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НИЛ АСЭМ Научно - исследовательская лаборатория автоматизированных систем экологического мониторинга

Экологический мониторинг

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by Admin » Mon Aug 27, 2018 2:56 pm

31. Nikhilesh M. Dharmadhikari and Dr. Y. B. Thakare. Designing and Applications of PIC Microcontroller Based Green House Monitoring and Controlling System // International Journal of Electronics and Communication Engineering. - 2015. V.8, №2, pp. 107-121.

Abstract: Greenhouses play an important part in the agriculture and horticulture sectors in our country, as they can be used to grow plants under controlled climatic conditions during any period of year for optimum produce. While tradition crop cultivation requires a tremendous amount of hard work and attention and there are several disadvantages in implementing traditional cultivation techniques. Automation of a greenhouse for monitoring and controlling of various climatic conditions which directly or indirectly govern the plant growth and hence their yield is very important. Automation is process control of industrial machinery and processes, thereby replacing human operators. This system will be useful for farmers for cultivation of economically important plants. Most crops can only be grown in certain climates during certain times of the year. The rise of Controlled Environment Agriculture (CEA) proposes a new direction for agriculture. CEA is an agriculture technique which allows for the growth of plants in controlled conditions. The main focus of the present study is on building user friendly and cheap greenhouse monitoring and control system for a farmer which is provided with the facility of plant selection. This system can be readily used for the growth of various plants throughout the year by providing the favourable conditions required for their growth.

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References
1. Muhammad Ali Mazidi, Rolin D McKilany, Danny causey PIC Microcontroller and embedded systems. 2008.ISBN: 0-13-600902-6
2. Siuli Roy and Somprakash Bandyopadhyay. A Test-bed on Real-time Monitoring of Agricultural Parameters using Wireless Sensor Networks for Precision Agriculture.
3. Marshall R. Hafercamp. Environment factors affecting plant Productivity. Fort Keogh research symposium.1987.27-36
4. Directorate of horticulture and plantation crop. Crop Production Techniques of Horticultural Crops by Directorate of Horticulture and Plantation Crops.2004.
5. http://www.techrepublic.com/resource-library/whitepapers/digitally-greenhouse-monitoring-and-controlling-of-system-based-on-embedded-system
6. http://www.%20ijarcsse.com/docs/papers/Volume_3/5_May2013/V3I5-0227.pdf
7. http://www.%20tnau.ac.%20in/tech/hortcg2004.pdf
8. For pH circuit-http://www.%2066%20pacific.%20com/ph/simplest_ph.aspx%209. http://www.ijsrp.org/research_paper_may2012/ijsrp-may-2012-29.pdf
10. http://ojs.academypublisher.com/index.php/jnw/article/viewFile/jnw0705838844/4638
11. http://aggie-horticulture.tamu.edu/ornamental/a-reference-guide-to-plant-care-handling-and-merchandising/light-temperature-and-humidity
12. https://ag.%20arizona.edu/pubs/garden/mg/botany/environmental.%20html
13. http://www.gardeningknowhow.com/plant-problems/environmental/how-light-affects-the-growth-of-a-plant-problems-with-too-little-light.%20html
14. http://www.vellag.com/index.php/articles/what-plants-require-for-growth
15. http://www.controlledenvironments.org/Growth_Chamber_Handbook/Ch03.%20pdf
16. http://unesdoc.unesco.org/images/0014/001488/148851eb.pdf
17. http://www.gardeningsingapore.org/index.php?option=com_content&view=article&id=52:effects-of-soil-ph-on-plant-growth&catid=35:plant-care&Itemid=5318
18. Rodrigo Castaeda-Miranda, Eusebio Ventura-Ramos Jr, Rebeca del Rocio Peniche-Vera, et al. ―Fuzzy greenhouse climate control system based on a field programmable gate array‖,Bio systems Engineering,2006, vol. 94, pp. 165~177.
19. Bennis. N. Duplaix. J and Enea G. ―Greenhouse climate modelling and robust control‖, Computers and electronics in agriculture, 2008, vol. 61, pp. 96–107
20. Gu Jinan, Mao Hanping. ―A mathematical model on intelligent control of greenhouse environment‖, Transactions of the Chinese Society for Agricultural Machinery, 2001, vol. 32,: pp. 63~66.
21. Wang, N., Zhang, N., & Wang, M.. Wireless sensors in agriculture and food industry—recent development and future perspective. Computer and Electronics in Agriculture, 2006, vol. 50, pp. 1–14.
22. Serôdio, C., Cunha, J. B., Morais, R., Couto, C., & Monteiro, J.. A networked platform for agricultural management systems. Computers and Electronics in Agriculture, 2001, vol. 31, pp. 75–90.
23. Zhang Q. ,Yang X. ,Zhou Y., ―A wireless solution for greenhouse monitoring and control system based on ZigBee technology‖, Journal of Zhejiang University SCIENCE A, 2007, vol. 8, pp. 1584-1587.
24. Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) Published by Atlantis Press, Paris, France. © The authors 2364.
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by Admin » Tue Aug 28, 2018 7:26 pm

