Atrial fibrillation is diagnosed in 1-2% of the population, in next decades, it expects a significant increase in the number of patients with this arrhythmia in connection with the aging of the population and the higher incidence of some diseases that are considered as risk factors of atrial fibrillation. The aim of this work is to describe the problem of atrial fibrillation and the methods that allow its detection in the ECG record. In the first part of work there is a theory dealing with cardiac physiology and atrial fibrillation. There is also basic descreption of the detection of atrial fibrillation. In the practical part of work, there is described software for detection of atrial fibrillation, which is provided by BTL company. Furthermore, an atrial fibrillation detector is designed. Several parameters were selected to detect the variation of RR intervals. These are the parameters of the standard deviation, coefficient of skewness and kurtosis, coefficient of variation, root mean square of the successive differences, normalized absolute deviation, normalized absolute difference, median absolute deviation and entropy. Three different classification models were used: support vector machine (SVM), k-nearest neighbor (KNN) and discriminant analysis classification. The SVM classification model achieves the best results. Results of success indicators (sensitivity: 67.1%; specificity: 97.0%; F-measure: 66.8%; accuracy: 92.9%).

This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.

This masters's thesis contains description of designed and implemented tool for classification of plant microRNA without genome. Properties of mature and star sequences in microRNA duplexes are used. Implemented method is based on clustering of RNA sequences (with CD-HIT) to mainly reduce their count. Selected representants from each clusters are classified using support vector machine. Performance of classification is more than 96% (based on cross-validation method using the training data).

The aim of this work is to get basic overview about the process of obtaining knowledge from databases - datamining and to analyze the datamining system developed at FIT BUT on the NetBeans platform in order to create a new mining module. We decided to implement a module for mining outliers and to extend existing regression module with multiple linear regression using generalized linear models. New methods using existing methods of Oracle Data Mining.

This thesis compares hand-designed features with features learned by feature learning methods in video classification. The features learned by Principal Component Analysis whitening, Independent subspace analysis and Sparse Autoencoders were tested in a standard Bag of Visual Word classification paradigm replacing hand-designed features (e.g. SIFT, HOG, HOF). The classification performance was measured on Human Motion DataBase and YouTube Action Data Set. Learned features showed better performance than the hand-desined features. The combination of hand-designed features and learned features by Multiple Kernel Learning method showed even better performance, including cases when hand-designed features and learned features achieved not so good performance separately.

This master's thesis deals with data mining techniques and classification of the data into specified categories. The goal of this thesis is to implement a web portal for administration and classification of data from distributed sources. To achieve the goal, it is necessary to test different methods and find the most appropriate one for web articles classification. From the results obtained, there will be developed an automated application for downloading and classification of data from different sources, which would ultimately be able to substitute a user, who would process all the tasks manually.

Main subjects of this thesis are texture classification and texture-based object recognition. Various texture features are being explored, including several variants of local binary patterns (LBP). A novel modification of LBP (weighted spatial LBP) is proposed, with intention to improve on the spatial coverage of the traditional LBP. Rarely used color texture features are being discussed as well. Artificial neural networks and support vector machines are used to classify all the aforementioned features. Using these methods, framework for the texture classification and image segmentation is implemented. Comprehensive texture database is employed to test its performance under different conditions. In the end, the system is applied to solve a real-world problem - the segmentation of aerial photos.

