Recognising Gestures Using Ambient Light and Convolutional Neural Networks: Adapting Convolutional Neural Networks for Gesture Recognition on Resource-constrained Microcontrollers

This paper presents how a convolutional neural network can be constructed in order to recognise gestures using photodiodes and ambient light. A number of candidates are presented and evaluated, with the most performant being adopted for in-depth analysis. This network is then compressed in order to be ran on an Arduino Nano 33 BLE...

Tikhonov and Sobolev regularisers compared to user-based KNN collaborative filtering

Collaborative filtering is used to predict the preference or rating of a user for a certain item. Collaborative filtering is based on the notion that similar users rate similarly. A lot of research is done on how to improve this algorithm, mostly with deep learning. A less investigated field for recommender systems is graph signal processing....

The Performance of Total Variation Regularizer for User Collaborative Filtering

Recommender systems (RS) assist users in making decisions by filtering content that the user would likely find relevant. Standard techniques like collaborative filtering exploit user similarities to find the recommendations assuming that similar users are likely to be interested in the same items. On the other hand, graph RS borrow techniques...

Total Variation Regularisation for Item KNN Collaborative Filtering: Performance Analysis

Algorithms that recommend items to users are known as recommender systems and have become an important part of online ecosystems. These systems calculate the similarity between given users or items based on the ratings they have received. Such similarities can be modeled as a graph where users or items (nodes) have connections (edges) to other...

Designing a Software Receiver for Gesture Recognition with Ambient Light

There is a growing need for touch-free interaction with public utilities such as coffeemakers and vending machines that will help prevent the spread of diseases such as COVID-19. One solution is the integration of embedded gesture recognition systems relying on ambient light. However, existing work so far is found to be inefficient in terms of...

Hand Gesture Recognition on Arduino Using Recurrent Neural Networks and Ambient Light

Touching physical buttons to interact with public electronic devices has raised some concerns regrading disease transmission following the COVID-19 pandemic. The use of hand gestures as a touchless replacement sounds appealing, but comes with the challenge of recognizing which gesture is being performed by the user, with only the processing...

Item-Item Collaborative Filtering via Graph Regularization

A recommendation algorithm aims to predict the quality of a user's future interaction with certain items based on their previous interactions. As research progresses, these algorithms are becoming increasingly more complicated with the use of machine learning and neural networks. This paper looks into a more simple solution. The recommendation...

Collaboration on Safety of Dutch Chemical clusters

This project aims to answer the question of how cross-plant safety can be improved in a general way, including the prevention of domino effects. The main research question is: How do we improve collaboration between companies in a chemical cluster, in so that an overall safety gain is achieved? From the literature review and the interviews a...

Comparing multichannel mixed CNN-RNN to individual models for earthquake prediction

Earthquakes can do great harm to the environment and people's daily lives. Being able to predict an earthquake moments before it happens could therefore reduce harm and save human lives. Traditional methods have not been successful yet, but with the rise of techniques focused on deep learning, there is a growing interest to apply them to the...

Earthquake Prediction: A MLP & SVM Comparison

Multi-Layer Perceptron and Support Vector Machine have both been widely used in machine learning. In this research paper, these models have been applied to binary classification on an individual time series basis. The goal was to see whether they can predict earthquakes, using earthquakes measured at specific stations across New Zealand. As it...

Short-term Earthquake Prediction via Recurrent Neural Network Models: Comparison among vanilla RNN, LSTM and Bi-LSTM

Earthquake prediction has raised many concerns nowadays, due to the massive loss caused by earthquakes, as well as the significance of accurate forecasting. Lots of trials have been investigated and experimented but few achieved satisfying results on short-term prediction (i.e., usually those earthquakes that will happen in three months). It is...

How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?

Earthquakes are one of the most dangerous natural disasters that occur worldwide. Predicting them is one of the unsolved problems in the field of science. In the past decade, there has been an increase in seismic monitoring stations worldwide, which has allowed us to design and implement data-driven and deep learning solutions. In this paper, we...

Technological Framework to assess the implementation of Power to Hydrogen

The current EU member states have set targets to reduce annual greenhouse emissions for 2021-2030. Sixty per cent of EU emissions include sectors like buildings, agriculture, non-ETS industry and waste. Much research cannot be found to reduce emissions in the buildings, houses and community-based scenarios. Therefore, this research focuses on...

Green hydrogen potential for the Dutch built environment

The term “Green Hydrogen Economy” has been prevalent in the global economies in recent years. Interest in hydrogen as an energy carrier has increased due to the global increase in air pollution, greenhouse gas emissions and increased energy demand. The Netherlands aspires to be a European leader in hydrogen deployment, as hydrogen is widely seen...