scholarly journals A Configurable Heterogeneous Multicore Architecture With Cellular Neural Network for Real-Time Object Recognition

2009 ◽  
Vol 19 (11) ◽  
pp. 1612-1622 ◽  
Author(s):  
Kwanho Kim ◽  
Seungjin Lee ◽  
Joo-Young Kim ◽  
Minsu Kim ◽  
Hoi-Jun Yoo
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Real Time ◽  
Cellular Neural Network ◽  
Multicore Architecture ◽  
Heterogeneous Multicore

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Optimal Robot Path Planning with Cellular Neural Network

2013 ◽  
pp. 19-38
Author(s):  
Yongmin Zhong ◽  
Bijan Shirinzadeh ◽  
Xiaobu Yuan
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
State Space ◽  
Real Time ◽  
Cellular Neural Network ◽  
Neural Dynamics ◽  
Robot Path Planning ◽  
Local Connectivity ◽  
Target Activity ◽  
Robot Path

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

Optimal Robot Path Planning With Cellular Neural Network

2019 ◽  
pp. 491-511
Author(s):  
Yongmin Zhong ◽  
Bijan Shirinzadeh ◽  
Xiaobu Yuan
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
State Space ◽  
Real Time ◽  
Cellular Neural Network ◽  
Neural Dynamics ◽  
Robot Path Planning ◽  
Local Connectivity ◽  
Target Activity ◽  
Robot Path

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

Implementation of deep learning neural network for real-time object recognition in OpenCL framework

2017 ◽  
Author(s):  
Yukui Luo ◽  
Shuai Li ◽  
Kuangyuan Sun ◽  
Raul Renteria ◽  
Ken Choi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Real Time ◽  
Deep Learning Neural Network
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