As technology advances, the possibility of using satellite data and observations to aid inagricultural activities comes closer to reality. Swedish farmers can apply for subsidies for their land in which crop management and animal grazing occurs, and every year thousands of manual follow-up checks are conducted by Svenska Jordbruksverket (Swedish Board of Agriculture) to validate the farmers’ claims to financial aid. RISE (Research Institutes of Sweden) is currently researching a replacement for the manual follow-up checks using an automated process with optical satellite observations from primarily the ESA-made satellite constellation Sentinel-2, and secondarily the radar observations of the Sentinel-1 constellation. The optical observations from Sentinel-2 are greatly hindered by the presence of weather on the Earth’s atmosphere and lack of sunlight, but the radar-based observations of Sentinel-1 are able to penetrate any weather conditions entirely independently from sunlight. By using the optical index NDVI (Normalized Difference Vegetation Index) which is strongly correlated with plant chlorophyll, and the radar index RVI (Radar Vegetation Index), classifications on animal grazing activities are sought to be made. Dynamic Time Warping and hierarchical clustering are used to analyse and attempt to make classifications on the two selected datasets of sizes 959 and 20 fields. Five experiments were conducted to analyse the observational data from mainly Sentinel-2, but also Sentinel-1. The results were inconclusive and were unable to perform successful classifications primarily on the 959 fields large dataset. An indication is given in one of the experiments, performed on the smaller dataset of 20 fields, that classification is indeed possible by using mean valued NDVI time series. However, it is difficult to draw conclusions due to the small size of the 20 fields large dataset. To validate any possible methods classification a larger dataset must be used.

The main objective of this thesis is to develop novel misfit functions for full-waveform inversion such that (a) the estimation of the long-wavelength model will less likely stagnate in spurious local minima and (b) the inversion is immune to wavelet inaccuracy. First, I investigate the pros and cons of misfit functions based on optimal transport theory to indicate the traveltime discrepancy for seismic data. Even though the mathematically well-defined optimal transport theory is robust to highlight the traveltime difference between two probability distributions, it becomes restricted as applied to seismic data mainly because the seismic data are not probability distribution functions. We then develop a misfit function combining the local cross-correlation and dynamic time warping. This combination enables the proposed misfit automatically identify arrivals associated with a phase shift. Numerical and field data examples demonstrate its robustness for early arrivals and limitations for later arrivals.%, which means that a proper pre-processing step is still required. Next, we introduce differentiable dynamic time warping distance as the misfit function highlighting the traveltime discrepancy without non-trivial human intervention. Compared to the conventional warping distance, the differentiable version retains the property of representing the traveltime difference; moreover, it can eliminate abrupt changes in the adjoint source, which helps full-waveform inversion converge to geologically relevant estimates. Finally, we develop a misfit function entitled the deconvolutional double-difference measurement. The new misfit measures the first difference by deconvolution rather than cross-correlation. We also present the derivation of the adjoint source with the new misfit function. Numerical examples and mathematical proof demonstrate that this modification makes full-waveform inversion with the deconvolutional double-difference measurement immune to wavelet inaccuracy.

En av de mest centrala delarna av undervisningen i lågstadiet fokuserar på svenskämnet och speciellt elevers läsförmåga. Trots obligatoriska screeningmoment och nationella bedömningsstöd uppfattar personal inom skola att kartläggning av läskunskaper är tidskrävande, komplext och subjektivt till följd av dess manuella format. Denna studie undersöker hur en automatiserad läs-screening kan implementeras som ett kompletterande verktyg i lågstadiet genom forced alignment. Syftet är att fastställa om ett program är tillräckligt pålitligt för att underlätta screeningprocessen och tidigare kunna identifiera elever med lässvårigheter. Genom intervjuer med personer som jobbar inom skola och framtagandet av en prototyp har resultaten analyserats. Studiens slutsats blev att det finns fördelar med att komplettera den manuella läs-screeningen med ett automatiskt verktyg, men att det inte går att dra några slutsatser om huruvida verktyget går att implementera i samtliga skolor med positiva effekter. Det kan vara mer fördelaktigt att använda sig av ett automatiskt verktyg i mellanstadiet, men till följd av låg svarsfrekvens vid intervjuer kan detta inte fastställas. / One of the most central parts of the education in lower school is Swedish and especially reading skills. Even though there are mandatory screening moments and national evaluation support, employees in schools think of the screenings as a timeconsuming, complex, and subjective process due to their manual format. This study investigates how an automatized reading screening can be implemented in lower school as a complemental tool by forced alignment. The purpose is to confirm whether a program is reliable enough to make it easier to perform reading screening and earlier identify pupils with reading difficulties. By interviewing employees in schools and creating a prototype, the results have been analyzed. The study concluded was that there are advantages of having an automatic complementary tool in the reading screening process. Still, there was not possible to make any conclusions about whether the tool can be implemented in all schools with positive effects. It could be more beneficial to use an automatic tool in middle school, but due to low answering frequency, this cannot be established.

