Radar-based respiration monitoring has been increasingly popular among researchers in biomedical fields during the last decades since it is a contactless monitoring technique. It is very convenient for subjects because it does not impose any restrictions on subjects or require their cooperation. Meanwhile, recognizing alternations in respiratory patterns is an important early clue of the diagnosis of several cardiorespiratory diseases. Thus, a study of biomedical radar-based respiration monitoring and respiratory pattern classification is carried out in this thesis. Radar-based respiration monitoring technology has a shortcoming that the collected respiratory signal will be easily distorted by the body movement of the monitoring subjects or disturbed by environment noise because of the contactless measurement attribute. This shortcoming limits the application of the respiratory pattern classification model, that is, the existing models cannot be applied automatically since the distorted respiratory signal needs to be manually filtered out ahead of the classification. In this study, a new respiratory pattern classification strategy, which can be implemented full-automatic, is proposed. In this strategy, a class “moving” is introduced to classify the distorted signal, and the sampling window length is shortened to reduce the effect caused by the signal distortion. A performance requirement for the continuous respiratory pattern classification is also proposed based on its expected function that can alert the occurrence of the abnormal breathing patterns. Several models which can meet the proposed performance requirement are developed in this thesis based on the state-of-the-art pattern classification technique and the time-series-based shapelet transform algorithm. The proposed models can classify four breathing patterns including eupnea, Cheyne Stokes respiration, Kussmaul breathing and apnea. A radar-collected respiratory signal database is built in this study, and a respiration simulation model which can generate breath samples for pattern classification is developed in this thesis. The proposed models were tested and validated in batch and stream processing manner with independently collected data and continuously collected data, respectively.

In this dissertation, a multi-classifier, decision fusion framework is proposed for robust classification of high dimensional data in small-sample-size conditions. Such datasets present two key challenges. (1) The high dimensional feature spaces compromise the classifiers’ generalization ability in that the classifier tends to overit decision boundaries to the training data. This phenomenon is commonly known as the Hughes phenomenon in the pattern classification community. (2) The small-sample-size of the training data results in ill-conditioned estimates of its statistics. Most classifiers rely on accurate estimation of these statistics for modeling training data and labeling test data, and hence ill-conditioned statistical estimates result in poorer classification performance. This dissertation tests the efficacy of the proposed algorithms to classify primarily remotely sensed hyperspectral data and secondarily diagnostic digital mammograms, since these applications naturally result in very high dimensional feature spaces and often do not have sufficiently large training datasets to support the dimensionality of the feature space. Conventional approaches, such as Stepwise LDA (S-LDA) are sub-optimal, in that they utilize a small subset of the rich spectral information provided by hyperspectral data for classification. In contrast, the approach proposed in this dissertation utilizes the entire high dimensional feature space for classification by identifying a suitable partition of this space, employing a bank-of-classifiers to perform “local” classification over this partition, and then merging these local decisions using an appropriate decision fusion mechanism. Adaptive classifier weight assignment and nonlinear pre-processing (in kernel induced spaces) are also proposed within this framework to improve its robustness over a wide range of fidelity conditions. Experimental results demonstrate that the proposed framework results in significant improvements in classification accuracies (as high as a 12% increase) over conventional approaches.

The autonomic specificity of emotion is intrinsic for many major theories of emotion. One of the goals of this study was to validate a standardized set of music clips to be used in studies of emotion and affect. This was accomplished using self-reported affective responses to 40 music pieces, noise, and silence clips in a sample of 71 college-aged individuals. Following the music selection phase of the study; the validated music clips as well as film clips previously shown to induce a wide array of emotional responses were presented to 50 college-aged subjects while a montage of autonomic variables were measured. Evidence for autonomic discrimination of emotion was found via pattern classification analysis replicating findings from previous research. It was theorized that groups of individuals could be identified based upon individual response specificity using cluster analytic techniques. Single cluster solutions for all emotion conditions indicated that stimulus response stereotypy of emotions was more powerful than individual patterns. Results from pattern classification analysis and cluster analysis support the concept of autonomic specificity of emotion. / Master of Science / [Appendix B: Beck Depression Inventory, p. 61-64, was removed Oct. 4, 2011 GMc]

