izvestswsuИзвестия Юго-Западного государственного университетаProceedings of the Southwest State University2223-15602686-6757ЮЗГУ10.21869/2223-1560-2022-26-3-129-150izvestswsu-1041Research ArticleИнформатика, вычислительная техника и управлениеComputer science, computer engineering and IT managmentПодход и алгоритм оценки допустимых значений отношения сигнал/шум лидаров роботов в условиях внешних воздействийApproach and Algorithm for Evaluating the Allowed Signal/Noise Ratio of Robotic Lidars under External Influenceshttps://orcid.org/0000-0002-6366-9786МамченкоМ. В.MamchenkoM. V.

Мамченко Марк Владиславович, научный  сотрудник лаборатории киберфизических систем

ул. Профсоюзная, д. 65, г. Москва 117997

Mark V. Mamchenko, Researcher, Cyberphysical Systems Lab.

65, Profsoyuznaya str., Moscow 117997

markmamcha@gmail.comИнститут проблем управления им. В.А. Трапезникова Российской академии наукV.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences202221022023263129150Copyright © Мамченко М.В., 20232023Мамченко М.В.Mamchenko M.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://izvestswsu.elpub.ru/jour/article/view/1041

Цель исследования заключается в обеспечении безопасного функционирования робототехнических средств за счет разработки методов, подходов и алгоритмов обработки информации и описания их функционирования.

Методы

Методы. В работе предлагается подход к оценке допустимого отношения сигнал/шум (ОСШ) для лидаров роботов на основе заданной вероятности появления «ложной тревоги» в условиях непреднамеренных воздействий. В основе представленного синтезированного вероятностного подхода лежат физические основы инфракрасного излучения и байесовская теория с применением критерия Неймана-Пирсона. Особенностью предлагаемого подхода является использование в аналитическом аппарате не только заданного порога появления ложной тревоги и вероятности возникновения интерференции, но и учет характеристик фотоприемных устройств лидаров. Это позволяет аналитически рассчитать величину допустимого ОСШ при стабилизации уровня «ложных тревог» на фоне шумов, вызванных данным видом помех.

Результаты

Результаты. Сформированные и представленные в работе зависимости могут использоваться в качестве одной из эксплуатационных характеристик при разработке и выборе оптоэлектронной системы измерения лидаров. Исходя из фиксированного значения «ложной тревоги» и полученного графического выражения полученной рабочей характеристики (полученных характеристик) возможно подобрать лидар с необходимыми техническими параметрами.

Заключение

Заключение. Разработан вероятностный подход и соответствующий алгоритм выбора порогового значения ОСШ, основанный на сущности критерия Неймана-Пирсона. Подход позволяет минимизировать значение вероятности «игнорирования» объекта при сканировании за счет недопущения превышения вероятности «ложной тревоги» заданного порогового значения. Представлено математическое и методологическое обеспечение для проектирования лидаров с учетом априорной оценки допустимого значения ОСШ и вероятности обнаружения отраженного импульса, без учета предварительных оценок вероятностных характеристик обнаружения объектов лидаром. В представленном алгоритме на вход подается набор необработанных данных – в виде значений полученного сигнала с шумовой составляющей. Выходные данные представлены множеством зависимостей вероятности ошибок для различных пороговых значений отношения сигнал/шум.

Purpose or research

Purpose or research. The aim of the study is to ensure the safe operation of robotics by developing methods, approaches and algorithms for information processing, and describing their functioning.

Methods

Methods. The paper proposes an approach to estimation allowed signal/noise ratio (SNR) for robotic LiDARs based on the predetermined probability of occurrence of «false alarm» under unintended influences. The synthesized probabilistic approach is based on the physical fundaments of infrared radiation, and the Bayesian theory using the Neyman-Pearson criterion. The feature of the proposed approach is the use of the given threshold of «false alarm» occurrence, and the probability of occurrence of interference in the analytical apparatus, as well as consideration of the characteristics of photodetectors. This allows expressing analytically and calculating the value of the allowed SNR when stabilizing the level of «false alarms» against background noise caused by this type of interference.

