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Arduino with Processing
Published in Amartya Mukherjee, Nilanjan Dey, Smart Computing with Open Source Platforms, 2019
Amartya Mukherjee, Nilanjan Dey
As we install the processing environment on our computer, our task is to make some programs. In windows environment, we can open the IDE by double-clicking the icon. If the package is a .zip extension, we simply extract it at any directory and run the executable file. In case of Linux, to execute it, we must write ./processing (as it is a shell script having a .sh extension). Before that, we must change the mode of the processing executable file to the executable mode. We must give the command chmod 777 processing to do so. Once it is successful, it will open the IDE.
Monte Carlo simulations of an innovative molecular breast imaging system for the small breast cancer diagnosis using GATE
Published in Radiation Effects and Defects in Solids, 2019
G. E. Poma, F. Garibaldi, F. Giuliani, T. Insero, M. Lucentini, A. Marcucci, P. Musico, C. Petta, F. Santavenere, C. Sutera, E. Cisbani
A shell script automates the simulation and digitization, in terms of the following main parameters: detector type, Large or Small Head system;collimator type for the Small Head, parallel hole or pinhole;radioisotope (usually Tc is used);source diameter and its depth inside the breast phantom;number of γ-ray emitted isotropically from the source;source type, tumor or breast background;geometrical setup: shift and rotation of the Small Head in LAT configuration.
Semantic driven code generation for networking testbed experimentation
Published in Enterprise Information Systems, 2018
Filip Jelenkovic, Milorad Tosic, Valentina Nejkovic
This paper presents a novel semantic-based approach and algorithm for automatic code generation in complex scalable infrastructure environments. It automatically generates OEDL and Shell Script code, which is then used to execute experiments on heterogeneous networking and cloud testbeds. Effectiveness of the approach is practically verified on one class of experiments that also indicate a great potential in future applications. Since automatic code generation does not depend on the experiment type, it could be extended to support general NFV/SDN or 5G applications, or any other type of experiments that may be executed at infrastructure similar to FIRE testbeds. Once the automatic code generation solution has been proven on the testbed infrastructure, it would represent one of the first end-user application development components for the 5G. Consequently, we would focus future research on such application development environments as attractive tool for end-user participation and delivering practical value in the future 5G installations.
Improvement of Automatic Physics Data Analysis Environment for the LHD Experiment
Published in Fusion Science and Technology, 2018
M. Emoto, C. Suzuki, M. Yokoyama, M. Yoshinuma, R. Seki, K. Ida
The analysis program receives a shot number as an argument, calculates the target physical data, and registers it to the analyzed data server. Generally, analysis programs are offered by scientists who are specialists in their fields. Therefore, the programs are written in various languages, for example, Python, FORTRAN, PV-Wave, Shell script, and others. There is also a potential requirement to use other proprietary tools such as IDL and MATLAB because both tools are widely used as data analysis tools in the nuclear fusion community. However, both tools were not supported because the number of run-time licenses is limited. As previously mentioned, execution programs run simultaneously. The number of concurrent processes is 116 at most, but the number is easy to increase by adding Executer PC to the AutoAna, which consumes limited licenses. In order to make use of the limited license, a resource management scheme has been introduced. Figure 5 shows the procedure to manage computer resources. All the available resources and the resources that the module uses are written in a new JSON file, “resource.json.” In this example, there are ten licenses for IDL and five licenses for MATLAB. Then, a new tag “USE” is added in the module definition file. In the example, “module 1” uses one IDL license. When Executer runs module 1, it asks ResourceManager whether an IDL license is available. If the license is available, it rents one license from ResourceManager. When the execution is terminated, it returns the license to ResourceManager.