scholarly journals Real Time Implementation of SIGN LMS Adaptive Filters using Xilinx System Generator

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Least Mean Square ◽  
Path Delay ◽  
Medical Computing ◽  
System Generator ◽  
Filter Output ◽  
Lms Filter ◽  
Xilinx System Generator

Sign Least Mean Square (SLMS) adaptive filter can adapt dynamically based on corresponding filter output. One of the major applications of adaptive filter is Noise cancellation. In real time applications like medical computing, speed of the process developing hardware is essential hence the hardware realization of SLMS adaptive filter using Xilinx System generator is proposed in this work. The propose architecture aims to reduce convergence rate, path delay and increasing speed. In this work (i) Modified architecture is designed for a 8-tap SLMS adaptive filter and (ii) multiplier less structure for Modified DLMS Filter. The designed architecture tested for ECG signal. The functionality of the algorithm is verified in MATLAB with various ECG data from the MIT-BIH database as input. Both LMS and SLMS are designed, simulated, synthesized and implemented in Virtex-5 FPGA using Xilnix ISE 14.3 . The result shows 5% decrease in total real time router completion and also decrease in the number of adders and subtractors, the maximum combinational path delay has been reduced by 48.84% in Systolic Sign LMS Filter when compared to LMS Filter.

Noise Cancellation in ECG Signals with an Unbiased Adaptive Filter

2011 ◽  
pp. 348-366 ◽  
Author(s):  
Yunfeng Wu ◽  
Rangaraj M. Rangayyan
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Random Noise ◽  
Recursive Least Squares ◽  
Ecg Signal ◽  
Least Mean Square ◽  
Ecg Signals ◽  
Lms Filter ◽  
Filtered Noise

The electrocardiographic (ECG) signal is a transthoracic manifestation of the electrical activity of the heart and is widely used in clinical applications. This chapter describes an unbiased linear adaptive filter (ULAF) to attenuate high-frequency random noise present in ECG signals. The ULAF does not contain a bias in its summation unit and the filter coefficients are normalized. During the adaptation process, the normalized coefficients are updated with the steepest-descent algorithm to achieve efficient filtering of noisy ECG signals. A total of 16 ECG signals were tested in the adaptive filtering experiments with the ULAF, the least-mean-square (LMS), and the recursive-least-squares (RLS) adaptive filters. The filtering performance was quantified in terms of the root-mean-squared error (RMSE), normalized correlation coefficient (NCC), and filtered noise entropy (FNE). A template derived from each ECG signal was used as the reference to compute the measures of filtering performance. The results indicated that the ULAF was able to provide noise-free ECG signals with an average RMSE of 0.0287, which was lower than the second-best RMSE obtained with the LMS filter. With respect to waveform fidelity, the ULAF provided the highest average NCC (0.9964) among the three filters studied. In addition, the ULAF effectively removed more noise, measured by FNE, in comparison with the LMS and RLS filters in most of the ECG signals tested. The issues of adaptive filter setting for noise reduction in ECG signals are discussed at the end of this chapter.

An Unbiased Linear Adaptive Filter with Normalized Coefficients for the Removal of Noise in Electrocardiographic Signals

2009 ◽  
Vol 3 (4) ◽  
pp. 73-90 ◽  
Author(s):  
Yunfeng Wu ◽  
Rangaraj M. Rangayyan
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Random Noise ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Ecg Signals ◽  
Squared Error ◽  
Lms Filter ◽  
Filtered Noise

The authors propose an unbiased linear adaptive filter (ULAF) to eliminate high-frequency random noise in electrocardiographic (ECG) signals. The ULAF does not contain a bias in its summation unit, and the filter coefficients are normalized. During the adaptation process, the normalized coefficients are updated with the steepest-descent algorithm in order to achieve efficient filtering of noisy ECG signals. The authors tested the ULAF with ECG signals recorded from 16 subjects, and compared the performance of the ULAF with that of the least-mean-square (LMS) and recursive-least-squares (RLS) adaptive filters. The filtering performance was quantified in terms of the root-mean-squared error (RMSE), normalized correlation coefficient (NCC), and filtered noise entropy (FNE). A template derived from each ECG signal was used as the reference to compute the measures of filtering performance. The results indicated that the ULAF was able to provided noise-free ECG signals with an average RMSE of 0.0287, which was lower than the second best RMSE (0.0365) obtained with the LMS filter. With respect to waveform fidelity, the proposed ULAF provided the highest average NCC (0.9964) among the three filters studied. In addition, the ULAF effectively removed more noise measured by FNE versus the LMS and RLS filters in most of the ECG signals tested.