Results

Results. The formed and presented dependencies can be used as one of the operating characteristics in the development and selection of optoelectronic system of LiDAR’s measurement system. Based on the fixed value of «false alarm», and the resulting graphical expression of the operating characteristic (obtained characteristics) it is possible to choose a LiDARs system with necessary technical parameters.

Conclusion

Conclusion. The probabilistic approach and the corresponding algorithm for selecting the threshold SNR value based on the Neyman-Pearson criterion were developed. The approach allows minimizing the probability of «ignoring» the object when scanning, since the probability of «false alarm» does not exceed the given threshold value. Mathematical and methodological support for the design of LiDARs is presented, taking into account a priori estimation of the allowed SNR value, and the probability of reflected pulse detection, without preliminary estimates of probabilistic characteristics of object detection. The presented algorithm has a set of raw data (in the form of the values of the received signal with a noise component) as an input. Its output is represented by a set of error probability dependencies for different SNR thresholds.

вероятностный подходлидаротношение сигнал/шумОСШложная тревогакритерий Неймана-Пирсонапомехавнешние воздействияИсследование выполнено при частичной финансовой поддержке РФФИ в рамках научного проекта № 19-29-06044. Автор выражает благодарность и признательность научному сотруднику лаборатории киберфизических систем ИПУ РАН Романовой Марии Андреевне за существенный вклад в подготовку и написание настоящей статьи.The reported study was partially funded by RFBR, number 19-29-06044. The author expresses gratitude and appreciation to Mariya A. Romanova, researcher of Cyberphysical Systems Laboratory of ICS RAS, for the significant contribution to the preparation and writing of this article.ReferencesHeinzler R., Schindler P., Seekircher J., Ritter W., Stork W. Weather Influence and Classification with Automotive Lidar Sensors. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV). 2019; 1527–1534. https://doi.org/10.1109/IVS.2019.8814205.Heinzler R., Schindler P., Seekircher J., Ritter W., Stork W. Weather Influence and Classification with Automotive Lidar Sensors. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV). 2019; 1527–1534. https://doi.org/10.1109/IVS.2019.8814205.Lin S.-L., Wu B.-H. Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather. Applied Sciences. 2021; 11(7:3018): 1–16. https://doi.org/10.3390/app11073018.Lin S.-L., Wu B.-H. Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather. Applied Sciences. 2021; 11(7:3018): 1–16. https://doi.org/10.3390/app11073018.Charron N., Phillips S., Waslander S.L. De-noising of Lidar Point Clouds Corrupted by Snowfall. Proceedings of the 2018 15th Conference on Computer and Robot Vision (CRV). 2018; 254–261. https://doi.org/10.1109/CRV.2018.00043.Charron N., Phillips S., Waslander S.L. De-noising of Lidar Point Clouds Corrupted by Snowfall. Proceedings of the 2018 15th Conference on Computer and Robot Vision (CRV). 2018; 254–261. https://doi.org/10.1109/CRV.2018.00043.Changalvala R., Malik H. LiDAR Data Integrity Verification for Autonomous Vehicle. IEEE Access. 