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Number of results: 6
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Abstract

This paper presents a 3D distance measurement accuracy improvement for stereo vision systems using optimization methods A Stereo Vision system is developed and tested to identify common uncertainty sources. As the optimization methods are used to train a neural network, the resulting equation can be implemented in real time stereo vision systems. Computational experiments and a comparative analysis are conducted to identify a training function with a minimal error performance for such method. The offered method provides a general purpose modelling technique, attending diverse problems that affect stereo vision systems. Finally, the proposed method is applied in the developed stereo vision system and a statistical analysis is performed to validate the obtained improvements.

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Authors and Affiliations

J.C. Rodríguez-Quiñonez
O. Sergiyenko
W. Flores-Fuentes
M. Rivas-lopez
D. Hernandez-Balbuena
R. Rascón
P. Mercorelli
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Abstract

In the article, three types of proximity sensors that might be used in bicycle rangefinder to measure the distance between the bicycle and an overtaking car are compared. The influence of various factors on the accuracy of the distance measurements obtained using ultrasonic, infrared and laser sensors is tested, among others, light conditions, car surface type and colour, rain, pollination and vibrations.
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Bibliography

[1] M. De Angelis, V.M. Puchades, F. Fraboni, L. Pietrantoni, and G. Prati, “Negative attitudes towards cyclists influence the acceptance of an in-vehicle cyclist detection system,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 49, pp. 244–256, Aug. 2017. DOI: https://doi.org/10.1016/j.trf.2017.06.021
[2] E. Robartes, and T. D. Chen, “Crash histories, safety perceptions, and attitudes among Virginia bicyclists.” Journal of Safety Research, vol. 67, pp. 189–196, Dec. 2018. DOI: https://doi.org/10.1016/j.jsr.2018.10.009
[3] I. Walker, I. Garrard, and F. Jowitt, “The influence of a bicycle commuter's appearance on drivers’ overtaking proximities: An on-road test of bicyclist stereotypes, high-visibility clothing and safety aids in the United Kingdom,” Accident Analysis & Prevention, vol. 64, pp. 69–77, Mar. 2014. DOI: https://doi.org/10.1016/j.aap.2013.11.007
[4] B. Beck, D. Chong, J. Olivier, M. Perkins, A. Tsay, A. Rushford, L. Li, P. Cameron, R. Fry, and M. Johnson, “How much space do drivers provide when passing cyclists? Understanding the impact of motor vehicle and infrastructure characteristics on passing distance,” Accident Analysis & Prevention, vol. 128, pp. 253–260, Jul. 2019. DOI: https://doi.org/10.1016/j.aap.2019.03.007
[5] M. O'Reilly, “The device that measures cyclist passing distances,” http://www.executivestyle.com.au/the-device-that-measures-cyclist-passing-distances-gpehki (accessed on 8 July 2020).
[6] M. Dozza, R. Schindler, G. Bianchi-Piccinini, and J. Karlsson, “How do drivers overtake cyclists?” Accident Analysis & Prevention, vol. 88, pp. 29-36, Mar. 2016. DOI: https://doi.org/10.1016/j.aap.2015.12.008
[7] C3FT v1 | Codaxus LLC: http://codaxus.com/c3ft/c3ft-v1/ (accessed on 8 July 2020).
[8] C3FT v2 | Codaxus LLC: http://codaxus.com/c3ft/c3ft-v2/ (accessed on 8 July 2020).
[9] C3FT v3 | Codaxus LLC. http://codaxus.com/c3ft/c3ft-v3/ (accessed on 8 July 2020).
[10] A. K. Debnath, N. Haworth, A. Schramm, K. C.Heesch, and K. Somoray, “Factors influencing noncompliance with bicycle passing distance laws,”, Accident Analysis & Prevention, vol. 115, pp. 137-142, Jun. 2018. DOI: https://doi.org/10.1016/j.aap.2018.03.016
[11] J. Coburn, “Distance Sensor.” In: Build Your Own Car Dashboard with a Raspberry Pi. Apress, Berkeley, CA (2020). DOI: https://doi.org/10.1007/978-1-4842-6080-7_13
[12] Heckathorn, B.; MacPherson, T.; Schumacher, T., “ Distance Sensors,” http://www.eecs.umich.edu/courses/eecs270/270lab/270_docs/Distance%20Sensor%20Presentation.pdf (accessed on 8 July 2020).
[13] B.G. Pavithra, P. Siva Subba Rao, A. Sharmila, S. Raja, and S.J.Sushma, “Characteristics of different sensors used for Distance Measurement,” International Research Journal of Engineering and Technology (IRJET), vol. 4, pp. 698-702, Dec. 2017.
[14] S. Adarsh, S. Mohamed Kaleemuddin, B. Dinesh, and K.I. Ramachandran, “Performance comparison of Infrared and Ultrasonic sensors for obstacles of different materials in vehicle/ robot navigation applications,” Proc. IOP Conf. Series: Materials Science and Engineering, 149, 2016. DOI: 10.1088/1757-899X/149/1/012141
[15] J. Majchrzak, M. Michalski, and G. Wilczyński, “Distance Estimation With a Long-Range Ultrasonic Sensor System,” IEEE Sensors Journal, vol. 9, pp. 767–773, 2009.
[16] T. Mohammad, “Using Ultrasonic and Infrared Sensors for Distance Measurement,” International Journal of Mechanical and Mechatronics Engineering, vol. 3, no. 3, pp. 273-278, 2009.
[17] S. Rzydzik, A. Saltarski, M. Roziński, and K Psiuk, “Infrared Distance Sensors for Autonomous Model of Truck with Semi-trailer,” 2020 6th International Conference on Mechatronics and Robotics Engineering (ICMRE), Barcelona, Spain, 2020, pp. 104-109 (2020). DOI: 10.1109/ICMRE49073.2020.9065091
[18] W. Xu, C. Yan, W. Jia, X, Ji, and J. Liu, “Analyzing and Enhancing the Security of Ultrasonic Sensors for Autonomous Vehicles,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 5015–5029, Dec. 2018. DOI: 10.1109/JIOT.2018.2867917.
[19] R. Burnett, Ultrasonic vs Infrared (IR) Sensors – Which is better? https://www.maxbotix.com/articles/ultrasonic-or-infrared-sensors.htm (accessed on 8 July 2020).
[20] Distance Sensor Comparison Guide. https://www.sparkfun.com/distance_sensor_comparison_guide (accessed on 8 July 2020).
[21] HC-SR04 (ultrasound) vs Sharp GP2Y0A02YK0F (IR) vs VL53L0X (Laser), which solution to choose for distance measurement with Arduino or Raspberry Pi. https://diyprojects.io/hc-sr04-ultrasound-vs-sharp-gp2y0a02yk0f-ir-vl53l0x-laser-solutions-choose-distance-measurement-arduino-raspberrypi/#.XSWSkBLTAsc (accessed on 8 July 2020).
[22] https://diyprojects.io/proximity-sensor-a02yk0-test-calibration-sharp-gp2y0a02yk0f-asian-clone/#.XSMH7xLTAsc (accessed on 8 July 2020).
[23] Product User’s Manual – HC-SR04 Ultrasonic Sensor. Cytron Technologies, 2013. GP2Y0A02YK0F. Sharp Corporations, 2006
[24] A. Szydło, A device that measures the distance between a bicycle and a car. Master thesis written under supervision of Bartłomiej Zieliński, Silesian University of Technology, Institute of Computer Science, Gliwice 2017 [in Polish].
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Authors and Affiliations

