The Research on a Rapid Appearance Detection Method for Vertical Polarized Electrolytic Capacitors

2013 ◽  
Vol 415 ◽  
pp. 333-337
Author(s):  
Min Dai ◽  
Chen Yang Wang ◽  
Kai Chen ◽  
Zhi Sheng Zhang
Keyword(s):  
Processing Speed ◽  
Detection Method ◽  
Low Cost ◽  
Detection Algorithm ◽  
Feature Points ◽  
Electrolytic Capacitors ◽  
Fast Processing ◽  
High Production ◽  
Vision Detection ◽  
Rapid Appearance

For detecting the defects of the vertical polarized electrolytic capacitors in the process of production, an online machine vision detection method based on DSP is proposed in this paper. The hardware framework is designed emphatically with DM642 as the core processor, which has the advantages of small size, fast processing speed, low cost and low power consumption. The software workflow of the capacitors defects detection is introduced. To meet the requirement of high production and real-time detection of capacitors, a rapid appearance detection algorithm is introduced by using a few number of feature points instead of the whole. The experimental results show that the proposed method can achieve many types of defects detection of the capacitors.

Yawning Detection Based on Mouth Feature Points Curve Fitting

2012 ◽  
Vol 605-607 ◽  
pp. 2227-2231
Author(s):  
Wu Yang Ding ◽  
Ling Zhang ◽  
Yun Hua Chen
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Curve Fitting ◽  
Detection Method ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Feature Points ◽  
Harris Corner ◽  
Fatigue Monitoring ◽  
Harris Corner Detection

A yawning detection method which can be used in drivers’ fatigue monitoring is proposed. To adapt to the variance in different mouth shapes and sizes, it based on mouth inner contour corner detection and curve fitting. First, the Harris corner detection algorithm was used to detect inner mouth feature points. Second, we established the open mouths’ mathematical model by curve fitting those points, calculated the degree of mouth openness using the mouth model, and generated the real-time M-curve. Third, the duration of big openness in successive images is divided into levels for further judgment. The validation results show that the method can obtain more precise mouth parameters and distinguish yawn from complex mouth activities. So the method achieves a higher level of accuracy.

Research on Real-Time Vision Detection Method for Sanitary Labels

2013 ◽  
Vol 300-301 ◽  
pp. 484-489
Author(s):  
Chao Luo ◽  
Le Song ◽  
Mei Rong Zhao ◽  
Yu Chi Lin ◽  
Jian Li
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Inspection System ◽  
Matching Method ◽  
Accurate Identification ◽  
Vision Detection

Taking diaper which is a representative production of sanitary supplies as an example, a real-time detection method for diaper label based on machine vision is developed. To identify the location of diaper surface label position rapidly, a visual inspection system platform applies to production line is built. Images are captured with high-resolution colorful CCD industrial camera and NC template matching method is adopted as the surface label detection algorithm. Meanwhile, the comparative experiments results among NC, ABS method and Moment Matching method are presented. Experimental results show that this label detection system can realize accurate identification on the condition of different light, whose recognition rate can reach up to 97% and detection algorithm is of preferable instantaneity and stability.

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

scholarly journals A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1179
Author(s):  
Carolina Del-Valle-Soto ◽  
Carlos Mex-Perera ◽  
Juan Arturo Nolazco-Flores ◽  
Alma Rodríguez ◽  
Julio C. Rosas-Caro ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Performance Metrics ◽  
Low Cost ◽  
Detection Algorithm ◽  
Anomalous Behavior ◽  
Wireless Sensor ◽  
Jamming Detection ◽  
Minimal Energy Expenditure

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

scholarly journals A Note on Advantages of the Fuzzy Gabor Filter in Object and Text Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 678
Author(s):  
Vladimir Tadic ◽  
Tatjana Loncar-Turukalo ◽  
Akos Odry ◽  
Zeljen Trpovski ◽  
Attila Toth ◽  
...  
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Gabor Filter ◽  
Low Cost ◽  
Fuzzy Optimization ◽  
Detection Performance ◽  
Text Detection ◽  
License Plate ◽  
2D Gabor Filter ◽  
Fine Tune

This note presents a fuzzy optimization of Gabor filter-based object and text detection. The derivation of a 2D Gabor filter and the guidelines for the fuzzification of the filter parameters are described. The fuzzy Gabor filter proved to be a robust text an object detection method in low-quality input images as extensively evaluated in the problem of license plate localization. The extended set of examples confirmed that the fuzzy optimized Gabor filter with adequately fuzzified parameters detected the desired license plate texture components and highly improved the object detection when compared to the classic Gabor filter. The robustness of the proposed approach was further demonstrated on other images of various origin containing text and different textures, captured using low-cost or modest quality acquisition procedures. The possibility to fine tune the fuzzification procedure to better suit certain applications offers the potential to further boost detection performance.

A pragmatic eLCR for an ultrasensitive detection of methicillin-resistant Staphylococcus aureus in joint synovial fluid: superior to qPCR

The Analyst ◽  
2021 ◽  
Author(s):  
Jinyuan Chen ◽  
Hongxiang Wei ◽  
Xinyu Fang ◽  
Yuanqing Cai ◽  
Zhenzhen Zhang ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Synovial Fluid ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Detection Method ◽  
Low Cost ◽  
Ultrasensitive Detection ◽  
Gene Detection ◽  
Methicillin Resistant ◽  
Cost Identification

A pragmatic electrochemical mecA gene detection method for a rapid, accurate and low-cost identification of MRSA in the joint synovial fluid of PJI patients.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

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