2019; 7: 138018–138031. https://doi.org/10.1109/ACCESS.2019.2943207.Changalvala R., Malik H. LiDAR Data Integrity Verification for Autonomous Vehicle. IEEE Access. 2019; 7: 138018–138031. https://doi.org/10.1109/ACCESS.2019.2943207.Shin H., Kim D., Kwon Y., Kim Y. Illusion and Dazzle: Adversarial Optical Channel Exploits against Lidars for Automotive Applications. Lecture Notes in Computer Science. 2017; 10529: 445–467. https://doi.org/10.1007/978-3-319-66787-4_22.Shin H., Kim D., Kwon Y., Kim Y. Illusion and Dazzle: Adversarial Optical Channel Exploits against Lidars for Automotive Applications. Lecture Notes in Computer Science.Hwang I.-P., Yun S.-j., Lee C.-H. Study on the Frequency-Modulated ContinuousWave LiDAR Mutual Interference. Proceedings of the 2019 IEEE 19th International Conference on Communication Technology (ICCT). 2019; 1053–1056. https://doi.org/10.1109/ICCT46805.2019.8947067.; 10529: 445–467. https://doi.org/10.1007/978-3-319-66787-4_22.Godbaz J.P., Dorrington A.A., Cree M.J. Understanding and Ameliorating Mixed Pixels and Multipath Interference in AMCW Lidar. TOF Range-Imaging Cameras. 2013; 91–116. https://doi.org/10.1007/978-3-642-27523-4_5.Hwang I.-P., Yun S.-j., Lee C.-H. Study on the Frequency-Modulated ContinuousWave LiDAR Mutual Interference. Proceedings of the 2019 IEEE 19th International Conference on Communication Technology (ICCT). 2019; 1053–1056. https://doi.org/10.1109/ICCT46805.2019.8947067.Kim G., Eom J., Hur S., Park Y. Analysis on the characteristics of mutual interference between pulsed terrestrial LIDAR scanners. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015; 2151–2154. https://doi.org/10.1109/IGARSS.2015.7326229.Godbaz J.P., Dorrington A.A., Cree M.J. Understanding and Ameliorating Mixed Pixels and Multipath Interference in AMCW Lidar. TOF Range-Imaging Cameras. 2013; 91–116. https://doi.org/10.1007/978-3-642-27523-4_5.Kim G., Eom J., Park S., Park Y. Occurrence and characteristics of mutual interference between LIDAR scanners. Photon Counting Applications. Proceedings. 2015; 9504: 1–9. https://doi.org/10.1117/12.2178502.Kim G., Eom J., Hur S., Park Y. Analysis on the characteristics of mutual interference between pulsed terrestrial LIDAR scanners. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015; 2151–2154. https://doi.org/10.1109/IGARSS.2015.7326229.Kim G., Eom J., Park Y. Investigation on the occurrence of mutual interference between pulsed terrestrial LIDAR scanners. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV). 2015; 437–442. https://doi.org/10.1109/IVS.2015.7225724.Kim G., Eom J., Park S., Park Y. Occurrence and characteristics of mutual interference between LIDAR scanners. Photon Counting Applications. Proceedings. 2015; 9504: 1– 9. https://doi.org/10.1117/12.2178502.Eom J., Kim G., Hur S., Park Y. Assessment of Mutual Interference Potential and Impact with off-the-Shelf Mobile LIDAR. Advanced Photonics 2018 (BGPP, IPR, NP, NOMA, Sensors, Networks, SPPCom, SOF). 2018; (JTu2A.66): 1–14. ttps://doi.org/10.1364/BGPPM.2018.JTu2A.66.Kim G., Eom J., Park Y. Investigation on the occurrence of mutual interference between pulsed terrestrial LIDAR scanners. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV). 2015; 437–442. https://doi.org/10.1109/IVS.2015.7225724.Park Y., Kim G., Eom J. Design of pulsed scanning lidar without mutual interferences. Smart Photonic and Optoelectronic Integrated Circuits XX. Proceedings. 