M-ary phase-shift keying using finite impulse response filter based on window function method

Digital filters are vastly utilized in the area of communication. A perfect digital filter efficiency is significant and hence to design a digital finite impulse response filter (FIR) favorable all the wanted situations is necessary. In this paper, a new proposed FIR digital filter designed, the fineness of the submitted filter is tested in terms of BER and then matched with another window, namely Hamming, Hanning, and Blackman. The design procedure done in the MATLAB software. It is concluded that the Blackman window is the best window to design the FIR digital filter, because it is bit error rate is better than another window.

Uniform patterns based area-efficient and accurate stochastic computing finite impulse response filter

Stochastic computing has recently gained attention due to its low hardware complexity and better fault tolerance against soft errors. However, stochastic computing based circuits suffer from different errors which affect the output accuracy of these circuits. In this paper, an accurate and area-efficient stochastic computing based digital finite impulse response filter is designed. In the proposed work, constant uniform patterns are used as stochastic numbers for the select lines of different MUXes in the filter and the error performance of filter is analysed. Based on the error performance, the combinations of these patterns are proposed for reducing the output error of stochastic computing based filters. The architectures for generating these uniform patterns are also proposed. Results show that the proposed design methodology has better error performance and comparable hardware complexity as compared to the state-of-the-art implementations.

Continuous convolution and nonlinear transformation for multi-shaker non-Gaussian random vibration control

This study proposes a continuous convolution method combined with memoryless nonlinear transformation for multi-input multi-output stationary non-Gaussian random vibration tests. The challenge of the multi-shaker non-Gaussian random vibration test lies in the coupling problems that are manifested in the inherent physical system and in the existence of cross-spectral densities. In the presented method, the independent stationary Gaussian random signals pass through a designed finite impulse response filter with a convolution manipulation first, and then the resulting signals are transformed to the non-Gaussian random signals by the memoryless nonlinear transformation method. The desired drive signals are obtained by the input–output relationship in the frequency domain. The finite impulse response filter is constructed by the frequency sampling technique in which the amplitude characteristics of the filter are determined by the predefined reference power spectral densities. A new monotonic nonlinear transformation function with an approximate kurtosis solution is provided. It only contains one parameter for kurtosis control both in sub-Gaussian and super-Gaussian cases. The memoryless nonlinear transformation is used to maintain the cross-spectral densities, although some distortions are introduced to the power spectra during the transformation process. The inverse system method is used to overcome the coupling problem caused by the inherent physical system. A simulation example and a triaxial vibration test are carried out, and the results indicate the validity and feasibility of the proposed method.