Automated malaria diagnosis using convolutional neural networks in an on-field setting: The analysis of low quality smartphone based microscope images

This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone...

Solving Vapor-Liquid Flash Problems Using Artificial Neural Networks

Vapor-liquid phase equilibrium (flash) calculations largely contribute to the total computation time of many process simulation models. As a result, process simulations, especially dynamic cases, are limited in the amount of detail that can be included due to time restrictions. In addition, under certain conditions flash calculations can fail to...

Optimal scheduling for agile Earth observation satellites

The past decade has seen a continuous increase of Earth observation missions, since they are regarded as an important tool to address global problems such as climate change or disaster mitigation. A commercial trend exists now towards higher resolution imagery, which drives the use of agile satellites. Nevertheless, a disadvantage of agile...

Short-Term Traffic Forecasting on Highways Using Neural Networks: A Congestion Pattern-Based Assessment

As traffic demands are ever increasing and building new infrastructure poses challenges in densely populated areas, it is important to optimally utilise existing infrastructure. Short-term traffic forecasting can help with this task, as its predictions can help to prevent congestion by rerouting vehicles. Recently, neural networks developed for...

Enhancing early-stage energy consumption predictions using dynamic operational voyage data: a grey-box modelling investigation

The adverse human contribution to global climate change has forced the yachting industry to acknowledge the need to reduce its environmental impact due to the client's increasing pressure and potential future regulations to limit the ecological effects. Unfortunately, current real-world data presents a significant disparity between predicted and...

Complement-based Stochastic Computing Multiplier Design for Convolutional Neural Network Acceleration

Recently, it has become popular to use Convolutional Neural Networks (CNNs) in embedded and portable devices. The popularity is based on their high accuracy rate in the field of Computer Vision (CV). However, CNNs are computationally intensive due to the convolutional layer, which accounts for over 90% of the operations. To overcome this problem...

Predicting Truck Cycle Time in Earthworks Using a Machine Learning Approach

Inaccurate truck cycle time (TCT) prediction in earthworks impacts construction projects because more equipment and human resources must be added to complete the project. It also increases fuel consumption and emissions from the machinery. However, the current method gives inaccurate prediction because of subjectivity and human error. <br/>This...

FPGA Based Deep Learning Accelerator for RF Applications: A Design Framework

Recently, interest in the use of deep learning technology for RF applications has increased. However, many of these studies are focused on developing deep learning models for a particular RF application. Therefore this master thesis focuses on the implementation of these kinds of deep learning models by using FPGAs such that these deep learning...

Moment tensor estimation through inversion of borehole microseismic data with machine learning

The analysis of microseismic measurements acquired during borehole acquisition surveys is essential for a thorough understanding of the source mechanisms in hydraulic fracturing operations. Due to the injection of high-pressure fluids, induced fractures can produce seismic events of low magnitude that can be recorded and subsequently analyzed to...

Stabilization Strategies for a Variational MultiScale Method coupled with an Artificial Neural Network

The use of Large Eddy Simulations(LES) in industry is steadily increasing as the complexity of the problems are increasing. However there remains a potential for significant improvements in LES. The Variational Multiscale Method(VMM) introduced by Hughes et al. (1998) provides a more consistent framework for turbulence modelling. The use of an...

Hardware Spiking Neural Network based Sbox AES

Hardware cryptographic algorithm implementation is easy to attack by side-channel attacks. The power-based side-channel attacks are powerful among several side-channel attacks. This attack methods use the relationship between the leakage model and power traces to reveal the secret key. Some existing countermeasures like mask and hide can protect...