Improving the energy efficiency of buildings is important for many reasons. There is a large body of data detailing the hourly energy consumption of buildings. This work studies a large data set from the Swedish energy market. This thesis proposes a data analysis methodology for identifying abnormal consumption patterns using two steps of clustering. First, typical weekly energy usage profiles are extracted from each building by clustering week-long segments of the building’s lifetime consumption, and by extracting the medoids of the clusters. Second, all the typical weekly energyusage profiles are clustered using agglomerative hierarchical clustering. Large clusters are assumed to contain normal consumption pattens, and small clusters are assumed to have abnormal patterns. Buildings with a large presence in small clusters are said to be abnormal, and vice versa. The method employs Dynamic Time Warping distance for dissimilarity measure. Using a set of 160 buildings, manually classified by domain experts, this thesis shows that the mean abnormality-score is higher for abnormal buildings compared to normal buildings with p ≈ 0.0036.

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Eine Hauptanwendung der Einzelpartikel-Analyse in der Kryo-Elektronenmikroskopie ist die Charakterisierung der dreidimensionalen Struktur makromolekularer Komplexe. Dazu werden zehntausende Bilder verwendet, die verrauschte zweidimensionale Projektionen des Partikels zeigen. Im ersten Schritt werden ein niedrig aufgelöstetes Anfangsmodell rekonstruiert sowie die unbekannten Bildorientierungen geschätzt. Dies ist ein schwieriges inverses Problem mit vielen Unbekannten, einschließlich einer unbekannten Orientierung für jedes Projektionsbild. Ein gutes Anfangsmodell ist entscheidend für den Erfolg des anschließenden Verfeinerungsschrittes. Meine Dissertation stellt zwei neue Algorithmen zur Rekonstruktion eines Anfangsmodells in der Kryo-Elektronenmikroskopie vor, welche auf einer groben Darstellung der Elektronendichte basieren. Die beiden wesentlichen Beiträge meiner Arbeit sind zum einen das Modell, welches die Elektronendichte darstellt, und zum anderen die neuen Rekonstruktionsalgorithmen. Der erste Hauptbeitrag liegt in der Verwendung Gaußscher Mischverteilungen zur Darstellung von Elektrondichten im Rekonstruktionsschritt. Ich verwende kugelförmige Mischungskomponenten mit unbekannten Positionen, Ausdehnungen und Gewichtungen. Diese Darstellung hat viele Vorteile im Vergleich zu einer gitterbasierten Elektronendichte, die andere Rekonstruktionsalgorithmen üblicherweise verwenden. Zum Beispiel benötigt sie wesentlich weniger Parameter, was zu schnelleren und robusteren Algorithmen führt. Der zweite Hauptbeitrag ist die Entwicklung von Markovketten-Monte-Carlo-Verfahren im Rahmen eines Bayes'schen Ansatzes zur Schätzung der Modellparameter. Der erste Algorithmus kann aus dem Gibbs-Sampling, welches Gaußsche Mischverteilungen an Punktwolken anpasst, abgeleitet werden. Dieser Algorithmus wird hier so erweitert, dass er auch mit Bildern, Projektionen sowie unbekannten Drehungen und Verschiebungen funktioniert. Der zweite Algorithmus wählt einen anderen Zugang. Das Vorwärtsmodell nimmt nun Gaußsche Fehler an. Sampling-Algorithmen wie Hamiltonian Monte Carlo (HMC) erlauben es, die Positionen der Mischungskomponenten und die Bildorientierungen zu schätzen. Meine Dissertation zeigt umfassende numerische Experimente mit simulierten und echten Daten, die die vorgestellten Algorithmen in der Praxis testen und mit anderen Rekonstruktionsverfahren vergleichen.

