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GUI Programming with Python and Qt
Published in Vasudevan Lakshminarayanan, Hassen Ghalila, Ahmed Ammar, L. Srinivasa Varadharajan, Understanding Optics with Python, 2018
Vasudevan Lakshminarayanan, Hassen Ghalila, Ahmed Ammar, L. Srinivasa Varadharajan
To convert the design file to Python code saved as UiMainApp.ui, use the cd command to change to the MyApp directory holding the UiMainApp.ui file and simply run: For Windows users \scripts\MyApp>pyuic5 -x UiMainApp.ui -o UiMainApp.py For Linux/MacOS users: /scripts/MyApp$pyuic5 -x UiMainApp.ui -o UiMainApp.py The design details are now stored in the Python file UiMainApp.py file.
Digital video compression
Published in Steve Heath, Multimedia and Communications Technology, 1999
Apple’s video compressor uses a proprietary image compression method developed by Apple. The technology involves the use of both spatial and temporal redundancy, i.e. chrominance sub-sampling and motion estimation, but the actual details are closely guarded. The algorithm has been ported to both the MacOS and the Microsoft Windows operating systems. It provides compression ratios typically in a range between 5:1 and 25:1. One advantage the algorithm provides is a relatively low level of performance needed to decode the video and play it back. A typical QuickTime movie which was compressed using this algorithm can achieve play back rates of 15 frames per second with a picture size of 160x120 pixels on a 20 MHz MC68020 based Macintosh. Larger picture sizes and frame rates are supported by faster processors such as the PowerPC based MACs.
Monte Carlo Simulation of Nuclear Medicine Imaging Systems
Published in Michael Ljungberg, Handbook of Nuclear Medicine and Molecular Imaging for Physicists, 2022
David Sarrut, Michael Ljungberg
The entire code was written in Fortran and includes versions that are fully operational on Linux, Windows, and MacOS operating systems. No imaging routines or reconstruction programs are provided. However, the distribution includes a conversion program, SMC2CASTOR; this program converts SIMIND projection files to be readable by the CASToR tomographic reconstruction program, which is freely available [22].
GeoBox: design and evaluation of a tool for resilient and decentralised data management in agriculture
Published in Behaviour & Information Technology, 2023
Franz Kuntke, Marc-André Kaufhold, Sebastian Linsner, Christian Reuter
The end-users mainly interact with the whole system by using the front-end. To reach multiple devices (R3) within the same code base, we decided to develop a PWA, that could be translated into software for smartphones, tablets, and desktop computers and their different operating systems (Microsoft Windows, Apple macOS, GNU/Linux, Google Android, Apple iOS/iPadOS). Our first development stage should establish a basic functionality set with a low complexity (R2), but a cross-domain usage (R1). We decided to implement the following application features to have a usable application: visualisation of spatial data on a map (e.g. all cultivated fields of a company),documentation of processes in a journal (e.g. applied fertilisation),sending/receiving orders/jobs in a form management (e.g. soil sample examination),creating calculations (e.g. calculation of optimal amount of fertiliser), andgetting an overview of business data in a tabular view (e.g. how many fields have been fertilised).
Machine vs. deep learning comparision for developing an international sign language translator
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Meltem Eryilmaz, Ecem Balkaya, Eylül Uçan, Gizem Turan, Seden Gülay Oral
As the first step of the study, machine learning and deep learning concepts were investigated. In machine learning, before classifying the algorithm, the output is obtained with feature separation. In contrast, in deep learning, features are automatically extracted and learned thanks to the artificial neural network structure of the algorithm. A detailed comparison of the differences is shown in Table 1 (Microsoft, 2020). Considering the advantages and disadvantages, it was decided to continue the project with the deep learning process. Considering deep learning, it can run on different operating systems such as Windows, Linux, Android, IOS, macOS. There are multiple software languages and libraries developed for deep learning. Deep learning has many architectures such as recurrent neural networks, constrained Boltzmann machines, deep autoencoders, convolutional neural networks (CNN) (Doğan & Türkoğlu, 2019). Since there is no significant data set to be used within the project’s scope, the created data sets are trained in the VGG16. This last layer is one of the Convolution Neural Network (CNN) architectures known for its success in image classification and object identification.
DeCE: the ENDF-6 data interface and nuclear data evaluation assist code
Published in Journal of Nuclear Science and Technology, 2019
The computer program DeCE, ‘Descriptive Correction of ENDF-6 format,’ is open-source software developed at Los Alamos National Laboratory, to provide the interface between ENDF-6 and C++. DeCE facilitates manipulating the evaluated nuclear data files, as well as making a new evaluation in the legitimate ENDF-6 format. The code is built on top of core C++ libraries, ENDFLIB and ENDFIO, which offer an easy access to the past ENDF-6 formatted files. This paper presents the computational technique employed in DeCE and demonstrates how DeCE can promote the data science in the nuclear technology field. DeCE is a modest size C++ code, 1.2 MBytes, 20,000 lines, and available at GitHub (https://github.com/toshihikokawano/DeCE). DeCE runs on a UNIX-like platform, such as Linux and MacOS. At this moment Windows is not supported, but we plan to provide the Windows version as we anticipate there could be such a demand.