Analyzing the Performance of 7nm FinFET Based Logic Circuit for the Signal Processing in Neural Network

2020 ◽  
Author(s):  
Rajeev Kumar Pandey ◽  
Sanjeev Kumar Pandey
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Logic Circuit

LOCALLY IMPLEMENTABLE MATRIX FLOWS AND THEIR APPLICATION TO NEURAL NETWORK LEARNING

1994 ◽  
Vol 04 (01) ◽  
pp. 23-51 ◽  
Author(s):  
JEROEN DEHAENE ◽  
JOOS VANDEWALLE
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Structure ◽  
Adaptive Signal Processing ◽  
External Signal ◽  
Neural Network Learning ◽  
Network Learning ◽  
Adaptive Signal

A number of matrix flows, based on isospectral and isodirectional flows, is studied and modified for the purpose of local implementability on a network structure. The flows converge to matrices with a predefined spectrum and eigenvectors which are determined by an external signal. The flows can be useful for adaptive signal processing applications and are applied to neural network learning.

Neural Network Signal Processing in Spark Assisted Chemical Engraving (SACE) Micromachining

2021 ◽  
Author(s):  
Seyed Mahmoud Seyedi Sahebari ◽  
Ahmad Barari ◽  
Jana D. Abou Ziki
Keyword(s):  
Neural Network ◽  
Signal Processing

Handbook of Neural Network Signal Processing

2002 ◽  
Vol 111 (6) ◽  
pp. 2525-2526 ◽  
Author(s):  
Stuart W. Perry
Keyword(s):  
Neural Network ◽  
Signal Processing

Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal processing

2021 ◽  
pp. 1-14
Author(s):  
Sergey Filist ◽  
Riad Taha Al-kasasbeh ◽  
Olga Shatalova ◽  
Altyn Aikeyeva ◽  
Nikolay Korenevskiy ◽  
...  
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Model ◽  
Neural Network Model ◽  
Cardiovascular Health ◽  
Bioelectrical Signal

Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface

2021 ◽  
Vol 11 (12) ◽  
pp. 2918-2927
Author(s):  
A. Shankar ◽  
S. Muttan ◽  
D. Vaithiyanathan
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Discrete Wavelet ◽  
Feature Sets ◽  
Evaluation Of Performance ◽  
The Brain

Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

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