A: Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. K : Computerized Tomography (CT) and Computed Radiography (CR); Plasma diagnostics -interferometry, spectroscopy and imaging 1Corresponding author. 2See the author list of Overview of the JET preparation for Deuterium-Tritium Operation by E. Joffrin et al. in Nucl.
INTRODUCTION: Wireless Sensor Network is an interesting technology, which has great deal of node power, which affects the quality of various service parameters. The sensor nodes have been built with fixed energy and spend certain amount of energy for each data transmission. The user cannot access the sensor nodes once they are deployed. OBJECTIVES: In this paper, we proposes the multi attribute depletion measure using enhanced jumper fire fly algorithm which is based on various parameters of the nodes like location, the number of nodes around the node, number of transmissions it involved, energy value it has. METHODS: Based on the factors the method computes the multi attribute depletion measure, which represents the lifetime of the nodes, which serves as the cluster head. The Enhanced Jumper Firefly algorithm is to select the cluster head at each time interval, which computes the multi attribute depletion measure to choose the cluster head. RESULTS: This method performs 90% of delivery ratio then minimizes the average end-to-end delay up to 149.19 ms and improves the clustering performance. CONCLUSION: The MADM-FF method achieves the highest throughput compared to LODE-LEACH.
The key objective of this project is to design a decoder which can be used for hardware purposes. Hardware, here accompanies with software which is more we can discuss as a Software Defined Radio application. The decoder implemented here offers to new radio equipment (SDR), the flexibility of a programmable system. Nowadays, the behavior of a communication system can be modified by simply changing its software. Large tree decoder is made by reusing smaller similar sub-modules. Thus the structure is symmetric. The symmetric and regular structure of tree decoder makes the system a less complexity one. The structure obeys regularity and modularity concepts of VLSI circuit, thus is easy to fabricate using cell library elements. Design a Tree Decoder proposed architecture for SDR application on FPGA. The Structures made here are hardware synthesizable on FPGA board and are done in a respective manner. The design to be implementing by using Verilog-HDL language. The Simulation and Synthesis by using Xilinx Vivado design suite.
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