Design of Restricted Coulomb Energy Neural Network Processor for Multi-modal Sensor Fusion

2021 ◽  
pp. 441-446
Author(s):  
Jaechan Cho ◽  
Minwoo Kim ◽  
Yongchul Jung ◽  
Yunho Jung
Keyword(s):  
Neural Network ◽  
Sensor Fusion ◽  
Network Processor ◽  
Coulomb Energy

scholarly journals Architecture-Accuracy Co-optimization of ReRAM-based Low-cost Neural Network Processor

2020 ◽  
Author(s):  
Segi Lee ◽  
Sugil Lee ◽  
Jongeun Lee ◽  
Jong-Moon Choi ◽  
Do-Wan Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Network Processor

Neural network processor

2004 ◽  
Author(s):  
Michele R. Banish ◽  
David B. Chenault ◽  
John S. Harchanko
Keyword(s):  
Neural Network ◽  
Network Processor

Watermelon Sorting Process by Frequency Identification and Artificial Neural Network

2021 ◽  
Author(s):  
Komsan Wongkalasin ◽  
Teerapon Upachaban ◽  
Wacharawish Daosawang ◽  
Nattadon Pannucharoenwong ◽  
Phadungsak Ratanadecho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Selection Process ◽  
Network Processor ◽  
Data Set ◽  
The Neural Network ◽  
Frequency Identification ◽  
Sorting Process ◽  
Artificial Neural ◽  
Reasonable Prediction

This research aims to enhance the watermelon’s quality selection process, which was traditionally conducted by knocking the watermelon fruit and sort out by the sound’s character. The proposed method in this research is generating the sound spectrum through the watermelon and then analyzes the response signal’s frequency and the amplitude by Fast Fourier Transform (FFT). Then the obtained data were used to train and verify the neural network processor. The result shows that, the frequencies of 129 and 172 Hz were suit to be used in the comparison. Thirty watermelons, which were randomly selected from the orchard, were used to create a data set, and then were cut to manually check and match to the fruits’ quality. The 129 Hz frequency gave the response ranging from 13.57 and above in 3 groups of watermelons quality, including, not fully ripened, fully ripened, and close to rotten watermelons. When the 172 Hz gave the response between 11.11–12.72 in not fully ripened watermelons and those of 13.00 or more in the group of close to rotten and hollow watermelons. The response was then used as a training condition for the artificial neural network processor of the sorting machine prototype. The verification results provided a reasonable prediction of the ripeness level of watermelon and can be used as a pilot prototype to improve the efficiency of the tools to obtain a modern-watermelon quality selection tool, which could enhance the competitiveness of the local farmers on the product quality control.

Real-time stable adaptive control implementation using a neural network processor

1995 ◽  
Author(s):  
Timothy Robinson ◽  
Mohammad Bodruzzaman ◽  
Kevin L. Priddy ◽  
Karl Mathia
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Network Processor ◽  
Control Implementation

Experimenting with Recognition Accelerator for Pavement Distress Identification

1996 ◽  
Vol 1536 (1) ◽  
pp. 130-135 ◽  
Author(s):  
Sudhakar Nallamothu ◽  
Kelvin C. P. Wang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coulomb Energy ◽  
Processing Unit ◽  
Basic Principles ◽  
Pavement Distress ◽  
The Core ◽  
Artificial Neural Network Ann ◽  
Computer Board

A study was conducted using a computer board embedded with an artificial neural network (ANN) microchip for pattern recognition of pavement distress classification. The basic principles behind ANNs and pattern recognition are discussed. The hardware architecture of the Ni1000 recognition accelerator chip, which is the core of the ANN computer board, is presented, and the principle of operation of the restricted coulomb energy algorithm used in the chip is discussed. It is demonstrated that the Ni1000 Recognition Accelerator chip can be used for pattern recognition of pavement distress. Distresses in pavement images have been successfully classified using the Ni1000 recognition accelerator. The Ni1000 has the potential to be used as the core processing unit for distress classification at highway speeds.

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