Pipelined adaptive IIR filter architecture

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

Download Full-text

The Monte Carlo framework for adaptive IIR filter & ARMA parameters estimation

IET Conference on Wireless, Mobile and Sensor Networks 2007 (CCWMSN07) ◽

10.1049/cp:20070251 ◽

2007 ◽

Author(s):

Li-xin Wang ◽

Wei Xiang ◽

Jing-yuan Zhang

Keyword(s):

Monte Carlo ◽

Parameters Estimation ◽

Iir Filter ◽

Monte Carlo Framework

Download Full-text

Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189778 ◽

2021 ◽

pp. 1-14

Author(s):

Sachin Sharma ◽

Vineet Kumar ◽

K.P.S. Rana

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Filter Design ◽

Support Vector ◽

Control Loop ◽

External Interference ◽

Iir Filter ◽

Prony Method ◽

Oscillation Detection ◽

Detection And Quantification

Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.

Download Full-text