Goa Engineering College or Goa College of Engineering (abbreviated and colloquially referred to as GEC) is a public college in Goa, India, offering courses in engineering disciplines and affiliated to Goa University. Founded in 1967 and situated at Farmagudi plateau, Ponda, it is the oldest engineering college in Goa, with over 2,200 students.
Traditional multi-object tracking (MOT) relies primarily on visual cues and lacks the ability to track entities based on semantic, language-driven intent. This work introduces a unified multi-modal framework for language-guided multi-object tracking, designed as an initial step toward improving person re-identification (ReID) in complex scenes. The system integrates YOLOv11 for real-time detection, Contrastive Language-Image Pre-training (CLIP) for cross-modal grounding, and AlignedReID++ for appearance-based feature extraction. A Unified Assignment Engine, implemented as a global optimization solved by the Hungarian algorithm, manages active, lost, and re-associated tracks to ensure stable data association. This design reduces identity switches, improves temporal consistency, and enables tracking driven directly by natural-language queries. While full ReID optimization remains a direction for future work, this study effectively constrains the search space through language-guided initialization, providing a critical foundation for developing more accurate and efficient ReID systems. Experimental evaluations validate the framework’s robustness across varied surveillance scenarios, highlighting its potential for intelligent video analytics, human–AI collaboration, and adaptive monitoring.
This paper presents a Novel six-switch inverter designed to mitigate common mode voltage fluctuations, reduce leakage current, conduction losses, and enhance the overall efficiency in transformer-less grid-connected inverters. The operation and performance of the proposed inverter is further evaluated in comparison with existing transformer-less topologies, namely H5, H6 and HERIC. Particular emphasis is placed on analyzing the common-mode voltage and common-mode current generated by these inverters. All four inverter topologies are modeled in MATLAB/Simulink and their performance is compared using simulation results. The simulation results are subsequently validated using OPALRT. Practical implementation of Novel six switch inverter is carried out and it provides tangible proof of the proposed inverter’s effectiveness, thereby strengthening the reliability of the study’s findings.
Emotions play a vital role in shaping our behavior and decisions, influencing our physiological and mental state. Affective computing focuses on developing computer systems to understand and simulate human emotions. The method of emotion classification involves thorough collection, preprocessing, and modelling using advanced algorithms such as machine learning and deep learning. The review covers various techniques for emotion elicitation, self-assessment, preprocessing, and unimodal and multimodal classification, along with the utilization of physiological signals. The study examines openly available databases, emotion labels, feature extraction, feature selection, and feature reduction techniques used in emotion classification. This article focuses on physiological signals collected from wearable devices with sensors, including blood volume pulse, skin temperature, optomyography, and galvanic skin response. The goal is to highlight the latest advancements and identify opportunities for innovative machine learning, deep learning, and fusion techniques in classifying emotions.
Fatigue reliability analysis based upon limited and uncertain data brings uncertainties in the inputs such as probability distributions and their respective parameters. However, in practice, the data is compiled by conducting physical tests. The uncertainties based on limited physical test data need to be carefully evaluated. Underestimation or overestimation of reliability based upon uncertain data and the variability in experimental conditions needs to be evaluated. The uncertainties pertaining to distribution are mitigated using statistical tools. Literatures are available wherein either parametric or non-parametric distributions are used to estimate reliability for uncertain data. However, this paper attempts to use both parametric as well as non-parametric distributions on a set of uncertain data and tries to compare the reliability. First, the experimental data is assumed to follow the Weibull and Lognormal distribution. The fit of these distributions with the assumed distributions are evaluated and then the reliability is estimated for these distributions. Since the data is limited and uncertain, a non-parametric estimator such as the Kaplan–Meier estimate is used to compute reliability. The approach is applied on steel plate welded joints and the data on a number of cycles up to failure was studied. This study shows that when dealing with limited and uncertain fatigue data, the choice of failure distribution significantly affects the reliability estimate. Comparing parametric (Weibull and Lognormal) and non-parametric (Kaplan–Meier) methods indicates that each captures different aspects of data uncertainty. Using both approaches provides a more reliable and balanced interpretation of fatigue behavior than relying on any single model.
Soft-switching techniques are particularly relevant and beneficial for converters used in electric vehicles (EVs). EVs rely on various types of power converters to efficiently manage energy flow between different components such as batteries, motors, and other subsystems. This work proposes a non-isolated half bridge topology-based bidirectional soft-switched DC–DC converter. The converter regulates the power flow between battery pack and traction motor in either direction by balancing the voltage levels at both of its ends. Soft switching lowers power loss and increases range, which is one of the primary requirements for EVs. Reduction in switching loss will boost the converter’s effectiveness, allowing more battery energy to be used for drive during regular vehicle operation. Additionally, more regenerated energy can be stored in the battery during regenerative braking. Through simulation, the system performance is confirmed. A 250 W converter is used for simulating the soft-switching action, and it is found to be consistent with the waveforms produced by the theoretical study. Comparing it to the traditional hard-switched converter allows for performance evaluation. The maximum efficiency at full load in both the boost and buck modes is evaluated at 97.17
