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Single-tone frequency estimation based on reformed covariance for half-length autocorrelation

Sergiusz Sienkowski Mariusz Krajewski
Metrology and Measurement Systems | 2020 | vol. 27 | No 3 | 473-493 | DOI: 10.24425/mms.2020.134590
Keywords frequency estimator sinusoidal signal autocorrelation function mean squared error
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Abstract

This paper presents a new simple and accurate frequency estimator of a sinusoidal signal based on the signal autocorrelation function (ACF). Such an estimator was termed as the reformed covariance for half-length autocorrelation (RC-HLA). The designed estimator was compared with frequency estimators well-known from the literature, such as the modified covariance for half-length autocorrelation (MC-HLA), reformed Pisarenko harmonic decomposition for half-length autocorrelation(RPHD-HLA), modified Pisarenko harmonic decomposition for half-length autocorrelation (MPHD-HLA), zero-crossing (ZC), and iterative interpolated DFT (IpDFT-IR) estimators. We determined the samples of the ACF of a sinusoidal signal disturbed by Gaussian noise (simulations studies) and the samples of the ACF of a sinusoidal voltage(experimental studies), calculated estimators based on the obtained samples, and computed the mean squared error(MSE) to compare the estimators. The errorswere juxtaposed with the Cramér–Rao lower bound (CRLB). The research results have shown that the proposed estimator is one of the most accurate, especially for SNR > 25 dB. Then the RC-HLA estimator errors are comparable to the MPHD-HLA estimator errors. However, the biggest advantage of the developed estimator is the ability to quickly and accurately determine the frequency based on samples collected from no more than five signal periods. In this case, the RC-HLA estimator is the most accurate of the estimators tested.

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Authors and Affiliations

Sergiusz Sienkowski
Mariusz Krajewski

Analysis of development tendencies of metrological technologies to control rangefinders of an electronic distance measurement instruments

Vsevolod Burachek Dmytro Khomushko Oleksiy Tereshchuk Sergíy Kryachok Vadim Belenok
Advances in Geodesy and Geoinformation | 2022 | vol. 71 | No 1 | e13 | DOI: 10.24425/gac.2022.140504
Keywords calibration rangefinder electronic distance measurement reference linear station root mean square error of distance measurement
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Abstract

The article analyses the development of metrological control technologies for electronic distance measurement rangefinders to determine their main characteristic of accuracy – the root mean square error of distance measurement. It is established that the current reference linear bases are reliable and serve as the main means of transmitting a unit of length from the standards to the working means of measuring length. The article describes the existing linear reference bases and specifies their accuracy and disadvantages. It is concluded that the disadvantages of linear reference bases are deprived of the reference linear bases built in special laboratories. They use distances measured by the differential method with laser interferometers as reference distances. The application of such technology allowed to automate the processes of measurements and calculations. There is development of fibre-optic linear bases, in which optical fibres of known length are used as model lines. The article offers a new technical solution – a combination of fiber-optic and interference linear bases, which allows to qualitatively improve the system of metrological support of laser rangefinders. This is achieved by having a fiber-optic unit, which allows you to create baselines of increased length, while ensuring small dimensions of the baseline, and relative interference base, which provides high accuracy of linear measurements and does not require calibration of the base with a precision rangefinder, which eliminates several difficulties associated with changes in the refractive index, makes measurements independent of the wavelength of the radiation source and almost independent of the ambient temperature.
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Authors and Affiliations

Vsevolod Burachek
1
ORCID: ORCID
Dmytro Khomushko
2
ORCID: ORCID
Oleksiy Tereshchuk
3
ORCID: ORCID
Sergíy Kryachok
3
ORCID: ORCID
Vadim Belenok
4
ORCID: ORCID
  1. University of Emerging Tehnologies, Kyiv, Ukraine
  2. Private entrepreneur, Chernihiv, Ukraine
  3. Chernihiv Polytechnic National University, Chernihiv, Ukraine
  4. National Aviation University, Kyiv, Ukraine

