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Our earlier work on support vector machines (SVM) and ultrasonic flaw detection algorithms demonstrated i) highly accurate classifier performance and ii) the feasibility of the algorithm for real-time implementation on low-cost embedded systems with graphical processing units (GPU) and CUDA library (a parallel computing platform and programming model) support. This works extends the implementation...
Research on Deep Learning algorithms has progressed rapidly in recent years. Since the inception of deep learning, numerous architectures have been proposed for various applications targeting pattern recognition, image, audio and information analysis. For example, often audio signal classifications use variations of Deep Belief Networks (DBN), while a Deep Neural Network (DNN) called AlexNet is widely...
This study investigates the performance of different hardware platforms and development frameworks for efficient realization of an ultrasonic flaw detection algorithm based on the Support Vector Machine (SVM) classifier. The proposed algorithm is based on subband decomposition of ultrasonic signals followed by classification with a trained SVM model that uses subband filter outputs as feature inputs...
This work presents an embedded hardware architecture for real-time ultrasonic NDE applications that incorporate Hidden Markov Model (HMM) based statistical signal methods. HMM has been successfully used in applications like audio segment retrieval, speech/language recognition and image processing applications. Recently, we proposed a new Hidden Markov Model (HMM) based ultrasonic flaw detection algorithm...
This work presents an embedded hardware architecture for real-time ultrasonic NDE applications that incorporate Hidden Markov Model (HMM) based statistical signal methods. Proposed algorithm is a combination of Discrete Wavelet Transform (DWT) for pre-processing A-scan signals and HMM for classification of the flaw presence. For this study, a MicroZed FPGA with Xilinx Zynq-7020 System-on-Chip (SoC)...
In this work, we investigate the hardware implementation of Support Vector Machine (SVM) prediction on an FPGA platform for industrial ultrasound applications. Specifically, SVM is used as classifier for identifying ultrasonic A-scan signals as signals with flaw or signals without flaw. Hardware acceleration using FPGA is the main theme of the presented work. The architecture used to implement the...
Support vector machine (SVM) based classifiers can be used to predict the presence and location of flaw echoes in Ultrasonic NDE signals with high accuracy. In this work, we present the implementation of the SVM based flaw detection algorithm on an embedded hardware platform (Tegra TK1 board) based on ARM CPU cores and a graphics processing unit (GPU). This implementation exploits high level of parallelism...
In this work, modeling of a stochastic non stationary process is used to detect flaw echoes in ultrasonic NDE signals. This research aims at locating the flaw (if any) present in the NDE signal using Hidden Markov Model (HMM). HMM works on the principle that any non-stationary stochastic signals are generated by a hidden process which can be approximated by a Markov Model. The states present in the...
In this work, a Support Vector Machine (SVM) classifier is introduced for ultrasonic flaw detection based on features extracted from the output of the subband decomposition filters. SVM is a machine learning method used for classification and regression analysis of complex real-world problems that may be difficult to analyze theoretically. A dataset constituting feature vectors of ultrasonic signals...
In this paper, a robust traffic sign recognition system is introduced for driver assistance applications and/or autonomous cars. The system incorporates two major operations, traffic sign detection and classification. The sign detection is based on color segmentation and incorporates hue detection, morphological filter and labeling. A nearest neighbor classifier is introduced for sign classification...
Driver Assistance Systems such as traffic sign detection and autonomous car research are largely facilitated with the recent advances on computer vision and pattern recognition. In this work, Bag of visual Words technique has been implemented on Speeded Up Robust Feature (SURF) descriptors of the traffic signs and later the sturdy classifier Support Vector Machine (SVM) is used to categorize the traffic...
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