Over 300,000 individuals are injured every year by vehicle related accidents in the United Kingdom alone. Government and the vehicle manufacturers are not only bringing new legislation but are also investing in vehicle safety research to bring this figure down. A private self-driven car is an important factor in maintaining the independence and quality of life of the third age individuals. However, since older people brings deterioration of cognitive, physical and visual abilities, resulting in slower reaction times and lapses while driving. The third age individuals are involved in more vehicle related accidents than middle aged individuals. This scenario is corrected by the fact that the number of third age individuals is increasing, especially in developed countries. It is expected that the percentage of third age individuals in the United Kingdom will increase to 20% of the total population by 2010. Several safety systems have been developed by the automotive industry including intelligent airbags, Electronic Stability Control (ESC) and pre-tensioned seat belts, but nothing has been specifically developed for the third age related problems. This thesis proposes a driver posture identification system using low resolution infrared imaging. The use of a low resolution thermal imager provides a reliable non-contact based posture identification system at a relatively low cost and is shown to provide robust performance over a wide range of conditions. The low resolution also protects the privacy of the driver. In order to develop the proposed safety system an Artificial Intelligent Thermal Imaging algorithm (AITl) is created in MatLAB. Experimentation is conducted in real and simulated environment, with human subjects, to evaluate the results of the algorithm. The result shows that the safety system is able to identify eighteen different driving postures. The system also provides other valuable information about the driver such as driver physical built, fatigue, smoking, mobile phone usage, eye-height and trunk stability. It is clear that in incorporating this safety system in the overall automotive central strategy, better safety for third age individual can be achieved. This thesis provides various contributions to knowledge including a novel neural network design, a safety system using low resolution infrared imager and an algorithm that can identify driver posture.

Manual sleep scoring, executed by visual inspection of the EEG, is a very time consuming activity, with an inherent subjective decisional component. Automatic sleep scoring could ease the job of the technicians, because faster and more accurate. Frequency information characterizing the main brain rhythms, and consequently the sleep stages, needs to be extracted from the EEG data. The approach used in this study involves a wavelet filter bank for the EEG frequency features extraction. The wavelet packet analysis tool in MATLAB has been employed and the frequency information subsequently used for the automatic sleep scoring by means of an artificial neural network. Finally, the automatic sleep scoring has been employed for epoching the fMRI data, thus allowing for studying brain resting state networks during sleep. Three resting state networks have been inspected; the Default Mode Network, The Attentional Network and the Salience Network. The networks functional connectivity variations have been inspected in both healthy and narcoleptic subjects. Narcolepsy is a neurobiological disorder characterized by an excessive daytime sleepiness, whose aetiology may be linked to a loss of neurons in the hypothalamic region.

Can symmetry be utilized as a design principle to constrain evolutionary search, making it more effective? This dissertation aims to show that this is indeed the case, in two ways. First, an approach called ENSO is developed to evolve modular neural network controllers for simulated multilegged robots. Inspired by how symmetric organisms have evolved in nature, ENSO utilizes group theory to break symmetry systematically, constraining evolution to explore promising regions of the search space. As a result, it evolves effective controllers even when the appropriate symmetry constraints are difficult to design by hand. The controllers perform equally well when transferred from simulation to a physical robot. Second, the same principle is used to evolve minimal-size sorting networks. In this different domain, a different instantiation of the same principle is effective: building the desired symmetry step-by-step. This approach is more scalable than previous methods and finds smaller networks, thereby demonstrating that the principle is general. Thus, evolutionary search that utilizes symmetry constraints is shown to be effective in a range of challenging applications. / text

