Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation.

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This work proposes a new approach to fast detection of abnormal behaviour of cooling, IT, and power distribution systems in micro data centers based on machine learning techniques. Conventional protection of micro data centers focuses on monitoring individual parameters such as temperature at different locations and when these parameters reach certain high values, then an alarm will be triggered. This research employs machine learning techniques to extract normal and abnormal behaviour of the cooling and IT systems. Developed data acquisition system together with unsupervised learning methods quickly learns the physical dynamics of normal operation and can detect deviations from such behaviours. This provides an efficient way for not only producing health index for the micro data center, but also a rich label logging system that will be used for the supervised learning methods. The effectiveness of the proposed detection technique is evaluated on an micro data center placed at Computing Infrastructure Research Center (CIRC) in McMaster Innovation Park (MIP), McMaster University. / Thesis / Master of Science (MSc)

This thesis investigated how shapes can be mapped to programming constructs, offering a new way to compose and understand code with the long term goal of creating a tactile programming tool. By doing so it delved into the challenges of translating shapes into abstract programming concepts. Existing programming tools rely heavily on visual interfaces, making them inaccessible to individuals with visual impairments. Similar endeavours to create tactile programming tools were analysed and were shown to be domain-specific rather than Turing-complete which greatly limits their usefulness. The solution was to map a set of shapes to a set of Brainfuck (BF) instructions and classifying these shapes with a Support Vector Machine (SVM). Results are promising but are as of yet untested in less than ideal conditions, such as it would be in a real world application. More work has to be done to reach the goal of a tactile programming tool accessible to individuals with visual impairments. / Denna avhandling undersökte hur former kan kartläggas till programmerings-konstruktioner, vilket erbjuder ett nytt sätt att komponera och förstå kod med ett långsiktigt mål att skapa ett taktilt programmingsverktyg. Genom att göra det går den in på utmaningarna med att översätta former till abstrakta programmeringskoncept. Befintliga programmeringsverktyg förlitar sig i hög grad på visuella gränssnitt, vilket gör dem otillgängliga för personer med synnedsättningar. Liknande försök att skapa taktila programmeringsverktyg analyserades och visades vara domänspecifika snarare än Turing-kompletta, vilket starkt begränsar deras användbarhet. Lösningen var att kartlägga en uppsättning former till en uppsättning Brainfuck (BF)-instruktioner och klassificera dessa former med en Support Vector Machine (SVM). Resultaten är lovande men har ännu inte testats under mindre än ideala förhållanden, såsom det skulle vara i en verklig tillämpning. Mer arbete måste göras för att nå målet med ett taktilt programmeringsverktyg som är tillgängligt för personer med synnedsättningar.

Unmanned ground vehicles (UGVs) are increasingly being used for civilian and military applications. Passive sensing, such as visible cameras, are being used for navigation and object detection. An additional object of interest in many environments is text. Text information can supplement the autonomy of unmanned ground vehicles. Text most often appears in the environment in the form of road signs and storefront signs. Road hazard information, unmapped route detours and traffic information are available to human drivers through road signs. Premade road maps lack these traffic details, but with text localization the vehicle could fill the information gaps. Leading text localization algorithms achieve ~60% accuracy; however, practical applications are cited to require at least 80% accuracy [49]. The goal of this thesis is to test existing text localization algorithms against challenging scenes, identify the best candidate and optimize it for scenes a UGV would encounter. Promising text localization methods were tested against a custom dataset created to best represent scenes a UGV would encounter. The dataset includes road signs and storefront signs against complex background. The methods tested were adaptive thresholding, the stroke filter and the stroke width transform. A temporal tracking proof of concept was also tested. It tracked text through a series of frames in order to reduce false positives. Best results were obtained using the stroke width transform with temporal tracking which achieved an accuracy of 79%. That level of performance approaches requirements for use in practical applications. Without temporal tracking the stroke width transform yielded an accuracy of 46%. The runtime was 8.9 seconds per image, which is 44.5 times slower than necessary for real-time object tracking. Converting the MATLAB code to C++ and running the text localization on a GPU could provide the necessary speedup. / Master of Science