Patents have a great research value and it is also beneficial to the community of industrial, commercial, legal and policymaking. Effective analysis of patent literature can reveal important technical details and relationships, and it can also explain business trends, propose novel industrial solutions, and make crucial investment decisions. Therefore, we should carefully analyze patent documents and use the value of patents. Generally, patent analysts need to have a certain degree of expertise in various research fields, including information retrieval, data processing, text mining, field-specific technology, and business intelligence. In real life, it is difficult to find and nurture such an analyst in a relatively short period of time, enabling him or her to meet the requirement of multiple disciplines. Patent classification is also crucial in processing patent applications because it will empower people with the ability to manage and maintain patent texts better and more flexible. In recent years, the number of patents worldwide has increased dramatically, which makes it very important to design an automatic patent classification system. This system can replace the time-consuming manual classification, thus providing patent analysis managers with an effective method of managing patent texts. This paper designs a patent classification system based on data mining methods and machine learning techniques and use KNIME software to conduct a comparative analysis. This paper will research by using different machine learning methods and different parts of a patent. The purpose of this thesis is to use text data processing methods and machine learning techniques to classify patents automatically. It mainly includes two parts, the first is data preprocessing and the second is the application of machine learning techniques. The research questions include: Which part of a patent as input data performs best in relation to automatic classification? And which of the implemented machine learning algorithms performs best regarding the classification of IPC keywords? This thesis will use design science research as a method to research and analyze this topic. It will use the KNIME platform to apply the machine learning techniques, which include decision tree, XGBoost linear, XGBoost tree, SVM, and random forest. The implementation part includes collection data, preprocessing data, feature word extraction, and applying classification techniques. The patent document consists of many parts such as description, abstract, and claims. In this thesis, we will feed separately these three group input data to our models. Then, we will compare the performance of those three different parts. Based on the results obtained from these three experiments and making the comparison, we suggest using the description part data in the classification system because it shows the best performance in English patent text classification. The abstract can be as the auxiliary standard for classification. However, the classification based on the claims part proposed by some scholars has not achieved good performance in our research. Besides, the BoW and TFIDF methods can be used together to extract efficiently the features words in our research. In addition, we found that the SVM and XGBoost techniques have better performance in the automatic patent classification system in our research.

Satellites provide humanity with data to infer properties of the earth that were impossible a century ago. Humanity can now easily monitor the amount of ice found on the polar caps, the size of forests and deserts, the earth’s atmosphere, the seasonal variation on land and in the oceans and the surface temperature of the earth. In this thesis, new hypertemporal techniques are proposed for the settlement detection problem in South Africa. The hypertemporal techniques are applied to study areas in the Gauteng and Limpopo provinces of South Africa. To be more specific, new sequential (windowless) and non-sequential hypertemporal techniques are implemented. The time-series employed by the new hypertemporal techniques are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which is on board the earth observations satellites Aqua and Terra. One MODIS dataset is constructed for each province. A Support Vector Machine (SVM) [1] that uses a novel noise-harmonic feature set is implemented to detect existing human settlements. The noise-harmonic feature set is a non-sequential hypertemporal feature set and is constructed by using the Coloured Simple Harmonic Oscillator (CSHO) [2]. The CSHO consists of a Simple Harmonic Oscillator (SHO) [3], which is superimposed on the Ornstein-Uhlenbeck process [4]. The noise-harmonic feature set is an extension of the classic harmonic feature set [5]. The classic harmonic feature set consists of a mean and a seasonal component. For the case studies in this thesis, it is observed that the noise-harmonic feature set not only extends the harmonic feature set, but also improves on its classification capability. The Cumulative Sum (CUSUM) algorithm was developed by Page in 1954 [6]. In its original form it is a sequential (windowless) hypertemporal change detection technique. Windowed versions of the algorithm have been applied in a remote sensing context. In this thesis CUSUM is used in its original form to detect settlement expansion in South Africa and is benchmarked against the classic band differencing change detection approach of Lunetta et al., which was developed in 2006 [7]. In the case of the Gauteng study area, the CUSUM algorithm outperformed the band differencing technique. The exact opposite behaviour was seen in the case of the Limpopo dataset. Sequential hypertemporal techniques are data-intensive and an inductive MODIS simulator was therefore also developed (to augment datasets). The proposed simulator is also based on the CSHO. Two case studies showed that the proposed inductive simulator accurately replicates the temporal dynamics and spectral dependencies found in MODIS data. / Thesis (PhD(Eng))--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

