Indian Journal of Pure & Applied Physics (IJPAP)

Hybrid Multiscale Contextual Framework for Enhanced Fault Detection in Photovoltaic Electroluminescence Imaging

Prabhakar Sharma — 2025-11-10

Electroluminescence (EL) imaging has demonstrated efficacy in identifying cracks, inactive areas, and other concealed faults that are frequently undetectable in visible-spectrum examinations. Nonetheless, conventional deep learning models, such as standalone convolutional neural networks (CNNs), encounter limitations in generalization, sensitivity to complex features, and robustness at diverse fault sizes. This study introduces an innovative Hybrid Multiscale Contextual Framework (HMCF) architecture that combines two powerful networks, EfficientNetB0 and ResNet50 to extract diverse features. Proposed model also introduces a Multiscale Feature Fusion Block (MFFB) to handle diverse fault sizes. The findings validate the effectiveness of integrating hybrid CNN architectures with multiscale feature fusion for precise, scalable, and dependable fault classification in photovoltaic (PV) modules, facilitating the development of intelligent and automated solar farm inspection systems. The proposed architecture demonstrates a detection accuracy of 93.18%, significantly outperforming leading deep neural network approaches.

Objective Enhancement for Image and Video Compression Using Feature Extraction and Fast RNN-Based Motion Estimation Optimisation

Mudhavath Ramesh Naik — 2025-11-10

Enhancing compressed visual content remains challenging due to visual degradation, motion distortions, and poor temporal coherence. Existing methods often fail to balance detail preservation with accurate motion estimation, especially under high compression or motion. To address this, we introduce the Faster-Recurrent Neural Network-Swarm Intelligence Metaheuristic of the CT Optimisation Algorithm (F-RNN-SIMCT), a novel method combining a fast recurrent neural network with swarm intelligence inspired by coyotes and tuna fish. This hybrid approach optimises motion estimation and preserves spatial-temporal details under harsh compression. F-RNN-SIMCT leverages advanced feature extraction and metaheuristic optimisation to improve motion accuracy and perceptual quality. Experiments show it outperforms standard methods, making it suitable for video transmission and storage in bandwidth-limited environments.

Effects of Major Geomagnetic Storms on Total Electron Content near the Equatorial Ionization Anomaly (EIA) and Low Latitude Regions

Vishakha . — 2025-11-10

In this paper, weinvestigated the fluctuations in Total Electron Content (TEC) during major geomagnetic storms from 2012 to 2021, using the data from six IGS stations: GUAM, GUUG, IISC, DARW, KAT1, and ALIC. We studied the effect of six major geomagnetic storms on TEC, which occurred on 8 September 2017, 17 March 2015, 15 July2012, 8 May 2016, 7 October 2015, and 20 December 2015. These events showed substantial variations in TEC, with noticeable increasesfrom 2.4% to346%. We also used the global aTEC maps to demonstrate the spatial diversity of TEC enhancements. The analysis indicated that the most significant TEC variations occurred in equatorial and low-latitude regions.The stations located near the equator region responded earlier to the geomagnetic storms of 17 March 2015, 20 December 2015, 8 May 2016. Further, theionosphericeffects are found to be subsequently shifting toward low-latitude stations due to the equatorial electrodynamics and prompt penetration electric fields (PPEF). However,during the geomagnetic storm of15 July 2012 the low latitude stations responded earlierthan equatorial stations. The findings emphasize the crucial role of geomagnetic activity in driving changes in the ionosphere, which in turn affects global TEC levels.

Ultrafast All Optical N-Bit Comparator Using 2D Photonic Crystals with Hexagonal Lattice Structure

Kajal Maji — 2025-11-10

The manuscript represents a design of all optical N-bit comparator using 2D photonic crystals. The device can compare two N-bit numbers by comparing the most significant bit (MSB) and successive bits serially. The device is numerically simulated for optimized performance and an operating speed of 5 Tb/s is calculated from response time diagram. The design ensures high contrast ratios, compact footprint, and scalability to higher bit levels. Numerical simulations using the finite-difference time-domain (FDTD) method confirm the efficient performance of the comparator, with an operational speed in the tera bite regime and negligible signal degradation. The proposed N-bit comparator is a promising candidate for integration in all-optical signal processing and high-speed photonic computing systems.

Wide Bandgap and Structural Homogeneity in Spray-Pyrolyzed CuNiFeO Spinel Ferrite Thin Films for Multifunctional Optoelectronics

S D Shakyamuni — 2025-11-10

This study presents the synthesis of quaternary (Cu6Ni0.4Fe2O4 ) CuNiFeO thin films via spray pyrolysis, exhibiting high phase purity and structural uniformity. XRD confirmed a polycrystalline cubic spinel structure (Fd-3m) with a lattice parameter of 8.242 Å. Crystallite sizes (96.065–597.477 nm) and minimal lattice strain (0.250×10-3 to 2.294×10-3), derived from Debye-Scherrer and W-H analyses, respectively, indicate highlight low crystallographic defects. SEM revealed dense, homogeneous nanoscale grains (11.45-17.48 nm), consistent with aligning with the fine-grained microstructure. FTIR identified metal-oxygen bonds (464 cm-1), confirming Ni-O, Cu-O, and Fe-O vibrations in the spinel framework. UV-Vis-NIR spectra showed absorption peaks at 217 nm (Cu2+ charge transfer), 344 nm (Ni3+ transitions), and additional peaks corresponding to Fe3+ d-d excitations. A direct bandgap of 5.26 eV (via Tauc’s plot) underpins strong UV-C/UV-B absorption (>90% below 300 nm) and high visible transparency (>80% at 500-800 nm). These optical traits, coupled with structural coherence, position the films for UV-filtering and transparent conductive technologies, leveraging their dual functionality.

