Indoor positioning using artificial neural network with field programmable gate array implementation
Indoor positioning required fast and accurate result. This paper applied the artificial neural network (ANN) as a system for calculating the target in indoor environment. To speed up the calculation time, ANN then is run into field programmable gate array (FPGA). Since the original sigmoid function...
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| Format: | Article |
| Language: | en |
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American Scientific Publisher
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19956/1/49.%20Indoor%20Positioning%20Using%20Artificial%20Neural%20Network%20with%20Field%20Programmable%20Gate%20Array%20Implementation1.pdf http://umpir.ump.edu.my/id/eprint/19956/ https://doi.org/10.1166/asl.2018.12985 |
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| author | Syahrulanuar, Ngah Rohani, Abu Bakar Suryanti, Awang |
| author_facet | Syahrulanuar, Ngah Rohani, Abu Bakar Suryanti, Awang |
| author_sort | Syahrulanuar, Ngah |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Indoor positioning required fast and accurate result. This paper applied the artificial neural network (ANN) as a system for calculating the target in indoor environment. To speed up the calculation time, ANN then is run into field programmable gate array (FPGA). Since the original sigmoid function in ANN is not feasible to be applied into FPGA, two-steps sigmoid function calculation proposed by previous researcher then is used as a replacement. A new design of the FPGA is proposed to suite the requirement for implementing the previous researcher method. The results showing that FPGA can calculate 20 times faster with the maximum error 0.04 meters, slightly higher than the software implementation. |
| format | Article |
| id | my.ump.umpir.19956 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2018 |
| publisher | American Scientific Publisher |
| record_format | eprints |
| spelling | my.ump.umpir.199562024-01-08T04:42:36Z http://umpir.ump.edu.my/id/eprint/19956/ Indoor positioning using artificial neural network with field programmable gate array implementation Syahrulanuar, Ngah Rohani, Abu Bakar Suryanti, Awang QA76 Computer software Indoor positioning required fast and accurate result. This paper applied the artificial neural network (ANN) as a system for calculating the target in indoor environment. To speed up the calculation time, ANN then is run into field programmable gate array (FPGA). Since the original sigmoid function in ANN is not feasible to be applied into FPGA, two-steps sigmoid function calculation proposed by previous researcher then is used as a replacement. A new design of the FPGA is proposed to suite the requirement for implementing the previous researcher method. The results showing that FPGA can calculate 20 times faster with the maximum error 0.04 meters, slightly higher than the software implementation. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19956/1/49.%20Indoor%20Positioning%20Using%20Artificial%20Neural%20Network%20with%20Field%20Programmable%20Gate%20Array%20Implementation1.pdf Syahrulanuar, Ngah and Rohani, Abu Bakar and Suryanti, Awang (2018) Indoor positioning using artificial neural network with field programmable gate array implementation. Advanced Science Letters, 24 (10). pp. 7598-7601. ISSN 1936-6612. (Published) https://doi.org/10.1166/asl.2018.12985 doi: 10.1166/asl.2018.12985 |
| spellingShingle | QA76 Computer software Syahrulanuar, Ngah Rohani, Abu Bakar Suryanti, Awang Indoor positioning using artificial neural network with field programmable gate array implementation |
| title | Indoor positioning using artificial neural network with field programmable gate array implementation |
| title_full | Indoor positioning using artificial neural network with field programmable gate array implementation |
| title_fullStr | Indoor positioning using artificial neural network with field programmable gate array implementation |
| title_full_unstemmed | Indoor positioning using artificial neural network with field programmable gate array implementation |
| title_short | Indoor positioning using artificial neural network with field programmable gate array implementation |
| title_sort | indoor positioning using artificial neural network with field programmable gate array implementation |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/19956/1/49.%20Indoor%20Positioning%20Using%20Artificial%20Neural%20Network%20with%20Field%20Programmable%20Gate%20Array%20Implementation1.pdf http://umpir.ump.edu.my/id/eprint/19956/ https://doi.org/10.1166/asl.2018.12985 |
| url_provider | http://umpir.ump.edu.my/ |