Σκοπός αυτής της διπλωματικής εργασίας είναι η σύγκριση δύο μεγάλων κατηγοριών, των Τεχνητών Νευρωνικών Δικτύων και των πολύ δημοφιλείς τα τελευταία χρόνια, Μηχανών Διανυσματικής Υποστήριξης (SVMs) στην Κατηγοριο-ποίηση δεδομένων και στην Παλινδρόμηση. Στο πρώτο κεφάλαιο έχουν γραφτεί θέματα σχετικά με την Εξόρυξη γνώσης και την Κατηγοριοποίηση δεδομένων, το δεύτερο κεφάλαιο προσεγγίζει αρκετά θέματα από το τεράστιο κεφάλαιο των Νευρωνικών Δικτύων. Αναλύει το λόγο που δημιουργή-θηκαν, το θεωρητικό τους μέρος, αρκετές από τις τοπολογίες τους – αρχιτεκτονικές τους και τέλος τις ανάγκες που δημιουργήθηκαν μέσα από τα πλεονεκτήματά και τα μειονεκτήματα τους, για ακόμη καλύτερα αποτελέσματα. Το τρίτο κεφάλαιο ασχολείται με τις Μηχανές Διανυσματικής Υποστήριξης, για πιο λόγο είναι τόσο δημοφιλείς, πως υλοποιούνται θεωρητικά και γεωμετρικά, τι πετυχαίνουν, τα πλεονεκτήματά και τα μειονεκτήματα τους. Το τέταρτο κεφάλαιο προσπαθεί μέσα από πειραματικά αποτελέσματα να συγκρίνει τα Τεχνητά Νευρωνικά Δίκτυα με τα SVMs με πραγματικά σύνολα δεδομένων (πρότυπα ή στιγμιότυπα), ποιοί δείκτες είναι αυτοί που θα μας δώσουν τελικά ποιος κατηγοριοποιητής είναι συνολικά καλύτερος; Όταν λέμε καλύτερος είναι αυτός που είναι πιο ακριβής ή πιο γρήγορος ή κάτι ενδιάμεσο; Το πέμπτο κεφάλαιο μας εξηγεί τι είναι παλινδρόμηση και συγκρίνει κύριους αλγορίθμους από τα Τεχνητά Νευρωνικά Δίκτυα και των Μηχανών Διανυσματικής Υποστήριξης. Στο έκτο κεφάλαιο και στα πλαίσια της διπλωματικής εργασίας υλοποίησα μία εφαρμογή σε Java, η οποία κάνει ταξινόμηση και παλινδρόμηση σε δεδομένα από αρχεία arff. Επικεντρώνεται μόνο στην ταξινόμηση και την παλινδρόμηση ενώ αυτό που το κάνει διαφορετικό από το Weka είναι η πρόβλεψη (Prediction) στο οποίο μπορούμε εμείς να δώσουμε κάποιο στιγμιότυπο και η εφαρμογή να μας κάνει πρόβλεψη για αυτό. Τέλος ακολουθούν ο επίλογος, τα παραρτήματα τα οποία καλύπτουν θεωρητικές βασικές έννοιες που αναφέρονται στα προηγούμενα κεφάλαια και διαγράμματα UML των κλάσεων που υλοποιούνται στην κατηγοριοποίηση (Classification) και στην πρόβλεψη (Prediction) στο Weka και κάποια κομμάτια κώδικα σε Java από την υλοποίηση του προγράμματος. Στην εργασία υπάρχει αρκετή βιβλιογραφία στην οποία γίνονται συνεχείς αναφορές. Στην εργασία υπάρχει αρκετή βιβλιογραφία στην οποία γίνονται συνεχείς αναφορές. Έγινε μεγάλη προσπάθεια στο να καταλάβει κάποιος πόσο σημαντική προσπάθεια έχει γίνει σε αυτό το χώρο της τεχνητής νοημοσύνης (Artificial intelligence) από τον Alan Turing και τους McCulloch και Pitts μέχρι τον Vapnik τον Osuna και τον Platt και πολλούς άλλους μετέπειτα. / The aim of this dissertation is the comparison of two major categories, the Artificial Neural Networks and the, very popular recently, Support Vector Machines on Data Classification and Regression. In the first chapter issues relevant to Data Mining and Data Classification are written, whereas in the second one, several issues from the enormous chapter of Artificial Neural Networks are approached. In this we analyze the reason for their creation, their theoretical part, several of their topologies – architectural and finally the needs that were created from their advantages and disadvantages for better results. In the third chapter we are dealing with the Support Vector Machines, the reason of their popularity, the way of their implementation theoretically and geometrically, their accomplishments and their advantages and disadvantages. In the fourth chapter, via experimental results, we are trying to compare the Artificial Neural Networks to the Support Vector Machines with real aggregate data, patterns or instances, which indicators are those that will finally give us the classifier that is the best. And by saying the best do we imply the most accurate, the fastest or something in between? In the fifth chapter we explain what Regression is and we compare major algorithms from Artificial Neural Networks and Support Vector Machines. In the sixth chapter we implemented an application into Java which performs classification and regression from arff files. It focuses only on classification and regression, while what differentiates it from Weka is Prediction on which we can give an instance and the application can make a prediction on/about it. Finally, we include the Conclusion/Epilogue, the appendices that cover basic theories which refer to previous chapters and UML diagrams of classes that are implemented on classification and Prediction in Weka, as well as some parts of the code in Java from the implementation of the program. In the Dissertation there is the Bibliography on which we constantly refer to. A great effort has been given so that anyone can understand the importance of the attempt that was done on the field of Artificial Intelligence by Alan Turing, McCulloch and Pitts up to Vapnik, Osuna and Platt and many others that followed.

