As an important indicator of biodiversity and ecological environment in a region, the number and distribution of animals has been given more and more attention by agencies such as nature reserves, wetland parks, and animal protection supervision departments. To protect biodiversity, we need to be able to detect and track the movement of animals to understand which animals are visiting the space. This thesis uses the improved You Only Look Once Version 5 (YOLOv5) target detection algorithm and Simple online and real-time tracking with a deep association metric (DeepSORT) tracking algorithm to provide technical support for bird monitoring, identification and tracking. Specifically, the thesis tries different improvement methods based on YOLOv5 to solve the problem that small targets in images are difficult to detect. In the backbone network, different attention modules are added to enhance the network feature extraction ability; in the neck network part, the Bi-Directional Feature Pyramid Network (BiFPN) structure is used to replace the Path Aggregation Network (PAN) structure to strengthen the utilization of underlying features; in the detection head part, a high-resolution detection head is added to improve the detection ability of tiny targets. In addition, a better loss function has been used to improve the algorithm’s performance on small birds. The improved algorithms in this paper have been used in multiple comparative experiments on the VisDrone data set and a data set of bird flight images, and the results show that compared with the baseline using YOLOv5, for VisDrone data set, Spatial-to-Depth (SPD)-Convolutional stride-free (Conv) gets the highest training mean Average Precision (mAP) of all methods with an increase from 0.325 to 0.419; for the bird data set, the best result of training mAP that could be achieved is adding a P2 layer, which reaches an improvement from 0.701 to 0.724. After combining the You Only Look Once (YOLO) with DeepSORT to implement the tracking function, the improved method makes the final tracking effect better. / Som en viktig indikator på biologisk mångfald och ekologisk miljö i en region har antal och utbredning av djur uppmärksammats mer och mer av organisationer som som naturreservat, våtmarksparker och djurskyddsmyndigheter. För att skydda den biologiska mångfalden måste vi kunna upptäcka och spåra djurs rörelser för att förstå vilka djur som besöker ett område. Uppsatsen använder den förbättrade YOLOv5-måldetektionsalgoritmen och DeepSORT-spårningsalgoritmen för fågelövervakning, identifiering och spårning. Specifikt undersöks olika förbättringsmetoder baserade på YOLOv5 för att lösa problemet med att små mål i bilder är svåra att upptäcka. I den första delen av nätverket läggs olika uppmärksamhetsmoduler till; i nästa används BiFPN-strukturen för att ersätta PAN-strukturen; i detektionsdelen läggs ett högupplöst detektionshuvud till för att förbättra detekteringsförmågan för små föremål. Dessutom har en bättre förlustfunktion använts för att förbättra algoritmens prestanda för små fåglar och andra djur. De förbättrade algoritmerna har testats flera jämförande experiment på VisDronedatamängden och en datamängd av bilder av flygande fåglar. Resultaten visar att jämfört med baslinjen med YOLOv5s, för VisDrone-datamängden får SPD-Conv det högsta tränings-mAP med en ökning från 0,325 till 0,419; för fågeldatamängden nås det bästa resultatet genom att lägga till ett P2-lager, vilket ger en förbättring från 0,701 till 0,724 av mAP. Efter att ha kombinerat YOLO med DeepSORT för att implementera spårningsfunktionen, blir den slutliga spårningseffekten bättre.

Human-robot collaboration (HRC) occurs when people and robots work together in a shared environment. Current robots often use rigid programs unsuitable for HRC. Multimodal robot programming offers an easier way to control robots using inputs like voice and gestures. In this scenario, human commands from different sensors trigger the robot’s actions. However, this data-driven approach has challenges: accurately understanding power dynamics, integrating inputs, and precisely controlling the robot. To address this, we introduce EEG signals to improve robot control, requiring reliable signal processing, feature extraction, and accurate classification using machine learning and deep learning. Existing deep learning models struggle to balance accuracy and efficiency. This thesis focuses on whether dilated convolutional neural networks can improve accuracy and reduce training and reaction times compared to the baseline. After using the Morlet wavelet for EEG feature extraction, in the thesis, an existing convolutional neural network as a benchmark is employed and uses the dilated convolution algorithm for comparison. Accuracy, precision, recall, and time are used to assess the comparison algorithm’s performance. The conclusion is that the dilated convolutional neural network performs better than the baseline in accuracy and time parameters. / Samarbete mellan människa och robot (HRC) inträffar när människor och robotar arbetar tillsammans i en delad miljö. Nuvarande robotar använder ofta rigida program som inte är lämpliga för HRC. Multimodal robotprogrammering erbjuder ett enklare sätt att styra robotar med hjälp av röst och gester. I detta scenario utlöser mänskliga kommandon från olika sensorer robotens handlingar. Dock har denna datadrivna ansats utmaningar: att noggrant förstå kraftdynamik, integrera inmatning och exakt styra roboten. För att hantera detta introducerar vi EEG-signaler för att förbättra robotstyrningen, vilket kräver pålitlig signalbehandling, funktionsextraktion och noggrann klassificering med maskininlärning och djupinlärning. Nuvarande djupinlärningsmodeller har svårt att balansera noggrannhet och effektivitet. Den här artikeln fokuserar på om dilaterade konvolutionella neurala nätverk kan förbättra noggrannheten och minska träningstider och reaktionstider jämfört med baslinjen. Efter att ha använt Morlet-våg för EEG-funktionsutvinning använder artikeln en befintlig konvolutionell neural modell som referens och jämför med dilaterad konvolution för att bedöma prestandan. Noggrannhet, precision, recall och tidsparametrar bedömer jämförelsealgoritmens prestanda. Slutsatsen är att det dilaterade konvolutionella neurala nätverket presterar bättre än baslinjen vad gäller noggrannhet och tidsparametrar.