Perceptual multistability is a phenomenon that is mostly studied in all modalities separately. The phenomenon reveals fundamental principles of the perceptual system in the formation of an emerging cognitive representation in the consciousness. The momentary perceptual organizations evoked during the stimulation with ambiguous stimuli switches between several perceptual organizations or percepts: The auditory streaming stimulus in audition and the moving plaids stimulus in vision, elicit different at least two percepts that dominate awareness exclusively for a random phase or dominance duration before an inevitable switch to another percept occurs. The similarity in the perceptual experience has led to propose a global mechanism contributing to the perceptual multistability phenomena crossmodally. Contrary, the difference in the perceptual experience has led to propose a distributed mechanism that is modality-specific. The development of a hybrid model has synergized both approaches. We accumulate empirical evidence for the contribution of a global mechanism, albeit distributed mechanisms play an indispensable role in this cross-modal interplay. The overt report of the perceptual experience in our experiments is accompanied by the recording of objective, cognitive markers of the consciousness: Reflexive movements of the eyes, namely the dilation of the pupil and the optokinetic nystagmus, correlate with the unobservable perceptual switches and perceptual states respectively and have their neuronal rooting in the brainstem. We complement earlier findings on the sensitivity of the pupil to visual multistability: It was shown in two independent experiments that the pupil dilates at the time of reported perceptual switches in auditory multistability. A control condition on confounding effects from the reporting process confines the results. Endogenous, evoked internally by the unchanged stimulus ambiguity, and exogenous, evoked externally by the changes in the physical properties of the stimulus, perceptual switches could be discriminated based on the maximal amplitude of the dilation. The effect of exogenous perceptual has on the pupil were captured in a report and no-report task to detect confounding perceptual effects. In two additional studies, the moment-by-moment coupling and coupling properties of percepts between concurrent multistable processes in audition, evoked by auditory streaming, and in vision, evoked by moving plaids, were found crossmodally. In the last study, the externally induced percept in the visual multistable process was not relayed to the simultaneous auditory multistable process: Still, the observed general coupling is fragile but existent. The requirement for the investigation of a moment-by-moment coupling of the multistable perceptual processes was the application of a no-report paradigm in vision: The visual stimulus evokes an optokinetic nystagmus that has machine learnable different properties when following either of the two percepts. In combination with the manually reported auditory percept, attentional bottlenecks due to a parallel report were circumvented. The two main findings, the dilation of the pupil along reported auditory perceptual switches and the crossmodal coupling of percepts in bimodal audiovisual multistability, speak in favor of a partly global mechanism being involved in control of perceptual multistability; the global mechanism is incarcerated by the, partly independent, distributed competition of percepts on modality level. Potentially, supramodal attention-related modulations consolidate the outcome of locally distributed perceptual competition in all modalities.:COVER 1 BIBLIOGRAPHISCHE BESCHREIBUNG 2 ACKNOWLEDGEMENTS 3 CONTENTS 4 CHAPTER 1: Introduction 6 C1.1: Stability and uncertainty in perception 6 C1.2: Auditory, visual and audio-visual multistability 14 C1.3: Capturing the subjective perceptual experience 25 C1.4: Limitations of preceding studies, objectives, and outline of the Thesis 33 CHAPTER 2: Study 1 “Pupillometry in auditory multistability” 36 C2.1.1 Experiment 1: Introduction 36 C2.1.2 Experiment 1: Material and Methods 38 C2.1.3 Experiment 1: Data analysis 44 C2.1.4 Experiment 1: Results 48 C2.1.5 Experiment 1: Discussion 52 C2.2.1 Experiment 2: Introduction 54 C2.2.2 Experiment 2: Material and Methods 54 C2.2.3 Experiment 2: Data analysis 56 C2.2.4 Experiment 2: Results 57 C2.3 Experiment 1 & 2: Discussion 61 C2.4 Supplement Study 1 65 CHAPTER 3: Study 2 “Multimodal moment-by-moment coupling in perceptual bistability” 71 C3.1.1 Experiment 1: Introduction 71 C3.1.2 Experiment 1: Results 74 C3.1.3 Experiment 1: Discussion 80 C3.1.4 Experiment 1: Material and Methods 84 C3.1.5 Experiment 1: Data analysis 87 C3.2 Supplement Study 2 92 CHAPTER 4: Study 3 “Boundaries of bimodal coupling in perceptual bistability” 93 C4.1.1 Experiment 1: Introduction 93 C4.1.2 Experiment 1: Material and Methods 98 C4.1.3 Experiment 1: Data analysis 102 C4.1.4 Experiment 1: Results 108 C4.1.5 Experiment 1: Discussion 114 C4.2.1 Experiment 2: Introduction 116 C4.2.2 Experiment 2: Material and Methods 119 C4.2.3 Experiment 2: Data analysis 125 C4.2.4 Experiment 2: Results 133 C4.3 Experiment 1 & 2: Discussion 144 C4.4 Supplement Study 3 151 CHAPTER 5: General Discussion 154 C5.1 Significance for models of multistability and implications for the perceptual architecture 162 C5.2 Recommendations for future research 166 C5.3 Conclusion 168 REFERENCES 170 APPENDIX 186 A1: List of Figures 186 A2: List of Tables 188 A3: List of Abbreviations and Symbols 189