This paper introduces SEDM, a Student Engagement Detection Model that leverages computer vision and deep learning to monitor student engagement in both classroom and online environments. SEDM combines YOLOv9 for face detection, HopeNet for head pose estimation, and DeepFace for emotion analysis, and it uses temporal smoothing and engagement buffer to minimize noise. A peer-discussion mode is created to ensure collaborative interactions are not misclassified as disengagement. SEDM was evaluated in both classroom and online settings, achieving accuracies of 98.6% and 97.1% respectively, which outperform or match existing models that rely only on head pose detection. SEDM's precise measurement of student engagement enables educators to identify drop-offs and refine their teaching strategies accordingly. The model is adaptable to various classroom settings and provides a scalable, data-driven approach to improving student learning experiences.
Achieving more reliable communication necessitates precise beam management, a process often challenged by the dynamic and unpredictable nature of mobile environments. This study proposes an advanced prediction methodology that integrates a beam sweeping technique to acquire user prototype trajectory information. The method leverages received signal strength (RSS) and interference signal to noise ratio (SINR) metrics collected over a defined period to construct a prototype trajectory that reflects the behavior of user equipment (UE) over time. To enhance the accuracy of user movement predictions, two distinct Kalman filter approachesDthe Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are applied to the prototype trajectory and data from proximity sensors. These filters are used to predict the user's position and derive an average path representing the movement from one point to another. This comparative analysis enables the evaluation of prediction accuracy and its implications for adaptive beam steering in future. The findings demonstrate the potential of this approach that enable us to dynamically direct beams toward the user, thereby enhancing connectivity and overall network performance in mmWave-based systems. Such advancements are instrumental in addressing the challenges posed by high mobility and intermittent connectivity in nextgeneration wireless communication networks.
As modern semiconductor devices have become increasingly complex, guaranteeing their functional correctness and security assurance has become more challenging. Assertion-based verification (ABV) is a typical method to detect functional and security issues at the RTL stage of the design process. Properties - often expressed as SystemVerilog Assertions (SVAs) - are constructed to formally specify a design’s expected behavior by enabling automatic checks during simulation and formal verification. Although writing assertions enhances semiconductor assurance before fabrication, it requires expertise and is a time-consuming process, motivating the development of automated methods to facilitate the entire procedure. This article explores the various methodologies and best practices to enhance the automation of the SVA generation and presents insights into the challenges and opportunities in this area.
The rapid evolution of cyber threats demands innovative solutions for real-time detection and mitigation. This paper presents a novel hybrid AI-powered framework for detecting cyberattacks by leveraging a multi-dimensional dataset comprising 40,000 network records. The dataset integrates key features, including IP addresses, protocols, anomaly scores, attack signatures, and geo-location data, providing a rich foundation for threat analysis. Our approach combines machine learning techniques with advanced feature engineering to identify patterns indicative of malicious activities. By employing supervised learning algorithms and anomaly detection models, the proposed framework achieves high accuracy in differentiating between benign and malicious traffic. Furthermore, the framework incorporates metadata analysis to enhance interpretability and facilitate informed decision-making. Experimental results demonstrate the model’s ability to detect diverse cyber threats, including fileless malware, DDoS attacks, and protocol-based exploits, with improved precision and reduced false positives compared to existing solutions. The findings highlight the effectiveness of leveraging multi-dimensional data and AI-driven methodologies for proactive cybersecurity. This study underscores the potential of integrating comprehensive datasets and advanced analytics to enhance cyber defense mechanisms. The proposed framework sets the stage for future research in real-time threat detection and response, contributing to the broader goal of securing digital ecosystems.
This paper presents a detailed performance evaluation of various power quality (PQ) monitoring devices utilizing the Hydro-Quebec benchmark and a combination of statistical analysis tests and machine learning techniques, specifically Partial Least Squares Discriminant Analysis (PLS-DA). The study examines six devices, assessing their compliance with IEC standards 61000-4-30, 61000-4-15, and 61000-4-7. The evaluation was conducted using a proprietary reference system developed by Hydro-Quebec/IREQ. The devices underwent rigorous testing, including stationary and variable frequency assessments, as well as event detection for voltage dips, swells, and interruptions. The combination of statistical tests and machine learning provided a robust analysis, identifying the most accurate and reliable devices, closely matching the Hydro-Quebec benchmark. This study highlights the importance of integrating statistical analysis with advanced machine learning techniques like PLS-DA to enhance the evaluation and selection process of PQ monitoring devices, ensuring compliance with industry standards and optimizing reliability at Hydro-Quebec.
