Functional Simulation of Human Blood Identification Device using Feed-Forward Artificial Neural Network for FPGA Implementation

被引：0

作者：

Darlis, Denny ^{[1
]}

Murwati, Heri ^{[1
]}

Priramadhi, Rizki Ardianto ^{[1
]}

Ramdhani, Mohamad ^{[1
]}

Nugraha, M. Bima ^{[1
]}

机构：

[1] Telkom Univ, Bandung, Indonesia

来源：

2018 INTERNATIONAL CONFERENCE ON SIGNALS AND SYSTEMS (ICSIGSYS) | 2018年

关键词：

feedforward propagation; artificial neural network; FPGA; human blood type identification;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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 32x32 pixels, 48x48 pixels, 64x64, 80x80, and 96x96 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%.

引用

页码：142 / 145

页数：4

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