Effect of phase in fast frequency measurements for sensors embedded in robotic systems

Sanchez-Lopez, Juan de Dios; Murrieta-Rico, Fabián N.; Petranovskii, Vitalii; Antúnez-García, Joel; Yocupicio-Gaxiola, Rosario I.; Sergiyenko, Oleg; Tyrsa, Vera; Hipólito, Juan Iván Nieto; Vazquez-Briseno, Mabel

doi:10.1177/1729881419869727

Cited by 10 publications

(3 citation statements)

References 20 publications

(37 reference statements)

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“…By accurately measuring physical parameters, robots are able to perceive their environment and fulfill their purpose correctly. Many sensors output signals, whose frequency depends on certain input stimuli [1,2]. These include piezoelectric sensors [3,4] or accelerometers [5].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Processing Time of Cross-Correlation Spectra for Frequency Measurements of Noisy Signals

Liu

Liu²,

Kennel

2022

Metrology

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Accurate frequency measurement plays an important role in many industrial and robotic systems. However, different influences from the application’s environment cause signal noises, which complicate frequency measurement. In rough environments, small signals are intensively disturbed by noises. Thus, even negative Signal-to-Noise Ratios (SNR) are possible in practice. Thus, frequency measuring methods, which can be used for low SNR signals, are in great demand. In previous work, the method of cross-correlation spectrum has been developed as an alternative to Fast Fourier-Transformation or Continuous Wavelet Transformation. It is able to determine the frequencies of a signal under strong noise and is not affected by Heisenberg’s uncertainty principle. However, in its current version, its creation is computationally very intensive. Thus, its application to real-time operations is limited. In this article, a new way to create the cross-correlation spectrum is presented. It is capable of reducing the calculation time by 89% without significant accuracy loss. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with an SNR of −14 dB and a signal length of 2000 data points. When applied to “self-mixing”-interferometry signals, the method can reach a normalized root-mean-square error of 0.21% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended.

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Section: Introductionmentioning

confidence: 99%

Optimization of the Processing Time of Cross-Correlation Spectra for Frequency Measurements of Noisy Signals

Liu

Liu²,

Kennel

2022

Metrology

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show abstract

“…By precisely measuring physical parameters, robots are capable of perceiving their environment and fulfilling their purpose correctly. Many sensors generate signals, whose frequency depends on certain input stimulus [1,2]. These include accelerometers [3] or piezoelectric sensors [4,5].…”

Section: Introductionmentioning

confidence: 99%

Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation

Liu

Liu²,

Kennel

2021

Machines

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Precise frequency measurement plays an essential role in many industrial and robotic systems. However, different effects in the application’s environment cause signal noises, which make frequency measurement more difficult. In small signals or rough environments, even negative Signal-to-Noise Ratios (SNRs) are possible. Thus, frequency measuring methods, which are suited for low SNR signals, are in great demand. While denoising methods such as autocorrelation do not suffice for small signal with low SNR, frequency measurement methods such as Fast-Fourier Transformation or Continuous Wavelet Transformation suffer from Heisenberg’s uncertainty principle, which makes simultaneous high frequency and time resolutions impossible. In this paper, the cross-correlation spectrum is presented as a new frequency measuring method. It can be used in any frequency domain, and provides greater denoising than autocorrelation. Furthermore, frequency and time resolutions are independent from one another, and can be set separately by the user. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with a SNR of −10 dB and a signal length of 1000 data points. When applied to “self-mixing”-interferometry signals, the method can reach a normalized root-mean square error of 0.2% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended.

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“…Another promising technique is based on ultrasounds, which provide inline, non-invasive tools that exploit the Doppler echo of the signal through the material. Important applications of ultrasound can be found in design and construction of frequency-domain sensors [7,8,9]. Velocity profile measurements were obtained by Kotzé et al [10], who proposed a method that combines the ultrasonic velocity profiling technique with pressure difference measurements.…”

Section: Introductionmentioning

confidence: 99%

In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Tronci

Neer²,

Giling³

et al. 2019

Sensors

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The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

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Effect of phase in fast frequency measurements for sensors embedded in robotic systems

Cited by 10 publications

References 20 publications

Optimization of the Processing Time of Cross-Correlation Spectra for Frequency Measurements of Noisy Signals

Optimization of the Processing Time of Cross-Correlation Spectra for Frequency Measurements of Noisy Signals

Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation

In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

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