KEYWORDS: Signal processing, General packet radio service, Algorithm development, Environmental sensing, Data processing, Computing systems, Visualization, Sensors, Ruby, Java
Running applications and platforms as containerized services is a trending technology for either personal computers and cloud systems. It simplifies various tasks of developers such as building all-in-one, ready-to-use development environments. This paper explains how to easily prepare a stand-alone containerized signal processing environment. The container consists of a Jupyter environment, which is a web-based interactive application that allows the user to write live code and visualize. In this paper we also share a ready-to-use containerized GPR signal processing environment.
In this study, we present a new, user friendly, easily extensible and platform independent (cross-platform) GPR signal processing tool named GPRStudio. The tool contains numerous pre-processing and post processing techniques such as background subtraction, various filtering, adjustable gain function, visualization of data, and automatic detection of buried objects. The tool has been developed in Python which is a very popular programming language with its rich and versatile free library alternatives. It has been aimed that the tool can be used on Windows, Linux and MacOS. For this reason, Qt5 is used in graphical user interface design. As an innovative approach to existing GPR signal processing tools, the GPRStudio allows users to write and import their own signal processing algorithms coded in Python. Thus, users can easily observe the effects and results of their own algorithms on GPR data. The project-based structure of the GPRStudio allows the user to work on different collections of GPR data without mixing things up and keep detailed log of every processing step. Users can also import multiple raw GPR data to a project. A raw GPR data can be used in multiple series of processing steps. The tool supports common GPR data types, such as GSSI’s “.dzt”, Sensors and Software’s “.dt1”, MALA’s ”.rd3" and “.rd7” and ASCII “.txt”. Processed data can be exported as “.csv”, “.txt” or “.jpg”.
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