Solar Photovoltaic (PV) systems typically convert solar irradiance into electricity, thereby helping to reduce the need for fossil fuels and the amount of greenhouse gases released. They provide a reliable and continuous renewable source of energy. However, PV systems are continuously exposed to diverse and changing environmental conditions, such as temperature, humidity, dust, and rain. Exposure to such conditions creates electrical and visible faults in the PV systems. These faults may reduce the PV system’s performance, reliability, and lifetime. In this regard, this paper aims to propose a framework/methodology for reliability modeling and assessment of large-scale grid-connected PV systems using a Fault Tree Analysis (FTA) approach. Specifically, an exhaustive literature survey is carried out to acquire the failure rates of different components/faults existing on the DC side of the PV system. Then, the Fussel-Vesely (F-V) importance measure is employed to identify critical faults and their criticality ranking. Results showed that solder bond failure, broken cell, broken interconnect (finger interruption), rack structure, grounding/lightning protection system, delamination, discoloration, and partial shading are the most critical faults which severely degrade the performance of the PV systems. The recommendations and scope for further study are provided to optimize operations and maintenance costs.

Solar photovoltaic (PV) systems are becoming increasingly popular because they offer a sustainable and cost-effective solution for generating electricity. PV panels are the most critical components of PV systems as they convert solar energy into electric energy. Therefore, analyzing their reliability, risk, safety, and degradation is crucial to ensuring continuous electricity generation based on its intended capacity. This paper develops a failure mode and effects analysis (FMEA) methodology to assess the reliability of and risk associated with polycrystalline PV panels. Generalized severity, occurrence, and detection rating criteria are developed that can be used to analyze various solar PV systems as they are or with few modifications. The analysis is based on various data sources, including field failures, literature reviews, testing, and expert evaluations. Generalized severity, occurrence, and detection rating tables are developed and applied to solar panels to estimate the risk priority number (RPN) and the overall risk value. The results show that the encapsulant, junction box, and failures due to external events are the most critical components from both the RPN and risk perspectives. Delamination and soiling are the panels’ most critical FMs, with RPN values of 224 and 140, respectively, contributing 16.2% to the total RPN. Further, moderately critical FMs are also identified which contribute 56.3% to the RPN. The encapsulant is the most critical component, with RPN and risk values of 940 (40.30%) and 145 (23.40%), respectively. This work crucially contributes to sustainable energy practices by enhancing the reliability of solar PV systems, thus reducing potential operational inefficiencies. Additionally, recommendations are provided to enhance system reliability and minimize the likelihood and severity of consequences.

Millimeter-wave and Terahertz communications consist of complex analog and mixed-signal transceivers where overall system performance is often limited by the weakest performing subsystem. While analog and mixed-signal integrated circuits have significantly advanced, the future of 5G and 6G transceiver design could be accelerated by including artificial intelligence. In this combination, analog integrated circuit design and operation would harness machine learning to identify, characterize, and act upon variations and anomalies in system performance. Focusing on 5G and 6G, this paper investigates solutions for a unified intelligent integrated transceiver: a conceptual combination of a traditional analog subsystem, a supporting digital subsystem that enables artificial intelligence, and dedicated feedback circuitry or sensors that monitor performance, efficiency, or reliability. Active and passive components and propagation channels are reviewed based on their merits of introducing intelligence. Holistically and for broader applicability, the paper conceptualizes and coins the notion of an “intelligent integrated system (IIS)”, which brings forward a novel unified vision and approach toward context-aware subsystems that dynamically interact with ambient and varying operating conditions. To demonstrate viability, the paper concatenates a select set of measurement results.

Autism Spectrum Disorder (ASD) is a major incident neurological disorder. Medical practitioners use different diagnostic techniques such as Electroencephalogram (EEG) Analysis, Magnetic Resonance Imaging (MRI) analysis, and traditional Behavioral Analysis for ASD detection. However, diagnosis success largely depends on specialists' knowledge and remains seldom accessible to remote patients. To address this issue, recently, various machine learning (ML) approaches have been developed for ASD detection using brain MRI images. The performance of these approaches is often limited because of poor feature discrimination, inferior quality of features, high feature length, and poor correlation of features. Thus, there is a need for robust feature extraction and selection techniques to improve the performance of ASD detection. The proposed work demonstrates a fusion of three features, namely Gray Level Co-occurrence Matrix (GLCM) based holistic texture features, Local Binary Pattern (LBP) based local texture features, and Geometrical Features of the Corpus Callosum (GFCC) from brain MRI images. Further, a correlation-based feature selection technique is employed for the salient feature selection from the GLCM, LBP, and GFCC set to improve the feature quality. The effectiveness of the selected feature is evaluated using three ML classifiers such as K-Nearest neighbor (KNN), Support Vector Machine (SVM), and Classification Tree (CT). The proposed ASD detection scheme provides an accuracy of 95.86% with 10-fold cross-validation with a CT classifier. It is observed that the accuracy of the proposed system is improved by 11.32% over the recent GLCM-based ASD system. The correlation-based feature selection techniques minimize the recognition time by 34.95% over the ASD system without feature selection.

In the agriculture sector, the rice crops getting diseased has become a significant concern recently, especially in India, where rice is one of the primary meals. Precise and early-stage detection of various diseases observed in the rice crops can help farmers to provide proper treatment to the crops. This paper presents a Convolutional Neural Network (CNN) based approach is used to detect rice plant leaf disease. CNN is one of the deep learning algorithms that help in image processing and classification with significant accuracy. The proposed algorithm is used for an image dataset of the diseased rice plant leaves, available on Kaggle. Two types of rice leaf diseases are considered for the analysis: brown spot and bacterial leaf blight. The images of these two diseases were pre-processed, segmented, and classified to identify the caused disease. The proposed model can also be used for the detection of the diseases present in other types of crops, faces recognition system, classifying animals, and car models. The overall accuracy of the developed model is nearly 67%.