This project is dedicated to the problem of vehicle classification using inductive loop sensors. We created the dataset that contains more than 11000 labeled inductive loop signatures collected at different times and from different parts of the world. Multiple classification methods and their optimizations were employed to the vehicle classification. Final model that combines K-nearest neighbors and logistic regression achieves 94\% accuracy on classification scheme with 9 classes. The vehicle classifier was implemented in C++.

Cette thèse étudie deux tâches fondamentales d'apprentissage non supervisé: la recherche des plus proches voisins et le clustering de données massives en grande dimension pour respecter d'importantes contraintes de temps et d'espace.Tout d'abord, un nouveau cadre théorique permet de réduire le coût spatial et d'augmenter le débit de traitement du Cross-polytope LSH pour la recherche du plus proche voisin presque sans aucune perte de précision.Ensuite, une méthode est conçue pour apprendre en une seule passe sur des données en grande dimension des codes compacts binaires. En plus de garanties théoriques, la qualité des sketches obtenus est mesurée dans le cadre de la recherche approximative des plus proches voisins. Puis, un algorithme de clustering sans paramètre et efficace en terme de coût de stockage est développé en s'appuyant sur l'extraction d'un arbre couvrant minimum approché du graphe de dissimilarité compressé auquel des coupes bien choisies sont effectuées. / This thesis focuses on how to perform efficiently unsupervised machine learning such as the fundamentally linked nearest neighbor search and clustering task, under time and space constraints for high-dimensional datasets. First, a new theoretical framework reduces the space cost and increases the rate of flow of data-independent Cross-polytope LSH for the approximative nearest neighbor search with almost no loss of accuracy.Second, a novel streaming data-dependent method is designed to learn compact binary codes from high-dimensional data points in only one pass. Besides some theoretical guarantees, the quality of the obtained embeddings are accessed on the approximate nearest neighbors search task.Finally, a space-efficient parameter-free clustering algorithm is conceived, based on the recovery of an approximate Minimum Spanning Tree of the sketched data dissimilarity graph on which suitable cuts are performed.

With advancements in technology, the dimensions of transistors are scaling down. It leads to shrinkage in the size of memory bitcells, increasing its sensitivity to process variations introduced during manufacturing. Failure of a single bitcell can cause the failure of an entire memory; hence careful statistical analysis is essential in estimating the highest reliable performance of the bitcell before using them in memory design. With high repetitiveness of bitcell, the traditional method of Monte Carlo simulation would require along time for accurate estimation of rare failure events. A more practical approach is importance sampling where more samples are collected from the failure region. Even though importance sampling is much faster than Monte Carlo simulations, it is still fairly time-consuming as it demands an iterative search making it impractical for large simulation sets. This thesis proposes two machine learning models that can be used in estimating the performance of a bitcell. The first model predicts the time taken by the bitcell for read or write operation. The second model predicts the minimum voltage required in maintaining the bitcell stability. The models were trained using the K-nearest neighbors algorithm and Gaussian process regression. Three sparse approximations were implemented in the time prediction model as a bigger dataset was available. The obtained results show that the models trained using Gaussian process regression were able to provide promising results.

Tracking a moving object and reconstructing its trajectory can be done with a stereo camera system, since the two cameras enable depth vision. However, such a system would not work if one of the cameras fails to detect the object. If that happens, it would be beneficial if the system could still use the functioning camera to make an approximate trajectory reconstruction. In this study, I have investigated how past observations from a stereo system can be used to recreate trajectories when video from only one of the cameras is available. Several approaches have been implemented and tested, with varying results. The best method was found to be a nearest neighbors-search optimized by a Kalman filter. On a test set with 10000 golf shots, the algorithm was able to create estimations which on average differed around 3.5 meters from the correct trajectory, with better results for trajec-tories originating close to the camera. / Att spåra ett objekt i rörelse och rekonstruera dess bana kan göras med ett stereokamerasystem, eftersom de två kamerorna möjliggör djupseende. Ett sådant system skulle dock inte fungera om en av kamerorna misslyckas med att detektera objektet. Om det händer skulle det vara fördelaktigt om systemet ändå kunde använda den fungerande kameran för att göra en approximativ rekonstruktion av banan. I den här studien har jag undersökt hur tidigare observationer från ett stereosystem kan användas för att rekonstruera banor när video från enbart en av kamerorna är tillgänglig. Ett flertal metoder har implementerats och testats, med varierande resultat. Den bästa metoden visade sig vara en närmaste-grannar-sökning optimerad med ett Kalman-filter. På en testmängd bestående av 10000 golfslag kunde algoritmen skapa uppskattningar som i genomsnitt skiljde sig 3.5 meter från den korrekta banan, med bättre resultat för banor som startat nära kameran.

