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All Journal International Journal of Electrical and Computer Engineering MEDIA KOMUNIKASI TEKNIK SIPIL Jurnal Teknik ITS JURNAL KARYA TEKNIK SIPIL IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Penelitian Hasil Hutan Jurnal Ilmiah Kursor Journal of Engineering and Technological Sciences Jurnal Riset Industri Seminar Nasional Informatika (SEMNASIF) Prosiding Forum Studi Transportasi Antar Perguruan Tinggi Jurnal Riset Industri Hasil Hutan Seminar Nasional Teknologi Informasi Komunikasi dan Industri Gelar : Jurnal Seni Budaya JAVA Journal of Electrical and Electronics Engineering Fairness and Justice: Jurnal Ilmiah Ilmu Hukum Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati Journal of Contemporary Law Studies Indonesian Journal of Law and Justice
Djoko Purwanto
Departemen Teknik Elektro Institut Teknologi Sepuluh Nopember
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Journal : International Journal of Electrical and Computer Engineering

 1 
Neural Network for Electronic Nose using Field Programmable Analog Arrays Helmy Widyantara; Muhammad Rivai; Djoko Purwanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 6: December 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (651.801 KB)

Abstract

Electronic nose is a device detecting odors which is designed to resemblethe ability of the human nose, usually applied to the robot. The process ofidentification of the electronic nose will run into a problem when the gaswhich is detected has the same chemical element. Misidentification due tothe similarity of chemical properties of gases is possible; it can be solvedusing neural network algorithms. The attendance of Field ProgrammableAnalog Array (FPAA) enables the design and implementation of ananalog neural network, while the advantage of analog neural networkwhich is an input signal from the sensor can be processed directly by theFPAA without having to be converted into a digital signal. Direct analogsignal process can reduce errors due to conversion and speed up thecomputing process. The small size and low power usage of FPAA are verysuitable when it is used for the implementation of the electronic nose thatwill be applied to the robot. From this study, it was shown that theimplementation of analog neural network in FPAA can support theperformance of electronic nose in terms of flexibility (resource componentrequired), speed, and power consumption. To build an analog neuralnetwork with three input nodes and two output nodes only need twopieces of Configurable Analog Block (CAB), of the four provided by theFPAA. Analog neural network construction has a speed of the process0.375 μs, and requires only 59 ± 18mW resources.DOI:http://dx.doi.org/10.11591/ijece.v2i6.1501
Implementation of Electronic Nose in Omni-directional Robot Harianto Harianto; Muhammad Rivai; Djoko Purwanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 3, No 3: June 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Electronic nose (E-nose) is a device detecting odors which is designed to resemble the ability of the human nose. E-nose can identifying chemical elements that contained in the odors. E-nose is made of arrays of gas sensor, each of it could detect certain chemical element. When detects gases, each sensor will generate a specific pattern for each gas. These patterns could be classified using neural network algorithm. Neural network is a computational method based on mathematical models which has the structure and operation of neural networks which imitate the human brain. Neural network consists of a group of neurons conected to each other with a connection named weight. The weights will determine wether neural networks could compute given inputs to produce a specified output. To generate the appropriate weight, the neural network needs to be trained using a number of gasoline and alcohol samples. The training process to generate appropriate weights is done by using back propagation algorithm on a personal computer. The appropriate weight then transferred to omni-directional robot equipped with e-nose. The result shows that the robot could identify the trained gas.DOI:http://dx.doi.org/10.11591/ijece.v3i3.2531
Co-Authors Achmad Jazidie Adrie Sentosa Ainuddin, Emha Amelia Kusuma Indriastuti Amelia Kusuma Indriastuti, Amelia Anas Maulidi Utama Anda Ferwira Andri Widihandoko Andrian, Muhammad Yunus Anifatul Faricha Arhamsyah Arhamsyah, Arhamsyah Ari Santoso Ari Santoso Ari Santoso Astria Nur Irfansyah Attamimi, Muhammad Bima Sena Bayu Dewantara Budi Tri Cahyana, Budi Tri Budiman, Fajar Chastine Fatichah Dermawan, Dimas Imam Devy Kuswidiastuti Diah Kusumawati, Diah Elfrin P. Hsb. Enny Zulaika EPF Eko Yulipriyono, EPF Epf. Eko Yulipriyono Felix Gunawan Furqan Aliyuddien Gangsarestu, Muhammad Soleh Garudio Kusuma Aji Handry Khoswanto Hani Avrilyantama Hany Ferdinando Hari Agus Sujono Harianto Harianto Haris Hariza Ekarinda Helmy Widyantara Hendra Kusuma Hendro Juwono Irfansyah, Astria Nur Ismiyati Ismiyati Jamhari Jamhari Kami Hari Basuki Kami Hari Basuki Kelvin Liusiani Kusuma, Hendra Siswanto M. Rivai Mannarul Hidayah Muchammad Ainur Fahd Muchammad Ainur Fahd Muhammad Attamimi Muhammad Hilman Fatoni Muhammad Ichlasul Salik Muhammad Rivai Muhammad Rivai Muhammad Rivai Muhammad Yunus Andrian Muharom, Syahri Nanda, Muhammad Achirul Naysila, Novita Prajna Wirya Kencana Putra Purbaningtyas, Retno Utami Putra, Karisma Trinanda Rahayu, Vidya Windi Retno Tri Wahyuni Rinne Nintasari Riza Agung Rizki Anhar R.P. Ronny Mardianto Ronny Mardiyanto, Ronny Rudy Dikairono Ruth Johana Hutagalung Setiawardhana Setiawardhana Setiawardhana Sonny H. Suryani, Santy Diah Suwito Suwito Takayuki Nagai Taufiq, Husen Totok Mujiono, Totok Tri Arief Sardjono Try Yuliandre Pajar Uroidhi, Ali Wahyudi Kushardjoko Widodo Budiharto Widodo Budiharto Widodo Budiharto Yunardi, Riky Tri