The present study investigated autonomic nervous system (ANS) patterning during experimentally manipulated emotion. Film clips previously shown to induce amusement, anger, contentment, disgust, fear, and sadness, in addition to a neutral control, were presented to 34 college-aged subjects while electrodermal activity, blood pressure and electrocardiogram (ECG) were recorded as was self-reported affect. Mean and mean successive difference of inter-beat interval were derived from the ECG. Pattern classification analysis revealed emotion-specific patterning for all emotion conditions except disgust. Discriminant function analysis was used to describe the location of discrete emotions within a dimensional affective state space, for both self-report and ANS activity. Findings suggest traditional dimensional emotion models accurately describe the state space for self-reported emotion, but may require modification in order to accurately describe the state space for ANS activity during discrete emotions. Proposed modifications are consistent with the adoption of a discrete-dimensional hybrid model as well as current trends in emotion theory. / Master of Science

In this thesis, we propose an improved fuzzy GBML¡]genetic-based machine learning¡^algorithm to construct a FRBCS¡]fuzzy rule-based classification system¡^for pattern classification problem. Existing hybrid fuzzy GBML algorithm is consuming more computational time since it used the SS fuzzy model and combined with the Michigan-style algorithm for increasing the convergent rate of the Pittsburgh-style algorithm. By contrast, our improved fuzzy GBML algorithm is consuming less computational time since it used the MW fuzzy model and instead of the role of the Michigan-style algorithm by a heuristic procedure. Experimental results show that improved fuzzy GBML algorithm possesses the shorter computational time, the faster convergent rate, and the slightly better classification rate.

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Classification of terrain cover from satellite radar imagery represents an area of considerable current interest and research. Most satellite sensors used for land applications are of the imaging type. They record data in a variety of spectral channels and at a variety of ground resolutions. Spectral pattern recognition refers to classification procedures utilizing pixel-by-pixel spectral information as the basis for automated land cover classification. A number of methods have been developed in the past to classify pixels [resolution cells] from multispectral imagery to a priori given land cover categories. Their ability to provide land cover information with high classification accuracies is significant for work where accurate and reliable thematic information is needed. The current trend towards the use of more spectral bands on satellite instruments, such as visible and infrared imaging spectrometers, and finer pixel and grey level resolutions will offer more precise possibilities for accurate identification. But as the complexity of the data grows, so too does the need for more powerful tools to analyse them. It is the major objective of this study to analyse the capabilities and applicability of the neural pattern recognition system, called fuzzy ARTMAP, to generate high quality classifications of urban land cover using remotely sensed images. Fuzzy ARTMAP synthesizes fuzzy logic and Adaptive Resonance Theory (ART) by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of category choice, search and learning. The paper describes design features, system dynamics and simulation algorithms of this learning system, which is trained and tested for classification (8 a priori given classes) of a multispectral image of a Landsat-5 Thematic Mapper scene (270 x 360 pixels) from the City of Vienna on a pixel-by-pixel basis. Fuzzy ARTMAP performance is compared with that of an error-based learning system based upon the multi-layer perceptron, and the Gaussian maximum likelihood classifier as conventional statistical benchmark on the same database. Both neural classifiers outperform the conventional classifier in terms of classification accuracy. Fuzzy ARTMAP leads to out-of-sample classification accuracies, very close to maximum performance, while the multi-layer perceptron - like the conventional classifier - shows difficulties to distinguish between some land use categories. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

