Generalized Metropolis-Hastings Algorithm for Distributed Averaging with Uniform Quantization Scheme

Abstract Advancement of wireless technology leads to some developments in current wireless electroencephalography. Through improving the transmission method of brainwaves, it would be possible to bring more convenience for the patients in need and give this opportunity to others for discovering other aspects of the amazing brainwave. What has been proposed in this study is a new type of adjustable backward quantization method which exploits the nature of the brainwave signal. This method is based on the nature of the captured brainwave and its quantization boundary changes based on the amplitude of each EEG captured signal. The proposed quantization scheme has been analyzed with uniform and Gaussian distribution of quantization level. Consequently, the Backward Gaussian Quantization with Adjustable Boundary and two Word Memories beside the Backward Uniform Quantization with Adjustable Boundary and two Word Memories are introduced by this experiment. In addition, the performance of wireless transmission system and the proposed quantizer’s efficiency for very low frequency (up to 100 Hz) and amplitude EEG signal have been noticed. With doing so, we simulated the transmitter and receiver by MATLAB® software. To model the medium, channel was assumed as Additive White Gaussian Noise (AWGN). Meanwhile analysis is done for the whole wireless system performance in terms of transmission range, compared with current available wireless transmission systems on the market. It should be noticed that the transmission range of the proposed wireless transmission system is compared to the transmission range of current wireless EEG systems when there is no obstacle between transmitter and receiver. Furthermore, some relevant parameters to evaluate the quality of the proposed quantization method were examined. To sum up, the proposed quantization schemes show considerable performance in terms of Quantization Rate for constant MSQE and SQNR in comparison with Uniform Quantization method and the achieved transmission range of our wireless system by using this method is higher than available wireless EEG systems on market.

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Non-Uniform Probabilistically Quantized Distributed Consensus Applied on Sensors Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.577.921 ◽

2014 ◽

Vol 577 ◽

pp. 921-925

Author(s):

De Hong Xu ◽

Huan Xin Peng ◽

Bin Liu

Keyword(s):

Consensus Algorithm ◽

Quantization Scheme ◽

Mean Square ◽

Distributed Consensus ◽

Step Size ◽

Quantization Step ◽

Uniform Quantization ◽

The Mean ◽

Initial States ◽

Mean Square Errors

In order to improve the accuracy of distributed consensus under digital communication, in the paper, assuming the initial states of nodes following uniform distribution, a non-uniform quantization scheme based on probabilistic quantization is proposed, and the entire data range is divided based on µ-law non-uniform quantization scheme. The quantization step-size near the average of initial states is smaller, and the corresponding quantization errors are smaller. Base on the proposed quantization scheme, a non-uniform probabilistically quantized distributed consensus algorithm is proposed. The performance and the mean square errors of the non-uniform probabilistically quantized distributed consensus algorithm is analyzed, by analyses and simulations, the results show the non-uniform probabilistically quantized distributed consensus can reach a consensus, and the mean square error is far smaller than that of probabilistically quantized distributed consensus.

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