Yes / This paper presents an approach to implement an automatically tuning antenna for optimising power transfer suitable for software defined radio (SDR). Automatic tuning is accomplished using a closed loop impedance tuning network comprising of an impedance sensor and control unit. The sensor provides the control unit with data on the transmit or receive power, and the algorithm is used to impedance of a T-network of LC components to optimize the antenna impedance to maximise power transmission or reception. The effectiveness of the proposed tuning algorithm in relation to impedance matching and convergence on the optimum matching network goal is shown to be superior compared with the conventional tuning algorithm. / This work is partially supported by innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424 and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/E022936/1

Motion estimation and compensation plays an important role in video processing applications. Two-dimensional block-based and mesh-based models are widely used in this area. A 2-D mesh-based model provides a better representation of complex real world motion than a block-based model. Mesh-based motion estimation algorithms are employed in both frame-based and object-based video compression and coding. A hierarchical mesh-based algorithm is applied to improve the motion field generated by a single-layer algorithm. 2-D mesh-based models also enable the manipulation of video objects which is included in the MPEG-4 standard. A video object in a video clip can be replaced by another object by the use of a dynamic mesh structure. In this thesis, a comparative analysis of 2-D block-based and mesh-based motion estimation algorithms in both frame-based and object-based video representations is performed. The experimental results indicate that a mesh-based algorithm produces better motion compensation results than a block-based algorithm. Moreover, a two-layer mesh-based algorithm shows improvement over a one-layer mesh-based algorithm. The application of mesh-based motion estimation and compensation to video object replacement and animation is also performed.

No description available.

Tidigare forskning visar att det finns en koppling mellan musiksmak och social attraktion mellan människor, eftersom delad musiksmak ofta innebär delade personliga värderingar, och delade personliga värderingar kan innebära större chans för social attraktion. Målet med undersökningen har varit att ta reda på om musiksmak har någon korrelation med personliga värderingar, och vilka algoritmer som i så fall skulle kunna användas för att beräkna korrelationen. En modell ställs upp för en teoretisk perfekt matchningsalgoritm mot vilken de undersökta algoritmerna testas och jämförs praktiskt. Studien, som är uppdelad i tre delar, undersöker algoritmerna närmare med hjälp av testdata i formen av datorgenererade värden i den första och andra delen. Den första delen använder data i formen av heltal (antalet förekomster av musikpreferens) och den andra använder data i formen av binära tal (förekomst eller ej av musikpreferens). Den tredje delen använder sig av användardata, från 13 deltagare, från Spotify samt från en enkät om personliga värderingar. Resultaten visar ingen uppenbar korrelation mellan personliga värderingar och musiksmak, vilket troligtvis beror på datamängderna; det kan vara så att det krävs mer detaljerad och strukturerad användardata än den som inhämtats och använts i denna undersökning för att få tydliga resultat. / Earlier research shows that there is a connection between music taste and social attraction between people, because shared music taste usually means shared personal values, and shared personal values could mean greater chance for social attraction. The goal with the project has been to find out if music taste is correlated with personal values, and what algorithms can be used to calculate that correlation. A model is defined for a perfect matching-algorithm against which the studied algorithms are tested and compared practically. The study, which is divided into three parts, investigates the algorithms closer using test data in the form of computer-generated values in the first and second part. The first part uses data in the form of integers (the number of occurences of a music preference) and the second part uses data in the form of binary numbers (occurence or not of a music preference). The third part uses real user data, from 13 participants, from Spotify and from a survey regarding personal values. The results show no apparent correlation between personal values and music taste, the cause of which is most likely the data; it could be that it takes more detailed and structured user data than the one used in this study to get clear results.

<div>This dissertation addresses the problem of how to design a convolutional neural network (CNN) for giving semantic labels to the points on the ground given the satellite image coverage over the area and, for the ground truth, given the noisy labels in OpenStreetMap (OSM). This problem is made challenging by the fact that -- (1) Most of the images are likely to have been recorded from off-nadir viewpoints for the area of interest on the ground; (2) The user-supplied labels in OSM are frequently inaccurate and, not uncommonly, entirely missing; and (3) The size of the area covered on the ground must be large enough to possess any engineering utility. As this dissertation demonstrates, solving this problem requires that we first construct a DSM (Digital Surface Model) from a stereo fusion of the available images, and subsequently use the DSM to map the individual pixels in the satellite images to points on the ground. That creates an association between the pixels in the images and the noisy labels in OSM. The CNN-based solution we present yields a 4-8% improvement in the per-class segmentation IoU (Intersection over Union) scores compared to the traditional approaches that use the views independently of one another. The system we present is end-to-end automated, which facilitates comparing the classifiers trained directly on true orthophotos vis-`a-vis first training them on the off-nadir images and subsequently translating the predicted labels to geographical coordinates. This work also presents, for arguably the first time, an in-depth discussion of large-area image alignment and DSM construction using tens of true multi-date and multi-view WorldView-3 satellite images on a distributed OpenStack cloud computing platform.</div>