John P. Basart

Depolarization asymmetry in the quasar 3C 47

It has been reported recently that in many extended high-luminosity extragalactic radio sources, the lobe on the jet side shows less depolarization than the lobe on the counterjet side. This depolarization asymmetry has been interpreted as being produced by an external foreground screen. Depolarization caused by such an external medium supports the idea that the jets are relativistically beamed, since higher depolarization is expected on the counterjet side. In this regard, the one-sided jet quasar 3C 47 was observed with the VLA at 6 and 20 cm. Maps of the projected magnetic field, depolarization, and rotation measure are derived

Wavelets in the solution of the volume integral equation: Application to eddy current modeling

There is growing interest in the applications of wavelets as basis functions in solutions of integral equations, especially in the area of electromagnetic field problems. In this article we apply a wavelet expansion to the solution of the three-dimensional eddy current modeling problem based on the volume integral method. Although this method shows promise for eddy current modeling of three-dimensional flaws, it is restricted by the computing power required to solve a large linear system. In this article we show that applying a wavelet basis to the volume integral method can dramatically reduce the size of the linear system to be solved. In our approach, the unknown total field is expressed as a twofold summation of shifted and dilated forms of a properly chosen basis function that is often referred to as the mother wavelet. The wavelet expansion can adaptively fit itself to the total field distribution by distributing the localized functions near the flaw boundary, where the field change is large, and th...

A very low frequency array for the lunar far-side

Our preliminary design of a lunar far-side correlation array is based on a two-dimensional nonuniformly spaced array of elemental dipoles extending to a maximum diameter of 1000 km. Phase 1 consists of deploying antennas over a 17 km diameter in the crater Tsiolkovsky on the lunar far-side while the outer antennas are deployed in the second phase. Array operation is over at least four bands from 1 to 30 MHz with variable bandwidths up to 5 MHz and resolutions varying from one degree at 1 MHz to one arcsecond at 30 MHz.

NDE X-RAY IMAGE ANALYSIS USING MATHEMATICAL MORPHOLOGY

Morphology is the study of form and structure. In image processing, morphology refers to the analysis of structure or texture within an image. The basic principle in mathematical morphology is to probe the microstructure of the image with various geometric structures to extract features of interest from the image. These geometric structures are known as structuring elements.

3-D Crack Reconstruction In Radiographic Images Using Projections Obtained From A Linear Sample Shift

A geometric reconstruction method for arbitrarily oriented crack-like flaws in radiographic images is presented. The reconstruction algorithm utilizes projections obtained from linear sample shifts rather than sample rotation. Reconstruction errors caused by film measurement uncertainty and source boresight inaccuracy are analyzed. The reconstruction method is verified using radiographs of constructed samples and is applied to an industrial sample.

Use of remote sensing to determine plant health and productivity

This project seeks to assess plant productivity and health in time and space by measuring spectral reflectance from soybean canopies using remote sensing images that do not require ground assessment. Aerial images and reflectance measurements from a multi-spectral radiometer were obtained simultaneously from a soybean field located in Story County, Iowa. The multi-spectral radiometer has eight wavelength bands, ranging from 460-nm to 810-nm and was used as a ground reference for the data analysis. Aerial images were obtained from altitudes ranging from 152 to 427 meters from the ground during summer 2000. Aerial images were analyzed using Matlab, ArcView and Imagine. Difficulties in image analysis and interpretation may occur as the sensing equipment increases in altitude because atmospheric influences become more pronounced. Scattering and absorption of electromagnetic waves in the atmosphere change the spectrum of the reflected wave emitting from the plants as it propagates from the plants to the sensors. Color calibration procedures were used with red, green and blue ground cloths to correct aerial images in the respective red, blue and green bands. Regression analysis was carried out to quantify the relationships between multi-spectral radiometer data and aerial image data.

The Application of Wavelets and Fuzzy Logic to Eddy Current Flaw Detection in Steam Generator Tubes

Eddy current testing is a widely used nondestructive testing method, especially for inspecting steam generator tubes in nuclear power plants. Due to the complex nature of this technique, the analysis of inspection data is a difficult task requiring a great deal of work by experienced human analysts. This is time consuming, expensive, and can be inconsistent due to human nature. Also, the presence in eddy current signals of interference from the tube support plates and deposits can make the data very difficult to analyze. To help overcome these obstacles, an automatic eddy-current analysis system is needed to aid the analysts.

FAST EDDY CURRENT FORWARD MODELS USING ARTIFICIAL NEURAL NETWORKS

Eddy current testing is a widely used nondestructive evaluation (NDE) technique in which flaw information is extracted from the impedance change of a coil placed above a metal testpiece. Typical applications of eddy current NDE are the inspection of heat-exchanger tubes in steam generators of nuclear power plants and detection of hidden corrosion in the lap-splices of aircraft skins. To obtain quantitative information about flaw size and shape, we would like to have a forward model which is able to predict the impedance change of a coil for different flaws in the test geometry. Analytical solutions exist for simple test geometry and flaws with good symmetry properties. However, for flaws with irregular shapes in complex geometry, an analytical solution usually is not available so we must find a numerical solution. There have been several numerical models in the literature, e.g., the finite element method [1], the boundary element method [2], and the volume integral method [3–5]. Those numerical models can be used in a wide range of applications with moderately complex geometry. However, numerical models are inherently computational intensive and thus are not suitable for applications in which modeling speed has the first priority. One application of a fast forward model is to build fast eddy current simulators which can be used for educational purpose. Another application of the fast forward model is in the solution of the nonlinear inverse problem in which a large number of forward solutions must be computed.

Feature Extraction and Classification in Automated Inspection of NDE Images

Industry currently relies on manual inspection of X-Ray images, which is expensive, time consuming and subject to inconsistent results by various inspectors. Automatic inspection of the industrial NDE images using high speed computers would make the flaw detection task more consistent and efficient. However, since the objects to be inspected usually have complex geometric structures, it is very difficult to separate automatically flaws from the complex geometric background of an image. Most of the automated inspection systems developed so far are customized packages which are tailored for particular types of applications, such as the inspections of aluminum wheel castings [1], the welds of space shuttle fuel tanks [2], and nodules in chest X-rays [3]. Customized packages have the advantages of relatively fast speed and good liability, but they can not be used in other applications without substantial changes in computer programs.

GENERAL AUTOMATED FLAW DETECTION SCHEME FOR NDE X-RAY IMAGES

This paper presents an approach to automated flaw detection (AFD) in an arbitrary X-ray image. The intensities in the digitized radiographic image are modeled as piecewise-smooth surface functions corrupted by noise and flaws. It has been observed that radiographs generated for NDE purposes containing flaws also have a combination of three unwanted features; background trends, geometrical structures, and noise. These features inhibit the performance of automated flaw detection algorithms. The proposed general processing scheme reduces the unwanted features in such a way that candidate flaws within the image can be identified. The proposed scheme is robust and is applicable to a wide variety of NDE imaging applications.