Darlis, Denny and Murwati, Heri and Priramadhi, Rizki Ardianto and Ramdhani, Mohammad and Nugraha, M. Bima (2018) Functional simulation of human blood identification device using feed-forward artificial neural network for FPGA implementation. In: 2018 International Conference on Signals and Systems (ICSigSys), 1-3 May 2018, Bali, Indonesia.
Full text not available from this repository.Abstract
The identification of human blood type still requires a fast and accurate device considering the number of blood samples that need to be distributed and transfused immediately. In this study we propose a hardware implementation of human blood type identification devices using feedforward neural network algorithms on grayscale images of blood samples. The images to be used are 32×32 pixels, 48×48 pixels, 64×64, 80×80, and 96×96 pixels. The algorithm were implemented using VHSIC Hardware Description Language. With artifical neural network implemented on Xilinx FPGA Spartan 3S1000, the success rate of detection by grouping by the mean and median ratios of the number of `1' bits is more than 75%.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | feedforward propagation , artificial neural network , FPGA , human blood type identification |
| Subjects: | 500 Science and Mathematic > 500 Science > 507 Education, Research, Related Topics |
| Divisions: | Faculty of Engineering & Informatics > Electrical Engineering |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 08 Dec 2021 10:22 |
| Last Modified: | 08 Dec 2021 10:22 |
| URI: | https://kc.umn.ac.id/id/eprint/19408 |
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