Machine learning is an area of computer science that only grows as time goes on, and there are applications in areas such as finance, biology, and computer vision. Some common applications are stock price prediction, data analysis of DNA expressions, and optical character recognition. This thesis uses machine learning techniques to predict prices for different stocks and indices on the Swedish stock market. These techniques are then compared to see which performs best and why. To accomplish this, we used some of the most popular models with sets of historical stock and index data. Our best-performing models are linear regression and neural networks, this is because they are the best at handling the big spikes in price action that occur in certain cases. However, all models are affected by overfitting, indicating that feature selection and hyperparameter optimization could be improved.

Public transport is essential for both residents and city planners because of its environmentally and economically beneficial characteristics. During the past decade climatechange, coupled with fuel and energy crises have attracted significant attention toward public transportation. Increasing the demand for public transport on the one hand and its complexity on the other hand have made the optimum network design quite challenging for city planners. The ridership is affected by numerous variables and features like space and time. These fluctuations, coupled with inherent uncertaintiesdue to different travel behaviors, make this procedure challenging. Any demand and supply mismatching can result in great user dissatisfaction and waste of energy on the horizon. During the past years, due to recent technologies in recording and storing data and advances in data analysis techniques, finding patterns, and predicting ridership based on historical data have improved significantly. This study aims to develop forecasting models by regressing boardings toward population, time of day, month, and station. Using the available boarding dataset for blue bus line number 4 in Stockholm, Sweden, seven different machine learning algorithms were assessed for prediction: Multiple Linear Regression, Decision Tree, Random Forest, Bayesian Ridge Regression, Neural Networks, Support Vector Machines, K-Nearest Neighbors. The models were trained and tested on the dataset from 2012 to 2019, before the start of the pandemic. The best model, KNN, with an average R-squared of 0.65 in 10-fold cross-validation was accepted as the best model. This model is then used to predict reduced ridership during the pandemic in 2020 and 2021. The results showed a reduction of 48.93% in 2020 and 82.24% in 2021 for the studied bus line.

Background: Cloud computing’s rapid growth has highlighted the need for efficientresource allocation. While cloud platforms offer scalability and cost-effectiveness for a variety of applications, managing resources to match dynamic workloads remains a challenge. Auto-scaling, the dynamic allocation of resources in response to real-time demand and performance metrics, has emerged as a solution. Traditional rule-based methods struggle with the increasing complexity of cloud applications. Machine Learning models offer promising accuracy by learning from performance metrics and adapting resource allocations accordingly.  Objectives: This thesis addresses the topic of cloud environments auto-scaling recommendations emphasizing the integration of Machine Learning models and significant application metrics. Its primary objectives are determining the critical metrics for accurate recommendations and evaluating the best recommendation techniques for auto-scaling. Methods: The study initially identifies the crucial metrics—like CPU usage and memory consumption that have a substantial impact on auto-scaling selections through thorough experimentation and analysis. Machine Learning(ML) techniques are selected based on literature review, and then further evaluated through thorough experimentation and analysis. These findings establish a foundation for the subsequent evaluation of ML techniques for auto-scaling recommendations. Results: The performance of Random Forests (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) are investigated in this research. The results show that RF have higher accuracy, precision, and recall which is consistent with the significance of the metrics which are identified earlier. Conclusions: This thesis enhances the understanding of auto-scaling recommendations by combining the findings from metric importance and recommendation technique performance. The findings show the complex interactions between metrics and recommendation methods, establishing the way for the development of adaptive auto-scaling systems that improve resource efficiency and application functionality.

