23,905 research outputs found
Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data
Many systems are partially stochastic in nature. We have derived data driven
approaches for extracting stochastic state machines (Markov models) directly
from observed data. This chapter provides an overview of our approach with
numerous practical applications. We have used this approach for inferring
shipping patterns, exploiting computer system side-channel information, and
detecting botnet activities. For contrast, we include a related data-driven
statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems
and Securit
J-PET Framework: Software platform for PET tomography data reconstruction and analysis
J-PET Framework is an open-source software platform for data analysis,
written in C++ and based on the ROOT package. It provides a common environment
for implementation of reconstruction, calibration and filtering procedures, as
well as for user-level analyses of Positron Emission Tomography data. The
library contains a set of building blocks that can be combined by users with
even little programming experience, into chains of processing tasks through a
convenient, simple and well-documented API. The generic input-output interface
allows processing the data from various sources: low-level data from the
tomography acquisition system or from diagnostic setups such as digital
oscilloscopes, as well as high-level tomography structures e.g. sinograms or a
list of lines-of-response. Moreover, the environment can be interfaced with
Monte Carlo simulation packages such as GEANT and GATE, which are commonly used
in the medical scientific community.Comment: 14 pages, 5 figure
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