Den här uppsatsen är en undersökning av Svenska Dagbladets musikbevakning under 30 år, från 1980 till 2010. I en inledande kvantitativ delstudie undersöktes Svenska Dagbladets innehåll två veckor vart tionde år för att få svar på frågor som hur mycket det skrivits om olika musikstilar genom åren och hur bevakningen utvecklats och förändrats. Erfarenheter från den kvantitativa delstudien låg sedan till grund för en uppföljande kvalitativ delstudie där musikjournalister med lång erfarenhet av att arbeta på tidningen intervjuades. Det samlade resultatet visar att musikbevakningen i Svenska Dagbladet förändrats från att tidigare främst ha fokuserat på den klassiska musiken, till att i allt större utsträckning handla om modern populärmusik. Detta kan förklaras av ett sämre ekonomiskt läge för Svenska Dagbladet, en pågående trend i tidningsvärlden att skriva alltmer om populärkultur, en generationsväxling bland skribenterna och ett uppluckrande av tidningens tidigare kulturella identitet.

Cerebral small vessel disease (SVD) is highly prevalent in older adults and is a predictor of stroke, dementia, and death. SVD is also associated with cognitive dysfunction, gait problems, and urinary incontinence. SVD is diagnosed based on white matter hyperintensities on T2 weighted scans. This thesis investigates the cardiac frequency component of resting state functional magnetic resonance imaging data in young healthy adults, older healthy adults, and older adults with pronounced SVD. A cardiac pulsatility metric is defined, and a tissue type contrast is observed between white matter, grey matter, and cerebrospinal fluid. Aging and disease effects are observed on cardiac pulsatility in white matter. The increased pulsatility may reflect the pathology of venous collagenosis and draining vein stenosis. Developing a better understanding of the etiology of SVD is an important step towards treating the disease.

Cerebral small vessel disease (SVD) is highly prevalent in older adults and is a predictor of stroke, dementia, and death. SVD is also associated with cognitive dysfunction, gait problems, and urinary incontinence. SVD is diagnosed based on white matter hyperintensities on T2 weighted scans. This thesis investigates the cardiac frequency component of resting state functional magnetic resonance imaging data in young healthy adults, older healthy adults, and older adults with pronounced SVD. A cardiac pulsatility metric is defined, and a tissue type contrast is observed between white matter, grey matter, and cerebrospinal fluid. Aging and disease effects are observed on cardiac pulsatility in white matter. The increased pulsatility may reflect the pathology of venous collagenosis and draining vein stenosis. Developing a better understanding of the etiology of SVD is an important step towards treating the disease.

I denna uppsats har vi genom en kvalitativ textanalys jämfört hur Al Jazeera och SvD rapporteratom Sydsudan under ett tidsspann på arton månader. Underlaget för forskningen är tio artiklarfrån SvD:s webbsida och tio artiklar från Al Jazeeras webbsida. Våra frågeställningar är; Vilkaretoriska grepp använder sig de olika medierna av och skiljer sig användningen på något sätt, ochi så fall hur? Vi har även formulerat en hypotes där vi påstår att en analys av de retoriska greppenkommer att synliggöra ideologiska underliggande meningar hos respektive nyhetsleverantör.Genom att formulera tio frågor utifrån den massmedieretoriska innehållsanalysen har vikunnat synliggöra olika retoriska grepp som används av skribenterna; däribland metaforer,liknelser, personifiering, värdeladdade ord och miljöbeskrivningar.Detta är en diskursanalys där vi tillämpat semiotiska teorier om språk och retorik. Våraresultat visade att det fanns flera likheter än skillnader gällande den språkliga gestaltningen hosde båda medierna. Hypotesen motbevisades och resultaten visade snarare att mediernasideologiska undertoner överensstämde.

The rise in digital technology has also rose the use of digital images. The digital imagesrequire much storage space. The compression techniques are used to compress the dataso that it takes up less storage space. In this regard wavelets play important role. Inthis thesis, we studied the Haar wavelet system, which is a complete orthonormal systemin L2(R): This system consists of the functions j the father wavelet, and y the motherwavelet. The Haar wavelet transformation is an example of multiresolution analysis. Ourpurpose is to use the Haar wavelet basis to compress an image data. The method ofaveraging and differencing is used to construct the Haar wavelet basis. We have shownthat averaging and differencing method is an application of Haar wavelet transform. Afterdiscussing the compression by using Haar wavelet transform we used another method tocompress that is based on singular value decomposition. We used mathematical softwareMATLAB to compress the image data by using Haar wavelet transformation, and singularvalue decomposition.