32. João Ricardo Baptista de Matos / IoT Enabled Aquatic Drone for Environment Monitoring // ISCTE-IUL. 2016.

Abstract: This thesis presents a platform that tackles environment monitoring by using air and water quality sensors to provide data for the user to know what is happening in that surveilled area. The hardware is incorporated in a sensing module in order to be used with an Unmanned Surface Vehicle (USV).
It presents a monitoring system based on Raspberry Pi platform and a multichannel sensing module associated with water quality and air quality measurement parameters. Thus, the temperature, relative humidity and gas concentration are measured as well as the underwater acoustic signals using a hydrophone. The data is stored on the memory of the drone’s computational platform (Raspberry Pi), and synchronized with a remote server database. Audio streaming capabilities were implemented in the server side. Additionally, a mobile application was developed to be used by people working in the field for data visualization, audio streaming playback and statistical analysis (by showing plotted data).This thesis presents a platform that tackles environment monitoring by using air and water quality sensors to provide data for the user to know what is happening in that surveilled area. The hardware is incorporated in a sensing module in order to be used with an Unmanned Surface Vehicle (USV).
It presents a monitoring system based on Raspberry Pi platform and a multichannel sensing module associated with water quality and air quality measurement parameters. Thus, the temperature, relative humidity and gas concentration are measured as well as the underwater acoustic signals using a hydrophone. The data is stored on the memory of the drone’s computational platform (Raspberry Pi), and synchronized with a remote server database. Audio streaming capabilities were implemented in the server side. Additionally, a mobile application was developed to be used by people working in the field for data visualization, audio streaming playback and statistical analysis (by showing plotted data).

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by Admin » Thu Aug 30, 2018 6:29 am

33. Natalia Blasco, María de Diego, Román Belda, Ismael de Fez, Pau Arce, Francisco José Martínez-Zaldívar, Alberto González, Juan Carlos Guerri. Distributed Sensor Network for Noise Monitoring in Industrial Environment with Raspberry Pi / INTELLI 2017 : The Sixth International Conference on Intelligent Systems and Applications (includes InManEnt). P.51-55.

Abstract: Monitoring the noise in working places is essential to protect the health of workers. There are two main factors that must be taken into account, and thus controlled, when considering noise exposition during the working hours: the level of perceived noise and the time exposed to that level of noise. In industrial environments, these two factors represent a high priority due to the quantity of equipment inside the factory. In this paper, we present a low cost system to measure and monitor noise conditions in an industrial environment. The proposed solution is based on ad hoc wireless probes and a server in the cloud, which acts as a centralized data sink. Specifically, the probes are based on Raspberry Pi 3, while the server may be placed anywhere on the Internet. The proposed system helps to detect critical levels of noise for workers, sending warning messages to predefined contacts by means of a text message or email when hazardous situations occur.

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References
[1] World Health Organization Europe, “Night noise guidelines for Europe,” 2009, available online at: http://www.euro.who.int/__data/assets/p ... 5.pdf?ua=1, accessed: Jun. 2017.
[2] International Labour Office, “Protection of workers against noise and vibration,” ILO Codes of Practice, 1977, avaible online at: http://www.ilo.org/wcmsp5/groups/public ... 107878.pdf, accessed: Jun. 2017.
[3] K. S. Low, W. N. N. Win, and M. J. Er, “Wireless Sensor Networks for Industrial Environments,” Int. Conf. on Computational Intelligence for Modelling, Control and Automation and Int. Conf. on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC), Vienna, Austria, Nov. 2005, pp. 271-276, ISBN: 0-7695-2504-0.
[4] T. A. Onkar and P. T. Karule, “Web based Maintenance for Industrial Application using Raspberry-pi,” Online Int. Conference on Green Engineering and TEchnologies (IC-GET), Coimbatore, India, Nov. 2016, pp. 1-4, ISBN: 978-1-5090-4556-3.
[5] J. Segura-Garcia, S. Felici-Castell, J. J. Pérez-Solano, M. Cobos, and J. M. Navarro, “Low-cost Alternatives for Urban Noise Nuisance Monitoring using Wireless Sensor Networks,” IEEE Sensors Journal, vol. 15, no. 2, pp. 836-844, 2015.
[6] T. Clausen and P. Jacquet, “Optimized Link State Routing Protocol (OLSR),” IETF RFC, vol. 3626, Oct. 2003.
[7] T. Clausen, C. Dearlove, P. Jacquet, and U. Herberg, “The Optimized Link State Routing Protocol (OLSR) version 2,” IETF RFC, vol. 7181, Apr. 2014.
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by Admin » Sat Sep 01, 2018 7:58 pm

34. Swaroop. P., Sheshank Reddy. Y.,Syed Saif. E.,Sasikala.,Ms.Christeena Joseph. The Real Time Temperature Sensing using Raspberry PI // IJIRST –International Journal for Innovative Research in Science & Technology. - 2015. V.1. Issue 12. P.232-237.

