Development Of Iot Smart Home Controller Using Biosensor

Development Of Iot Smart Home Controller Using Biosensor

At present, the challenge of ultrasonic rotary machine is for material hard and brittle is booming for advanced materials with high mechanical properties such as high hardness, high resistance to wear, low density and resistance to abrasion at high temperatures. However, the high cost of machining,...

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Main Author: Ab Aziz, Muhammad Azraei
Format: Thesis
Language:English
English
Published: 2017
Subjects:
T Technology (General)
Online Access:http://eprints.utem.edu.my/id/eprint/22397/1/Development%20Of%20Iot%20Smart%20Home%20Controller%20Using%20Biosensor%20-%20Muhammad%20Azraei%20Ab%20Aziz.pdf
http://eprints.utem.edu.my/id/eprint/22397/2/Development%20Of%20Iot%20Smart%20Home%20Controller%20Using%20Biosensor.pdf
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id my-utem-ep.22397
record_format uketd_dc
institution Universiti Teknikal Malaysia Melaka
collection UTeM Repository
language English
English
topic T Technology (General)
T Technology (General)
spellingShingle T Technology (General)
T Technology (General)
Ab Aziz, Muhammad Azraei
Development Of Iot Smart Home Controller Using Biosensor
description At present, the challenge of ultrasonic rotary machine is for material hard and brittle is booming for advanced materials with high mechanical properties such as high hardness, high resistance to wear, low density and resistance to abrasion at high temperatures. However, the high cost of machining, ranging from 30-60% and even 90% of the cost of production, has clearly slowed effect on the part manufacture. Composites are materials that are widely used in a varies industry especially airospace industry due to their lightweight and strong features. Machining during panel fabrication of these materials cannot be avoided, even if they are produced to net shape component. This becomes more important when a new product design and shape dimension is critical where high surface finish, accuracy of dimensional tolerances and high material removal rate are required. Therefore, the application of Rotary Ultrasonic Assisted Trimming (RUAT) is proposed, it was said that a composite material can be machined smooth and machining costs can be reduced. The feasibility to machine composite is investigated experimentally using RUAT. Effects of input variable of RUAT process (rotation speed, feedrate, frequency and amplitude) on the machining output; cutting force, cutting temperature and surface roughness were studied. The optimum values of machining parameters can be obtained by applying cutting speed, feedrate, amplitude and frequency of 2838.46 rpm, 1499.97 mm/min, 2.73 μm and 27 kHz.
format Thesis
qualification_name Master of Philosophy (M.Phil.)
qualification_level Master's degree
author Ab Aziz, Muhammad Azraei
author_facet Ab Aziz, Muhammad Azraei
author_sort Ab Aziz, Muhammad Azraei
title Development Of Iot Smart Home Controller Using Biosensor
title_short Development Of Iot Smart Home Controller Using Biosensor
title_full Development Of Iot Smart Home Controller Using Biosensor
title_fullStr Development Of Iot Smart Home Controller Using Biosensor
title_full_unstemmed Development Of Iot Smart Home Controller Using Biosensor
title_sort development of iot smart home controller using biosensor
granting_institution Universiti Teknikal Malaysia Melaka
granting_department Faculty of Engineering Technology
publishDate 2017
url http://eprints.utem.edu.my/id/eprint/22397/1/Development%20Of%20Iot%20Smart%20Home%20Controller%20Using%20Biosensor%20-%20Muhammad%20Azraei%20Ab%20Aziz.pdf
http://eprints.utem.edu.my/id/eprint/22397/2/Development%20Of%20Iot%20Smart%20Home%20Controller%20Using%20Biosensor.pdf
_version_ 1747834016259637248
