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Functional Magnetic Resonance Imaging Signal Modelling and Contrasts : an Example of Manual Praxis Tasks

Buchwald M.

Computational Methods in Science and Technology

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2021

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Vol. 27, No. 4

159--167

EN

The goal of neuroscience as a discipline is to understand how the neural system is organized in the brain, giving rise to mental processes and the control of behavior. One of the most frequently utilized methods in neuroscientific studies is the functional magnetic resonance imaging (fMRI), which is a non-invasive technique for quantifying brain processes dynamics. In a standard fMRI procedure, the hypothesis of the correlation between a cognitive task and the observed physiological signal is tested. This way, a certain computational model of a given brain mechanism can be validated. The procedure of modelling fMRI signal time course will be explained in this article as exemplified by planning functional grasps of tools. Subsequently, the results of contrasting model parameter estimates will be presented for a different experiment on manual praxis skills, i.e., bimanual tool grasps and manipulations.

2

Automated blurred image region classification

Bera A., Sacharski B ., Sychel D.

Journal of Theoretical and Applied Computer Science

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2014

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Vol. 8, nr 2

37--47

EN

In this paper authors present a simple method for recognizing blurred regions in the image. Proposed algorithm is based on 81 simple features — moments of histogram of image subbands, that were obtained during image decomposition, and ratio derived from gray level co-occurrence matrix (GLCM) are used. The method is compared with a different method, that is based on approaches found in literature. To increase the efficiency of algorithms, authors combined three solutions (edge-detection, gray level co-occurrence matrix and fast image sharpness). The aim of the research was to verify whether it is possible to use simpler methods of feature extraction to achieve similar, or even better, results.

3

On the detection of distributed roughness on ball bearings via stator current energy: experimental results

Curcuru G., Cocconcelli M., Immovilli F., Rubini R.

Diagnostyka

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2009

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nr 3(51)

17-21

EN

This paper deals with the detection of distributed roughness on ball-bearings mounted on electric motors. Most of the literature techniques focus on the early detection of localized faults on bearing (e.g. on the outer ring) in order to determine the bearing life and to plan the bearing replacing. Localized faults can be detected because they have characteristic signatures which is revealed in the frequency spectrum of the vibration signal acquired by an external sensor, e.g. accelerometer. Unfortunately other faults exist which do not have a characteristic signatures and then they could not be foreseen accurately: e.g. the distributed roughness. In this paper the motor stator current energy is proposed as a fault indicator to identify the presence of the distributed roughness on the bearing. Moreover an orthogonal experiment is set to analyse, through a General Linear Model (GLM), the dependencies of the current energy to the roughness level, and two environmental conditions: the motor velocity and the loads applied externally. ANOVA investigates the statistical significance of the considered factors.