STAR recursive least square lattice adaptive filters

1997 ◽  
Vol 44 (12) ◽  
pp. 1040-1054 ◽  
Author(s):  
Yuet Li ◽  
K.K. Parhi
Keyword(s):  
Adaptive Filters ◽  
Least Square ◽  
Square Lattice ◽  
Recursive Least Square

scholarly journals Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying Self-Interference Channel

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Benjamin Henson ◽  
Nils Morozs ◽  
Paul Mitchell
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Least Square ◽  
Full Duplex ◽  
Time Varying ◽  
Recursive Least Square ◽  
Exponential Window ◽  
Evaluation Metric ◽  
High Level

<div>Abstract:</div><div><br></div><div>To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.</div>

Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm

2020 ◽  
Vol 19 (04) ◽  
pp. 2050039
Author(s):  
B. Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Maximum Error ◽  
Noise Removal ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Emg Signal

Electromyogram (EMG) signals are mostly affected by a large number of artifacts. Most commonly affecting artifacts are power line interference (PLW), baseline noise and ECG noise. This work focuses on a novel attenuation noise removal strategy which is concentrated on adaptive filtering concepts. In this paper, an enhanced squirrel search (ESS) algorithm is applied to remove noise using adaptive filters. The noise eliminating filters namely adaptive least mean square (LMS) filter and adaptive recursive least square (RLS) filters are designed, which is correlated with an ESS. This novel algorithm yields better performance than other existing algorithms. Here the performances are measured in terms of signal-to-noise ratio (SNR) in decibel, maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The proposed work has been implemented at the MATLAB simulation platform. Testing of their noise attenuation capability is also validated with different evolutionary algorithms namely squirrel search, particle swarm optimization (PSO), artificial bee colony (ABC), firefly, ant colony optimization (ACO) and cuckoo search (CS). The proposed work eliminates the noises and provides noise-free EMG signal at the output which is highly efficient when compared with existing methodologies. Our proposed work achieves 4%, 40%, 4%, 7%, 9% and 70% better performance than the literature mentioned in the results.

scholarly journals Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying Self-Interference Channel

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Benjamin Henson ◽  
Nils Morozs ◽  
Paul Mitchell
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Least Square ◽  
Full Duplex ◽  
Time Varying ◽  
Recursive Least Square ◽  
Exponential Window ◽  
Evaluation Metric ◽  
High Level

<div>Abstract:</div><div><br></div><div>To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.</div>

scholarly journals Design and Application of Signal Modeling, Segmentation and Classification Methods for High-Frequency Ultrasound Backscatter Signals

2021 ◽  
Author(s):  
Noushin R. Farnoud
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Monitoring Program ◽  
Least Square ◽  
Machine Learning Techniques ◽  
Square Lattice ◽  
High Frequency Ultrasound ◽  
Recursive Least Square ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

In this study, we explore the possibility of monitoring program cell death (apoptosis) and classifying clusters of apoptotic cells based on the changes in high frequency ultrasound backscatter signals from these cells. One of the hallmarks of cancer is that the fail [sic] in the apoptosis mechanism in cells. Therefore this research carries the promise of designing more refined and more effective cancer therapies. The ultrasound signals are modeled through the Autoregressive (AR) modeling technique. The proper model order is calculated by tracking the error criteria derived from statistical properties of the original and modeled signal. In the next stage, five machine learning classifiers are developed to classify backscatter signals based on their AR coefficients. In clinical applications ultrasound backscatter signals from tissues and tumors are most likely to be non-stationary. Therefore analyzing such signals requires signal segmentation techniques. We developed recursive least square lattice filter for adaptive segmentation of ultrasound backscatter signals from multiple cell types into blocks of stationary segments, and model and classify the segments individually. In this thesis we demonstrate the accuracy of modeling, segmentation and classification techniques to detect signals from different cell pellets based on the signal processing and machine learning techniques.

Design and Application of Signal Modeling, Segmentation and Classification Methods for High-Frequency Ultrasound Backscatter Signals

2021 ◽  
Author(s):  
Noushin R. Farnoud
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Monitoring Program ◽  
Least Square ◽  
Machine Learning Techniques ◽  
Square Lattice ◽  
High Frequency Ultrasound ◽  
Recursive Least Square ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

In this study, we explore the possibility of monitoring program cell death (apoptosis) and classifying clusters of apoptotic cells based on the changes in high frequency ultrasound backscatter signals from these cells. One of the hallmarks of cancer is that the fail [sic] in the apoptosis mechanism in cells. Therefore this research carries the promise of designing more refined and more effective cancer therapies. The ultrasound signals are modeled through the Autoregressive (AR) modeling technique. The proper model order is calculated by tracking the error criteria derived from statistical properties of the original and modeled signal. In the next stage, five machine learning classifiers are developed to classify backscatter signals based on their AR coefficients. In clinical applications ultrasound backscatter signals from tissues and tumors are most likely to be non-stationary. Therefore analyzing such signals requires signal segmentation techniques. We developed recursive least square lattice filter for adaptive segmentation of ultrasound backscatter signals from multiple cell types into blocks of stationary segments, and model and classify the segments individually. In this thesis we demonstrate the accuracy of modeling, segmentation and classification techniques to detect signals from different cell pellets based on the signal processing and machine learning techniques.

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