Radio map estimation (RME) is the task of predicting radio power at all locations in a two-dimensional area and at all frequencies in a given band. This thesis explores four deep learning approaches to RME: dual path autoencoders, skip connection autoencoders, diffusion, and joint learning with transmitter localization.

Myoelectric control schemes aim to utilize the surface electromyography (EMG) signals which are the electric potentials directly measured from skeletal muscles to control wearable robots such as exoskeletons and prostheses. The main challenge of myoelectric controls is to increase and preserve the signal quality by minimizing the effect of confounding factors such as muscle fatigue or electrode shift. Current research in myoelectric control schemes are developed to work in ideal laboratory conditions, but there is a persistent need to have these control schemes be more robust and work in real-world environments. Following the manifold hypothesis, complexity in the world can be broken down from a high-dimensional space to a lower-dimensional form or representation that can explain how the higher-dimensional real world operates. From this premise, the biological actions and their relevant multimodal signals can be compressed and optimally pertinent when performed in both laboratory and non-laboratory settings once the learned representation or manifold is discovered. This thesis outlines a method that incorporates the use of a contrastive variational autoencoder with an integrated classifier on multimodal sensor data to create a compressed latent space representation that can be used in future myoelectric control schemes.

Mechanical devices in industriy are equipped with numerous sensors to capture thehealth state of the machines. The reliability of the machine’s health system depends on thequality of sensor data. In order to predict the health state of sensors, abnormal behaviourof sensors must be detected to avoid unnecessary cost.We proposed LSTM autoencoder in which the objective is to reconstruct input time seriesand predict the next time instance based on historical data, and we evaluate anomaliesin multivariate time series via reconstructed error. We also used exponential moving averageas a preprocessing step to smooth the trend of time series to remove high frequencynoise and low frequency deviation in multivariate time series data.Our experiment results, based on different datasets of multivariate time series of gasturbines, demonstrate that the proposed model works well for injected anomalies and realworld data to detect the anomaly. The accuracy of the model under 5 percent infectedanomalies is 98.45%.

Dimensionality reduction techniques (DR) enhance data interpretability and reduce space complexity, though at the cost of information loss. Such methods have been prevalent in the Structural Health Monitoring (SHM) anomaly detection literature. While DR is favorable in supervised anomaly detection, where possible novelties are known a priori, the efficacy is less clear in unsupervised detection. In this work, we perform a detailed assessment of the DR performance trade-offs to determine whether the information loss imposed by DR can impact SHM performance for previously unseen novelties. As a basis for our analysis, we rely on an SHM anomaly detection method operating on input signals' fast Fourier transform (FFT). FFT is regarded as a raw, frequency-domain feature that allows studying various DR techniques. We design extensive experiments comparing various DR techniques, including neural autoencoder models, to capture the impact on two SHM benchmark datasets exclusively. Results imply the loss of information to be more detrimental, reducing the novelty detection accuracy by up to 60\% with autoencoder-based DR. Regularization can alleviate some of the challenges though unpredictable. Dimensions of substantial vibrational information mostly survive DR; thus, the regularization impact suggests that these dimensions are not reliable damage-sensitive features regarding unseen faults. Consequently, we argue that designing new SHM anomaly detection methods that can work with high-dimensional raw features is a necessary research direction and present open challenges and future directions. / M.S. / Structural health monitoring (SHM) aids the timely maintenance of infrastructures, saving human lives and natural resources. Infrastructure will undergo unseen damages in the future. Thus, data-driven SHM techniques for handling unlabeled data (i.e., unsupervised learning) are suitable for real-world usage. Lacking labels and defined data classes, data instances are categorized through similarities, i.e., distances. Still, distance metrics in high-dimensional spaces can become meaningless. As a result, applying methods to reduce data dimensions is currently practiced, yet, at the cost of information loss. Naturally, a trade-off exists between the loss of information and the increased interpretability of low-dimensional spaces induced by dimensionality reduction procedures. This study proposes an unsupervised SHM technique that works with low and high-dimensional data to assess that trade-off. Results show the negative impacts of dimensionality reduction to be more severe than its benefits. Developing unsupervised SHM methods with raw data is thus encouraged for real-world applications.

