Electric Machines: Tool in MATLAB

This chapter presents an educational modeling and parametric study of specific types of transformers, generators, and motors used in power system. Equivalent circuit models are presented and basic equations are developed. Through tests and operating conditions, essential parameters for each presented machine are extracted. Graphical user interface (GUI) on MATLAB software is used to study and analyze each element. GUI allows better comprehension and clearer vision to analyze the performance of each electric machine, thus, a complementary educational tool. In addition, GUI permits optimal collaborative learning situations when linked with the theoretical expansion and, thus, is a teaching process that forges the connection between traditional subjects and science education

Electric Power System Simulator Tool in MATLAB

An electric power system is a network of electrical components used to supply, transmit, and use electric power. An example of an electric power system is the network that supplies a region’s homes and industry with power. Due to the complexity and nonlinearity of the power system, hand calculations may be very complicated in some cases, especially when the number of buses or inputs is very large. Here comes the role of software for convergence, time saving, and accuracy. The “Electric Power System Simulator” focuses on three main concepts in power system analysis, the “Power Flow Calculation,” “Faults Calculation,” and “Economic Dispatch Calculation.

Surface wave propagation on humain body communications links

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Communication entre antennes implantées sur le corps humain par ondes de surface

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Study of Wave Propagation on the Human Body Surface in Normal Polarisation

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Analyse du canal de propagation corporel en régime impulsionnel

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A novel low SAR water-based mobile handset antenna

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Machine learning techniques on homological persistence features for prostate cancer diagnosis

International audienceThe rapid evolution of image processing equipment and techniques ensures the development of novel picture analysis methodologies. One of the most powerful yet computationally possible algebraic techniques for measuring the topological characteristics of functions is persistent homology. It's an algebraic invariant that can capture topological details at different spatial resolutions. Persistent homology investigates the topological features of a space using a set of sampled points, such as pixels. It can track the appearance and disappearance of topological features caused by changes in the nested space created by an operation known as filtration, in which a parameter scale, in our case the intensity of pixels, is increased to detect changes in the studied space over a range of varying scales. In addition, at the level of machine learning there were many studies and articles witnessing recently the combination between homological persistence and machine learning algorithms. On another level, prostate cancer is diagnosed referring to a scoring criterion describing the severity of the cancer called Gleason score. The classical Gleason system defines five histological growth patterns (grades). In our study we propose to study the Gleason score on some glands issued from a new optical microscopy technique called SLIM. This new optical microscopy technique that combines two classic ideas in light imaging: Zernike’s phase contrast microscopy and Gabor’s holography. Persistent homology features are computed on these images. We suggested machine learning methods to classify these images into the corresponding Gleason score. Machine learning techniques applied on homological persistence features was very effective in the detection of the right Gleason score of the prostate cancer in these kinds of images and showed an accuracy of above 95%