Theses and Projects: Difference between revisions

The aim of the project is to simulate representative natural hazards for hazard response, such as flooding and forest fire. A natural hazard response simulator will be implemented for both visualization and performance validation. For example, we can visualize the flooding of 2021 in Germany, and then validate the performance of drone deployment in hazard sensing and emergency communication. Here, we introduce some related works in virtual 3D scene which may help you to understand this project, e.g., Agents Toolkit (ML-Agents) [1], DisasterSim [2] and Airsim [3].

[3] S. Shah, D. Dey, C. Lovett, and A. Kapoor. “Airsim: High-fidelity visual and physical simulation forautonomous vehicles”. In:Field and Service Robotics. Springer. 2018, pp. 621–635.

The proliferation of video analytics is facilitated by the advances of deep learning and the low prices of high-resolution network-connected cameras. However, the accuracy improvement from deep learning is at the high computational cost. Although the state-of-the-art smart cameras can support deep learning method, the deployed surveillance and traffic camera paint a much bleaker resource picture. For example, DNNCam that ships with a high-end embedded NVIDIA TX2 GPU costs more than $2000 while the price of deployed traffic cameras today ranges $40-$200; these cameras typically loaded with a single-core CPU only provide very scarce compute resource. Because of this huge gap, typical video analytics applications, e.g., traffic cameras stream live video to remote server for further analysis.

As a result, a natural question occurs: which video streaming configuration also server decoding configuration should we select to guarantee high analysis accuracy as well as not incur network congestion? To answer this question, we attempt to explore the performance of deep reinforcement learning under this scenario.

Satellite images are a popular input to estimate wealth measures (e.g. income or consumption) on the spatial scale, so to determine which locations are richer or poorer than others within a certain time interval. However, the use of these images for estimation of the changes in these measures over time for given locations is investigated only insufficiently. This project/thesis topic intends to address this problem by applying Convolutional Neural Networks (CNNs) with Transfer Learning on a small data set of images from villages in Thailand and Vietnam. Among other things, this topic contains experimental comparisons of different approaches and CNNs in both Regression and Classification.

Identifying the socioeconomic status (SES) of users in social media like Twitter or Weibo is useful e.g., for digitized advertisements and social policies. This study aims to collect profiles of Twitter users on selected topics such as culture or foreign language learning, extract the temporal, spatial and linguistic features, and compare different classification algorithms (e.g., decision tree, random forest, na\"{i}ve Bayes, deep learning, and Gaussian processes classifier) to predict the socioeconomic status.