

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arXiv2024

Massimo: Public Queue Monitoring and Management using Mass-Spring Model

An efficient system of a queue control and regulation in public spaces is very important in order to avoid the traffic jams and to improve the customer satisfaction. This article offers a detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places. Through the utilization of different technologies i.e. computer vision, machine learning algorithms, deep learning our system provide accurate information about the place is crowded or not and the necessary efforts to be taken.

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arXiv2000

Projectile structure effects in the Coulomb breakup of one-neutron halo nuclei

We investigate the Coulomb breakup of neutron rich nuclei 11Be and (19,17,15)C within a theory developed in the framework of Distorted Wave Born Approximation. Finite range effects are included by a local momentum approximation, which allows incorporation of realistic wave functions for these nuclei in our calculations. Energy and angular as well as parallel momentum distributions of the fragments emitted in the breakup of these nuclei on heavy targets have been calculated using several structure models for their ground state. Comparison with the available experimental data shows that the results are selective about the ground state wave function of the projectile. Our investigations confirm that the nuclei 11Be, 19C and 15C have a one-neutron halo structure in their ground states. However, for 17C such a structure appears to be less likely. Calculations performed within our method have also been compared with those from an adiabatic model and the results are discussed.

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