In conventional monolithic operating system architecture, the kernel delivers all the necessary services to the application programs. The microkernel operating system is important in several areas such as industrial control systems, embedded systems, and real-time systems. As the requirements of the operating system increase, the kernel expands in size and increases complexity. The introduction of Operating Systems focusing on Microkernels was due to the difficulties mentioned above caused by Operating Systems with Monolithic kernels. Operating systems based on microkernels offer enhanced security and flexibility to the system. A comprehensive review of eleven distinct operating systems based on the microkernel architecture is presented in this study. Some microkernels provide great advantages like strong security, better performance, and reliability also some microkernels have disadvantages like increased complexity, high cost in making, and low performance. The three main components of the study are process scheduling, memory management, and inter-process communication. This overview provides a comprehension of the various advances in creating operating systems based on microkernel technology. The data sources included books, research papers, and official documentation of individual microkernels.

The issue of drowsiness while operating a motor vehicle is an increasingly common occurrence that has been found to contribute significantly to a substantial number of fatal accidents annually. The urgency of the current situation necessitates implementing a solution to mitigate accidents and fatalities. The present study aims to investigate a less intricate and less expensive but remarkably efficient approach for detecting drowsiness in drivers, in contrast to the existing complex systems developed for this purpose. This paper focuses on developing a simple drowsy driver detection system utilizing the Python programming language and integrating the OpenCV and Dlib models. The shape detector provided by Dlib is employed to accurately determine the spatial coordinates of the facial landmarks within the given video input. This enables the detection of drowsiness by monitoring various factors such as the aspect ratios of the eyes, mouth, and the angle of head tilt. The performance evaluation of the system under consideration is conducted through the utilization of standardized public datasets and real-time video footage. When tested with dataset image inputs, the system showed exceptional recognition accuracy. The performance comparison is done to show the efficacy of the proposed approach. Traveling can be made safer and more effective by combining the proposed system with additional safety features and automation technology in cars.