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No Date: IME 300 - Manufacturing Processes
No full textTo avoid an unnecessary pre-requisite hurdle, we propose removing MECH 307 (materials engineering) as a pre-req for IME 300. Relevant topics from materials science will added to course content (see syllabus). This will create more flexibility for students to take this course earlier in their studies. It will also help ease scheduling difficulties for these two courses
2/26/2020: Course Change Form BUSN 206
No full textBalancing the course offerings due to other curriculum changes. This change will enable students to get an exposure of both principles of management and principles of marketing concepts during their freshman year. Under the proposed changes, Introduction to Marketing (old BUSN 206/new BUSN 103) will be offered as Freshman 1 Summer-Fall course. An understanding of basic management and marketing concepts will prepare students adequately for the Economic Principles ECON 201 which is a sophomore 1 summer-fall course
Robust Sensor Fused Object Detection Using Convolutional Neural Networks for Autonomous Vehicles
No full textEnvironmental perception is considered an essential module for autonomous driving and Advanced Driver Assistance System (ADAS). Recently, deep Convolutional Neural Networks (CNNs) have become the State-of-the-Art with many different architectures in various object detection problems. However, performances of existing CNNs have been dropping when detecting small objects at a large distance. To deploy any environmental perception system in real world applications, it is important that the system achieves high accuracy regardless of the size of the object, distance, and weather conditions. In this paper, a robust sensor fused object detection system is proposed by utilizing the advantages of both vision and automotive radar sensors. The proposed system consists of three major components: 1) the Coordinate Conversion module, 2) Multi level-Sensor Fusion Detection (MSFD) system, and 3) Temporal Correlation filtering module. The proposed MSFD system employs the principles of artificial intelligence beyond simple comparison of data variance of the sensors. And then, its performance is further improved by using the temporal correlation information with an adaptive threshold scheme. The proposed system is evaluated with the collected video data (6,854 image frames with 18,918 labeled objects). Based on the laboratory testing and in-vehicle validation, the proposed system demonstrates its high accuracy for detecting any size of objects in real-world data
