Saudi Cultural Missions Theses & Dissertations
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Item Restricted Enhancing Wi-Fi Sensing Performance Using Advanced Deep Learning Approaches(Saudi Digital Library, 2025) Alatawi, Lafi; Lin, CaiSmart sensing technologies are increasingly integrated into our daily lives, creating demand for complementary sensing approaches that are unobtrusive, noninvasive, and capable of operating in complex indoor environments. Wi-Fi sensing has emerged as a promising solution as it leverages existing wireless infrastructure and relies on metrics such as Received Signal Strength (RSS) and Channel State Information (CSI) to capture spatial information and variations in signal propagation patterns. Existing methods can be broadly categorized as model-based or learning-based. Model-based approaches are interpretable but often limited by simplified assumptions, while learning-based approaches can capture complex features yet are hindered by noise, limited data, and environmental dynamics. To overcome these challenges and develop a more robust Wi-Fi sensing framework, it is necessary to both enhance the quality of CSI data and extract meaningful representations from it. In this thesis, we propose a simple yet effective Wi-Fi sensing method that combines Conditional Generative Adversarial Networks (CGANs) for CSI data augmentation with denoising and feature-extraction techniques based on Convolutional Neural Networks (CNNs). The proposed system increases the diversity of the training dataset, reduces the impact of noisy measurements, and improves the discriminative capability of learned features. Experimental evaluations demonstrate that our method achieves high recognition accuracy under limited-data conditions, surpasses baseline deep learning models, and maintains competitive performance on unseen data. These results indicate that integrating generative models with feature-enhanced learning-based techniques offers a promising direction for robust and practical Wi-Fi sensing applications.24 0Item Restricted Smart Glasses for the Blind and Visually Impaired (BVI)(NEWCASTLE UNIVERSITY, 2024-08-23) Aljamil, Ibtisam; Wang, ShidongTechnology has rapidly advanced to become a powerful tool for enhancing quality of life and providing creative solutions to the daily obstacles faced by different societal groups. Blindness is a significant obstacle that impacts the lives of millions worldwide. When carrying out everyday activities, blind individuals face many movements and independent living challenges. Consequently, there is an immediate need to create assistive tools and technologies that enhance the capabilities and independence of visually impaired individuals. The 'Smart Glasses for the Blind and Visually Impaired’ (BVI) project is an innovative solution that offers an approach based on computer vision and artificial intelligence technologies. This solution aims to assist blind individuals in recognising their surroundings and navigating safely and quickly. The fundamental goal of this project is to create intelligent eyewear that incorporates advanced cameras and sensors, as well as data analysis software and technologies for recognising objects. It also possesses text-to-speech capabilities, object detection, and user guidance through audio feedback. The glasses employ a Raspberry Pi 4 microprocessor for camera image processing, utilising Optical Character Recognition (OCR) technologies and OpenCV software to identify text accurately. The smart glasses use a Single Shot Multibox Detector (SSD) to detect objects in the environment. This technology enables visually impaired individuals to navigate independently while receiving real-time alerts about potential hazards along their path. The numerous benefits of glasses make them a valuable tool in the lives of visually impaired people.27 0Item Restricted Design and Implementation of a RISC Microprocessor(Saudi Digital Library, 2023-11-21) Aljishi, Hadi Fadel A; Khursheed, SaqibThe demand for compact, high-speed, and energy-efficient computing systems has made the innovation and advancement of microprocessor designs increasingly vital. This project concerns the evelopment of a fully-featured Reduced Instruction Set Computer microprocessor on an FPGA. A practical instruction set was chosen and used as the basis for a datapath design. Implementation was done on the Cyclone II featured on the Altera DE2 board. Two basic implementations were created based on internal and external memory. The maximum achievable clock frequency was determined to be 63.32 MHz for the internal memory implantation and 44.32 MHz for the external memory implementation. A third implementation featuring a multiplier and a floating-point unit was then developed which achieves a maximum clock frequency of 26.16 MHz and a total power consumption of 41.06 mW. Several programs were written using the new instruction set to test the three implementations, and all produced the expected outputs. However, some areas of the design and testing methodology could be improved.38 0