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<p> This thesis presents a novel hybrid methodology using Recurrent Neural Network and Dynamic Time Warping to solve the mode estimation problem of a radar warning receiver (RWR). The RWR is an electronic support (ES) system with the primary objective to estimate the threat posed by an unfriendly (hostile) radar in an electronic warfare (EW) environment. One such radar is the multi-function radar (MFR), which employs complex signal architecture to perform multiple tasks. As the threat posed by the radar directly depends on its current mode of operation, it is vital to estimate and track the mode of the radar. The proposed method uses a recurrent neural network (echo state network and recurrent multi-layer perceptron) trained in a supervised manner, with the dynamic time warping algorithm as the post processor to estimate the mode of operation. A grid filter in Bayesian framework is then applied to the dynamic time warp estimate to provide an accurate posterior estimate of the operational mode of the MFR. This novel approach is tested on an EW scenario via simulation by employing a hypothetical MFR. Based on the simulation results, we conclude that the hybrid echo state network is more suitable than its recurrent multi-layer perceptron counterpart for the mode estimation problem of a RWR.</p> / Thesis / Master of Applied Science (MASc)

Classification of human movement is a large field of interest to Human-Machine Interface researchers. The reason for this lies in the large emphasis humans place on gestures while communicating with each other and while interacting with machines. Such gestures can be digitized in a number of ways, including both passive methods, such as cameras, and active methods, such as wearable sensors. While passive methods might be the ideal, they are not always feasible, especially when dealing in unstructured environments. Instead, wearable sensors have gained interest as a method of gesture classification, especially in the upper limbs. Lower arm movements are made up of a combination of multiple electrical signals known as Motor Unit Action Potentials (MUAPs). These signals can be recorded from surface electrodes placed on the surface of the skin, and used for prosthetic control, sign language recognition, human machine interface, and a myriad of other applications. In order to move a step closer to these goal applications, this thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes. It further contrasts the applicability of these tools to noisy data in the form of the Ninapro dataset, a benchmarking tool put forth by a conglomerate of universities. Using this dataset as a basis, this work paves a path for the analysis required to optimize each of the three classifiers. Ultimately, care is taken to compare the three classifiers for their utility against noisy data, and a comparison is made against classification results put forth by other researchers in the field. The outcome of this work is 90+ % recognition of individual gestures from the Ninapro dataset whilst using two of the three distinct classifiers. Comparison against previous works by other researchers shows these results to outperform all other thus far. Through further work with these tools, an end user might control a robotic or prosthetic arm, or translate sign language, or perhaps simply interact with a computer. / Master of Science

Les données manquantes constituent un challenge commun en reconnaissance de forme et traitement de signal. Une grande partie des techniques actuelles de ces domaines ne gère pas l'absence de données et devient inutilisable face à des jeux incomplets. L'absence de données conduit aussi à une perte d'information, des difficultés à interpréter correctement le reste des données présentes et des résultats biaisés notamment avec de larges sous-séquences absentes. Ainsi, ce travail de thèse se focalise sur la complétion de larges séquences manquantes dans les séries monovariées puis multivariées peu ou faiblement corrélées. Un premier axe de travail a été une recherche d'une requête similaire à la fenêtre englobant (avant/après) le trou. Cette approche est basée sur une comparaison de signaux à partir d'un algorithme d'extraction de caractéristiques géométriques (formes) et d'une mesure d'appariement élastique (DTW - Dynamic Time Warping). Un package R CRAN a été développé, DTWBI pour la complétion de série monovariée et DTWUMI pour des séries multidimensionnelles dont les signaux sont non ou faiblement corrélés. Ces deux approches ont été comparées aux approches classiques et récentes de la littérature et ont montré leur faculté de respecter la forme et la dynamique du signal. Concernant les signaux peu ou pas corrélés, un package DTWUMI a aussi été développé. Le second axe a été de construire une similarité floue capable de prender en compte les incertitudes de formes et d'amplitude du signal. Le système FSMUMI proposé est basé sur une combinaison floue de similarités classiques et un ensemble de règles floues. Ces approches ont été appliquées à des données marines et météorologiques dans plusieurs contextes : classification supervisée de cytogrammes phytoplanctoniques, segmentation non supervisée en états environnementaux d'un jeu de 19 capteurs issus d'une station marine MAREL CARNOT en France et la prédiction météorologique de données collectées au Vietnam. / Missing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and low/un-correlated multivariate time series. We begin by investigating an imputation method to overcome these issues in univariate time series. This approach is based on the combination of shape-feature extraction algorithm and Dynamic Time Warping method. A new R-package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un-correlated multivariate time series (called DTWUMI) and a DTWUMI R-package is also established. The key of these two proposed methods is that using the elastic matching to retrieving similar values in the series before and/or after the missing values. This optimizes as much as possible the dynamics and shape of knowledge data, and while applying the shape-feature extraction algorithm allows to reduce the computing time. Successively, we introduce a new method for filling large successive missing values in low/un-correlated multivariate time series, namely FSMUMI, which enables to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database (ii) forecast various meteorological univariate time series collected in Vietnam

