International Journal of Computational Intelligence and Applications

In this paper, we propose a new hybrid Bivariate Complex Fourier Wavelet Regularized Deconvolution (Bi-ComForWaRD) that is an extension to the ComForWaRD algorithm, for medical imaging. This new algorithm is a two-step process, a global blur compensation using generalized Wiener filter and followed by a denoising algorithm using local adaptive Bivariate shrinkage function. It is a low-complexity denoising algorithm using the joint statistics of the wavelet coefficients and considers the statistical dependencies between the coefficients. And also, the performance of this system will be demonstrated on both the orthogonal wavelet transform and the dual-tree complex wavelet transform (DT-CWT) and some comparisons with the best available wavelet-based image denoising results will be given in order to illustrate the effectiveness of the system.

Wavelet transform has emerged as a powerful tool for time frequency analysis of complex nonstationary signals such as the electrocardiogram (ECG) signal. In this paper, the design of good wavelets for cardiac signal is discussed from the perspective of orthogonal filter banks. Optimum wavelet for ECG signal is designed and evaluated based on perfect reconstruction conditions and QRS complex detection. The performance is evaluated by using the ECG records from the MIT-BIH arrhythmia database. In the first step, the filter coefficients (optimum wavelet) is designed by reparametrization of filter coefficients. In the second step, ECG signal is decomposed to three levels using the optimum wavelet and reconstructed. From the reconstructed signal, the range of error signal is calculated and it is compared with the performance of other suitable wavelets already available in the literature. The optimum wavelet gives the maximum error range as 10^-14–10^-11 which is better than that of other wavelets existing in the literature. In the third step, the baseline wandering is removed from the ECG signal for better detection of QRS complex. The optimum wavelet detects all R peaks of all records. That is using optimum wavelet 100% sensitivity and positive predictions are achieved. Based on the performance, it is confirmed that optimum wavelet is more suitable for ECG signal.

Optimization of tapering windows for artifact reduction in two-dimensional (2D) Fourier electron magnetic resonance (EMR) tomography using genetic algorithm (GA) is presented. EMR imaging (EMRI) is a fast emerging functional imaging modality for studying free radicals in biological systems. EMRI by single point imaging (SPI) modality is a Fourier imaging technique. The bioclearance of the imaging agent as well as the need to minimize the radio frequency power deposition on the live animals, dictate reduced k-space sampling. This leads to ringing (Gibbs) artifacts in both directions of the 2D image, because, unlike the conventional MRI, SPI is phase encoding in both directions. To dampen the high-frequency components, data tapering windows are multiplicatively applied to provide tolerable blurred resultant image with reduced Gibbs ringing. To find a compromise between blur and ringing artifact, in this paper a method of optimizing the window functions by using GA is proposed. Our experiments suggest GA-based Kaiser window shows better performance by visual as well as quantitative evaluation.

The low-frequency instrumentation and imaging capabilities facilitate electron magnetic resonance imaging (EMRI) as an emerging non-invasive imaging technology for mapping free radicals in biological systems. Unlike MRI, EMRI is implemented as a pure phase–phase encoding technique. The fast bio-clearance of the imaging agent and the requirement to reduce radio frequency power deposition dictate collection of reduced k-space samples, compromising the quality and resolution of the EMR images. The present work evaluates various interpolation kernels to generate larger k-space samples for image reconstruction, from the acquired reduced k-space samples. Using k-space EMR data sets, acquired for phantom as well as live mice, the proposed technique is critically evaluated by computing quality metrics viz. signal-to-noise ratio (SNR), standard deviation error (SDE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR) and Lui's error function (F(I)). The quantitative evaluation of 24 different interpolation functions (including piecewise polynomial functions and many windowed sinc functions) to upsample the k-space data for the Fourier EMR image reconstruction shows that at the expense of a slight increase in computing time, the reconstructed images from upsampled data, produced using Spline-sinc, Welch-sinc, and Gaussian-sinc kernels, are closer to reference image with minimal distortion. Support of the interpolating kernel is a characteristic parameter deciding the quality of the reconstructed image and the time complexity. In this paper, a method to optimize the kernel support using genetic algorithm (GA) is also explored. Maximization of the fitness function has two conflicting objectives and it is approached as a multi-objective optimization problem.

