ஜர்னல் ஆஃப் கம்ப்யூட்டர் சயின்ஸ் & சிஸ்டம்ஸ் பயாலஜி

A new segmentation method of signals distorted by noise is proposed. Unlike other known methods, for example, the Canny method, a priori data on interference and/or a signal (image) is not used. Segmentation of signals and halftone images distorted by interference is one of the oldest problems in computer vision. But human vision solves this task almost independently of our consciousness. It was discovered for vision neurons, that sizes of receptive fields’ excitatory zones change during visual act, which eventually mean dynamical changes in visual system’s resolution i.e., coarse-to-fine phenomenon in living organism. We assumed that “coarse-to-fine” phenomenon, i.e., several different resolutions, is used in human vision to segment images. A “coarse-to-fine” algorithm for segmentation of experimental graphs was developed. The main difference of algorithm mentioned above from others is that decision is made taking into the account all partial solutions for all resolutions being used. This ensures stability of final global solution. The algorithm verification results are presented. It is expected that the method can naturally be expanded to segmentation of halftone images.

A significant proportion of e-Learning resources for engineering and computing education appear to be exclusively in English, requiring many learners to adapt themselves to learning within an English language context. Adaptive learning has a role to play in minimizing this adjustment and strengthening the learning. This research plans to understand learning needs, and take a Content and Language Integrated Learning (CLIL) approach to create algorithms to supply online learning experiences and content to meet these needs, adding novel mechanisms to help learners cope, develop their language capabilities, and enhance their ability to learn in another language. This work in progress describes the early stages of the research and we welcome insights into taxonomies of adaptive learning techniques, and mixed methods approaches to evaluating learning effectiveness, for those learning in an additional language.

Edge detection and feature extraction play an important role in digital image processing field. It reduces the amount of data and filters out useless information while preserving the important structural properties in an image. It was observed that using the same edge detection operator for different images make some images suffer from the details (high) and missing (low) edges. This limitation may affect the features for image understanding. Hence, the aim is enhancement of the edge pixels which suffer from the details and missing edge’s pixel by adjustment edge pixel in an automatic way for different images. This paper simulates the mechanism of how our body normally controls high and low blood pressure level to regulate the features of high and low edge images. The efficiency of proposed model is demonstrated experimentally on the hand posture dataset. The recognition accuracy obtained is 98.66%. The model provides better performance than conventional methods.

Objective: To assess how the modulation transfer function (MTF) was influenced by the k-space trajectory with partial Fourier fractions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), calculating MTF using computer simulations.

Methods: Reference data for signal intensity were acquired using breast model at different concentrations and used to create a digital phantom. Frequency images were created by fast Fourier transform, divided into parts, and a new image formed by taking one part from each. The MTFs were then calculated. Three linear signal intensity slope models (low, medium, and high) and three exponential curve models (slow, medium, and rapid) were created using the reference data.

Results: The smaller the partial Fourier fraction used, the faster the decline in MTF. The MTF of the three linear slope models showed that the higher gradient of the slope used, the more rapid was the decline in MTF. The MTF of all three exponential curve models were more gradually decreasing than all three linear slope models.

Conclusion: The MTF was influenced by the k-space trajectory with partial Fourier fractions in DCE-MRI using computer simulations. The reverse linear order was found to be less influenced than the linear order by the partial Fourier fraction.