Bachelor-Project base.camp (64-181)

We extend on last semester's deep dive into Edge IoT programming with small development boards including special accelleration hardware (TPU) for on-device machine-learning and AI capabilities (Coral.ai Dev Board Micro) or alternatively use their larger siblings (Dev Board), for a choice of different development environments. Participants will use these devices to implement an application with pre-trained or custom inference models, which shall communicate with a backend server on a separate device over the network.

This semester we want to dive deeper into microcontroller programming with small development boards including special accelleration hardware (TPU) for on-device machine-learning and AI capabilities (Coral.ai). Participants will use these devices to implement an application with pre-trained or custom inference models, which shall communicate with a backend server over the network.

With the help of small general purpose CPUs (e.g. Arduino) and sensors, various smart devices can be created inexpensively and easily. Within this project, the participants will use Arduino Nano 33 IoT and Raspberry Pis to develop a solution to record information on road conditions during a bicycle ride, transfer it to a suitable cloud backend and finally analyze, visualize and evaluate this data using suitable methods.

In the winter semester 2022 we will use parts of the results of SoSe22, including custom PCBs that were developed and manufactured here.

With the help of small general purpose CPUs (e.g. Arduino) and sensors, various smart devices can be created inexpensively and easily. Within this project, the participants will use Arduino Nano 33 IoT and Raspberry Pis to develop a solution to record information on road conditions during a bicycle ride, transfer it to a suitable cloud backend and finally analyze, visualize and evaluate this data using suitable methods.

Mobile devices with the ability to process data and communicate have already permeated our everyday lives. In the process, they support us in more and more situations in life and open up new possibilities for interacting with people, services and, increasingly, other objects in our daily lives.

As of 2008, more than 50% of the world's population lives in cities. By 2050, this figure is expected to increase to nearly 70%. This increasing urbanization poses special challenges for the lives of citizens and the businesses located there, among others. Among other things, increasing the quality of life, reducing resource consumption, strengthening resilience - these are all goals that can and should be achieved with the help of information and communication technologies.

Within the scope of the project, mobile (context-sensitive) applications and services or context-based information services for the smart city of tomorrow are to be developed for this purpose. For the implementation of such mobile distributed systems, the project offers the opportunity to learn relevant techniques and current technologies for the design, programming and testing of mobile and distributed application components.

We program AI applications that use machine learning to make predictions. The topics are:

Prediction of weather and particulate matter levels using the sensor network of luftdaten.info (https://luftdaten.info/).
In addition to analyzing and predicting the data, a visualization is required that processes the data based on search factors (location, time, sensor values).
Sentiment Analysis/Detection of Insults in Social Media (https://projects.fzai.h-da.de/iggsa/germeval/).
We build a good quality classifier for insults or sentiment analysis. With this, we create a text corpus of labeled tweets that is constantly augmented with new data. Current results are visualized.