الفهرس 
INDUSTRIAL RADIOISOTOPES APPLICATIONSOnly 14 pages are availabe for public view
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Abstract
Radioisotopes can be used to obtain signals or images in order to recognize the information inside the industrial systems. The main problems of using these techniques are the difficulty of identification of the obtained signals or images and the requirement of skilled experts for the interpretation process of the output data of these applications. Now, the interpretation of the output data from these applications is performed mainly manually, depending heavily on the skills and the experience of trained operators. This process is time consuming and the results typically suffer from inconsistency and errors.
The objective of the thesis is to apply the advanced digital signal processing techniques for improving the treatment and the interpretation of the output data from the different Industrial Radioisotopes Applications (IRA). This thesis focuses on two IRA; the Residence Time Distribution (RTD) measurement and the defect inspection of welded pipes using a gamma source (gamma radiography). In RTD measurement application, this thesis presents methods for signal pre-processing and modeling of the RTD signals. Simulation results have been presented for two case studies. The first case study is a laboratory experiment for measuring the RTD in a water flow rig. The second case study is an experiment for measuring the RTD in a phosphate production unit.
The thesis proposes an approach for RTD signal identification in the presence of noise. In this approach, after signal processing, the Mel Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients are extracted from the processed signal or from one of its transforms. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) have been tested and compared for efficient feature extraction. Neural networks have been used for matching of the extracted features. Furthermore, the Power Density Spectrum (PDS) of the RTD signal has been also used instead of the discrete transforms.
In the defect inspection of welded pipes application, this thesis used image processing techniques for radiographic image enhancement. Contrast enhancement, filtering, denoising, and interpolation processes have been carried out. After image enhancement, the thesis proposes a method for automatic segmentation (Region of Interest (ROI) determination) for radiographic images. The ROI is determined by thresholding the image with a threshold obtained automatically. Morphological operations and the wavelet transform have been used for improving the segmentation process. After segmentation, a cepstral approach for defect detection from gamma radiographic images is presented. In this approach, the image is lexicographically ordered into one dimension (1-D) signal and then the same algorithms of the RTD identification are applied.
Radioisotopes can be used to obtain signals or images in order to recognize the information inside the industrial systems. The main problems of using these techniques are the difficulty of identification of the obtained signals or images and the requirement of skilled experts for the interpretation process of the output data of these applications. Now, the interpretation of the output data from these applications is performed mainly manually, depending heavily on the skills and the experience of trained operators. This process is time consuming and the results typically suffer from inconsistency and errors.
The objective of the thesis is to apply the advanced digital signal processing techniques for improving the treatment and the interpretation of the output data from the different Industrial Radioisotopes Applications (IRA). This thesis focuses on two IRA; the Residence Time Distribution (RTD) measurement and the defect inspection of welded pipes using a gamma source (gamma radiography). In RTD measurement application, this thesis presents methods for signal pre-processing and modeling of the RTD signals. Simulation results have been presented for two case studies. The first case study is a laboratory experiment for measuring the RTD in a water flow rig. The second case study is an experiment for measuring the RTD in a phosphate production unit.
The thesis proposes an approach for RTD signal identification in the presence of noise. In this approach, after signal processing, the Mel Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients are extracted from the processed signal or from one of its transforms. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) have been tested and compared for efficient feature extraction. Neural networks have been used for matching of the extracted features. Furthermore, the Power Density Spectrum (PDS) of the RTD signal has been also used instead of the discrete transforms.
In the defect inspection of welded pipes application, this thesis used image processing techniques for radiographic image enhancement. Contrast enhancement, filtering, denoising, and interpolation processes have been carried out. After image enhancement, the thesis proposes a method for automatic segmentation (Region of Interest (ROI) determination) for radiographic images. The ROI is determined by thresholding the image with a threshold obtained automatically. Morphological operations and the wavelet transform have been used for improving the segmentation process. After segmentation, a cepstral approach for defect detection from gamma radiographic images is presented. In this approach, the image is lexicographically ordered into one dimension (1-D) signal and then the same algorithms of the RTD identification are applied.