This paper presents a nonlinear derivative approach to addressing the problem of discrete edge detection. This edge detection scheme is based on the nonlinear combination of two polarized derivatives. Its main property is a favorable signal-to-noise ratio (SNR) at a very low computation cost and without any regularization. A 2D extension of the method is presented and the benefits of the 2D localization are discussed. The performance of the localization and SNR are compared to that obtained using classical edge detection schemes. Tests of the regularized versions and a theoretical estimation of the SNR improvement complete this work.
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http://dx.doi.org/10.1109/TPAMI.2008.282 | DOI Listing |
Viruses
November 2024
MRC/UVRI & LSHTM Uganda Research Unit, Entebbe 256, Uganda.
The emergence of SARS-CoV-2 variants has heightened concerns about vaccine efficacy, posing challenges in controlling the spread of COVID-19. As part of the COVID-19 Vaccine Effectiveness and Variants (COVVAR) study in Uganda, this study aimed to genotype and characterize SARS-CoV-2 variants in patients with COVID-19-like symptoms who tested positive on a real-time PCR. Amplicon deep sequencing was performed on 163 oropharyngeal/nasopharyngeal swabs collected from symptomatic patients.
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November 2024
Institute of Industrial Crops, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.
Strawberry viruses are significant pathogenic agents in strawberry. The development and application of efficient virus detection technology can effectively reduce the economic losses incurred by virus diseases for strawberry cultivators. In order to rapidly identify strawberry virus species and prevent the spread of virus disease, a multiplex reverse transcription polymerase chain reaction system was established for the simultaneous detection and identification of strawberry mild yellow edge virus (SMYEV), strawberry vein banding virus (SVBV), strawberry mottle virus (SMoV), strawberry polerovirus 1 (SPV-1), strawberry pallidosis-associated virus (SPaV), and strawberry crinivirus 4 (SCrV-4).
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China.
Addressing the issue of excessive manual intervention in discharging fermented grains from underground tanks in traditional brewing technology, this paper proposes an intelligent grains-out strategy based on a multi-degree-of-freedom hybrid robot. The robot's structure and control system are introduced, along with analyses of kinematics solutions for its parallel components and end-effector speeds. According to its structural characteristics and working conditions, a visual-perception-based motion control method of discharging fermented grains is determined.
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December 2024
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Coronary artery stenosis detection remains a challenging task due to the complex vascular structure, poor quality of imaging pictures, poor vessel contouring caused by breathing artifacts and stenotic lesions that often appear in a small region of the image. In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. The framework consists of a histogram equalization and canny edge detection preprocessing (HEC) enhancement module, a double coordinate attention (DCA) feature extraction module and an output module that combines a newly designed loss function, named adaptive inner-CIoU (AICI).
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December 2024
School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK.
The rapid integration of Internet of Things (IoT) systems in various sectors has escalated security risks due to sophisticated multilayer attacks that compromise multiple security layers and lead to significant data loss, personal information theft, financial losses etc. Existing research on multilayer IoT attacks exhibits gaps in real-world applicability, due to reliance on outdated datasets with a limited focus on adaptive, dynamic approaches to address multilayer vulnerabilities. Additionally, the complete reliance on automated processes without integrating human expertise in feature selection and weighting processes may affect the reliability of detection models.
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