Exoskeleton robots hold tremendous promise in aiding the rehabilitation of patients with lower limb motor dysfunction. However, their effectiveness relies on the real-time and accurate prediction of knee joint angles. This study introduces KINETIQA (Kinematic Integration Network with Enhanced Temporal Intelligence and Quality-driven Attention), a novel model that significantly pushes the boundaries of knee angle prediction, offering substantial enhancements in clinical practice. The core strength of KINETIQA lies in its Kinematic Integration Network (KIN), a sophisticated architecture seamlessly integrating state-of-the-art techniques such as Transformers, temporal convolutions, and multi-headed attention mechanisms. This powerful synergy equips KINETIQA to capture the intricate dynamics of knee movement, both in the blink of an eye and over longer durations, resulting in a notable 20% reduction in prediction error compared to existing models. In addition to accuracy, KINETIQA employs rigorous feature selection and validation methods to guarantee clinical-grade precision. These processes offer clinicians crucial insights for tailored rehabilitation, injury prevention, and movement analysis. Our model's pioneering design integrates Transformers and temporal convolutions to capture detailed temporal dynamics of knee movement, leading to markedly enhanced prediction compared to current frameworks. These positive findings indicate that the proposed approach holds significant promise for application in exoskeleton robot control. The source code for KINETIQA is publicly available on GitHub at https://github.com/LyesSaadSaoud/KINETIQA/

Traditional maritime surveillance often relies on static cameras, limiting coverage and efficiency in detecting vessels. This paper proposes a novel real-time UAV-based vessel detection system that addresses these limitations. The system leverages a Dynamic Camera Control Strategy that overcomes fixed field-of-view constraints by adjusting camera angles based on predefined search patterns, historical data, and real-time sensor feedback. This systematic scanning approach ensures comprehensive coverage, minimizing missed detections. Furthermore, a Feature-Based Prioritization Scheme facilitates real-time target confirmation by analyzing features like size, shape, and, if applicable from additional sensors, other relevant data. This scheme prioritizes promising candidates for further analysis, reducing false positives. By comparing features with a reference vessel stored in the system (using a ResNet50-based module), the system effectively discriminates between vessels and other objects, while movement analysis helps distinguish stationary objects. Our system was evaluated using real-world testing and simulation, demonstrating significant improvements in detection accuracy and processing speed compared to state-of-the-art methods. This research presents a valuable contribution to UAV-based maritime surveillance, enhancing operational efficiency and detection accuracy for various real-world applications.