arXiv subjects

Arpan Maity

Publications and source records attributed to Arpan Maity.

3 recordsLinked to original sources

arXiv2026

Signal formation and induction-gap optimization in a THGEM coupled to a resistive plate anode

A Thick Gaseous Electron Multiplier (THGEM) detector coupled to a resistive plate anode was investigated for the first time, using an Ar:CO$_2$ (93:7) gas mixture. The resistive anode concept enabled stable operation with induction gaps significantly smaller than those typically employed in THGEM detectors, opening the possibility of improving signal formation and timing performance. The effect of the induction gap on the detector current- and charge-signal characteristics was systematically investigated. An optimal induction gap of 0.2 mm was identified based on several key signal parameters. Subsequently, detailed studies were carried out for this optimal configuration under different electric-field settings. The detector achieved a highest peak amplitude of $\sim$56 $\mu$A with a rise time of $\sim$5 ns at $\Delta\text{V}_{\text{THGEM}}$=1900 V, $\Delta\text{V}_{\text{induction}}$=100 V, and a drift field of 1 kV/cm when irradiated with 5.9 keV X-rays. Under the same operating conditions, a time resolution of $\sim$6.0 ns was measured detecting cosmic muons. This study establishes a new THGEM detector configuration and provides guidance for its potential application in particle-detection systems, such as muon spectrometers and sampling elements of digital hadronic calorimeters.

physics.ins-det↗

arXiv2025

Hybrid Deep Learning Framework for Enhanced Diabetic Retinopathy Detection: Integrating Traditional Features with AI-driven Insights

Diabetic Retinopathy (DR), a vision-threatening complication of Dia-betes Mellitus (DM), is a major global concern, particularly in India, which has one of the highest diabetic populations. Prolonged hyperglycemia damages reti-nal microvasculature, leading to DR symptoms like microaneurysms, hemor-rhages, and fluid leakage, which, if undetected, cause irreversible vision loss. Therefore, early screening is crucial as DR is asymptomatic in its initial stages. Fundus imaging aids precise diagnosis by detecting subtle retinal lesions. This paper introduces a hybrid diagnostic framework combining traditional feature extraction and deep learning (DL) to enhance DR detection. While handcrafted features capture key clinical markers, DL automates hierarchical pattern recog-nition, improving early diagnosis. The model synergizes interpretable clinical data with learned features, surpassing standalone DL approaches that demon-strate superior classification and reduce false negatives. This multimodal AI-driven approach enables scalable, accurate DR screening, crucial for diabetes-burdened regions.

cs.CV↗

arXiv2025

Comparative Analysis of Object Detection Algorithms for Surface Defect Detection

This article compares the performance of six prominent object detection algorithms, YOLOv11, RetinaNet, Fast R-CNN, YOLOv8, RT-DETR, and DETR, on the NEU-DET surface defect detection dataset, comprising images representing various metal surface defects, a crucial application in industrial quality control. Each model's performance was assessed regarding detection accuracy, speed, and robustness across different defect types such as scratches, inclusions, and rolled-in scales. YOLOv11, a state-of-the-art real-time object detection algorithm, demonstrated superior performance compared to the other methods, achieving a remarkable 70% higher accuracy on average. This improvement can be attributed to YOLOv11s enhanced feature extraction capabilities and ability to process the entire image in a single forward pass, making it faster and more efficient in detecting minor surface defects. Additionally, YOLOv11's architecture optimizations, such as improved anchor box generation and deeper convolutional layers, contributed to more precise localization of defects. In conclusion, YOLOv11's outstanding performance in accuracy and speed solidifies its position as the most effective model for surface defect detection on the NEU dataset, surpassing competing algorithms by a substantial margin.

cs.CV↗