A challenge to release autonomous vehicles to public roads is safety verification of the developed features. Safety test driving of vehicles is not practically feasible as the acceptance criterion is driving at least 2.1 billion kilometers [1]. An alternative to this distance-based testing is the scenario-based approach, where the intelligent vehicles are exposed to known scenarios. Identification of such scenarios from the driving data is crucial for this validation. The aim of this thesis is to investigate the possibility of unsupervised identification of driving scenarios from the driving data. The task is performed in two major parts. The first is the segmentation of the time series driving data by detecting changepoints, followed by the clustering of the previously obtained segments. Time-series segmentation is approached using a Deep Learning method, while the second task is performed using time series clustering. The work also includes a visual approach for validating the time-series segmentation, followed by a quantitative measure of the performance. The approach is also qualitatively compared against a Bayesian Nonparametric approach to identify the usefulness of the proposed method. Based on the analysis of results, there is a discussion about the usefulness and drawbacks of the method, followed by the scope for future research. / En utmaning att släppa autonoma fordon på allmänna vägar är säkerhetsverifiering av de utvecklade funktionerna. Säkerhetstestning av fordon är inte praktiskt genomförbart eftersom acceptanskriteriet kör minst 2,1 miljarder kilometer [1]. Ett alternativ till denna distansbaserade testning är det scenaribaserade tillväga-gångssättet, där intelligenta fordon utsätts för kända scenarier. Identifiering av sådana scenarier från kördata är avgörande för denna validering. Syftet med denna avhandling är att undersöka möjligheten till oövervakad identifiering av körscenarier från kördata. Uppgiften utförs i två huvuddelar. Den första är segmenteringen av tidsseriedrivdata genom att detektera ändringspunkter, följt av klustring av de tidigare erhållna segmenten. Tidsseriesegmentering närmar sig med en Deep Learningmetod, medan den andra uppgiften utförs med hjälp av tidsseriekluster. Arbetet innehåller också ett visuellt tillvägagångssätt för att validera tidsserierna, följt av ett kvantitativt mått på prestanda. Tillvägagångssättet jämförs också med en Bayesian icke-parametrisk metod för att identifiera användbarheten av den föreslagna metoden. Baserat på analysen av resultaten diskuteras metodens användbarhet och nackdelar, följt av möjligheten för framtida forskning.

In this dissertation, level set methods are employed to segment masses in digital mammographic images and to classify land cover classes in hyperspectral data. For the mammography computer aided diagnosis (CAD) application, level set-based segmentation methods are designed and validated for mass periphery segmentation, spiculation segmentation, and core segmentation. The proposed periphery segmentation uses the narrowband level set method in conjunction with an adaptive speed function based on a measure of the boundary complexity in the polar domain. The boundary complexity term is shown to be beneficial for delineating challenging masses with ill-defined and irregularly shaped borders. The proposed method is shown to outperform periphery segmentation methods currently reported in the literature. The proposed mass spiculation segmentation uses a generalized form of the Dixon and Taylor Line Operator along with narrowband level sets using a customized speed function. The resulting spiculation features are shown to be very beneficial for classifying the mass as benign or malignant. For example, when using patient age and texture features combined with a maximum likelihood (ML) classifier, the spiculation segmentation method increases the overall accuracy to 92% with 2 false negatives as compared to 87% with 4 false negatives when using periphery segmentation approaches. The proposed mass core segmentation uses the Chan-Vese level set method with a minimal variance criterion. The resulting core features are shown to be effective and comparable to periphery features, and are shown to reduce the number of false negatives in some cases. Most mammographic CAD systems use only a periphery segmentation, so those systems could potentially benefit from core features.

