Frequency Modulation (FM) based re-synthesis to find the parameter values which best make a FM-synthesizer produce an output sound as similar as possible to a given target sound is a challenging problem. The search space of a commercial synthesizer is often non-linear and high dimensional. Moreover, some crucial decisions need to be done such as choosing the number of modulating oscillators or the algorithm by which they modulate each other. In this work we propose to use Machine Learning (ML) to learn a mapping from target sound to the parameter space of an FM-synthesizer. In order to investigate the capabilities of ML to implicitly learn to make the mentioned key desicions in FM, we design and compare two approaches: first a concurrent approach where all parameter values are compared at once by one model, and second a sequential approach where the prediction is done by a mix of classifiers and regressors. We evaluate the performance of the approaches with respect to ability to reproduce instrumental sound samples from a dataset of 2255 samples from over 700 instrument in three different pitches with respect to four different distance metrics, . The results indicate that both approaches have similar performance at predicting parameters which reconstruct the frequency magnitude spectrum and envelope of a target sound. However the results also point at the sequential model being better at predicting the parameters which reconstruct the temporal evolution of the frequency magnitude spectrums. It is concluded that despite the sequential model outperforming the concurrent, it is likely possible for a model to make key decisions implicitly, without explicitly designed subproblems. / Denna masteruppsats undersöker återskapandet av instrumentala ljud genom användandet av maskininlärning och en synthesizer för frekvensmodulering (FM). Genom att använda maskininlärning kan rätt parametervärden för synthesizern förutspås, sådant att synthesizern skapar ett ljud som är så likt ett givet målljud som möjligt. Uppgiften görs svår då parametrarna för en FMsynthesizer är många och påverkar ljudet olinjärt, vilket skapar ett stort och komplext sökområde.I tidigare forskning har Genetiska Algorithmer använts frekvent för denna process. Det har förekommit olika meningar gällande huruvida det är nödvändigt att explicit dela upp prediktionsprocessen i subproblem, eller om det är bättre att låta förutspå alla parametrar samtidigt utan att explicit införa mänsklig expertis kring problemet. I denna uppsats jämförs därför två olika ansatser: en konkurrent där alla parametrar föruspås på samma gång, och en sekventiell där processen brytits ner till subproblem. De två ansatserna jämförs med avseende på deras förmåga att förutspå parametervärden som återskapar instrumentala ljud så väl som möjligt.Resultaten visar att den sekventiella ansatsen presterar bättre och skapar mer liknande ljud. Dock visas att de båda ansatserna har samma förmåga att återskapa frekvensspektrum. Alltså kan slutsatsen dras att det är möjligt att träna modeller som implicit tar beslut kring val av FM-parametrar lika bra som modeller som tar beslut baserat på explicit nedbrutna subproblem.

Autonomous cars involve problems with control and planning. In thispaper, we implement and evaluate an autonomous agent based ona Monte-Carlo Tree Search in continuous action space. To facilitatethe algorithm, we extend an existing simulation framework and usea GPU for faster calculations. We compare three action generatorsand two rewards functions. The results show that MCTS convergesto an effective driving agent in static environments. However, it onlysucceeds at driving slow speeds in real-time. We discuss the problemsthat arise in dynamic and static environments and look to future workin improving the simulation tool and the MCTS algorithm. See code, https://github.com/felrock/PyRacecarSimulator

Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This thesis evaluates the application of these methods to the problem domain of skin permeability. ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. Historically, refining mathematical models used to predict percutaneous drug absorption has been thought of as a key factor in this field. Quantitative Structure-Activity Relationships (QSARs) models are used extensively for this purpose. However, advanced ML methods successfully outperform the traditional linear QSAR models. In this thesis, the application of ML methods to percutaneous absorption are investigated and evaluated. The major approach used in this thesis is Gaussian process (GP) regression method. This research seeks to enhance the prediction performance by using local non-linear models obtained from applying clustering algorithms. In addition, to increase the model's quality, a kernel is generated based on both numerical chemical variables and categorical experimental descriptors. Monte Carlo algorithm is also employed to generate reliable models from variable data which is inevitable in biological experiments. The datasets used for this study are small and it may raise the over-fitting/under-fitting problem. In this research I attempt to find optimal values of skin permeability using GP optimisation algorithms within small datasets. Although these methods are applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.

Connaître la position 3D d’instruments robotisés peut être très utile dans le contexte chirurgical. Nous proposons deux méthodes automatiques pour déduire la pose 3D d’un instrument avec une unique section pliable et équipé avec des marqueurs colorés, en utilisant uniquement les images fournies par la caméra monoculaire incorporée dans l'endoscope. Une méthode basée sur les graphes permet segmenter les marqueurs et leurs coins apparents sont extraits en détectant la transition de couleur le long des courbes de Bézier qui modélisent les points du bord. Ces primitives sont utilisées pour estimer la pose 3D de l'instrument en utilisant un modèle adaptatif qui prend en compte les jeux mécaniques du système. Pour éviter les limites de cette approche dérivants des incertitudes sur le modèle géométrique, la fonction image-position-3D peut être appris selon un ensemble d’entrainement. Deux techniques ont été étudiées et améliorées : réseau des fonctions à base radiale avec noyaux gaussiens et une régression localement pondérée. Les méthodes proposées sont validées sur une cellule expérimentale robotique et sur des séquences in-vivo. / Knowing the 3D position of robotized instruments can be useful in surgical context for e.g. their automatic control or gesture guidance. We propose two methods to infer the 3D pose of a single bending section instrument equipped with colored markers using only the images provided by the monocular camera embedded in the endoscope. A graph-based method is used to segment the markers. Their corners are extracted by detecting color transitions along Bézier curves fitted on edge points. These features are used to estimate the 3D pose of the instrument using an adaptive model that takes into account the mechanical plays of the system. Since this method can be affected by model uncertainties, the image-to-3d function can be learned according to a training set. We opted for two techniques that have been improved : Radial Basis Function Network with Gaussian kernel and Locally Weighted Projection. The proposed methods are validated on a robotic experimental cell and in in-vivo sequences.