scholarly journals Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code

2021 ◽  
Author(s):  
Michael Schmuker ◽  
Rüdiger Kupper ◽  
Ad Aertsen ◽  
Thomas Wachtler ◽  
Marc-Oliver Gewaltig
Keyword(s):  
Feed Forward ◽  
Spiking Networks ◽  
Noise Tolerant

Design Methodology of High-Order Feed-Forward ΔΣ Modulators for Broadband Communications

2018 ◽  
Vol 13 (1) ◽  
pp. 45
Author(s):  
Houda Daoud ◽  
Dalila Laouej ◽  
Samir Ben Salem ◽  
Mourad Loulou
Keyword(s):  
Design Methodology ◽  
High Order ◽  
Feed Forward ◽  
Broadband Communications

A Feed-Forward Time Amplifier Using a Phase Detector and Variable Delay Lines

2013 ◽  
Vol E96.C (6) ◽  
pp. 920-922 ◽  
Author(s):  
Kiichi NIITSU ◽  
Naohiro HARIGAI ◽  
Takahiro J. YAMAGUCHI ◽  
Haruo KOBAYASHI
Keyword(s):  
Phase Detector ◽  
Variable Delay ◽  
Delay Lines ◽  
Feed Forward ◽  
Time Amplifier

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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