2018; 10536: 1–6. https://doi.org/10.1117/12.2288740.Eom J., Kim G., Hur S., Park Y. Assessment of Mutual Interference Potential and Impact with off-the-Shelf Mobile LIDAR. Advanced Photonics 2018 (BGPP, IPR, NP, NOMA, Sensors, Networks, SPPCom, SOF). 2018; (JTu2A.66): 1–14. ttps://doi.org/10.1364/BGPPM.2018.JTu2A.66.Popko G.B., Gaylord T.K., Valenta C.R. Geometric approximation model of interlidar interference. Optical Engineering. 2020; 59(3:033104): 1–21. https://doi.org/10.1117/1.OE.59.3.033104.Park Y., Kim G., Eom J. Design of pulsed scanning lidar without mutual interferences. Smart Photonic and Optoelectronic Integrated Circuits XX. Proceedings. 2018; 10536: 1–6. https://doi.org/10.1117/12.2288740.Zhang F., Du P., Liu Q., Gong M., Fu X. Adaptive strategy for CPPM single-photon collision avoidance LIDAR against dynamic crosstalk. Optics Express. 2017; 25(11): 12237–12250. https://doi.org/10.1364/OE.25.012237.Popko G.B., Gaylord T.K., Valenta C.R. Geometric approximation model of interlidar interference. Optical Engineering. 2020; 59(3:033104): 1–21. https://doi.org/10.1117/1.OE.59.3.033104.Diehm A.L., Hammer M., Hebel M., Arens M. Mitigation of crosstalk effects in multi-LiDAR configurations. Electro-Optical Remote Sensing XII. Proceedings. 2018; 10796(1079604): 1–12. https://doi.org/10.1117/12.2324305.Zhang F., Du P., Liu Q., Gong M., Fu X. Adaptive strategy for CPPM single-photon collision avoidance LIDAR against dynamic crosstalk. Optics Express. 2017; 25(11): 12237–12250. https://doi.org/10.1364/OE.25.012237.Wu J., Xu H., Tian Y., Pi R., Yue R. Vehicle Detection under Adverse Weather from Roadside LiDAR Data. Sensors. 2020; 20(12:3433): 1–17. https://doi.org/10.3390/s20123433.Diehm A.L., Hammer M., Hebel M., Arens M. Mitigation of crosstalk effects in multi-LiDAR configurations. Electro-Optical Remote Sensing XII. Proceedings. 2018; 10796(1079604): 1–12. https://doi.org/10.1117/12.2324305.Kutila M., Pyykönen P., Ritter W., Sawade O., Schäufele B. Automotive LIDAR sensor development scenarios for harsh weather conditions. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). 2016; 265–270. https://doi.org/10.1109/ITSC.2016.7795565.Wu J., Xu H., Tian Y., Pi R., Yue R. Vehicle Detection under Adverse Weather from Roadside LiDAR Data. Sensors. 2020; 20(12:3433): 1–17. https://doi.org/10.3390/s20123433.Jokela M., Pyykönen P., Kutila M., Kauvo K. LiDAR Performance Review in Arctic Conditions. Proceedings of the 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP). 2019; 27–31. https://doi.org/10.1109/ICCP48234.2019.8959554.Kutila M., Pyykönen P., Ritter W., Sawade O., Schäufele B. Automotive LIDAR sensor development scenarios for harsh weather conditions. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). 2016; 265–270. https://doi.org/10.1109/ITSC.2016.7795565.Park J.-I., Park J., Kim K.-S. Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds. IEEE Access. 2020; 8: 160202–160212. https://doi.org/10.1109/ACCESS.2020.3020266.Jokela M., Pyykönen P., Kutila M., Kauvo K. LiDAR Performance Review in Arctic Conditions. Proceedings of the 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP). 2019; 27–31. https://doi.org/10.1109/ICCP48234.2019.8959554.Ronen A., Agassi E., Yaron O. Sensing with Polarized LIDAR in Degraded Visibility Conditions Due to Fog and Low Clouds. Sensors. 