Home Skip to main content

DOTSC IT Section
About Us

The IT Section introduces North Dakota State University's (NDSU) computer science and management information system students with real-world information technology (IT) issues and projects.

The program familiarizes students with IT issues and governmental IT systems while encouraging them to stay in North Dakota and work with public or private organizations after graduation.

IT Projects

  • Web Development – The DOTSC IT students develop a very wide variety of web applications using a mix of Angular, React, or .NET as the front end, with many also using a C# API as a back end. These web projects can be deployed and accessed from anywhere that has Internet connectivity, eliminating the need to install programs on multiple machines.
  • Windows Desktop Development – IT interns develop desktop applications for use within UGPTI with the goal of increasing productivity and ease of use. Some examples of these projects include data entry and maintenance using Microsoft Access, wind and traffic data importers using Visual Studio and C#, data upload from XML files using Python, and traffic intersection snapshot collection also using Python. Generally, they are used to automate remedial tasks or to simplify complex data equations and calculations.
  • Database Design/Analysis – Many applications used by DOTSC IT utilize a database to store persistent data. Students learn how data should be stored and accessed to maximize efficiency, security, and reliability. Analyzing the data allows the IT department to create reports that help users better understand the data. The primary database tool students use is Microsoft SQL Server.
  • Research – Students are encouraged to research their projects and take time to learn about the challenges that come along with them. This helps the student become better equipped for current and future projects.

Machine Learning

  • Vehicle Identification – DOTSC IT students are charged with developing and utilizing an object detection machine learning model. This model is developed using YOLOv8 and aims to identify vehicles in video or image files and then classify them into one of several categories based on their design.
  • Vehicle Counting – Using the model mentioned above, students have utilized Python and developed an application that can track and count the number of vehicles that pass on a road. This application will also identify and classify the vehicles that pass. The program will output an excel file detailing the number of vehicles that pass going each direction (i.e. vehicles moving North or South), the total count of each type of vehicle that passed (number of cars, trucks, etc.), and the timestamps of when each vehicle passed the camera. The application utilizes a simple TKinter GUI to select any required parameters and input a video file.
  • Raspberry Pi – As an extension of these projects, students also developed an application using a Raspberry Pi 5 and a connected camera to run the machine learning model. This application also utilizes Python and works very similar to the prior application, but instead of a video file being uploaded, the Pi itself will capture the video in the field and then run the machine learning model at the same time. Students will gain experience working in a Linux environment while utilizing tools such as WinSCP, PuTTY, and TigerVNC to work with the Pi remotely.

Mobile Applications

  • PAVVET – The Performance Analysis Via Vehicle Electronic Telemetry (PAVVET) application has been developed and maintained by DOTSC IT students since 2017. There are several components of this project including two mobile applications (one developed in Java for Android, and another developed in C for IOS), a web application developed in React, and a server application developed in C#. The phones collect latitude, longitude, speed, and other data, which is then automatically sent to the server application. The server application performs data calculations and then uploads the final data to a database, where it is utilized by the ATAC department.
  • Roadway Image Capture – The RIC application was developed by an NDSU Computer Science capstone team in 2015 using both Swift for IOS and Java for Android. This application was developed to capture real time images of roads. Drivers would mount their phone with the camera facing the road and the camera would capture a picture after a set amount of time passed or distance traveled. These images would be uploaded to a database and eventually displayed in the GRIT application.
NDSU Dept 2880P.O. Box 6050Fargo, ND 58108-6050