This thesis explores the feasibility of clustering mixed data and unevenly spaced time series for customer segmentation. The proposed method implements the Gower dissimilarity as the local distance function in dynamic time warping to calculate dissimilarities between mixed data time series. The time series are then clustered with k−medoids and the clusters are evaluated with the silhouette score and t−SNE. The study further investigates the use of a time warping regularisation parameter. It is derived that implementing time as a feature has the same effect as penalising time warping, andtherefore time is implemented as a feature where the feature weight is equivalent to a regularisation parameter. The results show that the proposed method successfully identifies clusters in customer transaction data provided by Nordea. Furthermore, the results show a decrease in the silhouette score with an increase in the regularisation parameter, suggesting that the time at which a transaction occurred might not be of relevance to the given dataset. However, due to the method’s high computational complexity, it is limited to relatively small datasets and therefore a need exists for a more scalable and efficient clustering technique. / Denna uppsats utforskar klustring av ojämnt fördelade tidsserier med numeriska och kategoriska variabler för kundsegmentering. Den föreslagna metoden implementerar Gower dissimilaritet som avståndsfunktionen i dynamic time warping för att beräkna dissimilaritet mellan tidsserierna. Tidsserierna klustras sedan med k-medoids och klustren utvärderas med silhouette score och t-SNE. Studien undersökte vidare användningen av en regulariserings parameter. Det härledes att implementering av tid som en egenskap hade samma effekt som att bestraffa dynamic time warping, och därför implementerades tid som en egenskap där dess vikt är ekvivalent med en regulariseringsparameter.  Resultaten visade att den föreslagna metoden lyckades identifiera kluster i transaktionsdata från Nordea. Vidare visades det att silhouette score minskade då regulariseringsparametern ökade, vilket antyder att tiden transaktion då en transaktion sker inte är relevant för det givna datan. Det visade sig ytterligare att metoden är begränsad till reltaivt små dataset på grund av dess höga beräkningskomplexitet, och därför finns det behov av att utforksa en mer skalbar och effektiv klusteringsteknik.

The aim of this project is to use NI myRIO and LabVIEW for voice controlled home automation. The NI myRIO is an embedded device which has a Xilinx FPGA and a dual-core ARM Cortex-A9processor as well as analog input/output and digital input/output, and is programmed with theLabVIEW, a graphical programming language. The voice control is implemented in two differentsystems. The first system is based on an Amazon Echo Dot for voice recognition, which is acommercial smart speaker developed by Amazon Lab126. The Echo Dot devices are connectedvia the Internet to the voice-controlled intelligent personal assistant service known as Alexa(developed by Amazon), which is capable of voice interaction, music playback, and controllingsmart devices for home automation. This system in the present thesis project is more focusingon myRIO used for the wireless control of smart home devices, where smart lamps, sensors,speakers and a LCD-display was implemented. The other system is more focusing on myRIO for speech recognition and was built on myRIOwith a microphone connected. The speech recognition was implemented using mel frequencycepstral coefficients and dynamic time warping. A few commands could be recognized, includinga wake word ”Bosse” as well as other four commands for controlling the colors of a smart lamp. The thesis project is shown to be successful, having demonstrated that the implementation ofhome automation using the NI myRIO with two voice-controlled systems can correctly controlhome devices such as smart lamps, sensors, speakers and a LCD-display.

Dynamic programming has been widely used to solve various kinds of optimization problems.In this work, we show that two crucial problems in video-based sign language and gesture recognition systems can be attacked by dynamic programming with additional multiple observations. The first problem occurs at the higher (sentence) level. Movement epenthesis [1] (me), i.e., the necessary but meaningless movement between signs, can result in difficulties in modeling and scalability as the number of signs increases. The second problem occurs at the lower (feature) level. Ambiguity of hand detection and occlusion will propagate errors to the higher level. We construct a novel framework that can handle both of these problems based on a dynamic programming approach. The me has only be modeled explicitly in the past. Our proposed method tries to handle me in a dynamic programming framework where we model the me implicitly. We call this enhanced Level Building (eLB) algorithm. This formulation also allows the incorporation of statistical grammar models such as bigrams and trigrams. Another dynamic programming process that handles the problem of selecting among multiple hand candidates is also included in the feature level. This is different from most of the previous approaches, where a single observation is used. We also propose a grouping process that can generate multiple, overlapping hand candidates. We demonstrate our ideas on three continuous American Sign Language data sets and one hand gesture data set. The ASL data sets include one with a simple background, one with a simple background but with the signer wearing short sleeved clothes, and the last with a complex and changing background. The gesture data set contains color gloved gestures with a complex background. We achieve within 5% performance loss from the automatically chosen me score compared with the manually chosen me score. At the low level, we first over segment each frame to get a list of segments. Then we use a greedy method to group the segments based on different grouping cues. We also show that the performance loss is within 5% when we compare this method with manually selected feature vectors.