An Artificial Retina Chip Using Switch-Selective Resistive Network for Intelligent Sensor Systems

2006 ◽  
pp. 702-710 ◽  
Author(s):  
Jae-Sung Kong ◽  
Sang-Heon Kim ◽  
Jang-Kyoo Shin ◽  
Minho Lee
Keyword(s):  
Sensor Systems ◽  
Artificial Retina ◽  
Resistive Network ◽  
Intelligent Sensor ◽  
Retina Chip

Smooth pursuit eye movement system using artificial retina chip and shape memory alloy actuator

2005 ◽  
Vol 5 (3) ◽  
pp. 501-509 ◽  
Author(s):  
Won-Cheol Kim ◽  
Jung-Hwan Kim ◽  
Minho Lee ◽  
Jang-Kyoo Shin ◽  
Hyun-Seung Yang ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Eye Movement ◽  
Smooth Pursuit ◽  
Artificial Retina ◽  
Shape Memory Alloy Actuator ◽  
Smooth Pursuit Eye Movement ◽  
Retina Chip ◽  
Movement System

Intelligent sensor systems and smart sensors: concepts, focus points and technology

2011 ◽  
pp. 959-976 ◽  
Author(s):  
Gerard Meijer ◽  
Frank van der Goes ◽  
Ferry Toth ◽  
Sander van Herwaarden ◽  
Edval Santos
Keyword(s):  
Smart Sensors ◽  
Sensor Systems ◽  
Intelligent Sensor

Analog Learning Neural Network using Two-Stage Mode by Multiple and Sample Hold Circuits

2014 ◽  
Vol 2 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Masashi Kawaguchi ◽  
Naohiro Ishii ◽  
Takashi Jimbo
Keyword(s):  
Neural Network ◽  
Vision System ◽  
Network Models ◽  
Input Voltage ◽  
Neural Network Models ◽  
Artificial Retina ◽  
The Neural Network ◽  
Connection Coefficient ◽  
Retina Chip ◽  
Short Time

In the neural network field, many application models have been proposed. A neuro chip and an artificial retina chip are developed to comprise the neural network model and simulate the biomedical vision system. Previous analog neural network models were composed of the operational amplifier and fixed resistance. It is difficult to change the connection coefficient. In this study, we used analog electronic multiple and sample hold circuits. The connecting weights describe the input voltage. It is easy to change the connection coefficient. This model works only on analog electronic circuits. It can finish the learning process in a very short time and this model will enable more flexible learning.

scholarly journals An electromagnetic, vibration-powered generator for intelligent sensor systems

2004 ◽  
Vol 110 (1-3) ◽  
pp. 344-349 ◽  
Author(s):  
P. Glynne-Jones ◽  
M.J. Tudor ◽  
S.P. Beeby ◽  
N.M. White
Keyword(s):  
Sensor Systems ◽  
Intelligent Sensor ◽  
Electromagnetic Vibration

Intelligent sensor systems

1996 ◽  
Vol 55 (2-3) ◽  
pp. 226
Author(s):  
A. Bossche
Keyword(s):  
Sensor Systems ◽  
Intelligent Sensor

Intelligent sensor systems and smart sensors: applications

2011 ◽  
pp. 977-1018
Author(s):  
Gerard Meijer ◽  
Sander van Herwaarden ◽  
Gerben de Jong ◽  
Ferry Toth ◽  
Xiujun Li
Keyword(s):  
Smart Sensors ◽  
Sensor Systems ◽  
Intelligent Sensor
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