Influence of Sunspot Numbers and Solar Radio Flux on Geomagnetic Storm Activity during Solar Cycles 24 and 25

Swapnil Garg — 2025-11-10

This study systematically investigates the complicated link between solar activity indicators, specifically sunspot numbers (SSN) and solar radio flux (F10.7), and the occurrence of geomagnetic storms during Solar Cycles 24 and 25. The primary objective is to quantify the influence of these solar parameters on Earth's magnetospheric disturbances. The methodology involved a detailed analysis of 231 peak Disturbance Storm Time (Dst) events (P), 214 initial phase (P-I) geomagnetic storm events, and 201 recovery phase(P-R) geomagnetic storm events, all identified by a Dst threshold of ≤ -50 nT, covering the period from January 2008 to June 2024.

Statistical analysis, primarily through the calculation of Pearson correlation coefficients, revealed robust positive correlations. Peak Dst events exhibited correlation coefficients of 0.67 with sunspot numbers and 0.72 with solar radio flux. Similarly, geomagnetic storm events during the initial phase showed strong positive correlations with sunspot numbers (r = 0.69) and solar radio flux (r = 0.73). A comparable positive correlation was also observed during the recovery phase, with coefficients of 0.69 for sunspots and 0.73 for solar radio flux. A particularly striking finding was the exceptionally high correlation of 0.98 between the yearly average sunspot number and yearly average solar radio flux, underscoring the profound interconnectedness of these two solar parameters.

These findings highlight the significant predictive potential of sunspot numbers and solar radio flux in forecasting geomagnetic storm activity. The consistent strength of these correlations across different storm phases suggests that solar activity levels exert a sustained influence throughout the entire evolution of a geomagnetic disturbance, not merely its onset. Furthermore, the comparative analysis of Solar Cycle 24, which was historically weak, and the rapidly rising Solar Cycle 25 offers important clues about the evolving character of solar magnetic variability and its implications for space weather dynamics. The observed resurgence of activity in Cycle 25 implies a heightened likelihood of geo-effective space weather events compared to the preceding cycle.

Fractional Order Mem-Inductor and Mem-Capacitor Mutator using Single DXCCTA

Atul Kumar — 2025-11-10

The paper introduces new designs for fractional-order mem-element mutators utilizing an active component known as the dual-X current convey or transconductance amplifier (DXCCTA). These mutators serve as both meminductor and memcapacitor emulators. The proposed designs incorporate just one DXCCTA, one memristor and one fractional-order capacitor which is implemented using a constant phase element. The mem-element mutators can operate in both floating and grounded modes. An extensive analysis of the circuits is presented, addressing ideal, and non-ideal analysis. The theoretical performance of the designed mem-element mutator circuits is validated through SPICE simulations using 0.18 µm technology. The proposed mutator designs demonstrate a high operating frequency (up to 800 kHz), low power consumption (2.5 mW), low operating supply voltages (±1.25 V) and robust performance across variations in process, voltage, and temperature. The non-volatility of the proposed emulators is also tested.

Physics Inspired Optimisation and Explainable AI Framework for Enhanced BHP Flooding Attack Classification in Optical Burst Switching Networks

Arun Kumar S — 2025-11-10

Optical Burst Switching (OBS) networks offer high bandwidth efficiency and low latency, making it an ideal choice for next generation high-speed photonic communications. However, the burst based transmission architecture is highly vulnerable to flooding attacks, which can severely degrade network performance. In this work, a hybrid approach for Burst Header Packet (BHP) flooding attack classification is proposed. The method combines direct Machine Learning (ML) on tabular data, tabular to image conversion using EfficientNet-b0 fine-tuning. Further, deep EfficientNet-b0 features are optimized using physics inspired Black Hole Optimisation with Adaptive Mutation (BHO-AM). Finally, the optimised features are classified using a Bayesian-optimized Support Vector Machine (SVM) classifier. Explainable AI (XAI) techniques, including Grad-Class Activation Map (CAM) and Occlusion Sensitivity, are employed to enhance interpretability and identify the most critical features influencing classification. Experimental results show that Efficient Net fine-tuning achieves 99.50 % accuracy, while the BHO-AM optimized SVM model attains 99.60 % accuracy with significantly reduced training time. This study introduces a novel tabular to image conversion framework for OBS attack data, enabling Deep Learning models to achieve high accuracy. Thus, combining XAI methods, with DL classification the proposed work achieves better performance and enhanced interpretability in OBS networks.