En la presente memoria se describe el uso de líquidos eutécticos sostenibles (DESs en inglés) como medios de reacción, empleando diferentes catalizadores metálicos para llevar a cabo la síntesis de compuestos orgánicos de interés en química orgánica. En el Primer Capítulo se detalla el uso de un catalizador heterogéneo de cobre soportado sobre magnetita en el acoplamiento cruzado deshidrogenante de tetrahidroisoquinolinas en mezclas eutécticas. En el Segundo Capítulo se pormenoriza sobre la síntesis de un complejo tipo pinza de paladio y su empleo en la reacción de acoplamiento cruzado de Hiyama, tanto en mezclas eutécticas como en glicerol, como medios sostenibles de reacción. Asimismo, y con el fin de mejorar la compatibilidad de los catalizadores de paladio en estos líquidos eutécticos, se detalla el diseño y la síntesis de fosfinas catiónicas, las cuales han probado su efectividad como ligandos de paladio en reacciones típicas de acoplamiento cruzado (Suzuki, Heck y Sonogashira) en diferentes mezclas eutécticas. Finalmente, en el Tercer Capítulo se describen reacciones multicomponente de acoplamiento cruzado para la formación de enlaces C-S. Por un lado, se ha desarrollado una metodología para la inserción de SO2 catalizada por paladio a partir de ácidos borónicos y metabisulfito de sodio. Por otro lado, una variante de la metodología anterior permitió la síntesis de sulfonamidas sustituyendo los ácidos borónicos por compuestos de triarilbismuto y nitrocompuestos bajo catálsis de cobre. En este último caso, una nueva mezcla eutéctica ha sido descrita y caracterizada, tanto físco-química como biológicamente.

The tremendous technological advance experienced in recent years has allowed the development and implementation of algorithms capable of performing different tasks that help humans in their daily lives. Scene recognition is one of the fields most benefited by these advances. Scene recognition gives different systems the ability to define a context for the identification or recognition of objects or places. In this same line of research, semantic localization allows a robot to identify a place semantically. Semantic classification is currently an exciting topic and it is the main goal of a large number of works. Within this context, it is a challenge for a system or for a mobile robot to identify semantically an environment either because the environment is visually different or has been gradually modified. Changing environments are challenging scenarios because, in real-world applications, the system must be able to adapt to these environments. This research focuses on recent techniques for categorizing places that take advantage of DL to produce a semantic definition for a zone. As a contribution to the solution of this problem, in this work, a method capable of updating a previously trained model is designed. This method was used as a module of an agenda system to help people with cognitive problems in their daily tasks. An augmented reality mobile phone application was designed which uses DL techniques to locate a customer’s location and provide useful information, thus improving their shopping experience. These solutions will be described and explained in detail throughout the following document.

Estimating an object’s position can be of great interest in several applications,and there exists many different methods to do so. One approach is with Directionof Arrival (DOA) measurements from receivers to use the triangulation techniqueto estimate one or more transmitter’s position. One algorithm which can find theDOA measurements from several transmitters is the MUltiple SIgnal Classification(MUSIC) algorithm. However, this still leaves a ambiguity problem which givesfalse solutions, so called ghost points, if the number of receivers is not sufficient.In this report solving this problem with the help of deep learning is studied. Thethesis’s main objective is to investigate and study whether it is possible to performpositioning with measurements from the MUSIC-algorithm using deep learningand image processing methods. A deep neural network is built in TensorFlow and trained and tested using datagenerated from MATLAB. This thesis’s setup consists of two receivers, which areused to locate two transmitters. The network uses two MUSIC spectra from thetwo receivers, and returns a probability distribution of where the transmittersare located. The results are compared with a traditional method and are analysed.The results presented in this thesis show that it is possible to perform positioningusing deep learning methods. However, there is a lot of room for improvementwith accuracy, which can be an important future research direction to explore.

Matrices of Vision is a sonic deconstruction of a higher education dataset compiled by the influential Swedish higher education authority Universitetskanslersämbetet (UKÄ). The title Matrices of Vision and project theme is inspired by Indigenous cyberfeminist, scholar and artist Tiara Roxanne’s work into data colonialism. The method explores how practical applications of sound and theory can be used to meditate on political struggles and envision emancipatory modes of creation that hold space through a music-making practice. The artistic approach uses just intonation as a system, or grid of fixed points, which it refuses. The pitch strategy diverges from this approach by way of its political motivations: it disobeys just intonation’s rigid structure through practice and breaks with its order as a way to explore its experiential qualities. The approach seeks to engage beyond the structures designed to regulate behaviors and ways of perceiving and rather hold space for a multiplicity of viewpoints which are explored through cacophony, emotion and deep listening techniques.

500 of 690 training images used in optimized training runs.

