Developments of the generative topographic mapping

The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput. 10(1), 215-234) as a probabilistic re- formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an incremental version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern recognition algorithms.

The Information-analytical System for Diagnostics of Aircraft Navigation Units

The operation of technical processes requires increasingly advanced supervision and fault diagnostics to improve reliability and safety. This paper gives an introduction to the field of fault detection and diagnostics and has short methods classification. Growth of complexity and functional importance of inertial navigation systems leads to high losses at the equipment refusals. The paper is devoted to the INS diagnostics system development, allowing identifying the cause of malfunction. The practical realization of this system concerns a software package, performing a set of multidimensional information analysis. The project consists of three parts: subsystem for analyzing, subsystem for data collection and universal interface for open architecture realization. For a diagnostics improving in small analyzing samples new approaches based on pattern recognition algorithms voting and taking into account correlations between target and input parameters will be applied. The system now is at the development stage.

Detecting Calcareous Periphyton Mats in the Greater Everglades Using Passive Remote Sensing Methods

Use of remotely sensed data for environmental and ecological assessment has recently become more widespread in wetland research and management and advantages and limitations of this approach have been addresses (Ozesmi and Bauer 2002). Applications of remote sensing (RS) methods vary in spatial and temporal extent and resolution, in the types of data acquired, and in digital processing and pattern recognition algorithms used.

Cold War Legacies: Systems, Theory, Aesthetics

From futures research, pattern recognition algorithms, nuclear waste disposal and surveillance technologies, to smart weapons systems, contemporary fiction and art, this book shows that we are now living in a world imagined and engineered during the Cold War. Drawing on theorists such as Jean Baudrillard, Jacques Derrida, Michel Foucault, Luce Irigaray, Friedrich Kittler, Michel Serres, Peter Sloterdijk, Carl Schmitt, Bernard Stiegler and Paul Virilio this collection makes connections between Cold War material and conceptual technologies, as they relate to the arts, society, and culture.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Fingerprint Recognition: Enhancement, Feature Extraction and Automatic Evaluation of Algorithms

The identification of people by measuring some traits of individual anatomy or physiology has led to a specific research area called biometric recognition. This thesis is focused on improving fingerprint recognition systems considering three important problems: fingerprint enhancement, fingerprint orientation extraction and automatic evaluation of fingerprint algorithms. An effective extraction of salient fingerprint features depends on the quality of the input fingerprint. If the fingerprint is very noisy, we are not able to detect a reliable set of features. A new fingerprint enhancement method, which is both iterative and contextual, is proposed. This approach detects high-quality regions in fingerprints, selectively applies contextual filtering and iteratively expands like wildfire toward low-quality ones. A precise estimation of the orientation field would greatly simplify the estimation of other fingerprint features (singular points, minutiae) and improve the performance of a fingerprint recognition system. The fingerprint orientation extraction is improved following two directions. First, after the introduction of a new taxonomy of fingerprint orientation extraction methods, several variants of baseline methods are implemented and, pointing out the role of pre- and post- processing, we show how to improve the extraction. Second, the introduction of a new hybrid orientation extraction method, which follows an adaptive scheme, allows to improve significantly the orientation extraction in noisy fingerprints. Scientific papers typically propose recognition systems that integrate many modules and therefore an automatic evaluation of fingerprint algorithms is needed to isolate the contributions that determine an actual progress in the state-of-the-art. The lack of a publicly available framework to compare fingerprint orientation extraction algorithms, motivates the introduction of a new benchmark area called FOE (including fingerprints and manually-marked orientation ground-truth) along with fingerprint matching benchmarks in the FVC-onGoing framework. The success of such framework is discussed by providing relevant statistics: more than 1450 algorithms submitted and two international competitions.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

On Logical Correction of Neural Network Algorithms for Pattern Recognition

The paper is devoted to the description of hybrid pattern recognition method developed by research groups from Russia, Armenia and Spain. The method is based upon logical correction over the set of conventional neural networks. Output matrices of neural networks are processed according to the potentiality principle which allows increasing of recognition reliability.

Protein fold recognition without Boltzmann statistics or explicit physical basis

We present a fast method for finding optimal parameters for a low-resolution (threading) force field intended to distinguish correct from incorrect folds for a given protein sequence. In contrast to other methods, the parameterization uses information from >10(7) misfolded structures as well as a set of native sequence-structure pairs. In addition to testing the resulting force field's performance on the protein sequence threading problem, results are shown that characterize the number of parameters necessary for effective structure recognition.