Geografiska informationssystems användning blir allt viktigare i dagens samhälle där spatiala data kan lagras, hämtas, analyseras och visualiseras. Genom att sammanställa spatiala data kan en bild av verkligheten abstraheras. Detaljerad information om vägnat och företeelser (refuger, bullerplank, skyltar etcetera) för analys leder till ett effektivare drift- och underhållsarbete. Vilket i sin tur ger en ökad framkomlighet för trafikanter. Teknikföretaget Triona har en kartapplikation där utmaningar har uppstått gällande algoritmisk knytning av inmätta refuger (benämnd Norge-datasamlingen) till det norska vägnatet. En refug ar en upphöjning i gatan som avgränsar körfalt och påminner om en trottoar i utseendet. Denna fallstudie behandlade ett delproblem där klassificering av refuger skulle kunna underlätta knytningen och förutsättningarna for analys. Syftet med studien kan sammanfattas till att presentera förslag på metoder for att klassificera refugerna med övervakad maskininlärning. Med algoritmerna K-nearest neighbors (KNN) och Decision tree studerades möjligheten att automatiskt klassificera refugerna. En refug bestod av en vektorpolygon vilket är en lista med koordinater. Polygonens hörn bestod av koordinatparen latitud och longitud. Norge-datasamlingen var inte i forväg kategoriserad till sina elva typer och kunde därfor inte anvandas. En datasamling med 2157 refuger med sju typer från Portland, USA tillämpades i stället. De spatiala vektorpolygonerna transformerades med Elliptical Fourier Descriptors (EFD). Maskinlärningsmodellerna tränades på att klassificera refugerna baserat på matematiska approximationer av dess konturer från EFD. Slutsatser kunde dras genom att refugtypernas konturer analyserades och prestationer observerades. Prestationer utvärderades utifrån traffsäkerhet med kompletterande mätvarden som precision och återkallelse på Portland-datasamlingen. Traffsäkerhet är andelen rätta klassificeringar av refugerna. KNN uppnådde 64 % och Decisiontree 69 % traffsäkerhet. Då båda datasamlingarna var verkliga exempel på refuger i vägnat kunde ett antagande göras att det inte skulle bli en mycket högre traffsäkerhet om studiens metod appliceras på Norge-datasamlingen. Modellernas prestationer bedömdes därmed inte vara tillrackligt bra for en rekommendation. / Geographical information systems are becoming increasingly important in today´s society where spatial data can be stored, collected, analysed, and visualized. By compiling spatial data reality can be abstracted. Detailed information on road networks and objects (traffic islands, noise barriers, signs, etcetera) for analysis leads to more efficient operation and maintenance work. Which in turn provides increased accessibility for road users. The technology company Triona has a map application where algorithmic connection of traffic islands (Norway-dataset) to the Norwegian road network has been challenging. A traffic island is an elevation in the street that delimits lanes and is reminiscent of a sidewalk in appearance. This case study addressed a sub-problem where classification of traffic islands could facilitate the connection and prerequisites for analysis. The aim was to present methods that could classify the traffic islands with supervised machine learning. With the algorithms K-nearest neighbors (KNN) and Decision tree, the possibility of automatically classifying the traffic islands was studied. A traffic island consisted of a vector polygon which is a list storing its corners (latitude and longitude). The Norway-dataset was not previously labelled into its eleven types. A data collection of 2157 refuges with seven types from Portland, USA was therefore applied instead. The traffic islands were transformed with Elliptical Fourier Descriptors which extracted an approximation of its contours to train the machine learning models on. Conclusions could be drawn by analysing the contours and observing performance. Performance was evaluated based on accuracy with precision and recall on the Port-land-dataset. Accuracy is the proportion of correct classifications. KNN achieved 64% and Decision Tree 69% accuracy. As both datasets contained real traffic islands in road networks, an assumption could be made that the accuracy would not be much higher if applied on the Norway-dataset. The result was not considered sufficient for a recommendation.

