H. Kasban

Laboratory experiments and modeling for industrial radiotracer applications

This paper presents three laboratory experiments, which have been carried out using the Molybdenum-99 (Mo(99)) radiotracer to measure the residence time distribution (RTD), the mixing time and the flow rate in a water flow rig. The results of the RTD measurement experiment are preprocessed using the MATLAB software for background correction, radioactive decay correction, starting point correction, filtering, and data extrapolation. After preprocessing, six mathematical models are investigated on this data using the International Atomic Energy Agency (IAEA) RTD software. The parameters of each model are optimized to calculate the value of the RTD, and to determine the model, which gives the best match with the practical data. The selected model with the best match is used to calculate the RTD in this experiment. The mixing time experiment is carried out for different rotation speeds and repeated three times in each case. The results show that the mixing time is inversely proportional to the rotation speed. The flow rate experiment is carried out to measure the flow rate in the flow rig. The experimental results show a high reliability of the radiotracer used in the RTD, mixing time and flow rate measurements.

Homomorphic image watermarking with a singular value decomposition algorithm

Information embedding and retrieval in digital images.Digital Watermarking.Image processing. In this paper, a new homomorphic image watermarking method implementing the Singular Value Decomposition (SVD) algorithm is presented. The idea of the proposed method is based on embedding the watermark with the SVD algorithm in the reflectance component after applying the homomorphic transform. The reflectance component contains most of the image features but with low energy, and hence watermarks embedded in this component will be invisible. A block-by-block implementation of the proposed method is also introduced. The watermark embedding on a block-by-block basis makes the watermark more robust to attacks. A comparison study between the proposed method and the traditional SVD watermarking method is presented in the presence of attacks. The proposed method is more robust to various attacks. The embedding of chaotic encrypted watermarks is also investigated in this paper to increase the level of security.

Automatic object detection from acoustic to Seismic landmine images

This paper presents two innovative techniques developed and implemented for the automation of the landmine detection in a data scanned by the laser Doppler vibrometer (LDV) based acoustic to seismic (A/S) landmine detection system. These techniques are based on the intensity component of the color landmine images or on grayscale versions of these images. The obtained results are promising in terms of accuracy, consistency, reliability and processing time.

Power density spectrum for the identification of residence time distribution signals

One of the most important applications of radioisotopes in industry is the residence time distribution (RTD) measurement. RTD can be used for optimizing the design of industrial systems and determining their malfunctions. The RTD signal may be subject to different sorts of noise. This leads to errors in the RTD calculations, and hence leads to wrong analysis in the determination of system malfunctions. This paper presents a proposed approach for RTD signal identification based on power density spectrum (PDS). The cepstral features are extracted from the signal or/and its PDS. The PDS is estimated using nonparametric, parametric, and eigen-analysis methods. The identification results are analyzed and compared for different estimation methods in order to select the best PDS estimation method for RTD signal identification. Neural networks are used for training and testing in the proposed approach. The proposed approach is tested using RTD signals obtained from the measurements carried out with radiotracer technique. The experimental results show that the proposed approach with features extracted from the PDS of the RTD signals calculated using eigen-analysis methods is the most robust and reliable in RTD signal identification.

EFFICIENT DETECTION OF LANDMINES FROM ACOUSTIC IMAGES

The Laser Doppler Vibrometer (LDV)-based Acoustic to Seismic (A/S) landmine detection system is one of the reliable and powerful landmine detection systems. The interpretation of the LDVbased A/S data is performed off-line, manually, depending heavily on the skills, experience, alertness and consistency of a trained operator. This takes a long time. The manually obtained results suffer from errors, particularly when dealing with large volumes of data. This paper proposes some techniques for the automatic detection of objects from the acoustic images which are obtained from the LDV-based A/S landmine detection system. These techniques are based on Corresponding author: F. E. Abd El-Samie (fathi sayed@yahoo.com).

Principle component analysis for radiotracer signal separation.

Radiotracers can be used in several industrial applications by injecting the radiotracer into the industrial system and monitoring the radiation using radiation detectors for obtaining signals. These signals are analyzed to obtain indications about what is happening within the system or to determine the problems that may be present in the system. For multi-phase system analysis, more than one radiotracer is used and the result is a mixture of radiotracers signals. The problem is in such cases is how to separate these signals from each other. The paper presents a proposed method based on Principle Component Analysis (PCA) for separating mixed two radiotracer signals from each other. Two different radiotracers (Technetium-99m (Tc(99m)) and Barium-137m (Ba(137m))) were injected into a physical model for simulation of chemical reactor (PMSCR-MK2) for obtaining the radiotracer signals using radiation detectors and Data Acquisition System (DAS). The radiotracer signals are mixed and signal processing steps are performed include background correction and signal de-noising, then applying the signal separation algorithms. Three separation algorithms have been carried out; time domain based separation algorithm, Independent Component Analysis (ICA) based separation algorithm, and Principal Components Analysis (PCA) based separation algorithm. The results proved the superiority of the PCA based separation algorithm to the other based separation algorithm, and PCA based separation algorithm and the signal processing steps gives a considerable improvement of the separation process.

Distillation Column Malfunctions Identification using Higher Order Statistics

paper presents a proposed approach for distillation column malfunction identification using Higher Order Statistics (HOS). Gamma ray scanning techniques have been used for examining the inner details of a distillation column. In the proposed method, the signals are firstly divided into frames; each frame contains only the signal of one column tray. Secondly, HOS are estimated for these frame signals. Then features are extracted from the HOS estimate. Finally, features are used for training and testing of Artificial Neural Network (ANN) to identify the distillation column malfunctions. The simulation results show that the HOS can be used efficiently for the distillation column malfunction identification especially at high noisy scanning conditions. KeywordsCumulant, moment, and Trispectrum.

RTD signal identification using linear and nonlinear modified periodograms

One of the important applications of radioisotope in industry is the residence time distribution (RTD) measurement. RTD can be used for optimizing the design of the industrial system at the design stage and determination of the system malfunctions. The RTD signal may be subject to different sorts of noise; this leads to errors in the RTD calculations and hence leads to wrong analysis in determination of system malfunctions. This paper presents a proposed method for RTD signal identification based on power density spectrum (PDS). The cepstral features are extracted from the signal and from its linear and nonlinear modified periodograms. The neural networks are used for training and testing the proposed method. The proposed method is tested by RTD signals obtained from measurements carried out using radiotracer technique. The experimental results show that the proposed method with features extracted from the PDS of the RTD signal calculated using nonlinear modified periodogram (multitaper) is the most robust and reliable in RTD signal identification.