In this masters’ thesis report we present a graph theoretical method used for automatic bilingual lexicon acquisition with parallel texts. We analyze the concept of graph similarity and give an interpretation, of the parallel texts, connected to the vector space model. We represent the parallel texts by a directed, tripartite graph and from here use the corresponding adjacency matrix, A, to compute the similarity of the graph. By solving the eigenvalue problem ρS = ASAT + ATSA we obtain the self-similarity matrix S and the Perron root ρ. A rank k approximation of the self-similarity matrix is computed by implementations of the singular value decomposition and the non-negative matrix factorization algorithm GD-CLS. We construct an algorithm in order to extract the bilingual lexicon from the self-similarity matrix and apply a statistical model to estimate the precision, the correctness, of the translations in the bilingual lexicon. The best result is achieved with an application of the vector space model with a precision of about 80 %. This is a good result and can be compared with the precision of about 60 % found in the literature.

Singular value decomposition (SVD) is a popular decomposition method for solving least-squares estimation problems. However, for large datasets, SVD is very time consuming and memory demanding in obtaining least squares solutions. In this paper, we propose a least squares estimator based on an iterative divide-and-merge scheme for large-scale estimation problems. The estimator consists of several levels. At each level, the input matrices are subdivided into submatrices. The submatrices are decomposed by SVD respectively and the results are merged into smaller matrices which become the input of the next level. The process is iterated until the resulting matrices are small enough which can then be solved directly and efficiently by the SVD algorithm. However, the iterative divide-and-merge algorithms executed on a single machine is still time demanding on large scale datasets. We propose two distributed algorithms to overcome this shortcoming by permitting several machines to perform the decomposition and merging of the submatrices in each level in parallel. The first one is implemented in MapReduce on the Hadoop distributed platform which can run the tasks in parallel on a collection of computers. The second one is implemented on CUDA which can run the tasks in parallel using the Nvidia GPUs. Experimental results demonstrate that the proposed distributed algorithms can greatly reduce the time required to solve large-squares problems.

Recent theoretical and experimental studies have demonstrated that an estimate of the Green's function between two hydrophones can be extracted passively from the cross‐correlation of ambient noise recorded at these two points. Hence monitoring the temporal evolution of these estimated Green's functions can provide a means for noise‐based acoustic tomography using a distributed sensor network. However, obtaining unbiased Green's function estimate requires a sufficiently spatially and temporally diffuse ambient noise field. Broadband ambient noise ([200 Hz-20 kHz]) was recorded continuously for 2 days during the SWAMSI09 experiment (next to Panama City, FL) using two moored vertical line arrays (VLAs) spanning 7.5m of the 20‐m water column and separated by 150 m. The feasibility of noise‐based acoustic tomography ([300-1000 Hz]) was assessed in this dynamic coastal environment over the whole recording period. Furthermore, coherent array processing of the computed ocean noise cross‐correlations between all pairwise combinations of hydrophones was used to separate acoustic variations between the VLAs caused by genuine environmental fluctuations-such as internal waves-from the apparent variations in the same coherent arrivals caused when the ambient noise field becomes strongly directional, e.g., due to an isolated ship passing in the vicinity of the VLAs.

<p>In this masters’ thesis report we present a graph theoretical method used for automatic bilingual lexicon acquisition with parallel texts. We analyze the concept of graph similarity and give an interpretation, of the parallel texts, connected to the vector space model. We represent the parallel texts by a directed, tripartite graph and from here use the corresponding adjacency matrix, A, to compute the similarity of the graph. By solving the eigenvalue problem ρS = ASAT + ATSA we obtain the self-similarity matrix S and the Perron root ρ. A rank k approximation of the self-similarity matrix is computed by implementations of the singular value decomposition and the non-negative matrix factorization algorithm GD-CLS. We construct an algorithm in order to extract the bilingual lexicon from the self-similarity matrix and apply a statistical model to estimate the precision, the correctness, of the translations in the bilingual lexicon. The best result is achieved with an application of the vector space model with a precision of about 80 %. This is a good result and can be compared with the precision of about 60 % found in the literature.</p>

We consider two transceivers equipped with multiple antennas that intend to communicate i.e. both of which transmit and receive data in a TDD fashion. Assuming that the responses of the physical communication channels between these two nodes are linear and reciprocal (time invariant or with very slow time variations), and by exploiting the closed loop conversation between these nodes, we have proposed efficient algorithms allowing to adaptively identify the Best Singular Mode (BSM) of the channel (those algorithms are for training, blind, and semi-blind channel identification). Unlike other proposed algorithms, our proposed adaptive algorithms are robust to noise as the involved step-size allows a trade-off to reduce the impact of the additive noise at the expense of some estimation delay. In practice, however, the reciprocity of the equivalent channels is lost because of the mismatch between the transmit and the receive filters of the communicating nodes. This mismatch causes significant degradation in the performance of the BSM estimation. Therefore, we have also proposed adaptive self-calibrating algorithms (which do not require any additional RF circuitry) that account for such a mismatch. In addition, we have conducted a convergence analysis of the BSM algorithm and extended it to estimate multiple modes simultaneously. Finally, we have also proposed an adaptive, iterative algorithm that is capable of allocating power in such a way that maximizes the capacity of a SISO OFDM communication system. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2010-07-21 16:53:33.077