Timothy E. Smetek, Kenneth W. Bauer Jr. Hyperspectral anomaly detection is a useful means for using hyperspectral imagery to locate unusual objects. Current anomaly detection methods commonly use ...
Robust estimation and outlier detection play a critical role in modern data analysis, particularly when dealing with high-dimensional datasets. In such contexts, classical statistical methods often ...
Comparing die test results with other die on a wafer helps identify outliers, but combining that data with the exact location of an outlier offers a much deeper understanding of what can go wrong and ...
Our friends over at Noah Data have written a research style paper, Introduction to Statistical Analysis and Outlier Detection Methods, that discusses how statistical data can generally be classified ...
Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, ...
The next wave of automotive chips for assisted and autonomous driving is fueling the development of new approaches in a critical field called outlier detection. KLA-Tencor, Optimal+, as well as Mentor ...
Editing in surveys of economic populations is often complicated by the fact that outliers due to errors in the data are mixed in with correct, but extreme, data values. We describe and evaluate two ...
The method inputs Doppler observations, satellite positions (from ephemeris), elevation angles, azimuth angles, and C/N₀ values. It groups potential multipath/NLOS faults using elevation, azimuth ...
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