Efficiency Estimation of the Noise Digital Filtering Algorithms

Varshavskiy, Ilyas E.; Krasnova, Anastasiia I.; Polivanov, Vladimir V.

doi:10.1109/eiconrus.2019.8656672

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…The best results in the output data were obtained using a double exponential moving average (DEMA) filter and triple exponential moving average (TEMA) filter which was based on the exponential moving average (EMA) filter. Exponential averaging filters are floating averaging filters, often used for data smoothing and prediction [52,53]. Exponential filters are first order IIR filters.…”

Section: Measurementsmentioning

confidence: 99%

Design of the System for Measuring UAV Parameters

Novotňák

Fiľko

Lipovský

et al. 2022

Drones

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This article deals with the design and creation of a tensometric measuring system to measure the parameters of an unmanned aerial vehicle (UAV) of the quadcopter type. The system was designed to measure the total UAV thrust and the thrust of its individual motors. The distribution of forces from the UAV motors and their transmission to the sensors was ensured by a specially designed construction, for which the mechanical stresses were simulated and analysed for different modes of the UAV flight. The thrust measurement was performed by four pairs of strain gauges. A measurement system designed in this way and the measured parameters of the UAV can be used for tuning the flight control algorithms applied in the autopilot.

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Section: Measurementsmentioning

confidence: 99%

Design of the System for Measuring UAV Parameters

Novotňák

Fiľko

Lipovský

et al. 2022

Drones

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“…The sum of squares due to error(SSE) of EWMA is 206.37. Also, the simple moving average(SMA) and the weighted moving average(WMA) are used to process this data as a comparison [ 32 , 33 ]. SSE of raw sensor data, SMA, and WMA are 257.44, 238.65, and 220.11, respectively.…”

Section: Data Processing On Fpgamentioning

confidence: 99%

Design and Application of Mixed Natural Gas Monitoring System Using Artificial Neural Networks

Wang

Lei

et al. 2021

Sensors

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Natural gas component analysis is one of the significant technologies in the exploitation and utilization of natural gas. A stable and accurate online natural gas monitoring system is necessary for the gas extracting industry. We have developed an online monitoring system of natural gas with a novel hardware architecture. It improves the dependability and maintainability of the system. A specific instruction set is designed to facilitate the coordination of software and hardware. To reduce the sample noise, the exponentially weighted moving average (EWMA) method is used to preprocess the real-time raw data of the sensor array. A tailored neural network is designed for calibration. And the relationship between the performance and the structure of the gas neural network is demonstrated to find the optimal solution for accuracy and hardware scale. The design not only focuses on the optimization of individual components but also focuses on system-level improvement. The system has been running stably for several months in the gas fields. It meets the requirements of stability, ease of use, maintainability, and online monitoring in industrial applications.

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“…Nevertheless, such methods are not capable of adjusting to the local frequency content, except for the adaptive filters which are time-consuming and require a reference signal, which can be a complication. An approximation of the local frequencies by an adjustable window function is crucial for the nonstationary signals, where we observe time-varying frequency content over time [26][27][28]. For this reason, we apply the wavelet-based filtration with the goal of time-frequency localization of the CO 2 signal trend [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

Vaňuš

Fiedorova

Kubíček

et al. 2020

Sensors

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The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

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Efficiency Estimation of the Noise Digital Filtering Algorithms

Cited by 9 publications

References 4 publications

Design of the System for Measuring UAV Parameters

Design of the System for Measuring UAV Parameters

Design and Application of Mixed Natural Gas Monitoring System Using Artificial Neural Networks

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

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