A Guide on Using Xilinx System Generator to Design and Implement Real-Time Audio Effects on FPGA

2021 ◽  
pp. 38-44
Author(s):  
L. Merah ◽  
◽  
P. Lorenz ◽  
A. Ali-Pacha ◽  
N. Hadj-Said ◽  
...  
Keyword(s):  
Real Time ◽  
Power Efficiency ◽  
Audio Signal ◽  
Research Area ◽  
Audio Processing ◽  
The Real ◽  
System Generator ◽  
Xilinx System Generator ◽  
Active Research ◽  
Implementation Steps

The enormous progress in communication technology has led to a tremendous need to provide an ideal environment for the transmission, storing, and processing of digital multimedia content, where the audio signal takes the lion's share of it. Audio processing covers many diverse fields, its main aim is presenting sound to human listeners. Recently, digital audio processing became an active research area, it covers everything from theory to practice in relation to transmission, compression, filtering, and adding special effects to an audio signal. The aim of this work is to present the real-time implementation steps of some audio effects namely, the echo and Flanger effects on Field Programmable Gate Array (FPGA). Today, FPGAs are the best choice in data processing because they provide more flexibility, performance, and huge processing capabilities with great power efficiency. Designs are achieved using the XSG tool (Xilinx System Generator), which makes complex designs easier without prior knowledge of hardware description languages. The paper is presented as a guide with deep technical details about designing and real-time implementation steps. We decided to transfer some experience to designers who want to rapidly prototype their ideas using tools such as XSG. All the designs have been simulated and verified under Simulink/Matlab environment, then exported to Xilinx ISE (Integrated Synthesis Environment) tool for the rest of the implementation steps. The paper also gives an idea of interfacing the FPGA with the LM4550 AC’97 codec using VHDL coding. The ATLYS development board based on Xilinx Spartan-6 LX45 FPGA is used for the real-time implementation.

scholarly journals Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

2021 ◽  
Vol 34 (1) ◽  
pp. 133-140
Author(s):  
Teimour Tajdari
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Adaptive Filters ◽  
Adaptive Method ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Filter Output ◽  
System Output ◽  
Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

scholarly journals Multirate Audio-Integrated Feedback Active Noise Control Systems Using Decimated-Band Adaptive Filters for Reducing Narrowband Noises

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6693
Author(s):  
Antonius Siswanto ◽  
Cheng-Yuan Chang ◽  
Sen M. Kuo
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Adaptive Filters ◽  
Noise Control ◽  
Sampling Rate ◽  
Active Noise Control ◽  
Least Mean Square ◽  
Real Time System ◽  
Active Noise ◽  
Feedback Active Noise Control

Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments.

scholarly journals Speech Enhancement using Multiple Transducers

2021 ◽  
Author(s):  
◽  
Craig Anderson
Keyword(s):  
Speech Enhancement ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Processing System ◽  
Performance Characteristics ◽  
Data Sets ◽  
Noisy Environments ◽  
Noise Rejection ◽  
Lms Filter ◽  
Fastica Algorithm

<p>In this thesis, three methods of speech enhancement techniques are investigated with applications in extreme noise environments.  Various beamforming techniques are evaluated for their performance characteristics in terms of signal to (distant) noise ratio and tolerance to design imperfections. Two suitable designs are identified with contrasting performance characteristics — the second order differential array, with excellent noise rejection but poor robustness; and a least squares design, with adequate noise rejection and good robustness.  Adaptive filters are introduced in the context of a simple noise canceller and later a post-processor for a dual beamformer system. Modifications to the least mean squares (LMS) filter are introduced to tolerate cross-talk between microphones or beamformer outputs.  An adaptive filter based post-processor beamforming system is designed and evaluated using a simulation involving speech in noisy environments. The beamforming methods developed are combined with the modified LMS adaptive filter to further reduce noise (if possible) based on correlations between noise signals in a beamformer directed to the talker and a complementary beamformer (nullformer) directed away from the talker. This system shows small, but not insignificant, improvements in noise reduction over purely beamforming based methods.  Blind source separation is introduced briefly as a potential future method for enhancing speech in noisy environments. The FastICA algorithm is evaluated on existing data sets and found to perform similarly to the post-processing system developed in this thesis. Future avenues of research in this field are highlighted.</p>

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