Operator functional state classification using neural networks with combined physiological and performance features

1999 ◽  
Author(s):  
Glenn F. Wilson ◽  
Chris Russell
Keyword(s):  
Neural Networks ◽  
Functional State ◽  
State Classification ◽  
Operator Functional State ◽  
And Performance

The Effects of Day-to-Day Variability of Physiological Data on Operator Functional State Classification

2013 ◽  
Author(s):  
James C. Christensen ◽  
Justin R. Estepp ◽  
Glenn F. Wilson ◽  
Christopher A. Russell ◽  
Krystal M. Thomas
Keyword(s):  
Functional State ◽  
Physiological Data ◽  
State Classification ◽  
Operator Functional State

The effects of day-to-day variability of physiological data on operator functional state classification

NeuroImage ◽  
2012 ◽  
Vol 59 (1) ◽  
pp. 57-63 ◽  
Author(s):  
James C. Christensen ◽  
Justin R. Estepp ◽  
Glenn F. Wilson ◽  
Christopher A. Russell
Keyword(s):  
Functional State ◽  
Physiological Data ◽  
State Classification ◽  
Operator Functional State

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

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