Quantitative frequency-domain pulse oximetry with deep neural network (DNN) real-time processing

2019 ◽  
Author(s):  
Yanyu Zhao ◽  
Matthew B. Applegate ◽  
Raeef Istfan ◽  
Darren M. Roblyer
Keyword(s):  
Neural Network ◽  
Pulse Oximetry ◽  
Frequency Domain ◽  
Real Time ◽  
Deep Neural Network ◽  
Real Time Processing ◽  
Time Processing

scholarly journals Quantitative real-time pulse oximetry with ultrafast frequency-domain diffuse optics and deep neural network processing

2018 ◽  
Vol 9 (12) ◽  
pp. 5997 ◽  
Author(s):  
Yanyu Zhao ◽  
Mattew B. Applegate ◽  
Raeef Istfan ◽  
Ashvin Pande ◽  
Darren Roblyer
Keyword(s):  
Neural Network ◽  
Pulse Oximetry ◽  
Frequency Domain ◽  
Real Time ◽  
Deep Neural Network ◽  
Diffuse Optics ◽  
Time Pulse ◽  
Network Processing

Real-time processing using the frequency domain binaural model

2007 ◽  
Vol 68 (8) ◽  
pp. 923-938 ◽  
Author(s):  
Yoshifumi Chisaki ◽  
Kotaro Matsuo ◽  
Katsumori Hagiwara ◽  
Hidetoshi Nakashima ◽  
Tsuyoshi Usagawa
Keyword(s):  
Frequency Domain ◽  
Real Time ◽  
Real Time Processing ◽  
Time Processing

scholarly journals FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7399
Author(s):  
Ming-Hwa Sheu ◽  
S M Salahuddin Morsalin ◽  
Jia-Xiang Zheng ◽  
Shih-Chang Hsia ◽  
Cheng-Jian Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Sigmoid Function ◽  
Real Time Processing ◽  
Scale Choice ◽  
Training Stage

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.

scholarly journals High-speed camera with real time processing for frequency domain imaging

2011 ◽  
Vol 2 (7) ◽  
pp. 1931 ◽  
Author(s):  
Victor Shia ◽  
David Watt ◽  
Gregory W. Faris
Keyword(s):  
Frequency Domain ◽  
Real Time ◽  
High Speed ◽  
High Speed Camera ◽  
Real Time Processing ◽  
Time Processing
Sign in / Sign up

Export Citation Format

Share Document