This report consummates the Bachelor Project TI3806 course. To accumulate a Bachelor's degree in Computer Science and Engineering at the Delft University of Technology, it is compulsory to pass this course. The project, spanning a duration of ten weeks, is described in this report. The project client was Adviesgroep Strategisch Gebouwbeheer Nederland B.V. (ASG). This is a Dutch energy consultancy company based in Delft that uses big data innovations for energy management and for gaining insight into sustainability issues. The client encountered a real-life problem regarding the transmission of the data from their measuring devices. We were tasked to solve this problem. The project goal was to create a better data transmitter for the client. For naming convention purposes, we call the transmitter a gateway, as the device acts as a port between the measuring devices and the servers. It receives data from the measuring devices within its proximity and after filtering and processing this data, it is transmitted to the servers. The main objective was to design and develop a gateway that is better than the previously used gateway, the one that the client encountered numerous issues with. This improved gateway would then be deployed throughout the country such that the client enjoys numerous improvements. The most important improvements include cost reduction and a more robust, feature-rich and configurable solution. Furthermore, the goal of this report is to inform the reader about the work that has been completed during the duration of the project at ASG. This report also contains possible future recommendations for this project. The objective of the future recommendations is to act as advice, guidance and instructions for the continuity of this project as the client might decide to further expand the solution by co-workers.

Video temporal action localization is the task of identifying and localizing specific actions or activities within a video stream. Instead of only classifying which actions occur in the video stream, we aim to detect when an action begins and ends. In this work, we focus on solving this task without any supervision. Existing unsupervised methods solve this task by exploiting a combination of spatial and temporal information. We propose a new model that uses a MLP (multilayer perceptron to learn to sample prototype frames from a video. We use the distance between prototypes and video frames given by DTW (dynamic time warping) as a loss function to update the MLP. The sampled prototypes allow us to find the start and end boundaries of actions, when combined with DTW. Additionally, the prototype frames can be used for video summarization. We analyze our model in a controlled synthetic data setup, to show the weaknesses and strengths of our models. Additionally, we use the Breakfast dataset, and Cholec80 surgery dataset to compare our model to the state-of-the-art models in a real scenario.