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[pt] A Extração de Informação é uma tarefa importante no domínio jurídico. Embora a presença de dados estruturados seja escassa, dados não estruturados na forma de documentos jurídicos, como sentenças, estão amplamente disponíveis. Se processados adequadamente, tais documentos podem fornecer informações valiosas sobre processos judiciais anteriores, permitindo uma melhor avaliação por profissionais do direito e apoiando aplicativos baseados em dados. Este estudo aborda a Extração de Informação no domínio jurídico, extraindo valor de sentenças relacionados a reclamações de consumidores. Mais especificamente, a extração de cláusulas categóricas é abordada através de classificação, onde seis modelos baseados em diferentes estruturas são analisados. Complementarmente, a extração de valores monetários relacionados a indenizações por danos morais é abordada por um modelo de Reconhecimento de Entidade Nomeada. Para avaliação, um conjunto de dados foi criado, contendo 964 sentenças anotados manualmente (escritas em português) emitidas por juízes de primeira instância. Os resultados mostram uma média de aproximadamente 97 por cento de acurácia na extração de cláusulas categóricas, e 98,9 por cento na aplicação de NER para a extração de indenizações por danos morais. / [en] Information Extraction is an important task in the legal domain. While the presence of structured and machine-processable data is scarce, unstructured data in the form of legal documents, such as legal opinions, is largely available. If properly processed, such documents can provide valuable information with regards to past lawsuits, allowing better assessment by legal professionals and supporting data-driven applications. This study addresses Information Extraction in the legal domain by extracting value from legal opinions related to consumer complaints. More specifically, the extraction of categorical provisions is addressed by classification, where six models based on different frameworks are analyzed. Moreover, the extraction of monetary values related to moral damage compensations is addressed by a Named Entity Recognition (NER) model. For evaluation, a dataset was constructed, containing 964 manually annotated legal opinions (written in Brazilian Portuguese) enacted by lower court judges. The results show an average of approximately 97 percent of accuracy when extracting categorical provisions, and 98.9 percent when applying NER for the extraction of moral damage compensations.

<p>Emerging mobile mapping systems include a wide range of platforms, for instance, manned aircraft, unmanned aerial vehicles (UAV), terrestrial systems like trucks, tractors, robots, and backpacks, that can carry multiple sensors including LiDAR scanners, cameras, and georeferencing units. Such systems can maneuver in the field to quickly collect high-resolution data, capturing detailed information over an area of interest. With the increased volume and distinct characteristics of the data collected, practical quality control procedures that assess the agreement within/among datasets acquired by various sensors/systems at different times are crucial for accurate, robust interpretation. Moreover, the ability to derive semantic information from acquired data is the key to leveraging the complementary information captured by mobile mapping systems for diverse applications. This dissertation addresses these challenges for different systems (airborne and terrestrial), environments (urban and rural), and applications (agriculture, archaeology, hydraulics/hydrology, and transportation).</p> <p>In this dissertation, quality control procedures that utilize features automatically identified and extracted from acquired data are developed to evaluate the relative accuracy between multiple datasets. The proposed procedures do not rely on manually deployed ground control points or targets and can handle challenging environments such as coastal areas or agricultural fields. Moreover, considering the varying characteristics of acquired data, this dissertation improves several data processing/analysis techniques essential for meeting the needs of various applications. An existing ground filtering algorithm is modified to deal with variation in point density; digital surface model (DSM) smoothing and seamline control techniques are proposed for improving the orthophoto quality in agricultural fields. Finally, this dissertation derives semantic information for diverse applications, including 1) shoreline retreat quantification, 2) automated row/alley detection for plant phenotyping, 3) enhancement of orthophoto quality for tassel/panicle detection, and 4) point cloud semantic segmentation for mapping transportation corridors. The proposed approaches are tested using multiple datasets from UAV and wheel-based mobile mapping systems. Experimental results verify that the proposed approaches can effectively assess the data quality and provide reliable interpretation. This dissertation highlights the potential of modern mobile mapping systems to map challenging environments for a variety of applications.</p>

Music is created by composers to arouse different emotions and feelings in the listener, and in the case of soundtracks, to support the storytelling of scenes. The goal of this project is to seek the best method to evaluate the emotional content of soundtracks. This emotional content can be measured quantitatively thanks to Russell’s model of valence, arousal and dominance which converts moods labels into numbers. To conduct the analysis, MFCCs and VGGish features were extracted from the soundtracks and used as inputs to a CNN and an LSTM model, in order to study which one achieved a better prediction. A database of 6757 number of soundtracks with their correspondent VAD values was created to perform the mentioned analysis. As an ultimate purpose, the results of the experiments will contribute to the start-up Vionlabs to understand better the content of the movies and, therefore, make a more accurate recommendation on what users want to consume on Video on Demand platforms according to their emotions or moods. / Musik skapas av kompositörer för att väcka olika känslor och känslor hos lyssnaren, och när det gäller ljudspår, för att stödja berättandet av scener. Målet med detta projekt är att söka den bästa metoden för att utvärdera det emotionella innehållet i ljudspår. Detta känslomässiga innehåll kan mätas kvantitativt tack vare Russells modell av valens, upphetsning och dominans som omvandlar stämningsetiketter till siffror. För att genomföra analysen extraherades MFCC: er och VGGish-funktioner från ljudspåren och användes som ingångar till en CNN- och en LSTM-modell för att studera vilken som uppnådde en bättre förutsägelse. En databas med totalt 6757 ljudspår med deras korrespondent acrshort VAD-värden skapades för att utföra den nämnda analysen. Som ett yttersta syfte kommer resultaten av experimenten att bidra till att starta upp Vionlabs för att bättre förstå innehållet i filmerna och därför ge mer exakta rekommendationer på Video on Demand-plattformar baserat på användarnas känslor eller stämningar.