Fabric defect detection using the wavelet transform in an ARM processor

Small devices used in our day life are constructed with powerful architectures that can be used for industrial applications when requiring portability and communication facilities. We present in this paper an example of the use of an embedded system, the Zeus epic 520 single board computer, for defect detection in textiles using image processing. We implement the Haar wavelet transform using the embedded visual C++ 4.0 compiler for Windows CE 5. The algorithm was tested for defect detection using images of fabrics with five types of defects. An average of 95% in terms of correct defect detection was obtained, achieving a similar performance than using processors with float point arithmetic calculations

Phased electromagnetic acoustic transducer array for Rayleigh wave surface defect detection

A phased electromagnetic acoustic transducer (EMAT) array system has been developed for detection and characterisation of surface breaking defects. An array of four linear coils which are individually controlled are used to generate a Rayleigh wave. The high current electronics combined with the coil designs enables the array to generate either narrowband or broadband signals, and controlling the phase delay between the channels makes it possible to change the ultrasound wavelength without requiring the physical separation of the coils to be changed. Experimental results show that the four-coil phased array is able to generate a wavelength range from 3.0 mm to 11.7 mm. Surface breaking defects were characterised using a transmit-receive set-up with a broadband EMAT detector being used to detect the Rayleigh wave. Machined surface slots with different depths were used for technique validation. The results show that the array is sensitive to surface defects and that a wide depth sensitivity range for defect sizing can be easily achieved by applying phasing to tune the wavelength of operation. A large increase in detection flexibility is immediately shown

Microwave flaw detector Patent

Surface defect detection by reflected microwave radiation patter

2-D iteratively reweighted least squares lattice algorithm and its application to defect detection in textured images

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective

Towards a self-evolving software defect detection process

Software defect detection research typically focuses on individual inspection and testing techniques. However, to be effective in applying defect detection techniques, it is important to recognize when to use inspection techniques and when to use testing techniques. In addition, it is important to know when to deliver a product and use maintenance activities, such as trouble shooting and bug fixing, to address the remaining defects in the software.To be more effective detecting software defects, not only should defect detection techniques be studied and compared, but the entire software defect detection process should be studied to give us a better idea of how it can be conducted, controlled, evaluated and improved.This thesis presents a self-evolving software defect detection process (SEDD) that provides a systematic approach to software defect detection and guides us as to when inspection, testing or maintenance activities are best performed. The approach is self-evolving in that it is continuously improved by assessing the outcome of the defect detection techniques in comparison with historical data.A software architecture and prototype implementation of the approach is also presented along with a case study that was conducted to validate the approach. Initial results of using the self-evolving defect detection approach are promising

Duplicate Defect Detection

Discovering and fixing faults is an unavoidable process in Software Engineering. It is always a good practice to document and organize fault reports. This facilitates the effectiveness of development and maintenance process. Bug Tracking Repositories, such as Bugzilla, are designed to provide fault reporting facilities for developers, testers and users of the system. Allowing anyone to contribute finding and reporting faults has an immediate impact on software quality. However, this benefit comes with one side-effect. Users often file reports that describe the same fault. This increases the triaging time spent by the maintainers. At the same time, important information required to fix the fault is likely to be distributed across different reports.;The objective of this thesis is twofold. First, we want to understand the dynamics of bug report filing for a large, long duration open source project, Firefox. Second, we present a new approach that can reduce the number of duplicate reports. The novel element in the proposed approach is the ability to concentrate the search for duplicates on specific portions of the bug repository. This improves the performance of Information Retrieval techniques and classification runtime of our algorithm. Our system can be deployed as a search tool to help reporters query the repository or it can be adopted to help maintainers detect duplicate reports. In both cases the performance is satisfactory. When tested as a search tool our system is able to detect up to 53% of duplicate reports. The approach adapted for maintainers has a maximum recall rate of 59%