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This paper basically represents how to deblurring images using a wiener filter [1]. Basically wiener filter is used to produce an estimate of a desired or target random process by linear time-invariant filtering [2] of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise. The Wiener filter minimizes the mean square error between the estimated random process and the desired process. In here it has been introduced about the image processing capabilities in MATHLAB. Dividing some steps it has been shown how an image can be processed with the help of MATHLAB [3][4] command. Moreover how image can be effected by noise & how noise can be removed from image this also described in this paper properly

The theory of power system stability, necessary of power system stability and different methods for analysis of power system stability has been developed in this paper. The objective of this paper is to investigate and understand the stability of power system, with the main focus on stability theories and power system modeling. The paper first explained the definition of power system stability and the need for power system stability studies. Next the paper examined the concept of system stability and some stability theories. The paper then performed a power system modeling and simulation of a twomachine, three bus power systems. The performance of the power system was simulated. The operating points and system parameters were varied to test the robustness of the power system. From various stability systems, in this paper, only transient analysis is studied. Examples of the parameters that were varied include the fault position λ, the power angle δ and the mechanical power input m P. A software using MATLAB has been developed for this purpose. Finally we compare various stability responses by varying power angle, fault position and mechanical power.

This study proposes a novel distributed predictive cruise control (PCC) algorithm based on reinforcement learning. The algorithm aims at reducing idle time and maintaining an adjustable speed depending on the traffic signals. The effectiveness of the proposed approach has been validated through Paramics microscopic traffic simulations by proposing a scenario in Statesboro, Georgia. For different traffic demands, the travel time and fuel consumption rate of vehicles are compared between non‐PCC and PCC algorithms. Microscopic traffic simulation results demonstrate that the proposed PCC algorithm will reduce the fuel consumption rate by 4.24% and decrease the average travel time by 3.78%.

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