Email Integration and Analysis Component

The goal of this project is to develop a comprehensive component for Quote Control Ltd that can read and render emails from Gmail and Outlook, analyze their content, and sort them based on specific criteria. This project will allow learners to apply their classroom knowledge in software development, data analysis, and user interface design. The project involves integrating email services, creating a user-friendly interface for email rendering, and implementing algorithms to analyze and sort emails. The project is divided into five phases: Planning and Requirements Gathering, Design, Development, Testing, and Deployment and Review. Each phase includes specific tasks that need to be completed by team members with defined roles and skill sets.

Quote Request and Management System with Dashboard Reporting

Quote Control Ltd seeks to automate a quote request and management process through a comprehensive system that integrates email communications, smart forms, and real-time reporting. The goal is to develop a user-friendly interface that allows for seamless quote requests and responses via email or other communication channels. The system will utilize smart forms to parse and distribute components efficiently, integrate 3rd party lookup data, and employ APIs for smooth data flow. Additionally, a dashboard will be created to provide real-time reporting on key metrics related to quote requests and responses. This project aims to enhance operational efficiency and provide valuable insights through advanced reporting capabilities.

Edge Computing Solution for IoT Devices

The goal of this project is to implement an edge computing solution for IoT devices to process data locally, thereby reducing latency and reliance on cloud services. The project will be executed by a team of five members over four weeks, with each member contributing 40 hours. The team will start with planning and research to understand edge computing concepts and IoT devices. They will then design and prototype the architecture of the edge computing solution, followed by the development of data processing algorithms and communication protocols. Finally, the team will test the solution for performance and reliability, documenting their findings and recommendations. Tools like Raspberry Pi, Python, and MQTT will be utilized to achieve the project objectives.

AI and Machine Learning Integration for Edge Devices

The project aims to integrate AI and Machine Learning (ML) models into edge devices to enable real-time data processing and decision-making. This involves researching AI/ML concepts and edge computing, designing and prototyping AI/ML models, and deploying these models on edge devices such as Raspberry Pi or NVIDIA Jetson Nano. The project will allow learners to apply classroom knowledge in AI, ML, and edge computing to a practical scenario, enhancing their understanding of real-time data processing. The team will also gain hands-on experience with tools like TensorFlow Lite and Edge TPU, and will be responsible for training, optimizing, and testing the models for performance and accuracy.