Unsupervised anomaly detection is the task of identifying examples that differ from the normal or expected pattern without the use of labeled training data. Our research addresses shortcomings in two existing anomaly detection algorithms, Kernel Principal Component Analysis (KPCA) and Autoencoders (AE), and proposes novel solutions to improve both of their performances in the unsupervised settings. Anomaly detection has several useful applications, such as intrusion detection, fault monitoring, and vision processing. More specifically, anomaly detection can be used in autonomous driving to identify obscured signage or to monitor intersections. Kernel techniques are desirable because of their ability to model highly non-linear patterns, but they are limited in the unsupervised setting due to their sensitivity of parameter choices and the absence of a validation step. Additionally, conventionally KPCA suffers from a quadratic time and memory complexity in the construction of the gram matrix and a cubic time complexity in its eigendecomposition. The problem of tuning the Gaussian kernel parameter, $sigma$, is solved using the mini-batch stochastic gradient descent (SGD) optimization of a loss function that maximizes the dispersion of the kernel matrix entries. Secondly, the computational time is greatly reduced, while still maintaining high accuracy by using an ensemble of small, textit{skeleton} models and combining their scores. The performance of traditional machine learning approaches to anomaly detection plateaus as the volume and complexity of data increases. Deep anomaly detection (DAD) involves the applications of multilayer artificial neural networks to identify anomalous examples. AEs are fundamental to most DAD approaches. Conventional AEs rely on the assumption that a trained network will learn to reconstruct normal examples better than anomalous ones. In practice however, given sufficient capacity and training time, an AE will generalize to reconstruct even very rare examples. Three methods are introduced to more reliably train AEs for unsupervised anomaly detection: Cumulative Error Scoring (CES) leverages the entire history of training errors to minimize the importance of early stopping and Percentile Loss (PL) training aims to prevent anomalous examples from contributing to parameter updates. Lastly, early stopping via Knee detection aims to limit the risk of over training. Ultimately, the two new modified proposed methods of this research, Unsupervised Ensemble KPCA (UE-KPCA) and the modified training and scoring AE (MTS-AE), demonstrates improved detection performance and reliability compared to many baseline algorithms across a number of benchmark datasets. / Master of Science / Anomaly detection is the task of identifying examples that differ from the normal or expected pattern. The challenge of unsupervised anomaly detection is distinguishing normal and anomalous data without the use of labeled examples to demonstrate their differences. This thesis addresses shortcomings in two anomaly detection algorithms, Kernel Principal Component Analysis (KPCA) and Autoencoders (AE) and proposes new solutions to apply them in the unsupervised setting. Ultimately, the two modified methods, Unsupervised Ensemble KPCA (UE-KPCA) and the Modified Training and Scoring AE (MTS-AE), demonstrates improved detection performance and reliability compared to many baseline algorithms across a number of benchmark datasets.

This work aims to develop a deep learning model for land cover quantification through hyperspectral unmixing using an unsupervised autoencoder. Land cover identification and classification is instrumental in urban planning, environmental monitoring and land management. With the technological advancements in remote sensing, hyperspectral imagery which captures high resolution images of the earth's surface across hundreds of wavelength bands, is becoming increasingly popular. The high spectral information in these images can be analyzed to identify the various target materials present in the image scene based on their unique reflectance patterns. An autoencoder is a deep learning model that can perform spectral unmixing by decomposing the complex image spectra into its constituent materials and estimating their abundance compositions. The advantage of using this technique for land cover quantification is that it is completely unsupervised and eliminates the need for labelled data which generally requires years of field survey and formulation of detailed maps. We evaluate the performance of the autoencoder on various synthetic and real hyperspectral images consisting of different land covers using similarity metrics and abundance maps. The scalability of the technique with respect to landscapes is assessed by evaluating its performance on hyperspectral images spanning across 100m x 100m, 200m x 200m, 1000m x 1000m, 4000m x 4000m and 5000m x 5000m regions. Finally, we analyze the performance of this technique by comparing it to several supervised learning methods like Support Vector Machine (SVM), Random Forest (RF) and multilayer perceptron using F1-score, Precision and Recall metrics and other unsupervised techniques like K-Means, N-Findr, and VCA using cosine similarity, mean square error and estimated abundances. The land cover classification obtained using this technique is compared to the existing United States National Land Cover Database (NLCD) classification standard. / Master of Science / This work aims to develop an automated deep learning model for identifying and estimating the composition of the different land covers in a region using hyperspectral remote sensing imagery. With the technological advancements in remote sensing, hyperspectral imagery which captures high resolution images of the earth's surface across hundreds of wavelength bands, is becoming increasingly popular. As every surface has a unique reflectance pattern, the high spectral information contained in these images can be analyzed to identify the various target materials present in the image scene. An autoencoder is a deep learning model that can perform spectral unmixing by decomposing the complex image spectra into its constituent materials and estimate their percent compositions. The advantage of this method in land cover quantification is that it is an unsupervised technique which does not require labelled data which generally requires years of field survey and formulation of detailed maps. The performance of this technique is evaluated on various synthetic and real hyperspectral datasets consisting of different land covers. We assess the scalability of the model by evaluating its performance on images of different sizes spanning over a few hundred square meters to thousands of square meters. Finally, we compare the performance of the autoencoder based approach with other supervised and unsupervised deep learning techniques and with the current land cover classification standard.

