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SUPPORT VECTOR MACHINE BASED CLASSIFICATION FOR TRAFFIC SIGNS AND ULTRASONIC FLAW DETECTION
Title
SUPPORT VECTOR MACHINE BASED CLASSIFICATION FOR TRAFFIC SIGNS AND ULTRASONIC FLAW DETECTION
Creator
Virupakshappa, Kushal
Date
2015, 2015-12
Description
The use of machine learning techniques for the advanced signal and image processing applications is gaining importance due to performance...
Show moreThe use of machine learning techniques for the advanced signal and image processing applications is gaining importance due to performance increases in accuracy and robustness. Support Vector Machine (SVM) is a machine learning method used for classification and regression analysis of complex real-world problems that may be difficult to analyze theoretically. In this dissertation, the use of SVM for the application of ultrasonic flaw detection and traffic sign classification has been investigated and new methods are introduced. For traffic sign detection, Bag of visual Words technique has been implemented on Speeded Up Robust Feature (SURF) descriptors of the traffic signs and later the sturdy classifier SVM is used to categorize the traffic signs to its respective groups. Experimental results demonstrate that the proposed method of implementation can reach an accuracy of 95.2 % . For ultrasonic aw detection, subband decomposition filters are used to generate the necessary feature vectors for the SVM classifier. Experimental results, using A-scan data measurements from a steel block, show that a very high classification accuracy can be achieved. Robust performance of the classifier is due to proper selection of frequency-diverse feature vectors and successful training. SVM has also been used for regression analysis to locate and amplify the aw by suppressing the clutter noise. The results show that the use of SVM is reliable and achievable for both the applications.
M.S. in Electrical Engineering, December 2015
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EMBEDDED SYSTEM DESIGN FOR TRAFFIC SIGN RECOGNITION USING MACHINE LEARNING ALGORITHMS
Title
EMBEDDED SYSTEM DESIGN FOR TRAFFIC SIGN RECOGNITION USING MACHINE LEARNING ALGORITHMS
Creator
Han, Yan
Date
2016, 2016-12
Description
Traffic sign recognition system, taken as an important component of an intelligent vehicle system, has been an active research area and it has...
Show moreTraffic sign recognition system, taken as an important component of an intelligent vehicle system, has been an active research area and it has been investigated vigorously in the last decade. It is an important step for introducing intelligent vehicles into the current road transportation systems. Based on image processing and machine learning technologies, TSR systems are being developed cautiously by many manufacturers and have been set up on vehicles as part of a driving assistant system in recent years. Traffic signs are designed and placed in locations to be easily identified from its surroundings by human eyes. Hence, an intelligent system that can identify these signs as good as a human, needs to address a lot of challenges. Here, ―good‖ can be interpreted as accurate and fast. Therefore, developing a reliable, real-time and robust TSR system is the main motivation for this dissertation. Multiple TSR system approaches based on computer vision and machine learning technologies are introduced and they are implemented on different hardware platforms. Proposed TSR algorithms are comprised of two parts: sign detection based on color and shape analysis and sign classification based on machine learning technologies including nearest neighbor search, support vector machine and deep neural networks. Target hardware platforms include Xilinx ZedBoard FPGA and NVIDIA Jetson TX1 that provides GPU acceleration. Overall, based on a well-known benchmark suite, 96% detection accuracy is achieved while executing at 1.6 frames per seconds on the GPU board.
Ph.D. in Computer Engineering, December 2016
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APPLICATION OF MACHINE LEARNING TO ELECTRICAL DATA ANALYSIS
Title
APPLICATION OF MACHINE LEARNING TO ELECTRICAL DATA ANALYSIS
Creator
Bao, Zhen
Date
2017, 2017-05
Description
The dissertation is composed of four parts: modeling demand response capability by internet data centers processing batch computing jobs,...
Show moreThe dissertation is composed of four parts: modeling demand response capability by internet data centers processing batch computing jobs, cloud storage based power consumption management in internet data center, identifying hot socket problem in smart meters, and online event detection for non-intrusive load monitoring without knowing label. Mathematical models are constructed to fulfill the research of the four targets, and numerical examples are used to test the effectiveness of the models. The first two parts optimize jobs in Data Center in order to find the best way of utilizing the existing computing resources and storage. Mixed-integer programming (MIP) is used in the formulation. The purpose of the third part is to identify the hot socket problem in smart meter. Machine learning method has been used to locate the bad installation of smart meters by analyzing historical data from smart meters. The fourth part is non-intrusive load monitoring for residential load in houses. Signal processing and deep learning methods are used to identify the specific loads from high frequency signals.
Ph.D. in Electrical Engineering, May 2017
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