Questions of speaker’s recognition by voice are investigated in this dissertation. Speaker recognition systems, their evolution, problems of recognition, systems of features, questions of speaker modeling and matching used in text-independent and text-dependent speaker recognition are considered too. The text-independent speaker recognition system has been developed during this work. The Gaussian mixture model approach was used for speaker modeling and pattern matching. The automatic method for voice activity detection was proposed. This method is fast and does not require any additional actions from the user, such as indicating patterns of the speech signal and noise. The system of the features was proposed. This system consists of parameters of excitation source (glottal) and parameters of the vocal tract. The fundamental frequency was taken as an excitation source parameter and four formants with three antiformants were taken as parameters of the vocal tract. In order to equate dispersions of the formants and antiformants we propose to use them in mel-frequency scale. The standard mel-frequency cepstral coefficients (MFCC) for comparison of the results were implemented in the recognition system too. These features make baseline in speech and speaker recognition. The experiments of speaker recognition have shown that our proposed system of features outperformed standard mel-frequency cepstral coefficients. The equal error rate (EER) was equal to 5.17% using proposed... [to full text] / Disertacijoje nagrinėjami kalbančiojo atpažinimo pagal balsą klausimai. Aptartos kalbančiojo atpažinimo sistemos, jų raida, atpažinimo problemos, požymių sistemos įvairovė bei kalbančiojo modeliavimo ir požymių palyginimo metodai, naudojami nuo ištarto teksto nepriklausomame bei priklausomame kalbančiojo atpažinime. Darbo metu sukurta nuo ištarto teksto nepriklausanti kalbančiojo atpažinimo sistema. Kalbėtojų modelių kūrimui ir požymių palyginimui buvo panaudoti Gauso mišinių modeliai. Pasiūlytas automatinis vokalizuotų garsų išrinkimo (segmentavimo) metodas. Šis metodas yra greitai veikiantis ir nereikalaujantis iš vartotojo jokių papildomų veiksmų, tokių kaip kalbos signalo ir triukšmo pavyzdžių nurodymas. Pasiūlyta požymių vektorių sistema, susidedanti iš žadinimo signalo bei balso trakto parametrų. Kaip žadinimo signalo parametras, panaudotas žadinimo signalo pagrindinis dažnis, kaip balso trakto parametrai, panaudotos keturios formantės bei trys antiformantės. Siekiant suvienodinti žemesnių bei aukštesnių formančių ir antiformančių dispersijas, jas pasiūlėme skaičiuoti melų skalėje. Rezultatų palyginimui sistemoje buvo realizuoti standartiniai požymiai, naudojami kalbos bei asmens atpažinime – melų skalės kepstro koeficientai (MSKK). Atlikti kalbančiojo atpažinimo eksperimentai parodė, kad panaudojus pasiūlytą požymių sistemą buvo gauti geresni atpažinimo rezultatai, nei panaudojus standartinius požymius (MSKK). Gautas lygių klaidų lygis, panaudojant pasiūlytą požymių... [toliau žr. visą tekstą]

This thesis explores three major issues in the burgeoning empirical literature on the determinants of subjective well-being (SWB). While economic theory assumes that it is current consumption that matters to SWB, empirical work has focused almost exclusively on the effect of income. In Part 1, we use household panel data from Russia and Britain to show that neither the standard theoretical account, nor the standard empirical practice may be adequate. Consumption, income, and wealth each contribute separately to SWB, in particular via perceptions of status and anticipation of the future; and omitting consumption from SWB equations significantly understates the importance of money to SWB. Distinguishing between consumption and income is also important to identifying reference effects. In Part 2, we confirm earlier findings that others' income has a positive (informational) effect on SWB in Russia, but show that others' consumption has an offsetting negative (comparison) effect. The net effect depends on how we define individuals' reference groups. We develop a novel econometric model that lets us estimate these reference groups from the data. Contrary to previous results, we conclude that comparison dominates information. Most SWB analyses focus on the average effects of money, relationships, and other outcomes across a given population; yet there may be significant differences in what is important to different people. In Part 3, we employ parametric and semi-parametric random coefficient models to show that there are large differences in the determinants of individual SWB in Britain, and (in contrast to previous work) that such differences cannot simply be attributed to differences in individuals' reporting functions. While individual differences correlate with (some) observable demographic variables, they do not generally correlate with individuals' perceptions about what is important to them. The results of SWB research may therefore be a useful source of information.

Master of Science / Department of Computing and Information Sciences / William H. Hsu / Pitch detection and instrument identification can be achieved with relatively high accuracy when considering monophonic signals in music; however, accurately classifying polyphonic signals in music remains an unsolved research problem. Pitch and instrument classification is a subset of Music Information Retrieval (MIR) and automatic music transcription, both having numerous research and real-world applications. Several areas of research are covered in this thesis, including the fast Fourier transform, onset detection, convolution, and filtering. Basic music theory and terms are also presented in order to explain the context and structure of data used. The focus of this thesis is on the representation of musical signals in the frequency domain. Polyphonic signals with many different voices and frequencies can be exceptionally complex. This thesis presents a new model for representing the spectral structure of polyphonic signals: Uniform MAx Gaussian Envelope (UMAGE). The new spectral envelope precisely approximates the distribution of frequency parts in the spectrum while still being resilient to oscillating rapidly (noise) and is able to generalize well without losing the representation of the original spectrum. When subjectively compared to other spectral envelope methods, such as the linear predictive coding envelope method and the cepstrum envelope method, UMAGE is able to model high order polyphonic signals without dropping partials (frequencies present in the signal). In other words, UMAGE is able to model a signal independent of the signal’s periodicity. The performance of UMAGE is evaluated both objectively and subjectively. It is shown that UMAGE is robust at modeling the distribution of frequencies in simple and complex polyphonic signals. Combined classification (combiners), a methodology for learning large concepts, is used to simplify the learning process and boost classification results. The output of each learner is then averaged to get the final result. UMAGE is less accurate when identifying pitches; however, it is able to achieve accuracy in identifying instrument groups on order-10 polyphonic signals (ten voices), which is competitive with the current state of the field.