We address the problem of constructing a pair of node-disjoint paths connecting two given nodes in a geometric network. We propose a simple algorithm for constructing such a path that does not use any complicated data structure which can be easily implemented. We also consider two variations of disjoint path pair problems which we call narrow corridor problem and width-bounded corridor problem and present efficient algorithms for solving them.

Many of the automated testing tools concentrate on the automatic generation of test cases but do not worry about their optimization. In our paper, we analyzed the system model, which is represented as a state diagram and selected a very limited set of test sequences to be executed from the extreme large number (usually infinitely many) of potential ones. This paper demonstrates a way to generate optimal test sequences that are guaranteed to take the least possible time to execute and also satisfies both state and branch coverage criteria using Intelligent Agents.

In the proposed approach, the Intelligent Search Agent (ISA) takes the decision of optimized test sequences by searching through the SUT, which is represented as a graph in which each node is associated with a heuristic value and each edge is associated with an edge weight. The Intelligent Agent finds the best sequence by following the nodes that satisfy the fitness criteria and generates the optimized test sequences for the SUT. Finally we compared our approach with existing Ant Colony Optimization (ACO)-based test sequence optimization approach and proved that our approach produces better results.

In this paper, operational risk arising from the technological dimension is effectively modeled by efficiently forecasting software reliability. We propose the use of wavelet neural networks (WNN) to predict software reliability. Two kinds of wavelets were employed in WNN as transfer functions, viz. Morlet wavelet and Gaussian wavelet, thus giving rise to two variants of WNN. The effectiveness of WNN is demonstrated on a data set taken from literature. Its performance is compared with that of multiple linear regression, multivariate adaptive regression splines, back propagation trained neural network, threshold-accepting trained neural network, threshold accepting trained wavelet neural network, pi-sigma network, general regression neural network, dynamic evolving neuro-fuzzy inference system and TreeNet in terms of normalized root mean square error obtained on test data. Based on the experiments performed, it is observed that the WNN-based models outperformed all the other techniques.

The present generation faces a major problem due to pollution. The pollution from diesel engines contributes to nearly 33% of the total atmospheric on-road vehicle pollution. Diesel engine contributes more smoke and oxides of nitrogen (NO_x) which are harmful to the health and environment. Preventive steps could be taken if these emissions are predicted. In this paper, the prediction of smoke and NO_x has been attempted through fuzzy logic techniques. Using fuzzy logic, intermediate values could be defined between conventional evaluations. The combustion parameters considered for prediction of emissions are load, peak pressure, indicated mean effective pressure, combustion duration, and ignition delay. Based on the engine system, rules have been framed. The pollutants NO_x and smoke are the output parameters. On comparison, the actual experimental readings and the values through fuzzy logic show a good correlation. Since there is a good correlation, NO_x and smoke can be predicted for any intermediate load conditions. This helps the manufacturers to meet the emission regulations for Euro norms.

It has been known that time delays may not only degrade the performance of the closed-loop control systems, but also induce instability due to the introduction of extra phase lag. The focus of this paper is to find out a way to design a robust controller for networked control systems (NCSs) with time-varying communication delays. To this aim, a mixed fuzzy-PID/neural network compensating scheme is proposed so as to alleviate the influence resulting from uncertain communication delays while maintaining system performance. A condition ensuring stability of the NCS is derived from the system's input–output viewpoint. The effectiveness and superiority of our proposed approach are fully verified in an experimentally networked pneumatic control system.

There is an increasing need to apply emerging technologies in knowledge-based management system. This proposed system captures the knowledge of experts and the knowledge acquired for designing a new systems. Wind Tunnel Test data of missiles has been taken into consideration for knowledge management. Interpolation and Extrapolation of missile test data has been attempted using neural network technique General Regression Neural Networks training algorithm. The results produced by neural network training methodologies are validated with the existing test data. The knowledge extraction using neural network is found suitable for interpolation and to some extent for extrapolation, thereby reducing the cost as well as number of test runs in Wind Tunnel Test.