This paper proposes a smart dome model for mosques that uses AI to control dome movements based on weather conditions and overcrowding. The model utilizes Congested Scene Recognition Network (CSRNet) and fuzzy logic techniques in Python to determine when to open and close the domes to maintain fresh air and sunlight. The goal is to automatically manage dome operation based on real-time data, specifying the duration for which the domes should remain open each hour.
This paper proposes a smart dome system for mosques that uses machine learning to automatically control dome ventilation based on weather conditions and outside temperatures. The system was tested on the Prophet Mosque in Saudi Arabia using K-Nearest Neighbors and Decision Tree algorithms. The Decision Tree algorithm achieved a higher accuracy of 98% compared to 95% for the k-NN algorithm.
AI technology presents significant opportunities to enhance home safety and security across Saudi Arabia. Potential applications include intelligent surveillance systems, predictive analytics for detecting anomalies, and automated emergency response mechanisms. These solutions aim to provide comprehensive protection for residents against various threats, including intrusions, fires, and other domestic hazards. Why it matters: This highlights Saudi Arabia's proactive approach to adopting advanced AI solutions to improve the quality of life and enhance public safety within its residential communities.
KAUST researchers have developed a dual-use wireless sensor system that monitors both traffic congestion and flood incidents in cities. The system combines ultrasonic range finders and infrared thermal sensors to provide real-time, accurate data on traffic flow and roadway flooding. Data is sent to central servers and assimilated with satellite data to form real-time maps and forecasts. Why it matters: This technology can provide up-to-the-minute warnings for flash floods and traffic, enabling rapid emergency response and potentially saving lives in urban environments.
MBZUAI Professor Fakherddine Karray is developing deep learning algorithms for human activity recognition to monitor the health and safety of elderly people. The AI tools analyze movement, posture, and facial expressions to detect early warning signs of health emergencies. Remote patient monitoring systems integrate smart devices and secure communication to allow elderly patients to stay at home and communicate with healthcare providers. Why it matters: AI-powered smart homes can provide affordable healthcare solutions for the rapidly growing elderly population in the region and worldwide.
KAUST researchers in the Sensors Lab are developing neuromorphic circuits for vision sensors, drawing inspiration from the human eye. They created flexible photoreceptors using hybrid perovskite materials, with capacitance tunable by light stimulation, mimicking the human retina. The team collaborates with experts in image characterization and brain pattern recognition to connect the 'eye' to the 'brain' for object identification. Why it matters: This biomimetic approach promises advancements in AI, machine learning, and smart city development within the region.
KAUST has developed AirGo, a hybrid air quality monitoring system using mobile and stationary sensors. The system measures gases (carbon dioxide, carbon monoxide, sulfur dioxide, ozone, etc.) and particulate matter, providing real-time environmental data. AirGo is at technology readiness level 6 and is being scaled up for broader use through partnerships with manufacturers. Why it matters: This technology directly supports Saudi Vision 2030's environmental sustainability goals and the development of smart cities by providing granular air quality insights.