Automated Intelligent Real-Time System For Aggregate Classification

This research focuses on developing an intelligent real-time classification system called NeuralAgg. Penyelidikan ini memfokuskan untuk membina sistem pengkelasan pintar secara masa nyata dipanggil NeuralAgg

Signal processing and image restoration techniques for two-dimensional eddy current nondestructive evaluation

This dissertation presents a comprehensive study on the forward modeling methods, signal processing techniques, and image restoration techniques for two-dimensional eddy current nondestructive evaluation. The basic physical forward method adopted in this study is the volume integral method. We have applied this model to the eddy current modeling problem for half space geometry and thin plate geometry. To reduce the computational complexity of the volume integral method, we have developed a wavelet expansion method which utilizes the multiresolution compression capability of the wavelet basis to greatly reduce the amount of computation with small loss in accuracy. To further improve the speed of forward modeling, we have developed a fast eddy current model based on a radial basis function neural network. This dissertation also contains investigations on signal processing techniques to enhance flaw signals in two-dimensional eddy current inspection data. The processing procedures developed in this study include a set of preprocessing techniques, a background removal technique based on principal component analysis, and grayscale morphological operations to detect flaw signals. Another important part of the dissertation concerns image restoration techniques which can remove the blurring in impedance change images due to the diffusive nature of the eddy current testing. We have developed two approximate linear image restoration methods--the Wiener filtering method and the maximum entropy method. Both linear restoration methods are based on an approximate linear forward model formulated by using the Born approximation. To improve the quality of restoration, we have also developed nonlinear image restoration methods based on simulated annealing and a genetic algorithm. Those nonlinear methods are based on the neural network forward model which is more accurate than the approximate linear forward model

Neural networks to intrusion detection

Recent research indicates a lot of attempts to create an Intrusion Detection System that is capable of learning and recognizing attacks it faces for the first time. Benchmark datasets were created by the MIT Lincoln Lab and by the International Knowledge Discovery and Data Mining group (KDD). A few competitions were held and many systems developed. The overall preference was given to Expert Systems that were based on Decision Making Tree algorithms. This work is devoted to the problem of Neural Networks as means of Intrusion Detection. After multiple techniques and methodologies are investigated, we show that properly trained Neural Networks are capable of fast recognition and classification of different attacks. The advantage of the taken approach allows us to demonstrate the superiority of the Neural Networks over the systems that were created by the winner of the KDD Cups competition and later researchers due to their capability to recognize an attack, to differentiate one attack from another, i.e. classify attacks, and, the most important, to detect new attacks that were not included into the training set. The results obtained through simulations indicate that it is possible to recognize attacks that the Intrusion Detection System never faced before on an acceptably high level

Automated Design of Neural Network Architecture for Classification

This Ph.D. thesis deals with finding a good architecture of a neural network classifier. The focus is on methods to improve the performance of existing architectures (i.e. architectures that are initialised by a good academic guess) and automatically building neural networks. An introduction to the Multi-Layer feed-forward neural network is given and the most essential properties for neural networks; there ability to learn from examples is discussion. Topics like traning and generalisation are treated in more explicit. On the basic of this dissuscion methods for finding a good architecture of the network described. This includes methods like; Early stopping, Cross validation, Regularisation, Pruning and various constructions algorithms (methods that successively builds a network). New ideas of combining units with different types of transfer functions like radial basis functions and sigmoid or threshold functions led to the development of a new construction algorithm for classification. The algorithm called "GLOCAL" is fully described. Results from these experiments real life data from a Synthetic Aperture Radar (SAR) are provided.The thesis was written so people from the industry and graduate students who are interested in neural networks hopeful would find it useful.Key words: Neural networks, Architectures, Training, Generalisation deductive and construction algorithms

Flexible oxide thin film transistors: fabrication and photoresponse

Gli ossidi amorfi semiconduttori (AOS) sono nuovi candidati per l’elettronica flessibile e su grandi aree: grazie ai loro legami prevalentemente ionici hanno una mobilità relativamente alta (µ > 10cm^2/Vs) anche nella fase amorfa. Transistor a film sottile (TFT) basati sugli AOS saranno quindi più performanti di tecnologie a base di a-Si e più economici di quelle a base di silicio policristallino. Essendo amorfi, possono essere depositati a basse temperature e su substrati polimerici, caratteristica chiave per l’elettronica flessibile e su grandi aree. Per questa tesi, diversi TFT sono stati fabbricati e caratterizzati nei laboratori del CENIMAT all’Università Nova di Lisbona sotto la supervisione del Prof. P. Barquinha. Questi dispositivi sono composti di contatti in molibdeno, un canale semiconduttivo di ossido di zinco, gallio e indio (IGZO) e un dielettrico composto da 7 strati alternati di SiO2 e SiO2+Ta2O5. Tutti i dispositivi sono stati depositati mediante sputtering su sostrati flessibili (fogli di PEN). Le misure tensione-corrente mostrano che i dispositivi mantengono alte mobilità (decine di 10cm^2/Vs) anche quando fabbricati a temperature inferiori a 200°C. Si è analizzato il funzionamento dei dispositivi come fototransistor rilevando la risposta alla luce ultravioletta e in particolare la loro responsività e spostamento della tensione di soglia in funzione della lunghezza d’onda incidente. Questi risultati consentono di formulare ipotesi sul comportamento dei dispositivi alla scala microscopica. In particolare, indicano che i) la mobilità del canale non è influenzata dall’illuminazione, ii) sia l'IGZO sia il Ta2O5 contribuiscono al processo di fotoconduttività e iii) il processo di fotogenerazione non è adiabatico. La tesi contiene inoltre una descrizione del processo di ricombinazione e presenta un’applicazione pratica di tali dispositivi in un circuito per RFID. Infine, esplora la possibilità di migliorarne la flessibilità e le prestazioni

Predicting road transport GHG emissions with application for Canada

Prediction of greenhouse gas (GHG) emissions is vital to minimize their negative impact on climate change and global warming. In this thesis, we propose new models based on data mining and supervised machine learning algorithms (Regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four categories of models are investigated namely artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the application results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed Multilayer Perceptron model which significantly improved the model's predictive performance. The independent variable importance analysis conducted on multilayer perceptron model disclosed that among the input attributes Light truck emissions, Car emissions, GDP transportation, Heavy truck emission, Light duty truck fuel efficiency, Interest rate (overnight), Medium Trucks Emission, Passenger car fuel efficiency and Gasoline Price have higher sensitivity on the output of the predictive model of GHG emissions by Canadian road transport. Scenario analysis is conducted using widely available socioeconomic, emission and fuel efficiency attributes as inputs in multilayer perceptron (with bagging) model. The results show that in all Canadian road transport GHG emission projection scenarios, all the way through 2030, emissions from Light trucks will hold a major share of GHG emissions. Thereby, rigorous efforts should be made in mitigating GHG emissions from these trucks (freight transport) to meet the ambitious GHG emission target for Canadian road transport

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Sensorimotor neural systems for a predatory stealth behaviour camouflaging motion

A thesis submitted to the University of London in partial fulfillment of the requirements for the admission to the degree of Doctor of Philosophy