Tuning Iris Recognition for Noisy Images

The use of iris recognition for human authentication has been spreading in the past years. Daugman has proposed a method for iris recognition, composed by four stages: segmentation, normalization, feature extraction, and matching. In this paper we propose some modifications and extensions to Daugman's method to cope with noisy images. These modifications are proposed after a study of images of CASIA and UBIRIS databases. The major modification is on the computationally demanding segmentation stage, for which we propose a faster and equally accurate template matching approach. The extensions on the algorithm address the important issue of pre-processing that depends on the image database, being mandatory when we have a non infra-red camera, like a typical WebCam. For this scenario, we propose methods for reflection removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images show that the template matching segmentation method is more accurate and faster than the previous one, for noisy images. The proposed algorithms are found to be efficient and necessary when we deal with non infra-red images and non uniform illumination.

Embedded platform for ECG biometric recognition

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Hand gesture recognition system based in computer vision and machine learning : applications on human-machine interaction

Sendo uma forma natural de interação homem-máquina, o reconhecimento de gestos implica uma forte componente de investigação em áreas como a visão por computador e a aprendizagem computacional. O reconhecimento gestual é uma área com aplicações muito diversas, fornecendo aos utilizadores uma forma mais natural e mais simples de comunicar com sistemas baseados em computador, sem a necessidade de utilização de dispositivos extras. Assim, o objectivo principal da investigação na área de reconhecimento de gestos aplicada à interacção homemmáquina é o da criação de sistemas, que possam identificar gestos específicos e usálos para transmitir informações ou para controlar dispositivos. Para isso as interfaces baseados em visão para o reconhecimento de gestos, necessitam de detectar a mão de forma rápida e robusta e de serem capazes de efetuar o reconhecimento de gestos em tempo real. Hoje em dia, os sistemas de reconhecimento de gestos baseados em visão são capazes de trabalhar com soluções específicas, construídos para resolver um determinado problema e configurados para trabalhar de uma forma particular. Este projeto de investigação estudou e implementou soluções, suficientemente genéricas, com o recurso a algoritmos de aprendizagem computacional, permitindo a sua aplicação num conjunto alargado de sistemas de interface homem-máquina, para reconhecimento de gestos em tempo real. A solução proposta, Gesture Learning Module Architecture (GeLMA), permite de forma simples definir um conjunto de comandos que pode ser baseado em gestos estáticos e dinâmicos e que pode ser facilmente integrado e configurado para ser utilizado numa série de aplicações. É um sistema de baixo custo e fácil de treinar e usar, e uma vez que é construído unicamente com bibliotecas de código. As experiências realizadas permitiram mostrar que o sistema atingiu uma precisão de 99,2% em termos de reconhecimento de gestos estáticos e uma precisão média de 93,7% em termos de reconhecimento de gestos dinâmicos. Para validar a solução proposta, foram implementados dois sistemas completos. O primeiro é um sistema em tempo real capaz de ajudar um árbitro a arbitrar um jogo de futebol robótico. A solução proposta combina um sistema de reconhecimento de gestos baseada em visão com a definição de uma linguagem formal, o CommLang Referee, à qual demos a designação de Referee Command Language Interface System (ReCLIS). O sistema identifica os comandos baseados num conjunto de gestos estáticos e dinâmicos executados pelo árbitro, sendo este posteriormente enviado para um interface de computador que transmite a respectiva informação para os robôs. O segundo é um sistema em tempo real capaz de interpretar um subconjunto da Linguagem Gestual Portuguesa. As experiências demonstraram que o sistema foi capaz de reconhecer as vogais em tempo real de forma fiável. Embora a solução implementada apenas tenha sido treinada para reconhecer as cinco vogais, o sistema é facilmente extensível para reconhecer o resto do alfabeto. As experiências também permitiram mostrar que a base dos sistemas de interação baseados em visão pode ser a mesma para todas as aplicações e, deste modo facilitar a sua implementação. A solução proposta tem ainda a vantagem de ser suficientemente genérica e uma base sólida para o desenvolvimento de sistemas baseados em reconhecimento gestual que podem ser facilmente integrados com qualquer aplicação de interface homem-máquina. A linguagem formal de definição da interface pode ser redefinida e o sistema pode ser facilmente configurado e treinado com um conjunto de gestos diferentes de forma a serem integrados na solução final.

Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.