FPGA Implementation of Deep Neural Network Based Equalizers for High-Speed PON

2020 ◽  
Author(s):  
Noriaki Kaneda ◽  
Ziyi Zhu ◽  
Chun-Yen Chuang ◽  
Amitkumar Mahadevan ◽  
Bob Farah ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Deep Neural Network ◽  
Fpga Implementation

Fixed-Point Analysis and FPGA Implementation of Deep Neural Network Based Equalizers for High-Speed PON

2021 ◽  
pp. 1-1
Author(s):  
Noriaki Kaneda ◽  
Chun-Yen Chuang ◽  
Ziyi Zhu ◽  
Amitkumar Mahadevan ◽  
Bob Farah ◽  
...  
Keyword(s):  
Neural Network ◽  
Fixed Point ◽  
High Speed ◽  
Deep Neural Network ◽  
Fpga Implementation ◽  
Point Analysis

scholarly journals Fault Diagnosis of High-speed Train Bogie Based on Deep Neural Network

2019 ◽  
Vol 52 (24) ◽  
pp. 135-139 ◽  
Author(s):  
Yuanjie Zhang ◽  
Na Qin ◽  
Deqing Huang ◽  
Kaiwei Liang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Speed ◽  
Deep Neural Network ◽  
High Speed Train

scholarly journals Driver’s Seat Belt Detection Using CNN

2021 ◽  
Vol 12 (5) ◽  
pp. 776-785
Author(s):  
DS Bhupal Naik, G Sai Lakshmi, V Ramakrishna Sajja, D Venkatesulu,J Nageswara Rao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Error Rate ◽  
High Speed ◽  
Deep Neural Network ◽  
Seat Belt ◽  
Transportation System ◽  
Convolution Neural Network ◽  
Classification Algorithms ◽  
Convolution Neural Networks

Seat belt detection is one of the necessary task which are required in transportation system to reduce accidents due to abrupt stop or high speed accident with other vehicles. In this paper, a technique is proposed to detect whether the driver wears seat belt or not by using convolution neural networks. Convolution Neural Network is nothing but deep Neural Network. ConvNet automatically collects features using filters or kernels from images without human involvement to classify the output images. Compared to different classification algorithms, preprocessing required in ConvNet is least. In this proposed method, first ConvNet is built and trained using Seatbelt dataset of both standard and non-standard. ConvNet learns the features from the images of seat belt dataset and performed better with an accuracy of 91.4% over SVM with 87.17% and an error rate of 8.55% when compared with SVM with 12.83% in case of standard dataset.

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