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This project aims to classify different units on a wireless network with the use of their frequency response. This is in purpose to increase security when communicating over WiFi. We use a convolution neural network for finding symmetries in the frequency responses recorded from two different units. We used two pre-recorded sets of data which contained the same units but from two different locations. The project achieve an accuracy of 99.987%, with a 5 hidden layers CNN, when training and testing on one dataset. When training the neural network on one set and testing it on a second set, we achieve results below 54.12% for identifying the units. At the end we conclude that the amount of data needed, for achieving high enough accuracy, is to large for this method to be a practical solution for non-stationary units.

In this thesis, anomalies are defined as data points whose value differs significantly from the normal pattern of the data set. Anomalousobservations on time series measured on satellites has a growing need of being detected directly on board the space-orbit systems to for example prevent malfunction and have efficient data management. Unibap's service Spacecloud Framework (SCFW) is developed to allow the deployment of machine learning applications directly on the satellite systems. Neural Networks (NNs) is therefore a candidate for the possibility to predict anomalies on satellite time series. The work described in this reportaims to implement and create a benchmark for Convolutional Autoencoder NN (CNN) and a Long Short-term Memory Autoencoder NN (LSTM). These implementations are used to determine which NN can be applied in Unibap's SCFW and detect anomalies with accuracy.  The NNs are trained and tested using a public data-sets which containreal and artificial time-series with labelled anomalies. The anomaliesare detected by reconstructing the time series and creating a threshold between the output and the input. The algorithms classify a data pointas an anomaly if it lies above the threshold. The networks are evaluated based on accuracy, execution time and size, to assess whether they are suited for implementation in SCFW. The results from the NNs indicatethat CNN is best suited for further application. On this basis, anattempt to implement CNN in SCFW is performed, but failed due to time and documentation limitations. Therefore, further research is needed to identify whether CNN can be implemented in SCFW and successfully detect anomalies.

This thesis is divided into two distinct chapters. In the first chapter, we apply network representation learning to the field of materials science in order to predict aluminum grain boundaries' properties and locate the most influential atoms and subgraphs within each grain boundary. We create fixed-length representations of the aluminum grain boundaries that successfully capture grain boundary structure and allow us to accurately predict grain boundary energy. We do this through two distinct methods. The first method we use is a graph convolutional neural network, a semi-supervised deep learning algorithm, and the second method is graph2vec, an unsupervised representation learning algorithm. The second chapter presents our dynamic global value chain network, the combination of the dynamic global supply chain network and the dynamic global strategic alliance network. Our global value chain network provides a level of scope and accessibility not found in any other global value chain network, commercial or academic. Through applications of network theory, we discover business applications that would increase the robustness and resilience of the global value chain. We accomplish this through an analysis of the static, dynamic, and community structure of our global value chain network.

<p><em>Curvilinear structure detection in images has been investigated for decades. In general, the detection of curvilinear structures includes two aspects, binary segmentation of the image and  inference of the graph representation of the curvilinear network. In our work, we propose a connected-tube model based on a marked point process (MPP) for addressing the two issues. The proposed tube model is applied to fiber detection in microscopy images by combining connected-tube and ellipse models. Moreover, a tube-based segmentation algorithm has been proposed to improve the segmentation accuracy. Experiments on fiber-reinforced polymer images, satellite images, and retinal vessel images will be presented. Additionally, we extend the 2D tube model to a 3D tube model, with each tube be modeled as a cylinder. To investigate the supervised curvilinear structure detection method, we focus on the application of road detection in satellite images and propose a two-stage learning strategy for road segmentation. A probability map is generated in the first stage by a selected neural network, then we attach the probability map image to the original RGB images and feed the resulting four images to a U-Net-like network in the second stage to get a refined result.</em></p> <p><br></p> <p><em>Anomaly detection in time series is a key step in diagnosing abnormal behavior in some systems. Long Short-Term Memory networks (LSTMs) have been demonstrated to be useful for anomaly detection in time series, due to their predictive power. However, for a system with thousands of different time sequences, a single LSTM predictor may not perform well for all the sequences. To enhance adaptability, we propose a stacked predictor framework. Also, we propose a novel dynamic thresholding algorithm based on the prediction errors to extract the potential anomalies. To further improve the accuracy of anomaly detection, we propose a post-detection verification method based on a fast and accurate time series subsequence matching algorithm.</em></p> <p><br></p> <p><em>To detect anomalies from multi-channel time series, a bi-directional transformer-based predictor is applied to generate the prediction error sequences, and a statistical model referred as an anomaly marked point process (Anomaly-MPP) is proposed to extract the anomalies from the error sequences. The effectiveness of our methods is demonstrated by testing on a variety of time series datasets.</em></p>

There are scenarios in the modern world of today when several things are being cooked at the same time on the stove in the kitchen. You typically have a saucepan that is boiling a carbohydrate. This requires attention and can result in elevated levels of mental exertion. Would it not be useful then to aid the cooking process by removing the boiling process as a point of attention by automating the boiling process?   Food to be boiled can be identified through image recognition. There is thus a possibility to automate boiling by using machine learning. In this project machine learning is used to automate the boiling of carbohydrates. A prototype has been developed which consists of a camera and a Raspberry Pi in which a convolutional neural network (CNN) model has been implemented. The prototype can identify pasta, potato, rice, their corresponding boiling states, and give correct indication when any of them is ready. A dataset has been created from scratch, containing 5607 images that were taken and labeled, and then used to train the CNN model.   The CNN model has been evaluated through a confusion matrix applied to an image dataset which was captured by the prototype. It was also evaluated through tables of successful boiling trials. The evaluation results show that the performance of the CNN model can identify carbohydrates in limited stove scenarios. The confusion matrix shows that the precision scores are 0.846, 0.959, 0.870, 0.688 for pasta, potato, rice and "no boiling item", respectively. Recall scores are 0.967, 0.848, 0.844 and 0.681 for pasta, potato, rice and "no boiling item", respectively. But it is not sufficiently reliable to be able to work in a wide range of scenarios because of the limited dataset. It has also been shown that it is possible to use the CNN model to guide the boiling of carbohydrates. But still the dataset is not sufficiently large to quantify the error rate of the boiling system. There is potential for this type of application but further work is needed.

The thesis aims to develop a deep learning model for facial emotion recognition using Convolutional Neural Network algorithm and Multiclass Classification along with Hyper-parameter tuning using Bayesian Optimization to improve the performance of the model. The developed model recognizes seven basic emotions in images of human beings such as fear, happy, surprise, sad, neutral, disgust and angry using FER-2013 dataset.

Training a deep neural network from scratch can be very expensive in terms of resources.In addition, training a neural network on a new task is usually done by training themodel form scratch. Recently there are new approaches in machine learning which usesthe knowledge from a pre-trained deep neural network on a new task. The technique ofreusing the knowledge from previously trained deep neural networks is called Transferlearning. In this paper we are going to evaluate transfer learning capabilities of deep neuralnetwork architectures for image classification. This research attempts to implementtransfer learning with different datasets and models in order to investigate transfer learningin different situations.