Microprocessors course is a challenging course for both students and instructors. The challenge arises by the nature of this course which is a combination of assembly language and hardware interface. The assembly language develops the students' ability to program in a low level programming language utilizing the instruction set of the 8086 processor, understanding the addressing modes (pointer concept), and exploring the nature of different instructions along with their execution times, dependences, implicit addressing for certain instructions, as well as flow control concerning the flags. The hardware interface helps the students in connecting various system components to build up a working system. Memory and input/output communications (read/write) operations are explained using timing diagrams and handshaking signals. In this paper, the aforementioned contents are addressed through developing an interesting project that is capable of integrating both hardware and software in an attractive environment characterized by being simple and inexpensive. In this project, the PC parallel port extension is used to emulate the microprocessor bus activity in read/write operation. The results showed an impressive students' progress whereas they were so satisfied to not only their projects, but also the conceptual basics of this course. ß
With the ever-increasing vehicle population and introduction of autonomous and self-driving cars, innovative research is needed to ensure safety and reliability on the road. This work introduces an innovative solution that aims at understanding vehicle behavior based on sensors data. The behavior is classified according to driving events. Understanding driving events can play a significant role in road safety and estimating the expense and risks of driving and consuming a vehicle. Rather than relying on the distance and time driven, driving events can provide a more accurate measure of vehicle driving consumption. This measure will become more valuable as more autonomous vehicles and more ride sharing applications are introduced to roads around the world. Estimating driving events can also help better design the road infrastructure to reduce energy consumption. By sharing data from official vehicles and volunteers, crowd sensing can be used to better understand congestion and road safety. This work studies driving events and proposes using machine learning to classify these events into different categories. The acquired data is collected using embedded mobile device motion sensors and are used to train machine learning algorithms to classify the events.
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