Ramadhan J. Mstafa

The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring. Despite advances in multi-sensor fusion, a critical gap remains in developing unified architectures that effectively process and extract features from fundamentally different physiological signals. Another challenge is the inherent class imbalance in many biomedical datasets, often causing biased performance in traditional methods. This study addresses these issues by proposing a novel and unified deep learning framework that achieves state-of-the-art performance across different signal types. Our method integrates a ResNet-based CNN with an attention mechanism, enhanced by a novel data augmentation strategy: time-domain concatenation of multiple augmented variants of each signal to generate richer representations. Unlike prior work, we scientifically increase signal complexity to achieve future-reaching capabilities, which resulted in the best predictions compared to the state of the art. Preprocessing steps included wavelet denoising, baseline removal, and standardization. Class imbalance was effectively managed through the combined use of this advanced data augmentation and the Focal Loss function. Regularization techniques were applied during training to ensure generalization. We rigorously evaluated the proposed architecture on three benchmark datasets: UCI Seizure EEG, MIT-BIH Arrhythmia, and PTB Diagnostic ECG. It achieved accuracies of 99.96%, 99.78%, and 100%, respectively, demonstrating robustness across diverse signal types and clinical contexts. Finally, the architecture requires ~130 MB of memory and processes each sample in ~10 ms, suggesting suitability for deployment on low-end or wearable devices.

Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people \cite{WHO_epilepsy_50million}. Epileptic seizures result from sudden abnormal electrical activity in the brain, which can be read as sudden and significant changes in the EEG signal of the brain. The signal can vary in severity and frequency, which results in loss of consciousness and muscle contractions for a short period of time \cite{epilepsyfoundation_myoclonic}. Individuals with epilepsy often face significant employment challenges due to safety concerns in certain work environments. Many jobs that involve working at heights, operating heavy machinery, or in other potentially hazardous settings may be restricted for people with seizure disorders. This certainly limits job options and economic opportunities for those living with epilepsy.

Digital video coding aims to reduce the bitrate and keep the integrity of visual presentation. High-Efficiency Video Coding (HEVC) can effectively compress video content to be suitable for delivery over various networks and platforms. Finding the optimal coding configuration is challenging as the compression performance highly depends on the complexity of the encoded video sequence. This paper evaluates the effects of motion content on coding performance and suggests an adaptive encoding scheme based on the motion content of encoded video. To evaluate the effects of motion content on the compression performance of HEVC, we tested three coding configurations with different Group of Pictures (GOP) structures and intra refresh mechanisms. Namely, open GOP IPPP, open GOP Periodic-I, and closed GOP periodic-IDR coding structures were tested using several test sequences with a range of resolutions and motion activity. All sequences were first tested to check their motion activity. The rate–distortion curves were produced for all the test sequences and coding configurations. Our results show that the performance of IPPP coding configuration is significantly better (up to 4 dB) than periodic-I and periodic-IDR configurations for sequences with low motion activity. For test sequences with intermediate motion activity, IPPP configuration can still achieve a reasonable quality improvement over periodic-I and periodic-IDR configurations. However, for test sequences with high motion activity, IPPP configuration has a very small performance advantage over periodic-I and periodic-IDR configurations. Our results indicate the importance of selecting the appropriate coding structure according to the motion activity of the video being encoded.

With the widespread use of the internet, there is an increasing need to ensure the security and privacy of transmitted data. This has led to an intensified focus on the study of video steganography, which is a technique that hides data within a video cover to avoid detection. The effectiveness of any steganography method depends on its ability to embed data without altering the original video’s quality while maintaining high efficiency. This paper proposes a new method to video steganography, which involves utilizing a Genetic Algorithm (GA) for identifying the Region of Interest (ROI) in the cover video. The ROI is the area in the video that is the most suitable for data embedding. The secret data is encrypted using the Advanced Encryption Standard (AES), which is a widely accepted encryption standard, before being embedded into the cover video, utilizing up to 10% of the cover video. This process ensures the security and confidentiality of the embedded data. The performance metrics for assessing the proposed method are the Peak Signal-to-Noise Ratio (PSNR) and the encoding and decoding time. The results show that the proposed method has a high embedding capacity and efficiency, with a PSNR ranging between 64 and 75 dBs, which indicates that the embedded data is almost indistinguishable from the original video. Additionally, the method can encode and decode data quickly, making it efficient for real-time applications.