PHASE 2 - K-12 Education Virtual Tours & Virtual Reality Database Project

Digital Human Library (dHL) is looking for an individual or class to collaborate with our team to complete our Phase 2 our of Project. PHASE 2: Research new virtual tours and virtual reality experiences based on the criteria provided to grow our library of virtual content and create new learning opportunities for K-12 students.

Enhancing Website Based on UX/UI Prototypes

We hope to update and revamp our existing website based on existing UX/UI prototypes that we have designed. We would like to work with participants to develop a new website that is easy to maintain while providing an appealing interface for users. This might be achieved through common site building tools such as WordPress (which we currently use). This will involve several different steps for the participants, including: Developing Frontend and backend, without assistance in providing the content. Frontend - Using React.js, tailwindcss and sass to build the frontend - Calling backend endpoints development by backend devs to communicate with the database Backend - Using FastAPI to create backend endpoints - Using pytest to create unit tests for the backend endpoints - Creating documentation for endpoints APIs - Using Pydantic models for response types

Website Updates + Platform Integration

Our company has been serving thousands of teachers and students for decades. However, our current website requires updates, in addition to an integration within two third party vendor platforms to help us scale and grow our business. Platform information and detailed instructions will be provided. *Please see the linked information, resources, and instructions below to gauge whether this task is feasible for you before you apply. If you prefer to work on the website updates only, please specify that in your application. WEBSITE UPDATES Research and vet new educational virtual tours and virtual reality experiences Add new tours to the spreadsheet provided Add new tours to the SQL database (training provided) Review new added tours on the dHL website CLEVER INTEGRATION Clever data schema is here You can sign up for a Developer Sandbox here SSO Dev Docs can be found here   CLASSLINK developer.classlink.com

Computer software for visualization and implementation of engineering analysis of structure usingArtificial Intelligence & Machine Learning Application

Computer software for visualization and implementation of engineering analysis of structure against applied loads, strain, cracks, tilt measurement, and corrosion Our company is a structure health monitoring company that provides services to many different types of structure via health monitoring systems wirelessly sensors installed,analyzed and reported to the stakeholders. We want to leverage the latest technology in the market to create advantage for our stakeholders so they can take decisions real time with real time data. Applications of this technology include recommendation algorithms, and predictive analytics like lifetime value and classifications of the infrasturcture data.  About the project : It is an web app computer software for visualization and implementation of engineering analysis of structure against applied loads, strain, cracks, tilt measurement, and corrosion. The tool allows the client to visually understand the measuring variables causing any long-term or short-term effect on the structural integrity. The downloadable user-friendly reports allows the stakeholders to make informed decisions based on the real-time data input from sensors being directed on the portal.  We would like to collaborate with students in their Grad year, Master or PhD students to apply the latest engineering research in their field of experience computer systems, civil engineering or electrical engineering. They may also add their experience using artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students could also develop an AI / ML model related to  engineering analysis of structure against applied loads, strain, cracks, tilt measurement, and corrosion The Grad year, Master or PhD student will get to work with real-time problems and real-time data to understand the behaviour of structures. They will also learn and understand the practical use and delivery methods of the data for the stakeholders. The student will also get to work with 5-6 different types of sensor data and help classify and filtrate for the most optimal visualization of the data and its representation to the stakeholder/client. This will involve several different steps for the students, including: Conducting background research on our existing products and the dataset. Analyzing our current dataset. Researching the latest AI / ML techniques and how they could be applied to our data. Developing an AI / ML model that provides unique outcomes or insights into our data.(Optional )  Providing multiple solutions that can be applied to solve the same problem. Working alongside a team consisting of - a product manager, and computer software/developers.  Get access to and first-hand experience in different types of structural sensor data  Consider introducing different software or analytical methods that can be added to the platform. Classification and filtration of infrastructure data coming from 6-7 different type of sensors

AI-Driven Predictive Maintenance for Data Centers

The Predictive Maintenance for Data Centers project aims to create an AI-driven solution to address the operational challenges of modern data centers. By utilizing advanced deep learning techniques, specifically Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), the project seeks to enhance real-time monitoring and predictive analytics capabilities. The goal is to enable proactive maintenance of critical data center infrastructure, thereby reducing downtime and improving efficiency. This project provides learners with an opportunity to apply their knowledge of AI and machine learning in a practical setting, focusing on the development of predictive models that can analyze vast amounts of data to forecast potential failures. The project emphasizes the importance of integrating AI solutions into existing systems to optimize performance and reliability.