Intelligent transportation systems (ITS) have become a popular method to enhance the safety and efficiency of transportation. Pedestrians, as an essential participant of ITS, are very vulnerable in a traffic collision, compared with the passengers inside the vehicle. In order to protect the safety of all traffic participants and enhance transportation efficiency, the novel autonomous vehicles are required to detect pedestrians accurately and timely. In the area of pedestrian detection, deep learning-based pedestrian detection methods have gained significant development since the appearance of powerful GPUs. A large number of researchers are paying efforts to improve the accuracy of pedestrian detection by utilizing the Convolutional Neural Network (CNN)-based detectors. In this thesis, we propose a one-stage anchor-free pedestrian detector named Bi-Center Network (BCNet), which is aided by the semantic features of pedestrians' visible parts. The framework of our BCNet has two main modules: the feature extraction module produces the concatenated feature maps that extracted from different layers of ResNet, and the four parallel branches in the detection module produce the full body center keypoint heatmap, visible part center keypoint heatmap, heights, and offsets, respectively. The final bounding boxes are converted from the high response points on the fused center keypoint heatmap and corresponding predicted heights and offsets. The fused center keypoint heatmap contains the semantic feature fusion of the full body and the visible part of each pedestrian. Thus, we conduct ablation studies and discover the efficiency of feature fusion and how visibility features benefit the detector's performance by proposing two types of approaches: introducing two weighting hyper-parameters and applying three different attention mechanisms. Our BCNet gains 9.82% MR-2 (the lower the better) on the Reasonable setup of the CityPersons dataset, compared to baseline model which gains 12.14% MR-2 . The experimental results indicate that the performance of pedestrian detection could be significantly improved because the visibility semantic could prompt stronger responses on the heatmap. We compare our BCNet with state-of-the-art models on the CityPersons dataset and ETH dataset, which shows that our detector is effective and achieves a promising performance.

Connected vehicle networks and future autonomous driving systems call for characterization of risky driving events to improve safety applications and autonomous driving features. Precision of driving event characterization (\gls{dec}) systems in connected vehicles has become increasingly important with the responsive connectivity that is available to the modern vehicles. While risky behavior patterns entail potential safety issues on road networks, the advent of vehicular sensing and vehicular networks cannot guarantee accurate characterization of driving/movement behavior of vehicles and the precision of such systems still remains an open issue. Additionally, artificial intelligence-backed solutions are vital components towards highly accurate characterization systems in the modern transportation. However, such solutions require significant volume of driving event data for an acceptable level of performance. With this in mind, the proposal of this thesis is three-fold: 1) a reliable methodology to generate representative data under the scarcity of diverse anomalous sensory data, 2) classification of mobility/driving events of vehicles with attention-based deep learning methods, and 3) a modular prior-knowledge input method to the characterization methodologies in order to further improve the trustworthiness of the systems. Implementing the proposed steps, we are able to not only increase the consistency in the training process but also reduce the false positive detection instances compared to the previous models. One of the roadblocks against robust event characterization systems in connected vehicles that is tackled in this thesis is the scarcity of anomalous driving data to make the training of event classification models robust. To mitigate this issue an optimized deep recurrent neural network-based encoding model is introduced to extract the precise feature representation of the anomalous data. The utilization of the encoded input to the previous network allowed for a 12\% accuracy improvement. Furthermore, we introduced a framework for precise risky driving behavior detection that takes advantage of an attention-based neural networks model. Ultimately, the combination of prior knowledge modelling with our network and some optimizations to the network structure, the model outperforms the state-of-the-art solutions by reaching an average accuracy of 0.96 and F1-score of 0.92.

Every year, drivers are spending over 3 billions to repair damage on vehicle caused by potholes. Along with the financial disaster, potholes cause frustration in drivers. Also, with the emerging development of automated vehicles, road safety with automation in mind is being a necessity. Deep Learning techniques offer intelligent alternatives to reduce the loss caused by spotting pothole. The world is connected in such a way that the information can be shared in no time. Using the power of connectivity, we can communicate the information of potholes to other vehicles and also the department of Transportation for necessary action. A significant number of research efforts have been done with a view to help detect potholes in the pavements. In this thesis, we have compared two object detection algorithms belonging to two major classes i.e. single shot detectors and two stage detectors using our dataset. Comparing the results in the Faster RCNN and YOLOv5, we concluded that, potholes take a small portion in image which makes potholes detection with YOLOv5 less accurate than the Faster RCNN, but keeping the speed of detection in mind, we have suggested that YOLOv5 will be a better solution for this task. Using the YOLOv5 model and image processing technique, we calculated approximate area of potholes and visualized the shape of potholes. Thus obtained information can be used by the Department of Transportation for planning necessary construction tasks. Also, we can use these information to warn the drivers about the severity of potholes depending upon the shape and area.

