Simple hardware architecture for implementation of pairwise Support Vector Machine (SVM) classifiers on FPGA is presented. Training phase of the SVM is performed offline, and the extracted parameters used to implement testing phase of the SVM on the hardware. In the architecture, vector multiplication operation and classification of pairwise classifiers is designed in parallel and simultaneously. In order to realization, a dataset of Persian handwritten digits in three different classes is used for training and testing of SVM. Graphically simulator, System Generator, has been used to simulate the desired hardware design. Implementation of linear and nonlinear SVM classifier using simple blocks and functions, no limitation in the number of samples, generalized to multiple simultaneous pairwise classifiers, no complexity in hardware design, and simplicity of blocks and functions used in the design are view of the obvious characteristics of this research. According to simulation results, maximum frequency of 202.840 MHz in linear classification, and classification accuracy of 98.67% in nonlinear one has been achieved, which shows outstanding performance of the hardware designed architecture

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In this paper, a hybrid turboexpander-fuel cell (TE-FC) is investigated for extraction of electrical energy from high pressure gas in which the fuel cells are used for preheating the gas. Combination of expanders and fuel cells will reduce the fuel consumption and greenhouse gas emission. This study reveals that there are some circumstances in which the use of fuel cells in conjunction with a turboexpander is not recommended from an economic point of view. This paper seeks the region in which utilization of fuel cells along with a turboexpander presents maximum economic profit. Using the strategy provided in this paper one can decide whether to invest in the hybrid fuel cells-turboexpander or individually planned turboexpander with a conventional gas fired preheating system. Almost all effective parameters are taken into account and this can be considered a superiority of the present paper.

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