This study was conducted in collaboration with BAE Systems Hägglunds in Örnsköldsvik. They wanted help with their objective of researching the necessary components of a testability analysis. The primary goal was to enhance the efficiency of troubleshooting and diagnostics for their combat vehicle, Cv90. As their vehicles become increasingly advanced, troubleshooting can become challenging and time-consuming. An example of a problem that can arise is when an error code is displayed to the crew, but the technicians at the workshop are unable to identify the root cause of the error. To gather information, scientific articles and other relevant documents were extensively reviewed. Additionally, interviews were conducted with employees at BAE. The scope of the study was limited to troubleshooting within a workshop setting, rather than in the field. It was assumed that the troubleshooting equipment and software were functional, and the focus was solely on identifying faulty components. The research was conducted both in Umeå and on-site in Örnsköldsvik. A methodology for implementing a fault detection system with effective testability was developed. This encompassed considerations for the construction and content of the troubleshooting system. Determining the system's requirements and devising methods for testing their fulfillment were crucial aspects. Prioritization of different functions based on their criticality was recommended. Critical functions should be addressed first and may require more costly and intricate solutions. Various approaches to enhance troubleshooting at a more granular level were identified. These included establishing better threshold values, accounting for measurement uncertainties in the test equipment, and emphasizing the importance of a robust test design that considers deviations from system equilibrium. Additionally, worn components were recognized as a potential cause for false indications that are challenging to diagnose. It is important to note that these improved fault detection methods have not yet been implemented in the vehicles. / Studien genomfördes i samarbete med BAE Systems Hägglunds i Örnsköldsvik. De ville få hjälp med sitt mål att undersöka de nödvändiga komponenterna i en testabilityanalys. Det primära målet var att effektivisera felsökning och diagnostisering av deras stridsfordon, Cv90. När deras fordon blir alltmer avancerade kan felsökningen bli utmanande och tidskrävande. Ett exempel på ett problem som kan uppstå är att en felkod visas för besättningen, men teknikerna på verkstaden kan inte identifiera orsaken till felet. För att samla information gjordes en omfattande granskning av vetenskapliga artiklar och andra relevanta dokument. Dessutom genomfördes intervjuer med anställda på BAE. Studiens omfattning var begränsad till felsökning på verkstaden, snarare än i fält. Det antogs att felsökningsutrustningen och programvaran var funktionella, och fokus låg enbart på att identifiera felaktiga komponenter. Arbetet utfördes både i Umeå och på plats i Örnsköldsvik. En metodik för att implementera ett felsökningssystem med god testbarhet utvecklades. Detta omfattade överväganden för felsökningssystemets konstruktion och innehåll. Att bestämma systemets krav och utforma metoder för att testa deras uppfyllnad var avgörande aspekter. Prioritering av olika funktioner baserat på deras kritikalitet rekommenderades. Kritiska funktioner bör åtgärdas först och kan kräva mer kostsamma och invecklade lösningar. Olika metoder för att förbättra felsökning på en mer detaljerad nivå identifierades. Dessa inkluderade att fastställa bättre tröskelvärden, ta hänsyn till mätosäkerheter i testutrustningen och betona vikten av en robust testdesign som tar hänsyn till avvikelser från systemjämvikt. Dessutom identifierades slitna komponenter som en potentiell orsak till felaktiga indikationer som är svåra att diagnostisera. Det är viktigt att notera att dessa förbättrade felsökningsmetoder ännu inte har implementerats i fordonen.

Through the specialized lens of one-class classification, anomalies–irregular observations that uncharacteristically diverge from normative data patterns–are comprehensively studied. This dissertation focuses on advancing boundary-based methods in one-class classification, a critical approach to anomaly detection. These methodologies delineate optimal decision boundaries, thereby facilitating a distinct separation between normal and anomalous observations. Encompassing traditional approaches such as One-Class Support Vector Machine and Support Vector Data Description, recent adaptations in deep learning offer a rich ground for innovation in anomaly detection. This dissertation proposes three novel deep learning methods for one-class classification, aiming to enhance the efficacy and accuracy of anomaly detection in an era where data volume and complexity present unprecedented challenges. The first two methods are designed for tabular data from a least squares perspective. Formulating these optimization problems within a least squares framework offers notable advantages. It facilitates the derivation of closed-form solutions for critical gradients that largely influence the optimization procedure. Moreover, this approach circumvents the prevalent issue of degenerate or uninformative solutions, a challenge often associated with these types of deep learning algorithms. The third method is designed for second-order tensors. This proposed method has certain computational advantages and alleviates the need for vectorization, which can lead to structural information loss when spatial or contextual relationships exist in the data structure. The performance of the three proposed methods are demonstrated with simulation studies and real-world datasets. Compared to kernel-based one-class classification methods, the proposed deep learning methods achieve significantly better performance under the settings considered.