ZA-APA with Adaptive Zero Attractor Controller for Variable Sparsity Environment

S. Radhika A. Chandrasekar S. Nirmalraj
International Journal of Electronics and Telecommunications | 2020 | vol. 66 | No 4 | 695-700 | DOI: 10.24425/ijet.2020.134029
Keywords Zero Attraction APA sparse channel convergence steady state mean square error variable zero attraction controller
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Abstract

The zero attraction affine projection algorithm (ZA-APA) achieves better performance in terms of convergence rate and steady state error than standard APA when the system is sparse. It uses l1 norm penalty to exploit sparsity of the channel. The performance of ZA-APA depends on the value of zero attractor controller. Moreover a fixed attractor controller is not suitable for varying sparsity environment. This paper proposes an optimal adaptive zero attractor controller based on Mean Square Deviation (MSD) error to work in variable sparsity environment. Experiments were conducted to prove the suitability of the proposed algorithm for identification of unknown variable sparse system.

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Authors and Affiliations

S. Radhika
A. Chandrasekar
S. Nirmalraj

Using artificial neural networks to predict the reference evapotranspiration

Amal Abo El-Magd Shaimaa M. Baraka Samir F.M. Eid
Journal of Water and Land Development | 2023 | No 57 | 1-8 | DOI: 10.24425/jwld.2023.143768
Keywords climate data ETo calculator feed-forward artificial neural networks Penman–Monteith method reference evaporation root mean squared error
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Abstract

Artificial neural network models (ANNs) were used in this study to predict reference evapotranspiration ( ETo) using climatic data from the meteorological station at the test station in Kafr El-Sheikh Governorate as inputs and reference evaporation values computed using the Penman–Monteith (PM) equation. These datasets were used to train and test seven different ANN models that included different combinations of the five diurnal meteorological variables used in this study, namely, maximum and minimum air temperature ( Tmax and Tmin), dew point temperature ( Tdw), wind speed ( u), and precipitation (P), how well artificial neural networks could predict ETo values. A feed- forward multi-layer artificial neural network was used as the optimization algorithm. Using the tansig transfer function, the final architected has a 6-5-1 structure with 6 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer that corresponds to the reference evapotranspiration. The root mean square error ( RMSE) of 0.1295 mm∙day –1 and the correlation coefficient ( r) of 0.996 are estimated by artificial neural network ETo models. When fewer inputs are used, ETo values are affected. When three separate variables were employed, the RMSE test values were 0.379 and 0.411 mm∙day –1 and r values of 0.971 and 0.966, respectively, and when two input variables were used, the RMSE test was 0.595 mm∙day –1 and the r of 0.927. The study found that including the time indicator as an input to all groups increases the prediction of ETo values significantly, and that including the rain factor has no effect on network performance. Then, using the Penman–Monteith method to estimate the missing variables by using the ETo calculator the normalised root mean squared error ( NRMSE) reached about 30% to predict ETo if all data except temperature is calculated, while the NRMSE reached about of 13.6% when used ANN to predict ETo using variables of temperature only.
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Authors and Affiliations

Amal Abo El-Magd
1
ORCID: ORCID
Shaimaa M. Baraka
2
ORCID: ORCID
Samir F.M. Eid
1
ORCID: ORCID
  1. Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre (ARC) Nadi El-Said St. Dokki, P.O. Box 256, Giza, Egypt
  2. Ain Shams University, Faculty of Agriculture, Department of Agricultural Engineering, Cairo, Egypt

Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme for EEG Signals

Vivek Upadhyaya Mohammad Salim
International Journal of Electronics and Telecommunications | 2021 | vol. 67 | No 3 | 331-336 | DOI: 10.24425/ijet.2021.135985
Keywords Compressive Sensing (CS) Mean Square Error (MSE) Structural Similarity Index Measure (SSIM) EEG (Electroencephalogram) Digital Signal Processing (DSP) Block Sparse Bayesian Learning (BSBL)
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Abstract

Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.
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Bibliography

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Authors and Affiliations

Vivek Upadhyaya
1
ORCID: ORCID
Mohammad Salim
1
  1. Malaviya National Institute of Technology, India

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