Perception and control are foundational pillars of intelligent systems, enabling robots, automated machines, and intelligent devices to interact with dynamic environments effectively. This paper explores the integration of advanced perception technologies with adaptive control systems, highlighting how real-time sensing, cognitive processing, and responsive actuation collectively enhance system performance in complex...More >>

Audio and speech perception is central to human communication, relying on complex neural processes to decode acoustic signals into meaningful information. This paper synthesizes advances in speech recognition, auditory scene analysis (ASA), and multimodal audio-visual integration. It compares deep learning-based automatic speech recognition (ASR) with human speech processing, explores how...More >>

Automotive perception, which involves using sensor data to understand the external driving environment and the internal state of the vehicle cabin and occupants, is crucial for achieving high levels of safety and autonomy in driving. This paper focuses on sensor-based perception, specifically the utilization of LiDAR, radar, cameras, and other...More >>

This paper provides a comprehensive analysis of robotics and automation, focusing on motion control, path planning, and autonomous system design with integrated perception. Motion control strategies, from classical PID to advanced adaptive and model predictive control, are examined for their use in industrial robots, mobile platforms, and humanoid systems. Path...More >>

This paper reviews recent advances in computer vision and image processing, focusing on image enhancement, 3D reconstruction, motion analysis, and deep learning-driven systems. It explores classical and deep learning-based enhancement techniques in low-light, medical, and satellite imaging. 3D reconstruction methods, from stereo vision to deep learning, are assessed in applications...More >>