The macroscopic measure of traffic stream density is crucial in advanced traffic management systems. However, measuring the traffic stream density in the field is difficult since it is a spatial measurement. In this dissertation, several estimation approaches are developed to estimate the traffic stream density on signalized approaches using connected vehicle (CV) data. First, the dissertation introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of CVs. After that, the dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the traffic stream density using CV data only. The proposed model-driven approaches are evaluated using empirical and simulated data, the former of which were collected along a signalized approach in downtown Blacksburg, VA. Results indicate that density estimates produced by the linear KF approach are the most accurate. A sensitivity of the estimation approaches to various factors including the level of market penetration (LMP) of CVs, the initial conditions, the number of particles in the PF approach, traffic demand levels, traffic signal control methods, and vehicle length is presented. Results show that the accuracy of the density estimate increases as the LMP increases. The KF is the least sensitive to the initial traffic density estimate, while the PF is the most sensitive to the initial traffic density estimate. The results also demonstrate that the proposed estimation approaches work better at higher demand levels given that more CVs exist for the same LMP scenario. For traffic signal control methods, the results demonstrate a higher estimation accuracy for fixed traffic signal timings at low traffic demand levels, while the estimation accuracy is better when the adaptive phase split optimizer is activated for high traffic demand levels. The dissertation also investigates the sensitivity of the KF estimation approach to vehicle length, demonstrating that the presence of longer vehicles (e.g. trucks) in the traffic link reduces the estimation accuracy. Data-driven approaches are also developed to estimate the traffic stream density, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The data-driven approaches also utilize solely CV data. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Lastly, the dissertation compares the performance of the model-driven and the data-driven approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the linear KF approach is highly recommended in the application of traffic density estimation due to its simplicity and applicability in the field. / Doctor of Philosophy / Estimating the number of vehicles (vehicle counts) on a road segment is crucial in advanced traffic management systems. However, measuring the number of vehicles on a road segment in the field is difficult because of the need for installing multiple detection sensors in that road segment. In this dissertation, several estimation approaches are developed to estimate the number of vehicles on signalized roadways using connected vehicle (CV) data. The CV is defined as the vehicle that can share its instantaneous location every time t. The dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the number of vehicles using CV data only. The proposed model-driven approaches are evaluated using real and simulated data, the former of which were collected along a signalized roadway in downtown Blacksburg, VA. Results indicate that the number of vehicles produced by the linear KF approach is the most accurate. The results also show that the KF approach is the least sensitive approach to the initial conditions. Machine learning approaches are also developed to estimate the number of vehicles, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The machine learning approaches also use CV data only. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Finally, the dissertation compares the performance of the model-driven and the machine learning approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the huge amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the KF approach is highly recommended in the application of vehicle count estimation due to its simplicity and applicability in the field.

