Computational aspects of electromagnetic NDE phenomena

The development of theoretical models that characterize various physical phenomena is extremely crucial in all engineering disciplines. In nondestructive evaluation (NDE), theoretical models are used extensively to understand the physics of material/energy interaction, optimize experimental design parameters and solve the inverse problem of defect characterization. This dissertation describes methods for developing computational models for electromagnetic NDE applications. Two broad classes of issues that are addressed in this dissertation are related to (i) problem formulation and (ii) implementation of computers;The two main approaches for solving physical problems in NDE are the differential and integral equations. The relative advantages and disadvantages of the two approaches are illustrated and models are developed to simulate electromagnetic scattering from objects or inhomogeneities embedded in multilayered media which is applicable in many NDE problems. The low storage advantage of the differential approach and the finite solution domain feature of the integral approach are exploited. Hybrid techniques and other efficient modeling techniques are presented to minimize the storage requirements for both approaches;The second issue of computational models is the computational resources required for implementation. Implementations on conventional sequential computers, parallel architecture machines and more recent neural computers are presented. An example which requires the use of massive parallel computing is given where a probability of detection model is built for eddy current testing of 3D objects. The POD model based on the finite element formulation is implemented on an NCUBE parallel computer. The linear system of equations is solved using direct and iterative methods. The implementations are designed to minimize the interprocessor communication and optimize the number of simultaneous model runs to obtain a maximum effective speedup;Another form of parallel computing is the more recent neurocomputer which depends on building an artificial neural network composed of numerous simple neurons. Two classes of neural networks have been used to solve electromagnetic NDE inverse problems. The first approach depends on a direct solution of the governing integral equation and is done using a Hopfield type neural network. Design of the network structure and parameters is presented. The second approach depends on developing a mathematical transform between the input and output space of the problem. A multilayered perceptron type neural network is invoked for this implementation. The network is augmented to build an incremental learning network which is motivated by the dynamic and modular features of the human brain

A Novel Image Processing Algorithm for Enhancing the Probability of Detection of Flaws in X-Ray Images

Application of Digital Signal Processing (DSP) techniques in x-ray radiography is a field that is gaining a rapidly growing interest. Dealing with digital x-ray images and enhancing these images using DSP techniques allow the automation of x-ray inspection, which offers several advantages over the traditional film-based inspection. These advantages include reducing the inspection time and cost requirements, obtaing a consistent decision regarding the integrity of the object under test, and allowing the use of real-time inspection [1]. Typically processing of x-ray images to detect and size flaws involves edge detection. In this paper, we primarily focus on an image processing algorithm that is based on a new Gaussian weighted image moment vector edge operator. Application of this operator enhances image edges and suppresses the noise, which results in a significant improvement in the probability of detection of flaws in x-ray images

Nearfield Imaging for Noninvasive Monitoring of Hyperthermia Treatment

Hyperthermia treatment has the potential to enhance cancer therapy and reduce the side effects associated with conventional therapeutic plans. Commercial systems depend typically on narrowband operation and exploit single element applicators to drive energy into treated tissue. In addition, monitoring of thermal distribution depends on invasive intraluminal or interstitial probes. This research aims at developing a proficient platform that addresses some challenges of hyperthermia therapy. A system is suggested that depends on multichannel wideband operation, and implements applicator array. The configuration of this system allows the control of energy localization at various depth of tumors. In addition, the information associated with scattered wideband signals allow performing nearfield imaging, to reconstruct tissue characteristics maps. To investigate system performance, a model is developed of the forward problem, incorporating dispersive wideband models of tissue properties. A tool is developed to generate a dictionary that relates scattered signals to material features. Solution of the inverse problem is conducted based on compressed sensing techniques. Orthogonal matching pursuit OMP models are developed to enhance the resolution of image reconstruction. With the dependence of tissue electrical properties on temperature, thermal maps are generated. Practical aspects of the nonlinearity associated with wideband power amplifiers are incorporated in the model. Analysis of the reconstructed images reveals the validity of the proposed techniques. In particular, encouraging results are obtained of thermal mapping, denoting the potential of using nearfield imaging as a noninvasive thermometry tool, in monitoring hyperthermia treatment

Indexing multi-dimensional uncertain data with arbitrary probability density functions

Research Session 26: Spatial and Temporal DatabasesIn an "uncertain database", an object o is associated with a multi-dimensional probability density function (pdf), which describes the likelihood that o appears at each position in the data space. A fundamental operation is the "probabilistic range search" which, given a value p q and a rectangular area r q, retrieves the objects that appear in r q with probabilities at least p q. In this paper, we propose the U-tree, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data. The new structure is fully dynamic (i.e., objects can be incrementally inserted/deleted in any order), and does not place any constraints on the data pdfs. We verify the query and update efficiency of U-trees with extensive experiments.postprintThe 31st International Conference on Very Large Data Bases (VLDB 2005), Trondheim, Norway, 30 August-2 September 2005. In Proceedings of 31st VLDB, 2005, v. 3, p. 922-93

Reproductive Toxicity Induced by Low Dose Bisphenol A(BPA) in Male Rats

Bisphenol A(BPA) an endocrine disruptor used in the manufacturing process of plastic. BPA low doses on long exposure periods had many hazardous effects. The aim of the present study was to assess the effect of lowest dose of BPA on the male induced orally on the at reproductive performance and changes in the testes that takes place histologically. P53 gene expression by Realtime PCR was also evaluated. Seventy- two albino male rats were divided into two groups, group I (control male rats receiving corn oil only). Group II (64 male rats) which was treated with oral doses of 50 µg/ kg/ day daily for 8 weeks. Every week 8 male rats were dissected and subjected to the investigated parameters. BPA treated groups presented decrease in sperms count, motility, progression and viability as well as a significant increase in sperms head or/and tail abnormalities were recognized. Histology of the testes showed abnormal and irregular organization of seminiferous tubules and decreased sperms in the testes lumen. Moreover, up-regulation in p53 expression was recorded after BPA exposure. In conclusion, these results showed that exposure to BPA at low dose 50µg/kg can cause many reproductive disorders which may impair fertility