Detecting anomalies in backbone network traffic: a performance comparison among several change detection methods

2012 ◽  
Vol 11 (4) ◽  
pp. 205 ◽  
Author(s):  
Christian Callegari ◽  
Stefano Giordano ◽  
Michele Pagano ◽  
Teresa Pepe
Keyword(s):  
Change Detection ◽  
Network Traffic ◽  
Performance Comparison ◽  
Detection Methods ◽  
Backbone Network ◽  
A Performance

scholarly journals ANOMALY DETECTION PERFORMANCE COMPARISON ON ANOMALY-DETECTION BASED CHANGE DETECTION ON MARTIAN IMAGE PAIRS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1437-1441
Author(s):  
A. R. D. Putri ◽  
P. Sidiropoulos ◽  
J.-P. Muller
Keyword(s):  
High Resolution ◽  
Anomaly Detection ◽  
Change Detection ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Performance Comparison ◽  
Detection Methods ◽  
Large Area ◽  
Visible Wavelengths ◽  
Image Pairs

<p><strong>Abstract.</strong> The surface of Mars has been imaged in visible wavelengths for more than 40 years since the first flyby image taken by Mariner 4 in 1964. With higher resolution from orbit from MOC-NA, HRSC, CTX, THEMIS, and HiRISE, changes can now be observed on high-resolution images from different instruments, including spiders (Piqueux et al., 2003) near the south pole and Recurring Slope Lineae (McEwen et al., 2011) observable in HiRISE resolution. With the huge amount of data and the small number of datasets available on Martian changes, semi-automatic or automatic methods are preferred to help narrow down surface change candidates over a large area.</p><p>To detect changes automatically in Martian images, we propose a method based on a denoising autoencoder to map the first Martian image to the second Martian image. Both images have been automatically coregistered and orthorectified using ACRO (Autocoregistration and Orthorectification) (Sidiropoulos and Muller, 2018) to the same base image, HRSC (High-Resolution Stereo Camera) (Neukum and Jaumann, 2004; Putri et al., 2018) and CTX (Context Camera) (Tao et al., 2018) orthorectified using their DTMs (Digital Terrain Models) to reduce the number of false positives caused by the difference in instruments and viewing conditions. Subtraction of the codes of the images are then inputted to an anomaly detector to look for change candidates. We compare different anomaly detection methods in our change detection pipeline: OneClassSVM, Isolation Forest, and, Gaussian Mixture Models in known areas of changes such as Nicholson Crater (dark slope streak), using image pairs from the same and different instruments.</p>

scholarly journals Neyman-Pearson Criterion-Based Change Detection Methods for Wavelength-Resolution SAR Image Stacks

2021 ◽  
pp. 1-5
Author(s):  
Dimas I. Alves ◽  
Cristian Muller ◽  
Bruna G. Palm ◽  
Mats I. Pettersson ◽  
Viet T. Vu ◽  
...  
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Sar Image ◽  
Pearson Criterion ◽  
Wavelength Resolution

A Performance Comparison of Three Topologies of Circular Loop Antennas Transmitting Systems

2021 ◽  
Author(s):  
Ferdaous Abderrazak ◽  
Eva Antonino-Daviu ◽  
Larbi Talbi ◽  
Miguel Ferrando-Bataller
Keyword(s):  
Performance Comparison ◽  
Circular Loop ◽  
Loop Antennas ◽  
A Performance
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