2021; 21(7:2510): 1–13. https://doi.org/10.3390/s21072510.Park J.-I., Park J., Kim K.-S. Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds. IEEE Access. 2020; 8: 160202–160212. https://doi.org/10.1109/ACCESS.2020.3020266.Vargas Rivero J.R., Gerbich T., Buschardt B., Chen J. Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations. Sensors. 2021; 21(13:4503): 1–16. https://doi.org/10.3390/s21134503.Ronen A., Agassi E., Yaron O. Sensing with Polarized LIDAR in Degraded Visibility Conditions Due to Fog and Low Clouds. Sensors. 2021; 21(7:2510): 1–13. https://doi.org/10.3390/s21072510.Chen Y., Xie Y., Liu C., Chen L. Investigation of Anti-Interference Characteristics of Frequency-Hopping LiDAR. IEEE Photonics Technology Letters. 2021; 33(24): 1443–1446. https://doi.org/10.1109/LPT.2021.3125702.Vargas Rivero J.R., Gerbich T., Buschardt B., Chen J. Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations. Sensors. 2021; 21(13:4503): 1–16. https://doi.org/10.3390/s21134503.Yu M., Shi M., Hu W., Yi L. FPGA-Based Dual-Pulse Anti-Interference Lidar System Using Digital Chaotic Pulse Position Modulation. IEEE Photonics Technology Letters. 2021; 33(15): 757–760. https://doi.org/10.1109/LPT.2021.3093109.Chen Y., Xie Y., Liu C., Chen L. Investigation of Anti-Interference Characteristics of Frequency-Hopping LiDAR. IEEE Photonics Technology Letters. 2021; 33(24): 1443–1446. https://doi.org/10.1109/LPT.2021.3125702.Seo H., Cho G., Kim J., Bae J., Kim S.-J., Chun J.-H., Choi J. A CMOS LiDAR Sensor with Pre-Post Weighted-Histogramming for Sunlight Immunity over 105 klx and SPAD-based Infinite Interference Canceling. Proceedings of the 2021 Symposium on VLSI Circuits. 2021; 1–2. https://doi.org/10.23919/VLSICircuits52068.2021.9492328.Yu M., Shi M., Hu W., Yi L. FPGA-Based Dual-Pulse Anti-Interference Lidar System Using Digital Chaotic Pulse Position Modulation. IEEE Photonics Technology Letters. 2021; 33(15): 757–760. https://doi.org/10.1109/LPT.2021.3093109.Tsai C.-M., Liu Y.C. Anti-Interference Single-Photon LiDAR Using Stochastic Pulse Position Modulation. Optics Letters. 2020; 45(2): 439–442. https://doi.org/10.1364/OL.384894.Seo H., Cho G., Kim J., Bae J., Kim S.-J., Chun J.-H., Choi J. A CMOS LiDAR Sensor with Pre-Post Weighted-Histogramming for Sunlight Immunity over 105 klx and SPAD-based Infinite Interference Canceling. Proceedings of the 2021 Symposium on VLSI Circuits. 2021; 1–2. https://doi.org/10.23919/VLSICircuits52068.2021.9492328.Ishizaki Y., Zhang C., Set S.Y., Yamashita S. A Novel Software-Based Optical Sampling Scheme for High-Precision and Interference-Free Time-of-Flight LiDAR. Proceedings of the 2020 Conference on Lasers and Electro-Optics (CLEO). 2020; 1–2.Tsai C.-M., Liu Y.C. Anti-Interference Single-Photon LiDAR Using Stochastic Pulse Position Modulation. Optics Letters. 2020; 45(2): 439–442. https://doi.org/10.1364/OL.384894.Fersch T., Weigel R., Koelpin A. A CDMA Modulation Technique for Automotive Time-of-Flight LiDAR Systems. IEEE Sensors Journal. 2017; 17(11): 3507–3516. https://doi.org/10.1109/JSEN.2017.2688126.Ishizaki Y., Zhang C., Set S.Y., Yamashita S. A Novel Software-Based Optical Sampling Scheme for High-Precision and Interference-Free Time-of-Flight LiDAR. Proceedings of the 2020 Conference on Lasers and Electro-Optics (CLEO). 2020; 1–2.Cheng C.-H., Chen C.-Y., Chen J.-D., Pan D.-K., Ting K.-T., Lin F.