[ES] La Industria 4.0 se enfrenta a importantes retos a la hora de perseguir la transformación digital y la eficiencia operativa. La creciente complejidad de los entornos industriales modernos lleva a la necesidad de desplegar tecnologías digitales y, sobre todo, la automatización de la Industria. Sin embargo, este camino hacia la innovación va acompañado de numerosos obstáculos, ya que el entorno cambia constantemente. Por lo tanto, para adaptarse a esta evolución, es necesario emplear planteamientos más flexibles. Estos planteamientos están estrechamente relacionados con el uso de la AI y RL, ya que surgen como soluciones clave para abordar los retos cruciales de la navegación cooperativa de agentes dentro de entornos dinámicos. Mientras tanto, los algoritmos RL se enfrentan a las complejidades que implica la transmisión y el procesamiento de grandes cantidades de datos, para hacer frente a este desafío, la tecnología 5G emerge como un habilitador clave para las soluciones de escenarios de problemas evolutivos. Entre las principales ventajas de la 5G están que ofrece una transmisión rápida y segura de grandes volúmenes de datos con una latencia mínima. Al ser la única tecnología hasta ahora capaz de ofrecer estas capacidades, 5G se convierte en un componente esencial para desplegar servicios en tiempo real como la navegación cooperativa. Además, otra ventaja es que proporciona la infraestructura necesaria para intercambios de datos robustos y contribuye a la eficiencia del sistema y a la seguridad de los datos en entornos industriales dinámicos. A la vista de lo anterior, es evidente que la complejidad de los entornos industriales conduce a la necesidad de proponer sistemas basados en nuevas tecnologías como las redes AI y 5G, ya que su combinación proporciona una potente sinergia. Además, aparte de abordar los retos identificados en la navegación cooperativa, también abre la puerta a la implementación de fábricas inteligentes, dando lugar a mayores niveles de automatización, seguridad y productividad en las operaciones industriales. Es importante destacar que la aplicación de técnicas de AI conlleva la necesidad de utilizar software de simulación para probar los algoritmos propuestos en entornos virtuales. Esto permite abordar cuestiones esenciales sobre la validez de los algoritmos, reducir los riesgos de daños en el hardware y, sobre todo, optimizar las soluciones propuestas. Con el fin de proporcionar una solución a los retos fundamentales en la automatización de fábricas, esta Tesis se centra en la integración de la robótica móvil en la nube, especialmente en el contexto de la Industria 4.0. También abarca la investigación de las capacidades de las redes 5G, la evaluación de la viabilidad de simuladores como ROS y Gazebo, y la fusión de datos de sensores y el diseño de algoritmos de planificación de trayectorias basados en RL. En otras palabras, esta Tesis no solo identifica y aborda los retos clave de la Industria 4.0, sino que también presenta soluciones innovadoras e hipótesis concretas para la investigación. Además, promueve la combinación de AI y 5G para desplegar servicios en tiempo real, como la navegación cooperativa. Así, aborda retos críticos y demuestra que la colaboración tecnológica redefine la eficiencia y la adaptabilidad en la industria moderna. / [CA] La Indústria 4.0 s'enfronta a importants reptes a l'hora de perseguir la transformació digital i l'eficiència operativa. La creixent complexitat dels entorns industrials moderns porta a la necessitat de desplegar tecnologies digitals i, sobretot, l'automatització de la Indústria. No obstant això, este camí cap a la innovació va acompanyat de nombrosos obstacles, ja que l'entorn canvia constantment. Per tant, per a adaptar-se a esta evolució, és necessari emprar plantejaments més flexibles. Estos plantejaments estan estretament relacionats amb l'ús de l'AI i RL, ja que sorgixen com a solucions clau per a abordar els reptes crucials de la navegació cooperativa d'agents dins d'entorns dinàmics. Mentrestant, els algorismes RL s'enfronten a les complexitats que implica la transmissió i el processament de grans quantitats de dades, per a fer front a este desafiament, la tecnologia 5G emergix com un habilitador clau per a les solucions d'escenaris de problemes evolutius. Entre els principals avantatges de la 5G estan que oferix una transmissió ràpida i segura de grans volums de dades amb una latència mínima. A l'ésser l'única tecnologia fins ara capaç d'oferir estes capacitats, 5G es convertix en un component essencial per a desplegar servicis en temps real com la navegació cooperativa. A més, un altre avantatge és que proporciona la infraestructura necessària per a intercanvis de dades robustes i contribuïx a l'eficiència del sistema i a la seguretat de les dades en entorns industrials dinàmics. A la vista de l'anterior, és evident que la complexitat dels entorns industrials conduïx a la necessitat de proposar sistemes basats en noves tecnologies com les xarxes AI i 5G, ja que la seua combinació proporciona una potent sinergia. A més, a part d'abordar els reptes identificats en la