This thesis presents three distinct methodologies aimed at exploring pivotal aspects within the domain of fiber optics and piezoelectric materials. The first approach offers a comprehensive exploration of three pivotal aspects within the realm of fiber optics and piezoelectric materials. The study delves into the influence of voltage variation on piezoelectric displacement, examines the effects of bending multimode fiber (MMF) on data transmission, and scrutinizes the performance of an Autoencoder in MMF image reconstruction with and without additional noise. To assess the impact of voltage variation on piezoelectric displacement, experiments were conducted by applying varying voltages to a piezoelectric material, meticulously measuring its radial displacement. The results revealed a notable increase in displacement with higher voltage, presenting implications for fiber stability and overall performance. Additionally, the investigation into the effects of bending MMF on data transmission highlighted that the bending process causes the fiber to become leaky and radiate power radially, potentially affecting data transmission. This crucial insight emphasizes the necessity for further research to optimize data transmission in practical fiber systems. Furthermore, the performance of an Autoencoder model was evaluated using a dataset of MMF images, in diverse scenarios. The Autoencoder exhibited impressive accuracy in reconstructing MMF images with high fidelity. The results underscore the significance of ongoing research in these domains, propelling advancements in fiber optic technology.The second approach of this thesis entails a comparative investigation involving three distinct neural network models to assess their efficacy in improving image quality within optical transmissions through multimode fibers, with a specific focus on mitigating speckle patterns. Our proposed methodology integrates multimode fibers with a piezoelectric source, deliberately introducing noise into transmitted images to evaluate their performance using an autoencoder neural network. The autoencoder, trained on a dataset augmented with noise and speckle patterns, adeptly eliminates noise and reconstructs images with enhanced fidelity. Comparative analyses conducted with alternative neural network architectures, namely a single hidden layer (SHL) model and a U-Net architecture, reveal that while U-Net demonstrates superior performance in terms of image reconstruction fidelity, the autoencoder exhibits notable advantages in training efficiency. Notably, the autoencoder achieves saturation SSIM in 450 epochs and 24 minutes, surpassing the training durations of both U-Net (210 epochs, 1 hour) and SHL (160 epochs, 3 hours and 25 minutes) models. Impressively, the autoencoder's training time per epoch is six times faster than U-Net and fourteen times faster than SHL. The experimental setup involves the application of varying voltages via a piezoelectric source to induce noise, facilitating adaptation to real-world conditions. Furthermore, the study not only demonstrates the efficacy of the proposed methodology but also conducts comparative analyses with prior works, revealing significant improvements. Compared to Li et al.'s study, our methodology, particularly when utilizing the pre-trained autoencoder, demonstrates an average improvement of 15% for SSIM and 9% for PSNR in the worst-case scenario. Additionally, when compared to Lai et al.'s study employing a generative adversarial network for image reconstruction, our methodology achieves slightly superior SSIM outcomes in certain scenarios, reaching 96%. The versatility of the proposed method is underscored by its consistent performance across varying voltage scenarios, showcasing its potential applications in medical procedures and industrial inspections. This research not only presents a comprehensive and innovative approach to addressing challenges in optical image reconstruction but also signifies significant advancements compared to prior works. The final approach of this study entails employing Hermit Gaussian Functions with varying orders as activation functions within a U-Net model architecture, aiming to evaluate its effectiveness in image reconstruction. The performance of the model is rigorously assessed across five distinct voltage scenarios, and a supplementary evaluation is conducted with digit 5 excluded from the training set to gauge its generalization capability. The outcomes offer promising insights into the efficacy of the proposed methodologies, showcasing significant advancements in optical image reconstruction. Particularly noteworthy is the robust accuracy demonstrated by the higher orders of the Hermit Gaussian Function in reconstructing MMF images, even amidst the presence of noise introduced by the voltage source. However, a decline in accuracy is noted in the presence of voltage-induced noise, underscoring the imperative need for further research to bolster the model's resilience in real-world scenarios, especially in comparison to the utilization of the Rectified Linear Unit (ReLU) function.