Safe and secure transportation of hazardous materials (hazmat) is a challenging issue in terms of optimizing risk to society and simultaneously making the shipment delivery economical. The most important safety concern of hazardous material transportation is accidents causing multiple causalities. The potential risk to society from hazmat transportation has led to the evolution of a new threat from terrorism. Malicious entities can turn hazmat vehicles into weapons causing explosions in high profile locations. The present research is divided into two parts. First, a neural network model is developed to identify when a hazmat truck deviates from its pre-specified path based on its location in the road network. The model identifies abnormal diversions in hazmat carriers' paths considering normal diversions arising due to incidents. The second part of this thesis develops a methodology for predicting different paths that could be taken by malicious entities heading towards a target after successfully hijacking a hazmat vehicle. The path prediction methodology and the neural network methodology are implemented on the network between Baltimore, Maryland and Washington, DC. The trained neural network model classified nodes in the network with a satisfactory performance .The path prediction algorithm was used to calculate the paths to two targets located at the International Dulles Airport and the National Mall in Washington, DC. Based on this research, the neural network methodology is a promising technology for detecting a hijacked vehicle in its initial stages of diversion from its pre-specified path. Possible paths to potential targets are plotted and points of overlap among paths are identified. Overlaps are critical locations where extra security measures can be taken for preventing destruction. Thus, integrating both models gives a comprehensive methodology for detecting the initial diversion and then predicting the possible paths of malicious entities towards targets and could provide an important tool for law enforcement agencies minimizing catastrophic events. / Master of Science

An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate analysis frameworks. Two most prominent MVPA methods for information mapping are searchlight decoding and classifier weight mapping. The new MVPA brain mapping methods, however, have also posed new challenges for analysis and statistical inference on the group level. In this thesis, I discuss why the usual procedure of performing t-tests on MVPA derived information maps across subjects in order to produce a group statistic is inappropriate. I propose a fully nonparametric solution to this problem, which achieves higher sensitivity than the most commonly used t-based procedure. The proposed method is based on resampling methods and preserves the spatial dependencies in the MVPA-derived information maps. This enables to incorporate a cluster size control for the multiple testing problem. Using a volumetric searchlight decoding procedure and classifier weight maps, I demonstrate the validity and sensitivity of the new approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, the new results showed a higher sensitivity and spatial specificity. The second goal of this thesis is the comparison of the two widely used information mapping approaches -- the searchlight technique and classifier weight mapping. Both methods take into account the spatially distributed patterns of activation in order to predict stimulus conditions, however the searchlight method solely operates on the local scale. The searchlight decoding technique has furthermore been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. In this thesis, I compare searchlight decoding with linear classifier weight mapping, both using the formerly proposed non-parametric statistical framework using a simulation and ultra-high-field 7T experimental data. It was found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, the weight mapping method was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, such global multivariate methods provide a substantial improvement for characterizing structure-function relationships.

Garantir a qualidade da carne bovina produzida no Brasil tem sido uma preocupação dos produtores, pois contribui para aumentar a exportação e o consumo interno do produto. Por isso, tem-se pesquisado novos métodos que analisam e garantam a qualidade da carne, de forma rápida, eficiente e não destrutiva. A ressonância magnética nuclear (RMN) tem se destacado como uma das técnicas de controle de qualidade de carne. Neste trabalho as redes neurais artificiais estão sendo utilizadas para o reconhecimento de padrões dos dados de ressonância magnética nuclear oriundos de carne bovina. Mais especificamente, os respectivos dados têm sido utilizados por uma rede perceptron multicamadas para a extração de características da carne bovina, possibilitando a classificação do grupo genético e do sexo dos animais a partir de uma amostra da referida carne. Os resultados dos experimentos são também apresentados para ilustrar o desempenho da abordagem proposta. / Guaranteeing the quality of the bovine meat produced in Brazil has been a concern of the producers because it contributes to increase the export and the domestic consumption of the product. Therefore, new methods have been researched that analyze and guarantee the quality of the meat in a fast, efficient and non destructive way. Nuclear magnetic resonance (NMR) has been highlighted as one of the techniques of meat quality control. In this work study artificial neural networks are being used for pattern recognition from data obtained by the resonance equipment, originating from bovine meat. More specifically, the respective data have been used by a multilayer perceptron network for extraction of bovine meat characteristics, making possible the classification of both genetic group and animal sex starting from a single meat sample. Several results of experimental tests are also presented to illustrate the performance of the proposed approach.