Sonar systems are widely used for underwater sensing applications such as navigation, target detection, and environmental monitoring. However, conventional sonar signal processing techniques often rely on handcrafted features and model-based assumptions, which can limit performance in complex and noisy underwater environments. In recent years, learning-based approaches, particularly those using neural networks, have gained increasing attention …

A Review of Learning-Based Sonar Signal Processing Using Neural NetworksRead More

Sonar Acoustic feedback remains a critical limitation in sound reinforcement and communication systems, particularly in environments where microphones and loudspeakers operate close proximity. Traditional suppression methods such as notch filtering, phase shifting, and frequency shifting provide partial relief but often introduce latency, tonal coloration, and reduced audio quality. Recent work has increasingly explored adaptive approaches …

FPGA-Based Acoustic Feedback Suppression A Review of Algorithms, Implementations, and Future DirectionsRead More

The increasing penetration of large-scale photovoltaic power plants has intensified the challenge of managing power variability caused by rapid changes in solar irradiance. Short term and ultra short-term power fluctuations, mainly driven by cloud movement, can lead to severe ramp rate violations and grid instability, particularly in weak and island power systems. This review critically …

A Comprehensive Review of Vision-Based and Data-Driven Solar Power Forecasting TechniquesRead More

As Artificial Intelligence (AI) becomes increasingly embedded in higher education, concerns surrounding Responsible Artificial Intelligence (RAI), particularly trust and transparency have moved from abstract ethical discussions to practical institutional challenges. While existing research has largely focused on technological capabilities and learning outcomes, empirical evidence on university students’ perceptions of Responsible AI remains limited, especially in …

Trust and Transparency: Investigating University Students’ Attitudes Toward Responsible AIRead More

The growing demand for computational power has surpassed the capacity of traditional systems, creating an urgent need for innovative approaches. This study introduces a next-generation computing framework that integrates quantum-inspired algorithms, adaptive machine learning, and distributed architectures to achieve scalability, fault tolerance, and energy efficiency. The proposed framework reduced computation time by 40% (from 1050 …

Next-Generation Computing Frameworks – Harnessing Quantum-Inspired Algorithms for Scalable and Resilient SystemsRead More