scholarly journals Event-Driven Configuration of a Neural Network CMP System over a Homogeneous Interconnect Fabric

2009 ◽  
Author(s):  
Muhammad Mukaram Khan ◽  
Javier Navaridas Palma ◽  
Alexander D. Rast ◽  
Xin Jin ◽  
Luis A. Plana ◽  
...  
Keyword(s):  
Neural Network ◽  
Event Driven

scholarly journals Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2779
Author(s):  
Yaoming Zhuang ◽  
Chengdong Wu ◽  
Hao Wu ◽  
Zuyuan Zhang ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Single Type ◽  
Different Types ◽  
Event Driven ◽  
Robot Networks ◽  
Neural Network Algorithm ◽  
Complex Monitoring ◽  
Maximum Entropy Function

Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.

scholarly journals Event-driven simulations of a plastic, spiking neural network

2011 ◽  
Vol 84 (3) ◽  
Author(s):  
Chun-Chung Chen ◽  
David Jasnow
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Event Driven

Real-time event-driven spiking neural network object recognition on the SpiNNaker platform

2015 ◽  
Author(s):  
Garrick Orchard ◽  
Xavier Lagorce ◽  
Christoph Posch ◽  
Steve B. Furber ◽  
Ryad Benosman ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Real Time ◽  
Spiking Neural Network ◽  
Event Driven ◽  
Network Object
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