Abstract: This paper presents the detection of real time temperature employing Raspberry Pi. Temperature has an impact on all the activities surrounding us be it withering of the leaves, the brew of the coffee etc. The variation of the temperature plays a main role in field of electronics. A precise determination of temperature is a vital factor in countless industries and different fields of science. The temperature monitoring is crucial in lot of industries, like food industry, the workshop, and pharmaceutical industry and in environmental monitoring. The sensed temperature can be displayed in Raspberry pi kit using command.The proposed method aims at continuously monitoring the real time temperature in a cost effective way by setting fixed intervals.Here the monitoring node is raspberry pi. The Sensor utilized here is DS18B20 1-wire digital temperature sensor. This sensor come in a tiny three pi package like a transistor. The temperature is sensed using the digital sensor DS18B20 and is read ,stored and displayed by the raspberry pi kit. Other sensors like humidity, atmospheric pressure or vibration can be clubbed with this system with ease to measure the atmospheric parameters.

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References
[1] Temperature sensor characteristics and measurement system design J P Bentley 1984 J. Phys. E: Sci. Instrum. 17 430. doi:10.1088/0022-3735/17/6/002
[2] http://www.pcmag.com/article2/0,2817,2407058,00.asp
[3] http://www.rs-online.com/designspark/el ... -pi-b-plus
[4] http://www.kosmodrom.com.ua/el.php?name=DS18B20SPEZ
[5] Audio optimizations for the Raspberry Pi:
[6] Linuxaudio.org. "Linux Audio Wiki". http://wiki.linuxaudio.org/wiki/raspberrypi
[7] BeagleBone and Raspberry Pi accessories: Adafruit Industries. http://www.adafruit.com
[8] http://www.intel.com/content/www/us/en/ ... ticle.html
[9] BeagleBone DVI-Audio Cape:
[10] Beagle Board Toys. http://beagleboardtoys.info/index.php?t ... with_Audio
[11] Fiber Optic Temperature Sensor Based on Multimode Interference Effects 2011 J. Phys.: Conf. Ser. 274 012011 (http://iopscience.iop.org/1742-6596/274/1/012011)
[12] https://www.python.org
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by Admin » Thu Sep 13, 2018 7:20 pm

35. Macekova L., Žiga M. The wireless sensor network concept for measurement of water quality in water streams // Acta Electrotechnica et Informatica. – 2014. Vol. 14. No 2. P. 60-67.

Abstract: This article describes main aspects and steps of our design of water quality monitoring wireless sensor network. Interconnections between the ecologic-environmental, biologic-chemical measurements and electronic-telecommunications areas can be found in this matter. is Both the consultation with environmental specialists and data from the legislative documents from this area are unavoidable parts of such study. The result of study is selection of the measure indicators of water quality, proposal of suitable sensors, measurement method and architecture of the wireless sensor network. This work is a part of international crossborder co-operation, with the aim to build a system for both monitoring the ecologic-iological state of the cross-border river and early warning in case of an ecological disaster.

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References
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by Admin » Fri Sep 14, 2018 1:22 pm

36. SGX Sensortech (IS) Ltd Registered in England No. 08067077. A1A-EC_Sensors_AN2, Version 2, March 2010.

Abstract: The electrochemical gas sensor requires a bias circuit known as a potentiostat to maintain the correct bias potential between the sensing and reference electrodes as stated on the individual sensor datasheet. In many cases this will be 0 V but some devices require either a positive or negative bias potential. The gas sensor produces an output current proportional to the gas concentration. A current to voltage converter, also known as a transimpedance amplifier, is required to convert the small currents from the electrochemical cell into a useful voltage for measurement. The analog to digital converter (ADC) samples the output of the transimpedance amplifier and produces a digital reading of the voltage level. This is used by the
microprocessor to calculate the actual gas concentration.

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by Admin » Thu Oct 04, 2018 11:14 am

37. Oliva S. U. Smart Chemical Sensors: Concepts and Application / Programa de doctorat en Enginyeria i Tecnologies Avancades. – 2012.

Abstract: A chemical sensor can be defined as “a device that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an nalytically useful signal. In particular, said chemical information may originate from a chemical reaction of the analyte or from a physical property of the investigated system”.

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