spelling my-utem-ep.223972022-02-16T12:15:32Z Development Of Iot Smart Home Controller Using Biosensor 2017 Ab Aziz, Muhammad Azraei T Technology (General) TK Electrical engineering. Electronics Nuclear engineering At present, the challenge of ultrasonic rotary machine is for material hard and brittle is booming for advanced materials with high mechanical properties such as high hardness, high resistance to wear, low density and resistance to abrasion at high temperatures. However, the high cost of machining, ranging from 30-60% and even 90% of the cost of production, has clearly slowed effect on the part manufacture. Composites are materials that are widely used in a varies industry especially airospace industry due to their lightweight and strong features. Machining during panel fabrication of these materials cannot be avoided, even if they are produced to net shape component. This becomes more important when a new product design and shape dimension is critical where high surface finish, accuracy of dimensional tolerances and high material removal rate are required. Therefore, the application of Rotary Ultrasonic Assisted Trimming (RUAT) is proposed, it was said that a composite material can be machined smooth and machining costs can be reduced. The feasibility to machine composite is investigated experimentally using RUAT. Effects of input variable of RUAT process (rotation speed, feedrate, frequency and amplitude) on the machining output; cutting force, cutting temperature and surface roughness were studied. The optimum values of machining parameters can be obtained by applying cutting speed, feedrate, amplitude and frequency of 2838.46 rpm, 1499.97 mm/min, 2.73 μm and 27 kHz. 2017 Thesis http://eprints.utem.edu.my/id/eprint/22397/ http://eprints.utem.edu.my/id/eprint/22397/1/Development%20Of%20Iot%20Smart%20Home%20Controller%20Using%20Biosensor%20-%20Muhammad%20Azraei%20Ab%20Aziz.pdf text en public http://eprints.utem.edu.my/id/eprint/22397/2/Development%20Of%20Iot%20Smart%20Home%20Controller%20Using%20Biosensor.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=110229 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Engineering Technology 1. Arduino, 2017. Arduino. [Online] Available at: https://www.arduino.cc/en/Guide/Introduction [Accessed 9 4 2017]. 2. Ashton, K., 2009. That "Internet of Things" Thing. 3. Brown, E., 2016. 21 Open Source Project for Iot. 4. Christopher M. Sinclair, M. C. G. a. A. S. B., 2007. 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Ilawlor, 2014. Github. [Online] Available at: https://github.com/iobridge/thingspeak/blob/master/README.textile [Accessed 12 4 2017]. 13. Klimesch W., D. M. Y. A. K. N. L. M. R. D. G. W., 2001. Theta synchronization during episodic retrieval: neural correlates of conscious awareness. 14. Kübler A., K. B. K. J. W. J. B. N., 2001. Brain-computer communication: unlocking the locked in. 15. Kushner, D., 2011. IEEE Spectrum. [Online] Available at: https://spectrum.ieee.org/geek-life/hands-on/the-making-of-arduino [Accessed 9 4 2017]. 16. Leonard R. Johnson, J. H. B., 2003. Essential Medical Physiology. 3rd ed. California: Academic Press. 17. Luis Fernando Nicolas-Alonso, J. G.-G., 2012. Brain Computer Interfaces, a Review. pp. 1211-1279. 18. Mizuno, K., 1972. Essentials of Buddhism. Tokyo: Kosei Publishing Company. 19. Nobel Media AB 2014. Web., 2017. Nobelprize.org. [Online] Available at: <http://www.nobelprize.org/nobel_prizes/medicine/laureates/1924/einthoven-bio.html> [Accessed 6 4 2017]. 20. Pineda, J. A., 2005. The functional significance of mu rhythms: Translating ‘‘seeing’’and ‘‘hearing’’ into ‘‘doing’’. The functional significance of mu rhythms: Translating ‘‘seeing’’and ‘‘hearing’’ into ‘‘doing’’, p. 12. 21. Raspberry Pi, 2017. Raspberry Pi. [Online] Available at: https://www.raspberrypi.org/products/raspberry-pi-3-model-b/ [Accessed 19 April 2017]. 22. Silva, L. C. D., Morikawa, C. & Petra, I. M., 2012. State of the art of smart homes. Engineering Applications of Artificial Intelligence, 25(7), pp. 1313-1321. 23. Team, N., 2014. NodeMCU. [Online] Available at: http://www.nodemcu.com/index_en.html [Accessed 12 4 2017]. 24. Upton, E., 2016. Ten millionth Raspberry Pi, and a new kit - Raspberry Pi. [Online] Available at: https://www.raspberrypi.org/blog/ten-millionth-raspberry-pi-new-kit/ [Accessed 10 4 2017]. 25. Usakli, A. B., 2010. An Electrical Aspect for State of theArt of Front End. Improvement of EEG Signal Acquisition, p. 7.

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