The Performance Improvement of the Low-Cost Ultrasonic Range Finder (HC-SR04) Using Newton’s Polynomial Interpolation Algorithm

Gandha, Gutama Indra; Nurcipto, Dedi

doi:10.20895/infotel.v11i4.456

Cited by 5 publications

(6 citation statements)

References 11 publications

(15 reference statements)

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“…The measurement of fit level between original data and reconstructed data is represented by MSE (Mean Squared Error) shown in Table 2. The lower MSE level leads to higher accuracy of the generated model [22]. The MSE level for each experiment was categorized as low-level MSE.…”

Section: Resultsmentioning

confidence: 99%

“…Newton's polynomials result in lower error than Langrange's polynomial. The other advantages of Newton's polynomial are easy to implement in the computational platform, simple model structure, suitable for low-cost microprocessors and well-known properties [8], [22]. Since this paper focused on the degree reduction method, the discussion other than degree reduction is not discussed in depth.…”

Section: Methodsmentioning

confidence: 99%

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The reduction of polynomial degrees using moving average filter and derivative approach to decrease the computational load in polynomial classifiers

Santoso

Gandha

2022

J.INFOTEL

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Carbon monoxide is a type of pollutant that is harmful to human health and the environment. On the other hand, carbon monoxide also has benefits for industrial matter. Since the benefits and disadvantages of carbon monoxide, the measurement of carbon monoxide concentration is required. The measurement of carbon monoxide level is not easy moreover with low-cost sensors. The usage of 4 sensors namely TGS2611, TGS2612, TGS2610 and TGS2602 has been used along with feature extractor. The polynomial classifier is required to interpret the feature vector into the amount of substance concentration. The common classifier methods suffer fatal limitations. The polynomial classifiers method offers lower complexity in solution and lower computational effort. Since the involvement of a huge number of data points in the modelling process leads to high degree in the polynomial model. The occurrence of Runge's phenomenon is highly possible in this condition. This phenomenon affects the accuracy level of the generated model. The degree reduction algorithm is required to prevent the occurrence of Runge’s phenomenon. The combination of MAF (Mean Average Filter) and derivative approach as degree reductor algorithm has succeeded in reducing the polynomial model degree. The greater the number degree in the model means the greater the computational load. The model degree reductor algorithm has been succeeded to reduce computational load by 96.6%.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The reduction of polynomial degrees using moving average filter and derivative approach to decrease the computational load in polynomial classifiers

Santoso

Gandha

2022

J.INFOTEL

Self Cite

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“…The Newton polynomials method has been succeeded in decreasing the error level. The accuracy of this sensor has been increased by 55,54% [4].…”

Section: Methodsmentioning

confidence: 99%

“…Once the model update has been done, the microprocessor or microcontroller runs the most updated model to perform model-based calibration. In our previous research, the utilization of Newton's polynomials with manual model-making succeeded in improving sensor performance by 55.54% [4]. Since the manual model making require more time and cost [24], [25].…”

Section: Model Updatermentioning

confidence: 99%

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The Newton’s Polynomial Based - Automatic Model Generation (AMG) for Sensor Calibration to Improve the Performance of the Low-Cost Ultrasonic Range Finder (HC-SR04)

Gandha

Santoso

2020

J. INFOTEL

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The ultrasonic range finder sensors is a general-purpose sensor to measure the distance contactless. This sensor categorized as low-cost sensor that widely used in various application. This sensor has a significant deviation that lead to significant error in the measurement result. The error that produced by this sensor tends to increase proportionally to the measured distance. The implementation of the particular algorithm is required to reduce the error value. The model-based calibration is a solution to increase the accuracy. The model-based solutions are no longer feasible if the states of the model have changed. The longer of the usage of the sensor lead to sensor fatigue. Sensor fatigue is one of the causes of model state changes. As long as the drift still within the tolerance limit, the performance of the sensor still can be restored by using calibration method. The model-based calibration calibrates the sensor by using the model. The update of the model must be made whenever the changing of the model state occurred. Since the manual model making process is not an easy task, time and cost required, then the Newton polynomial-based AMG (Automatic Model Generation) have been implemented to this research. The AMG algorithm generates the new sensor model automatically based on the most updated states. This automatic model generation is implemented in the calibration process of the ultrasonic sensor. The implementation of polynomial-based AMG algorithm for sensor calibration have been succeeded to improve the accuracy of the calibrated sensor by 96.4% and reduce the MSE level from 25.6 to 0.914.

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Experimental Evaluation Performance of Ultrasonic Multisensor Deployment Geometries for a Data Acquisition System

Essiben,

Ihonock,

Matanga

et al. 2024

International Journal of Distributed Sensor Networks

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This paper proposed a model to estimate sensor spacing and a new deployment geometry and evaluates the performance of an ultrasonic sensor array according to the deployment geometry. The proposed spacing estimation model is based on the probability of false detection alarms, the aperture angle, and the sensor range. A design technique has been proposed for elliptical deployment geometry, giving expressions for its parameters to help designers of experimental prototypes to reduce errors related to sensor disposition. A practical method for calibrating ultrasonic sensors has been designed to reduce measurement errors. An experiment was conducted, and the data were analysed using a statistical significance test. An evaluation of the performance of an ultrasonic sensor array with linear and circular deployment geometry with and without our method was done and then compared to our proposed deployment geometry which was combined with the proposed method. The result of the statistical significance test shows that elliptical deployment geometry combined with the suggested strategy works better than circular and linear geometry. This applies to performance such as linear correlation, RMSE, MAPE, and standard deviation. These results show the importance of the choice of deployment geometry for application.

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The Performance Improvement of the Low-Cost Ultrasonic Range Finder (HC-SR04) Using Newton’s Polynomial Interpolation Algorithm

Cited by 5 publications

References 11 publications

The reduction of polynomial degrees using moving average filter and derivative approach to decrease the computational load in polynomial classifiers

The reduction of polynomial degrees using moving average filter and derivative approach to decrease the computational load in polynomial classifiers

The Newton’s Polynomial Based - Automatic Model Generation (AMG) for Sensor Calibration to Improve the Performance of the Low-Cost Ultrasonic Range Finder (HC-SR04)

Experimental Evaluation Performance of Ultrasonic Multisensor Deployment Geometries for a Data Acquisition System

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