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 Show less
In this study, a Support Vector Machine (SVM) classification method used for analyzing Ultrasound signals is implemented by FPGAs based on... Show moreIn this study, a Support Vector Machine (SVM) classification method used for analyzing Ultrasound signals is implemented by FPGAs based on Xilinx Zynq SoC. The SVM processor aims at classifying A-scan data obtained by an ultrasonic sensor. For reducing development time, hardware software co-design tools such as Xilinx System Generator and Vivado have been used. SVM kernel function is implemented by DSP slices and block RAMs. Advanced Extensible Interface bridges the ARM core and FPGAs for more convenient communication. The main objective of this study is to achieve robust detection of ultrasonic flaw echoes in real-time using an SVM algorithm. The implementation on the FPGA shows that the architecture can be realized with a Xilinx Zedboard FPGA. It runs at 100MHz clock frequency and can calculate the SVM classification for 1024 feature space points under 0.02ms. M.S. in Electrical Engineering, December 2016 Show less
