This article presents the stability and dynamic response of open loop and closed loop control of Bidirectional buck-boost converter(BBC) using PID and PIDN controllers through transfer function model implemented in MATLAB code. In order to ensure the stability of switch mode power supplies the control loop behaviors need to be characterized. Improvement of stability of BBC using PID/PIDN compensators is demonstrated in both analog and digital domains by plotting bode plots. Step response of BBC using PID /PIDN controllers are plotted that defines the dynamic behavior of the system.PIDN compensator is proposed to maintain a healthy balance between the stability and transient behavior since both are indirectly proportional.

Abstract--Image Fusion refers to the process of obtaining a single image by integrating two or more images. The fused image must have complete information which is more useful for human or machine perception .The main aim of image fusion in the satellite images is to improve the information content of the source images, so that the reliability and capability of the image is increased. The vital application of image fusion in remote sensing is to merge the Panchromatic and the Multispectral image. This paper mainly focuses on obtaining a pan sharpened satellite image from Multispectral (MS) and Panchromatic (PAN) images using Discrete Wavelet Transform (DWT) and an iterative algorithm called Tone Mapped image quality index (TMQI-II) is used for optimization. This algorithm mainly improves the structural fidelity and statistical naturalness of the fused image. The earliest fusion schemes perform the fusion on the source images, which often have serious side-effects such as reducing the contrast and spectral distortion. In order to overcome the spectral distortion, Discrete Wavelet Transform is used. IKONOS and Quick Bird satellite images are used to assess the quality of this technique.Keyword--Multispectral (MS), Panchromatic (PAN) images, Discrete Wavelet Transform (DWT), Tone Mapped image quality index (TMQI-II), structural fidelity, statistical naturalness.I. INTRODUCTION Image Fusion is a technique that integrates multiple images and produces a single image which retains the important information from each of its source image. The images are acquired from different instrument modalities of the same scene (like multi-sensor, multi-focus and multi modal images). Image fusion is widely used in image and signal processing applications such as aerial and satellite imaging, computer vision, robotics, concealed weapon detection and remote sensing. Pan sharpened images are produced by combining the high spectral resolution multispectral image and the high spatial resolution panchromatic image .Panchromatic images are collected in large visual wavelength range and it's rendered in black and white. Multispectral images are obtained from more than one spectral or wavelength interval [11]. Each and every individual image is usually of the same physical location but with various spectral bands [1]. The objective of image fusion algorithms is to make full use of spatial and spectral information in the Panchromatic and Multispectral images respectively, in order to reduce the potential colour distortion and provide clear image. Image fusion improves the quality and increase the application of usage of the data. Image fusion can be classified, depending on whether the images are fused either in the spatial domain or they are transformed into another domain [23].However, the spatial domain image fusion techniques produce spectral distortion while performing the fusion process. In this paper DWT (Discrete wavelet Transform) is used for image fusion using different fusion rules. Further two different types of wavelet are used f...

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