navegació cooperativa, també obri la porta a la implementació de fabriques intel·ligents, donant lloc a majors nivells d'automatització, seguretat i productivitat en les operacions industrials. És important destacar que l'aplicació de tècniques d'AI comporta la necessitat d'utilitzar programari de simulació per a provar els algorismes proposats en entorns virtuals. Això permet abordar qüestions essencials sobre la validesa dels algorismes, reduir els riscos de dona'ns en el maquinari i, sobretot, optimitzar les solucions proposades. Amb la finalitat de proporcionar una solució als reptes fonamentals en l'automatització de fabriques, esta Tesi se centra en la integració de la robòtica mòbil en el núvol, especialment en el context de la Indústria 4.0. També abasta la investigació de les capacitats de les xarxes 5G, l'avaluació de la viabilitat de simuladors com ROS i Gazebo, i la fusió de dades de sensors i el disseny d'algorismes de planificació de trajectòries basats en RL. En altres paraules, esta Tesi no sols identifica i aborda els reptes clau de la Indústria 4.0, sinó que també presenta solucions innovadores i hipòtesis concretes per a la investigació. A més, promou la combinació d'AI i 5G per a desplegar servicis en temps real, com la navegació cooperativa. Així, aborda reptes crítics i demostra que la col·laboració tecnològica redefinix l'eficiència i l'adaptabilitat en la indústria moderna. / [EN] Industry 4.0 faces significant challenges in pursuing digital transformation and operational efficiency. The increasing complexity of modern industrial environments leads to the need to deploy digital technologies and, above all, Industry automation. However, this path to innovation is accompanied by numerous obstacles, as the environment constantly changes. Therefore, to adapt to this evolution, it is necessary to employ more flexible approaches. These approaches are closely linked to the use of Artificial Intelligence (AI) and Reinforcement Learning (RL), as they emerge as pivotal solutions to address the crucial challenges of cooperative agent navigation within dynamic environments. Meanwhile, RL algorithms face the complexities involved in transmitting and processing large amounts of data. To address this challenge, Fifth Generation (5G) technology emerges as a key enabler for evolutionary problem scenario solutions. Among the main advantages of 5G is that it offers fast and secure transmission of large volumes of data with minimal latency. As the only technology so far capable of delivering these capabilities, 5G becomes an essential component for deploying real-time services such as cooperative navigation. Furthermore, another advantage is that it provides the necessary infrastructure for robust data exchanges and contributes to system efficiency and data security in dynamic industrial environments. In view of the above, it is clear that the complexity of industrial environments leads to the need to propose systems based on new technologies such as AI and 5G networks, as their combination provides a powerful synergy. Moreover, aside from tackling the challenges identified in cooperative navigation, it also opens the door to the implementation of smart factories, leading to higher levels of automation, safety, and productivity in industrial operations. It is important to note that the application of AI techniques entails the need to use simulation software to test the proposed algorithms in virtual environments. This makes it possible to address essential questions about the validity of the algorithms, reduce the risks of damage to the hardware, and, above all, optimize the proposed solutions. In order to provide a solution to the fundamental challenges in factory automation, this Thesis focuses on integrating mobile robotics in the cloud, especially in the context of Industry 4.0. It also covers the investigation of the capabilities of 5G networks, the evaluation of the feasibility of simulators such as Robot Operating System (ROS) and Gazebo, and the fusion of sensor data and the design of path planning algorithms based on RL. In other words, this Thesis not only identifies and addresses the key challenges of Industry 4.0 but also presents innovative solutions and concrete hypotheses for research. Furthermore, it promotes the combination of AI and 5G to deploy real-time services, such as cooperative navigation. Thus, it addresses critical challenges and demonstrates that technological collaboration redefines efficiency and adaptability in modern industry. / This research was funded by the Research and Development Grants Program (PAID-01-19) of the Universitat Politècnica de València. The research stay of the author at Technischen Universit¨at Darmstadt (Germany) was funded by the Program of Grants for Student Mobility of doctoral students at the Universitat Politècnica de València in 2022 from Spain and by Erasmus+ Student Mobility for Traineeship 2022 / Palacios Morocho, ME. (2024). Unleashing Technological Collaboration: AI, 5G, and Mobile Robotics for Industry 4.0 Advancements [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/204748