scholarly journals LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1229 ◽  
Author(s):  
Ziyang He ◽  
Xiaoqing Zhang ◽  
Yangjie Cao ◽  
Zhi Liu ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Devices ◽  
Resource Constrained

On effective offloading services for resource-constrained mobile devices running heavier mobile Internet applications

2008 ◽  
Vol 46 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Kun Yang ◽  
Shumao Ou ◽  
Hsiao-Hwa Chen
Keyword(s):  
Mobile Devices ◽  
Mobile Internet ◽  
Resource Constrained ◽  
Internet Applications

Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices

2020 ◽  
Author(s):  
Qinyong Wang ◽  
Hongzhi Yin ◽  
Tong Chen ◽  
Zi Huang ◽  
Hao Wang ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Resource Constrained ◽  
Point Of Interest

Deep neural network model optimizations for resource constrained tactical edge computing platforms

2021 ◽  
Author(s):  
Venkateswara R. Dasari ◽  
Billy E. Geerhart ◽  
Peng Wang ◽  
David Alexander
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Edge Computing ◽  
Resource Constrained ◽  
Computing Platforms

scholarly journals Integrated Image Sensor and Light Convolutional Neural Network for Image Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Cheng-Jian Lin ◽  
Chun-Hui Lin ◽  
Shyh-Hau Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Autonomous Vehicles ◽  
Model Performance ◽  
Image Sensor ◽  
Model Complexity ◽  
Resource Constrained ◽  
Smart Cameras ◽  
Resource Limited ◽  
Constrained Environments

Deep learning has accomplished huge success in computer vision applications such as self-driving vehicles, facial recognition, and controlling robots. A growing need for deploying systems on resource-limited or resource-constrained environments such as smart cameras, autonomous vehicles, robots, smartphones, and smart wearable devices drives one of the current mainstream developments of convolutional neural networks: reducing model complexity but maintaining fine accuracy. In this study, the proposed efficient light convolutional neural network (ELNet) comprises three convolutional modules which perform ELNet using fewer computations, which is able to be implemented in resource-constrained hardware equipment. The classification task using CIFAR-10 and CIFAR-100 datasets was used to verify the model performance. According to the experimental results, ELNet reached 92.3% and 69%, respectively, in CIFAR-10 and CIFAR-100 datasets; moreover, ELNet effectively lowered the computational complexity and parameters required in comparison with other CNN architectures.

A stochastic neural network for resource constrained scheduling

1992 ◽  
Vol 19 (3-4) ◽  
pp. 241-254 ◽  
Author(s):  
Shivakumar Vaithyanathan ◽  
James P. Ignizio
Keyword(s):  
Neural Network ◽  
Resource Constrained ◽  
Stochastic Neural Network
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