LoRa Link Quality Estimation Based on Support Vector Machine

2021 ◽  
pp. 92-102
Author(s):  
Van Dai Pham ◽  
Phuc Hao Do ◽  
Duc Tran Le ◽  
Ruslan Kirichek
Keyword(s):  
Support Vector Machine ◽  
Link Quality ◽  
Support Vector ◽  
Quality Estimation ◽  
Link Quality Estimation

scholarly journals Machine Learning for Wireless Link Quality Estimation: A Survey

2021 ◽  
pp. 1-1
Author(s):  
Gregor Cerar ◽  
Halil Yetgin ◽  
Mihael Mohorcic ◽  
Carolina Fortuna
Keyword(s):  
Machine Learning ◽  
Link Quality ◽  
Wireless Link ◽  
Quality Estimation ◽  
Link Quality Estimation

scholarly journals Radio Link Quality Estimation in Low-Power Wireless Networks

2013 ◽  
Author(s):  
Nouha Baccour ◽  
Anis Koubâa ◽  
Claro Noda ◽  
Hossein Fotouhi ◽  
Mário Alves ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Low Power ◽  
Link Quality ◽  
Radio Link ◽  
Quality Estimation ◽  
Low Power Wireless Networks ◽  
Link Quality Estimation

Link Quality Estimation of Cross-Technology Communication

2020 ◽  
Author(s):  
Jia Zhang ◽  
Xiuzhen Guo ◽  
Haotian Jiang ◽  
Xiaolong Zheng ◽  
Yuan He
Keyword(s):  
Link Quality ◽  
Quality Estimation ◽  
Link Quality Estimation

scholarly journals Enhancement in Quality Estimation of Resistance Spot Welding Using Vision System and Fuzzy Support Vector Machine

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1380
Author(s):  
Dima Younes ◽  
Essa Alghannam ◽  
Yuegang Tan ◽  
Hong Lu
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Real Time ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Failure Modes ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Quality Estimation ◽  
Resistance Spot

The current nondestructive testing methods such as ultrasonic, magnetic, or eddy current signals, and even the existing image processing methods, present certain challenges and show a lack of flexibility in building an effective and real-time quality estimation system of the resistance spot welding (RSW). This paper provides a significant improvement in the theory and practices for designing a robotized inspection station for RSW at the car manufacturing plants using image processing and fuzzy support vector machine (FSVM). The weld nuggets’ positions on each of the used car underbody models are detected mathematically. Then, to collect perfect pictures of the weld nuggets on each of these models, the required end-effector path is planned in real-time by establishing the Denavit-Hartenberg (D-H) model and solving the forward and inverse kinematics models of the used six-degrees of freedom (6-DOF) robotic arm. After that, the most frequent resistance spot-welding failure modes are reviewed. Improved image processing methods are employed to extract new features from the elliptical-shaped weld nugget’s surface and obtain a three-dimensional (3D) reconstruction model of the weld’s surface. The extracted artificial data of thousands of samples of the weld nuggets are divided into three groups. Then, the FSVM learning algorithm is formed by applying the fuzzy membership functions to each group. The improved image processing with the proposed FSVM method shows good performance in classifying the failure modes and dealing with the image noise. The experimental results show that the improvement of comprehensive automatic real-time quality evaluation of RSW surfaces is meaningful: the quality estimation could be processed within 0.5 s in very high accuracy.

Link Quality Estimation in Industrial Temporal Fading Channel With Augmented Kalman Filter

2019 ◽  
Vol 15 (4) ◽  
pp. 1936-1946 ◽  
Author(s):  
Fei Qin ◽  
Qilong Zhang ◽  
Wuxiong Zhang ◽  
Yang Yang ◽  
Jinliang Ding ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fading Channel ◽  
Link Quality ◽  
Quality Estimation ◽  
Link Quality Estimation

scholarly journals Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6430
Author(s):  
Ngoc Huy Nguyen ◽  
Myung Kyun Kim
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Link Quality ◽  
Wireless Sensor ◽  
Industrial Wireless Sensor Networks ◽  
Quality Estimation ◽  
Network Applications ◽  
Power Operations ◽  
Link Quality Estimation

Although mature industrial wireless sensor network applications increasingly require low-power operations, deterministic communications, and end-to-end reliability, it is very difficult to achieve these goals because of link burstiness and interference. In this paper, we propose a novel link quality estimation mechanism named the burstiness distribution metric, which uses the distribution of burstiness in the links to deal with variations in wireless link quality. First, we estimated the quality of the link at the receiver node by counting the number of consecutive packets lost in each link. Based on that, we created a burstiness distribution list and estimated the number of transmissions. Our simulation in the Cooja simulator from Contiki-NG showed that our proposal can be used in scheduling as an input metric to calculate the number of transmissions in order to achieve a reliability target in industrial wireless sensor networks.

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