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This work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multipleoutput (MIMO) wireless sensor network. The proposed designs that minimize the mean squared error (MSE) of the parameter estimate at the fusion center are based on majorization theory, which leads to non-iterative closed-form solutions for the precoders and combiners. Various scenarios are considered for parameter estimation such as networks with ideal high precision sensors as well as noisy non-ideal sensors. Moreover, inter parameter correlation is also incorporated, which makes the analysis comprehensive. The Bayesian Cramer-Rao bound (BCRB) and centralized MMSE bound are determined to characterize the estimation performance. Simulation results demonstrate the improved performance and also corroborate our analytical formulations.

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One of the biggest and important issues in the video watermarking is the distortion and attacks. The attacks and distortion affect the digital watermarking. Watermarking is an embedding process. With the help of watermarking, we insert the data into the digital objects. There are few methods are available for authentication of data, securing/protection of data. The watermarking technique also provides the data security, copyright protection and authentication of the data. Watermarking provides a comfortable life to authorized users. In my proposed work, we are working on distorted watermarked video. The distortion is present on the watermarked video is rational 7 th and 8 th order distortion model. In this paper, firstly we are embedding the watermark information into the original video and after that work on the distortion model which may be come into the watermarked video. We are also calculating the PSNR (Peak signal to noise ratio), SSIM (Structural similarity index measure), Correlation, BER (Bit Error Rate) and MSE (Mean Square Error) parameters for distorted watermarked video. We are showing the relationship between correlation and SSIM with BER, MSE and PSNR.