Showing 4 open source projects for "java spam detection"

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  • 1
    EDM Calibration Baseline Fitting

    EDM Calibration Baseline Fitting

    EDMFit is an adjustment tool to estimate EDM calibration parameters.

    An EDM calibration baseline is used to derive specific instrument errors of the electronic distance measurement (EDM) unit of a total station. EDMFit is a simple adjustment tool to estimate the EDM calibration parameters (addition constant and/or scale parameter). The adjustment process is according to ISO 17123-4. The parameters are checked for significance, to avoid an overestimation. Based on two classical hypothesis testing, EDMFit provides a reliable outlier detection. For...
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  • 2

    CONMAP

    Geobody connectivity analysis tool in geosciences. New version: 2.0

    ...It accepts input and outpts results in GEO-EAS (GSLIB) format for easy interoperability with most geoscience softwares. To get help in usage, simply run the program without arguments. CONMAP is a Java 1.7 program. Open a command prompt, go to the directory where you download CONMAP.jar and enter the command "java -jar CONMAP.jar <arguments>" (without quotation marks) to run the program. Geobody detection itself runs pretty fast, with almost all execution time spent in file loading, parsing and saving. New features in version 2.0: geobody walk, vertical transitions count and mean thickness. ...
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  • 3
    phenotemp

    phenotemp

    phenological trends using NOAA AVHRR time series

    Starting from GIMMS values as ascii data (each line will be treated as individual time series), the application can smooth the ts - values by applying different algorithm that are based on Fouriertransformation. Separation of seasonal figure as well as the detection of linear trends is possible. Possible breaks in longterm mean can be detected with a change-point analysis using CuSum algorithms. Phenological events such as start-of-season, day-of-max/day-of-min, end-of-season can be...
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  • 4
    This tool helps users for placing sensor nodes on smart highways and collecting data from the nodes. It optimizes the placement of sensor nodes on road, a critical issue that affects both the cost and detection capability of a sensor network.
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