-Y. 3D pulsed chaos lidar system. Optics Express. 2018; 26(9): 12230–12241. https://doi.org/10.1364/OE.26.012230.Fersch T., Weigel R., Koelpin A. A CDMA Modulation Technique for Automotive Time-of-Flight LiDAR Systems. IEEE Sensors Journal. 2017; 17(11): 3507–3516. https://doi.org/10.1109/JSEN.2017.2688126.Matsumura R., Sugawara T., Sakiyama K. A Secure LIDAR with AES-based SideChannel Fingerprinting. 2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW). 2018; 479–482. https://doi.org/10.1109/CANDARW.2018.00092.Cheng C.-H., Chen C.-Y., Chen J.-D., Pan D.-K., Ting K.-T., Lin F.-Y. 3D pulsed chaos lidar system. Optics Express. 2018; 26(9): 12230–12241. https://doi.org/10.1364/OE.26.012230.Takefuji Y. Connected Vehicle Security Vulnerabilities [Commentary]. IEEE Technology and Society Magazine. 2018; 37(1): 15–18. https://doi.org/10.1109/MTS.2018.2795093.Matsumura R., Sugawara T., Sakiyama K. A Secure LIDAR with AES-based SideChannel Fingerprinting. 2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW). 2018; 479–482. https://doi.org/10.1109/CANDARW.2018.00092.Boreysho A.S., Kim A.A., Konyaev M.A., Luginya V.S., Morozov A.V., Orlov A.E. Modern Lidar Systems for Atmosphere Remote Sensing. PHOTONICS RUSSIA. 2019; 7(13): 648–657. https://doi.org/10.22184/1992-7296.FRos.2019.13.7.648.657Takefuji Y. Connected Vehicle Security Vulnerabilities [Commentary]. IEEE Technology and Society Magazine. 2018; 37(1): 15–18. https://doi.org/10.1109/MTS.2018.2795093.Межерис Р. Лазерное дистанционное зондирование. М.: Мир, 1987.Boreysho A.S., Kim A.A., Konyaev M.A., Luginya V.S., Morozov A.V., Orlov A.E. Modern Lidar Systems for Atmosphere Remote Sensing. PHOTONICS RUSSIA. 2019; 7(13): 648–657. https://doi.org/10.22184/1992-7296.FRos.2019.13.7.648.657Салех Б., Тейх М. Оптика и фотоника. Принципы и применения: в 2 т. Долгопрудный: Издательский дом «Интеллект», 2012. Т. 2.Mejeris R. Lazernoe distantsionnoe zondirovanie [Remote laser sensing]. Moscow, Mir Publ., 1987.Hwang I.-P., Lee C.-H. Mutual Interferences of a True-Random LiDAR with Other LiDAR Signals. IEEE Access. 2020; 8: 124123–124133. https://doi.org/10.1109/ACCESS.2020.3004891.Saleh B., Teih M. Optika i fotonika. Printsipy i primeneniya [Optics and photonics.Meshcheryakov R., Iskhakov A., Mamchenko M., Romanova M., Uvaysov S., Amirgaliyev Y., Gromaszek K. A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences. Sensors. 2022; 22(2:609): 1–31. https://doi.org/10.3390/s22020609.Principles and applications] Dolgoprudny, 2012. Vol. 2.Hwang I.-P., Lee C.-H. Mutual Interferences of a True-Random LiDAR with Other LiDAR Signals. IEEE Access. 2020; 8: 124123–124133. https://doi.org/10.1109/ACCESS.2020.3004891.Hwang I.-P., Lee C.-H. Mutual Interferences of a True-Random LiDAR with Other LiDAR Signals. IEEE Access. 2020; 8: 124123–124133. https://doi.org/10.1109/ACCESS.2020.3004891.Meshcheryakov R., Iskhakov A., Mamchenko M., Romanova M., Uvaysov S., Amirgaliyev Y., Gromaszek K. A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences. Sensors.Meshcheryakov R., Iskhakov A., Mamchenko M., Romanova M., Uvaysov S., Amirgaliyev Y., Gromaszek K. A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences. Sensors.; 22(2:609): 1–31. https://doi.org/10.3390/s22020609.; 22(2:609): 1–31. https://doi.org/10.3390/s22020609.

The authors declare that there are no conflicts of interest present.