Вимірювальна техніка та метрологія. – 2023. – Випуск 84, №3

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Measuring Equipment and Metrology : scientific journal. – Lviv : Lviv Politechnic Publishing House, 2023. – Volume 84, № 3. – 45 р.

Вимірювальна техніка та метрологія

Зміст

1. Титульний аркуш до “Вимірювальна техніка та метрологія”	1
2. Prodan R., Shutka D., Tataryn V. Measurement and analysis of agricultural field state using cloud-based data processing pipeline Preventing potential robbery crimes using deep learning algorithm of data processing	5
3. Chaban V. Theoretical substantiation of the results of measuring anomalies of spacecraft trajectories	11
4. Shutka D., Prodan R., Tataryn V. Preventing potential robbery crimes using deep learning algorithm of data processing	16
5. Rudavskyi I., Klym H., Popov A. Design and evaluation of a smart indoor air quality monitoring system	23
6. Nikolskiy S., Klymenko I. Embedded IoT platform for remote traffic control in smart city IoT infrastructure	31
7. Yatsyshyn S., Zeng X. Design of the water strider-like robot	39
8. Skoropad P., Lihnovskyi I., Hamula P., Mastylo R. Criteria of materials metal glasses selection for thermometry	43
9. Gaman V., Kursin S., Velychko O. Generalized risk assessment procedure for software testing of legally regulated measuring instruments	47
10. Chaplia O., Klym H. Node.js project architecture with shared dependencies for microservices	53
11. Bubela T., Bohush B. Preconditions for the creation of a meat freshness control and identification system	59
12. Зміст до “Вимірювальна техніка та метрологія”	66

Content (Vol. 84, No 3)

1. Tytulnyi arkush do "Vymiriuvalna tekhnika ta metrolohiia"	1
2. Prodan R., Shutka D., Tataryn V. Measurement and analysis of agricultural field state using cloud-based data processing pipeline Preventing potential robbery crimes using deep learning algorithm of data processing	5
3. Chaban V. Theoretical substantiation of the results of measuring anomalies of spacecraft trajectories	11
4. Shutka D., Prodan R., Tataryn V. Preventing potential robbery crimes using deep learning algorithm of data processing	16
5. Rudavskyi I., Klym H., Popov A. Design and evaluation of a smart indoor air quality monitoring system	23
6. Nikolskiy S., Klymenko I. Embedded IoT platform for remote traffic control in smart city IoT infrastructure	31
7. Yatsyshyn S., Zeng X. Design of the water strider-like robot	39
8. Skoropad P., Lihnovskyi I., Hamula P., Mastylo R. Criteria of materials metal glasses selection for thermometry	43
9. Gaman V., Kursin S., Velychko O. Generalized risk assessment procedure for software testing of legally regulated measuring instruments	47
10. Chaplia O., Klym H. Node.js project architecture with shared dependencies for microservices	53
11. Bubela T., Bohush B. Preconditions for the creation of a meat freshness control and identification system	59
12. Zmist do "Vymiriuvalna tekhnika ta metrolohiia"	66

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Preconditions for the creation of a meat freshness control and identification system
(Видавництво Львівської політехніки, 2023-02-28) Bubela, Tetiana; Bohush, Bohdan; Lviv Polytechnic National University
The relevance of creating a comprehensive system for meat control and identification to determine its freshness level has been demonstrated in the study. The drawbacks of traditional organoleptic and laboratory methods commonly used for meat inspection were analyzed. The authors presented the advantages and challenges of employing an electronic nose. A design for a meat control and identification system was proposed, which includes an Arduino Uno microcontroller, Raspberry Pi, USB to TTL adapter, gas sensors, color sensor, thermal camera, and image sensor. The proposed implementation of the electronic nose system on a single-board computer demonstrates its success in controlling and identifying meat freshness. A matrix of semiconductor gas sensors, TGS2602, MQ137, and MQ138, is formed as olfactory sensors, and TCS3200 is used as an RGB vision sensor, enabling the identification of the smell and color of different degrees of meat freshness. To obtain clear output differences from the gas sensors that react to the freshness level of meat, the baseline method is proposed for use. Therefore, a system enhanced with neural network capabilities will replace traditional devices for identifying meat freshness.