scholarly journals Correction of Non-Linearity of Load Cell using Adaptive Technique with Mathematical Approximation

2020 ◽  
Vol 9 (1) ◽  
pp. 1112-1115
Keyword(s):  
Adaptive Filter ◽  
Reference Signal ◽  
Adaptive Methods ◽  
Load Cell ◽  
Least Square ◽  
Approximation Methods ◽  
Approximation Techniques ◽  
External Variables ◽  
Mathematical Approximation ◽  
Training Signal

Load Cell is used to evaluate unknown objects ' weight. It presents noise at the output due to different inner and external variables. The output deviates from the required response. This project's primary goal is to use Adaptive and Approximation methods to rectify a load cell's output reaction. Approximation is used to generate the reference or training signal at first using Approximation techniques. To generate the training signal, Least Square Approximation (LSA) and Particle Swarm Optimization (PSO) techniques are used and optimized to the desired value. This training signal is later used in an adaptive scheme as a reference signal. Adaptive methods are used to correct the load cell's output reaction. In the adaptive filter, Least Means Square Algorithms are used to remove the noisy load cell output with the adaptive filter. The noise is primarily caused by the creeping and drifting mistake at the output. The Adaptive Filter utilizes the reference signal produced by approximation methods to eliminate both creeping and drifting errors and to produce a load cell's required reaction.

scholarly journals Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

2009 ◽  
Vol 19 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Tomasz Barszcz
Keyword(s):  
Fault Detection ◽  
Adaptive Filter ◽  
Complex Structure ◽  
Reference Signal ◽  
The Self ◽  
Noise Component ◽  
Vibration Signals ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Self Adaptive

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and IdentificationThe paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then applied—first to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

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>

Signal Stability and the Height-Correction Method for Ground-Penetrating Radar In Situ Asphalt Concrete Density Prediction

2021 ◽  
pp. 036119812110045
Author(s):  
Qingqing Cao ◽  
Imad L. Al-Qadi
Keyword(s):  
Adaptive Filter ◽  
Ground Penetrating Radar ◽  
Asphalt Concrete ◽  
Correction Method ◽  
Least Square ◽  
Least Mean Square ◽  
Signal Stability ◽  
Density Prediction ◽  
Ground Penetrating ◽  
The Impact

Ground-penetrating radar (GPR) has shown great potential for asphalt concrete density prediction used in quality control and quality assurance. One challenge of continuous GPR measurements is that the measured dielectric constant could be affected by signal stability and antenna height. This would jeopardize the accuracy of the asphalt concrete density prediction along the pavement. In this study, signal instability and shifting antenna height during continuous real-time GPR measurements were identified as main sources of error. After using a bandpass filter to preprocess the signal, a least-square adaptive filter, using gradient descent and least mean square methods, was developed to reconstruct the received signal to improve its stability. In addition, simulations were performed to evaluate the impact of geometric spreading caused by shifting antenna height during testing. A height correction was developed using a power model to correct the height-change impact. The proposed filter and height-correction method were assessed using static and dynamic tests. The least-square adaptive filter improved signal stability by 50% and the height-correction method removed the effect of shifting antenna height almost entirely.

scholarly journals Effect of adaptive line enhancement filters on noise cancellation in ECG signals

2021 ◽  
Vol 18 (3) ◽  
pp. 291-302
Author(s):  
George Karraz
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Reference Signal ◽  
Power Line ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Filter Performance ◽  
Power Line Interference ◽  
Adaptive Line Enhancement ◽  
Line Interference

Power line interference is the main noise source that contaminates Electrocardiogram (ECG) signals and measurements. In recent years, adaptive filters with different approaches have been investigated to eliminate power line interference in ECG waveforms. Adaptive line enhancement filter is a special type of adaptive filter that, unlike other adaptive filters, does not require a reference signal and has potential application in ECG signal filtering. In this paper, a selflearning filter based on an adaptive line enhancement (ALE) filter is proposed to remove power line interference in ECG signals. We simulate the adaptive filter in MATLwith a noisy ECG signal and analyze the performance of algorithms in terms of signal-to-noise ratio (SNR) improvement. The proposed algorithm is validated with Physikalisch-Technische Bundesanstalt (PTB) ECG signals database. Additive white gaussian noise is added to the raw ECG signal. Influential parameters on the ALE filter performance such as filter delay, the convergence factor, and the filter length are analyzed and discussed.

scholarly journals Adaptive Noise Filter for Real-Time Stress ECG Signal Analysis

2020 ◽  
Vol 30.8 (147) ◽  
pp. 59-64
Author(s):  
Van Manh Hoang ◽  
◽  
Manh Thang Pham
Keyword(s):  
Adaptive Filter ◽  
Stress Test ◽  
Reference Signal ◽  
Motion Artifact ◽  
Ecg Signal ◽  
Step Size ◽  
Ecg Signals ◽  
Noise Filter ◽  
Adaptive Noise ◽  
Electrocardiogram Ecg

The stress Electrocardiogram (ECG) gives more efficient results for the diagnosis of cardiovascular diseases, which may not be apparent when the patients are at rest. However, the noise produced by the movement of the patient and the environment often contaminates the ECG signal. Motion artifact is the most prevalent and difficult type of interference to filter in stress test ECG. It corrupts the quality of the desired signal thus reducing the reliability of the stress test. In this work, we first describe a quantitative study of adaptive filtering for processing the stress ECG signals. The proposed method uses the motion information obtained from a 3-axis accelerometer as a noise reference signal for the adaptive filter and the optimal weight of the adaptive filter is adjusted by the Modified Error Data Normalized Step-Size (MEDNSS) algorithm. Finally, the performance of the proposed algorithm is tested on the stress ECG signal from the subject.

A mass and road slope integrated estimation strategy based on the joint iteration of least square method and Sage-Husa adaptive filter for autonomous logistics vehicle

2021 ◽  
pp. 095440702110417
Author(s):  
Weida Wang ◽  
Yuanbo Zhang ◽  
Ke Chen ◽  
Hua Zhang ◽  
Xiantao Wang ◽  
...  
Keyword(s):  
Adaptive Filter ◽  
Autonomous Vehicles ◽  
Least Square Method ◽  
Estimation Algorithm ◽  
Least Square ◽  
Recursive Least Square ◽  
Estimation Strategy ◽  
Vehicle Mass ◽  
Integrated Estimation ◽  
Road Slope

Autonomous logistics vehicles are characterised by large changes in mass and their performances are greatly influenced by slope. In addition, sensors on autonomous vehicles are expensive and difficult to be installed considering application environment. To address these problems, a novel integrated estimation strategy for vehicle mass and road slope, which is based on the joint iteration of multi-model recursive least square (MMRLS) and Sage-Husa adaptive filter with the strong tracking filter (SH-STF), is proposed by utilising information involving speed, nominal engine torque and inherent parameters of vehicles. Firstly, due to the separate slowly-changing and time-dependent characteristics, the vehicle mass and road slope are estimated by using MMRLS and SH-STF separately. Secondly, the longitudinal dynamics gain and the steering dynamics gain are calculated separately based on each model’s residual probability distribution. Then, the two estimations module are combined by employing an iterative algorithm. Finally, the proposed strategy is verified by simulation and real vehicle tests. The tests result reveals that the estimation algorithm can effective estimate vehicle mass and road slope in real-time under straight going and steering conditions.

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