The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image 'signature' based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard's Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye's intensity delineation capability in grayscale image segmentation tasks.

Dans ces travaux de thèse, nous proposons un modèle de prévision à court terme des flux de voyageurs basé sur les réseaux bayésiens. Ce modèle est destiné à répondre à des besoins opérationnels divers liés à l'information voyageurs, la régulation des flux ou encore la planification de l'offre de transport. Conçu pour s'adapter à tout type de configuration spatiale, il permet de combiner des sources de données hétérogènes (validations des titres de transport, comptages à bord des trains et offre de transport) et fournit une représentation intuitive des relations de causalité spatio-temporelles entre les flux. Sa capacité à gérer les données manquantes lui permet de réaliser des prédictions en temps réel même en cas de défaillances techniques ou d'absences de systèmes de collecte / In this thesis, we propose a Bayesian network model for short-term passenger flow forecasting. This model is intended to cater for various operational needs related to passenger information, passenger flow regulation or operation planning. As well as adapting to any spatial configuration, it is designed to combine heterogeneous data sources (ticket validation, on-board counts and transport service) and provides an intuitive representation of the causal spatio-temporal relationships between flows. Its ability to deal with missing data allows to make real-time predictions even in case of technical failures or absences of collection systems

Of the many existing eukaryotic gene finding software programs, none are able to guarantee accurate identification of genomic protein coding regions and other biological signals central to pathway from DNA to the protein. Eukaryotic gene finding is difficult mainly due to noncontiguous and non-continuous nature of genes. Existing approaches are heavily dependent on the compositional statistics of the sequences they learn from and are not equally suitable for all types of sequences. This thesis firstly develops efficient digital signal processing-based methods for the identification of genomic protein coding regions, and then combines the optimum signal processing-based non-data-driven technique with an existing data-driven statistical method in a novel system demonstrating improved identification of acceptor splice sites. Most existing well-known DNA symbolic-to-numeric representations map the DNA information into three or four numerical sequences, potentially increasing the computational requirement of the sequence analyzer. Proposed mapping schemes, to be used for signal processing-based gene and exon prediction, incorporate DNA structural properties in the representation, in addition to reducing complexity in subsequent processing. A detailed comparison of all DNA representations, in terms of computational complexity and relative accuracy for the gene and exon prediction problem, reveals the newly proposed ?paired numeric? to be the best DNA representation. Existing signal processing-based techniques rely mostly on the period-3 behaviour of exons to obtain one dimensional gene and exon prediction features, and are not well equipped to capture the complementary properties of exonic / intronic regions and deal with the background noise in detection of exons at their nucleotide levels. These issues have been addressed in this thesis, by proposing six one-dimensional and three multi-dimensional signal processing-based gene and exon prediction features. All one-dimensional and multi-dimensional features have been evaluated using standard datasets such as Burset/Guigo1996, HMR195, and the GENSCAN test set. This is the first time that different gene and exon prediction features have been compared using substantial databases and using nucleotide-level metrics. Furthermore, the first investigation of the suitability of different window sizes for period-3 exon detection is performed. Finally, the optimum signal processing-based gene and exon prediction scheme from our evaluations is combined with a data-driven statistical technique for the recognition of acceptor splice sites. The proposed DSP-statistical hybrid is shown to achieve 43% reduction in false positives over WWAM, as used in GENSCAN.

Road Networks play an important role in various applications such as urban and rural planning, infrastructure planning, transportation management, vehicle navigation. Extraction of Roads from Remote Sensed satellite images for updating road database in geographical information systems (GIS) is generally done manually by a human operator. However, manual extraction of roads is time consuming and labor intensive process. In the existing literature, there are a great number of researches published for the purpose of automating the road extraction process. However, automated processes still yield some erroneous and incomplete results and human intervention is still required. The aim of this research is to propose a framework for road network extraction from high spatial resolution multi-spectral imagery (MSI) to improve the accuracy of road extraction systems. The proposed framework begins with a spectral classification using One-class Support Vector Machines (SVM) and Gaussian Mixture Models (GMM) classifiers. Spectral Classification exploits the spectral signature of road surfaces to classify road pixels. Then, an iterative template matching filter is proposed to refine spectral classification results. K-medians clustering algorithm is employed to detect candidate road centerline points. Final road network formation is achieved by Markov Random Fields. The extracted road network is evaluated against a reference dataset using a set of quality metrics.