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MSP432 microcontroller development kits
Published in Ying Bai, Microcontroller Engineering with MSP432, 2016
Generally, a user project in Keil® MDK can be developed in the following steps: A new project is created using the Keil® MDK.The user’s source files with source codes, either C or ARM assembly codes, are added into the project.Depending on your source codes, the armcc (ARM® compiler) or the armasm (ARM® assembler) is called and executed to translate the user’s source codes to the object codes and stored in the host computer. The start-up codes with Project Settings will be involved in this compiling or assembling process.The object code files will be linked with all other system library files or MCU-related library files and converted to the executable files, or image file in the ARM® terminology, and downloaded into flash memory or RAM in the EVB.Finally, the executable codes can be sent to the debugger to perform the debugging or executing operations. In fact, the compiler, linker, and loader are integrated into one unit, the Builder in the ARM® μVersion® IDE.Alternatively, the executable file can also be converted to the binary file or text file for the users’ reference.
Advanced Topics
Published in Joe Follansbee, Hands-On Guide to Windows Media, 2012
Skin Definition File – After you’ve created the skin elements, you’ll write a skin definition file, which is a script that tells Windows Media Player what to do with the graphic elements and how to behave when users click on elements. A skin definition file is a plain text file that follows a defined format using XML. The code below gives you a sense of what a definition file looks like. <theme id=“main” title=“MyTheme”> <view id=“topView”> <subview id=“sub1”> <button id=“play” /> </subview> </view> </theme>
Nonlinear Equations
Published in Jeffery J. Leader, Numerical Analysis and Scientific Computation, 2022
The time has come to learn how to write programs in MATLAB. You may use any text editor, or MATLAB's edit command (which launches the MATLAB editor). Using the MATLAB editor is certainly easiest unless you have a strong preference for some other editor. If you are using Windows, be sure that the editor you use saves your program as a plain text file.
A container-based approach for sharing environmental models as web services
Published in International Journal of Digital Earth, 2021
Xiaohui Qiao, Zhiyu Li, Fengyuan Zhang, Daniel P. Ames, Min Chen, E. James Nelson, Rohit Khattar
There are many advantages of using Docker in environmental modeling. First, Docker keeps models functional by creating isolated containers in which all the software and dependencies are already installed, configured and tested. Second, Docker supports major platforms (Linux, Windows and macOS), which means Docker can avoid running environment conflicts, enabling models to be deployed across platforms (note that Windows containers can only run in Windows operating systems). Third, a Docker image is created through reading a ‘Dockerfile’, which is a simple script that defines all the commands, necessary dependencies with detailed versions, and the OS to assemble the image. The Dockerfile is a small plain text file that can be easily shared. Docker also provides a public repository (Docker Hub, https://hub.docker.com/) for publishing and sharing Docker images. All of these Docker capabilities significantly improve model sharing and versioning. Last but not least, Docker allows users to link any directory on the host OS to the running Docker container. This allows users to directly use data saved on the host OS and rely on familiar tools and environment for data collecting, preparing, and editing, while executing code in the development environment of the container, avoiding data transferring across different platforms.
Inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision
Published in Advanced Robotics, 2020
Shiying Sun, Xiaoguang Zhao, Qianzhong Li, Min Tan
The implementation of the path planning algorithm is based on ROS. Two-dimensional maps of experimental scenarios were first built using the gmapping package in ROS. The maximum velocity and acceleration of the robot are set to match those when capturing demonstration trajectories, with a planner frequency of 2 Hz. Moreover, when calculating the visual coverage, the radian Θ is set to , while the radius and resolution are r = 5 m, and , respectively. As the computational burden is onerous, the visual coverage feature is calculated off-lines: after establishing the two-dimensional map of the experimental scenario, visual coverage features of the robot that arrives at each non-obstacle point in the map at different rotation angles are calculated, however, the rotation angle of the robot has also to be discretised, having a resolution of . Hence, we can obtain a three-dimensional array off-line and the array is saved into a text file. In the on-line planning process, the text file is read and the corresponding elements in the array can be directly used so as to guarantee real time performance.
Review and Implementation of Topic Modeling in Hindi
Published in Applied Artificial Intelligence, 2019
Santosh Kumar Ray, Amir Ahmad, Ch. Aswani Kumar
jLDADMM is java implementation of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. a mixture of unigrams), using collapsed Gibbs sampling. jLDADMM supplies a document clustering evaluation to compare topic models, using two common metrics of Purity and normalized mutual information (NMI) (Nguyen 2018). jLDADMM is freely available and can be downloaded from GitHub6. The downloaded package contains jLDADMM tool as a .jar file, which can be run from the command line. This command line-based tool provides options for several parameters such as the name of the model (LDA or DMM), location of the corpus, number of topics, number of words per topic, number of iterations and so on. Many of these parameters have default values. Each line of the input text file is considered as one document. Figure 4 shows the output of jLDADMM for LDA model on the sample corpus provided with the tool itself. After running 2000 iterations (default), it produces an output that contains 15 topics, each topic containing default 20 words. However, this model does not support languages like Hindi.