Distibuted denial of service (DDoS) attacks are getting more and more common in society as the number of devices connected to the Internet is increasing. To reduce the impact of such attacks it is important to detect them as soon as possible. Many papers have investigated how well different machine learning algorithms can detect DDoS attacks. However, most papers are focusing on supervised learning algorithms which require a lot of labeled data, which is hard to find. This thesis compares two unsupervised learning algorithms, an autoencoder and principal component analysis (PCA), in how well they detect DDoS attacks. The models are implemented in the Python libraries Keras, Tensorflow and scikit-learn. They are then trained and tested with data that has its origin in the CICDDOS2019 dataset. There are normal data and nine different types of DDoS attacks in the used dataset. The models are compared by computing the Receiver Operating Characteristic (ROC) curve and its Area Under the Curve (AUC) score, and the F1 score of the models. For both measures the mean value of the results of all attack types are used. The computations show that the autoencoder perform better than PCA with respect to both the mean AUC score (0.981 compared to 0.967) and the mean F1 score (0.987 compared to 0.978). The thesis goes on to discussing why the autoencoder performs better than PCA and, finally draws conclusions based on the insights of the analysis.

For an economically sustainable fuel cell commercialization, robust manufacturing processes are essential. As current quality control is time-consuming and costly for manufacturers, standardized solutions are required that reduce cycle times needed to determine cell quality. With existing studies examining durability in field use, little is known about end-of-line detection of cell malfunctions. Applying machine learning algorithms to analyse performance measures of 3600 PEM fuel cells, this work presents a concept to automatically classify produced fuel cells according to cell performance indicators. Using a deep learning autoencoder and the extreme gradient boosting algorithm for anomaly detection and cell classification, models are created that detect cells associated with potential cell malfunctions. The work shows that the models developed predict key performance features in an early stage of the quality control phase and contributes to the overall goal of achieving cycle time reduction for manufacturers quality control procedures. / Für eine wirtschaftlich nachhaltige Kommerzialisierung von Brennstoffzellen sind robuste Herstellungsprozesse unerlässlich. Da die derzeitige Qualitätskontrolle zeitaufwändig und kostenintensiv ist, sind standardisierte Lösungen erforderlich. Während bisherige Arbeiten vorwiegend Lebensdaueruntersuchungen durchführen, ist nur wenig über die Erkennung von Zellfehlfunktionen am Ende der Produktionslinie bekannt. Durch die Anwendung von Algorithmen des maschinellen Lernens zur Analyse der Leistungsdaten von 3600 PEM-Brennstoffzellen wird in dieser Arbeit ein Konzept zur automatischen Klassifizierung produzierter Brennstoffzellen anhand von Leistungsindikatoren der Zellen vorgestellt. Unter Verwendung eines Deep-Learning-Autoencoders und des Extreme-Gradient-Boosting-Algorithmus zur Erkennung von Anomalien und zur Klassifizierung von Zellen werden Modelle erstellt, die Zellen erkennen, die mit potenziellen Zellfehlfunktionen in Verbindung stehen. Die Arbeit zeigt, dass die entwickelten Modelle wichtige Leistungsmerkmale in einem frühen Stadium der Qualitätskontrollphase vorhersagen und zum Gesamtziel der Reduzierung der Zykluszeit für die Qualitätskontrollverfahren der Hersteller beitragen.

Fraud is a common crime within the insurance industry, and insurance companies want to quickly identify fraudulent claimants as they often result in higher premiums for honest customers. Due to the digital transformation where the sheer volume and complexity of available data has grown, manual fraud detection is no longer suitable. This work aims to automate the detection of fraudulent claimants and gain practical insights into fraudulent behavior using unsupervised anomaly detection, which, compared to supervised methods, allows for a more cost-efficient and practical application in the insurance industry. To obtain interpretable results and benefit from the temporal dependencies in human behavior, we propose two variations of LSTM based autoencoders to classify sequences of insurance claims. Autoencoders can provide feature importances that give insight into the models' predictions, which is essential when models are put to practice. This approach relies on the assumption that outliers in the data are fraudulent. The models were trained and evaluated on a dataset we engineered using data from a Swedish insurance company, where the few labeled frauds that existed were solely used for validation and testing. Experimental results show state-of-the-art performance, and further evaluation shows that the combination of autoencoders and LSTMs are efficient but have similar performance to the employed baselines. This thesis provides an entry point for interested practitioners to learn key aspects of anomaly detection within fraud detection by thoroughly discussing the subject at hand and the details of our work. / <p>Gjordes digitalt via Zoom. </p>