Bartłomiej Zieliński
1

  1. Silesian University of Technology, Poland
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Abstract

A novel laser diode based length measuring interferometer for scientific and industrial metrology is presented. Wavelength the stabilization system applied in the interferometer is based on the optical wedge interferometer. Main components of the interferometer such as: laser diode stabilization assembly, photodetection system, measuring software, air parameters compensator and base optical assemblies are described. Metrological properties of the device such as resolution, measuring range, repeatability and accuracy are characterized.

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Authors and Affiliations

Marek Dobosz
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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
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Abstract

Different temperature sensors show different measurement values when excited by the same dynamic temperature source. Therefore, a method is needed to determine the difference between dynamic temperature measurements. This paper proposes a novelty approach to treating dynamic temperature measurements over a period of time as a temperature time series, and derives the formula for the distance between the measurement values using uniformsampling within the time series analysis. The similarity is defined in terms of distance to measure the difference. The distance measures were studied on the analog measurement datasets. The results show that the discrete Fréchet distance has stronger robustness and higher sensitivity. The two methods have also been applied to an experimental dataset. The experimental results also confirm that the discrete Fréchet distance performs better.
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Authors and Affiliations

Zhiwen Cui
1
Wenjun Li
1
Sisi Yu
1
Minjun Jin
1

  1. College of Metrological Technology and Engineering, China Jiliang University, Hangzhou 310018, China
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Abstract

The secretiveness of sonar operation can be achieved by using continuous frequency-modulated sounding signals with reduced power and significantly prolonged repeat time. The application of matched filtration in the sonar receiver provides optimal conditions for detection against the background of white noise and reverberation, and a very good resolution of distance measurements of motionless targets. The article shows that target movement causes large range measurement errors when linear and hyperbolic frequency modulations are used. The formulas for the calculation of these errors are given. It is shown that for signals with linear frequency modulation the range resolution and detection conditions deteriorate. The use of hyperbolic frequency modulation largely eliminates these adverse effects.

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Authors and Affiliations

Jacek Marszal
Roman Salamon

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