Microorganisms exist ubiquitously on Earth, yet their functions and ecological roles remain elusive. Investigating these microbes is accomplished by using culture-dependent and culture-independent methodologies. This study employs both methodologies to characterize: 1) the genomic potential of the novel deep-subsurface bacterial isolate Thermanaerosceptrum fracticalcis strain DRI-13T by combining next-generation and nanopore sequencing technologies and 2) the microbiome of the artificial marine environment for the Hawaiian Bobtail Squid in aquaculture using next-generation sequencing of 16S rRNA gene. Microbial ecology of the deep-subsurface remains understudied in terms of microbial diversity and function. The genomic information of DRI-13T revealed a potential for syntrophic relationships, diverse metabolic potential including prophages/antiviral defenses, and novel methylation motifs. Artificial marine environments housing marine the Hawaiian Bobtail Squid (Euprymna scolopes) contain microorganisms that can directly influence animal and aquaculture health. No studies presently show if bacterial communities of the tank environment correlate with the health and productivity of E. scolopes. This study sought to address this by sampling from a year of unproductive aquaculture yield and comparing the bacterial communities from productive cohorts. Bacterial communities from unproductive samples show less bacterial diversity and abundance coupled with shifts in bacterial composition. Nitrate and pH levels between the tanks were found to be strong influences on determining the bacterial populations of productive and unproductive cohorts.

In recent years the advancement in machine learning have solved more and more complex problems. But still these techniques are not commonly used in the industry. One problem is that many of the techniques are black boxes, it is hard to analyse them to make sure that their behaviour is safe. This property makes them unsuitable for safety critical systems. The goal of this thesis is to examine if the deep learning technique Deep Q-network could be used to create a behaviour tree that can solve the same problem. A behaviour tree is a tree representation of a flow structure that is used for representing behaviours, often used in video games or robotics. To solve the problem two simulators are used, one models a cart that shall balance a pole called cart pole, the other is a static world which needs to be navigated called grid world. Inspiration is taken from the learning from demonstration field to use the Deep Q-network as a teacher and then create a decision tree. During the creation of the decision tree two attributes are used for pruning; to look at the trees accuracy or performance. The thesis then compare three techniques, called Naive, BT Espresso, and BT Espresso Simplified. The techniques are used to transform the extracted decision tree into a behaviour tree. When it comes to the performance of the created behaviour trees they all manage to complete the simulator scenarios in the same, or close to, capacity as the trained Deep Q-network. The trees created from the performance pruned decision tree are generally smaller and less complex, but they have worse accuracy. For cart pole the trees created from the accuracy pruned tree has around 10 000 nodes but the performance pruned trees have around 10-20 nodes. The difference in grid world is smaller going from 35-45 nodes to 40-50 nodes. To get the smallest tree with the best performance then the performance pruned tree should be used with the BT Espresso Simplified algorithm. This thesis have shown that it is possible to use knowledge from a trained Deep Q-network model to create a Behaviour tree that can complete the same task. / Under de senaste åren har ett antal framsteg inom maskininlärning gjorts vilket har lett till att mer och mer komplexa problem har kunnat lösas. Dock är dessa tekniker ofta inte använda av industrin. Ett av problemen är att många av de bättre teknikerna beter sig som svarta lådor, det är väldigt svårt att analyser vad de kommer att göra. Denna egenskap gör att de inte är lämpliga att användas i säkerhetskritiska system. Målet med denna avhandling är att undersöka möjligheten att använda den djupa inlärningstekniken djupa q-nätverk kan användas för att skapa ett beteendeträd som är kapabelt att lösa samma problem. Ett beteendeträd är en flödesstruktur som används för att representera beteenden, ofta använt i dataspel eller för robotar. För att undersöka problemet så används två simulatorer, den ena modellerar en vagn som ska balansera en stav och kallas vagnstav (cart pole). Den andra simulatorn är en statisk värld där målet för agenten är att ta sig till en definierad målplats, vilken kallas rutvärld (grid world). För att lösa problemet tas inspiration från ett angränsande fält kallat inlärning från demonstration. Istället för att använda en mänsklig lärare ansätts det djupa q-nätverket som lärare och används för att skapa ett beslutsträd. Beslutsträdet är sedan reducerat genom att kolla på trädets träffsäkerhet eller hur mycket belöning trädet får. Tre tekniker jämförs för att transformera beslutsträdet till ett beteendeträd, teknikerna heter Naiv, BT Espresso och BT Espresso förenklad. Alla skapade beteendeträd lyckas klara av problemet i simulatorn de är skapade för. De hade liknande prestanda som det djupa q-nätverket. När beslutsträden var reducerat på belöning resulterade det i generellt mindre beteendeträd, dock så hade de inte full träffsäkerhet mot det djupa q-nätverket. För vagnstav simulatorn hade beteendeträden som skapats från träffsäkerhets beslutsträden runt 10 000 noder, mot belönings kapade träd som hade runt 10–20 noder. I rutvärlden var skillnaden mindre med 40–50 noder för träd skapade från träffsäkerhet reducerade beslutsträde och 35–45 noder för belöning reducerade beslutsträd. Denna avhandling har påvisat att det går att skapa beteende träd från en tränad djup q-nätverksmodell för ett scenario och om det minsta trädet som klarar scenariot är att önskat bör belönings reducerade beslutsträd användas med BT Espresso förenkling algoritmen.