The forecasted proportions of urban population to total population in Malaysia are steadily increasing from 26% in 1965 to 70% in 2020. Therefore, there is a need to fully appreciate the legacy of the urbanization of Malaysia by providing affordable housing. The main aim of this study is to focus on developing a model to forecast the demand of low cost housing in urban areas. The study is focused on eight states in Peninsular Malaysia, as most of these states are among the areas predicted to have achieved the highest urbanization level in the country. The states are Kedah, Penang, Perlis, Kelantan, Terengganu, Perak, Pahang and Johor. Monthly time-series data for six to eight years of nine indicators including: population growth; birth rate; child mortality rate; unemployment rate; household income rate; inflation rate; GDP; poverty rate and housing stocks have been used to forecast the demand on low cost housing using Artificial Neural Network (ANN) approach. The data is collected from the Department of Malaysian Statistics, the Ministry of Housing and the Housing Department of the State Secretary. The Principal Component Analysis (PCA) method has been adopted to analyze the data using SPSS 18.0 package. The performance of the Neural Network is evaluated using R squared (R2) and the accuracy of the model is measured using the Mean Absolute Percentage Error (MAPE). Lastly, a user friendly interface is developed using Visual Basic. From the results, it was found that the best Neural Network to forecast the demand on low cost housing in Kedah is 2-16-1, Pahang 2-15-1, Kelantan 2-25-1, Terengganu 2-30-1, Perlis 3-5-1, Pulau Pinang 3-7-1, Johor 3-38-1 and Perak 3-24-1. In conclusion, the evaluation performance of the model through the MAPE value shows that the NN model can forecast the low-cost housing demand very good in Pulau Pinang, Johor, Pahang and Kelantan, where else good in Kedah and Terengganu while in Perlis and Perak it is not accurate due to the lack of data. The study has successfully developed a user friendly interface to retrieve and view all the data easily.

No description available.

Power Amplifiers (PAs) are the key building blocks of the emerging wireless radios systems. They dominate the power consumption and sources of distortion, especially when driven with modulated signals. Several approaches have been devised to characterize the nonlinearity of a PA. Among these approaches, dynamic amplitude (AM/AM) and phase (AM/PM) distortion characteristics are widely used to characterize the PA nonlinearity and its effects on the output signal in power, frequency or time domains, when driven with realistic modulated signals. The inherent nonlinear behaviour of PAs generally yield output signals with an unacceptable quality, an undesirable level of out-of-band emission, high Error Vector Magnitudes (EVMs) and low Adjacent Channel Power Ratios (ACPRs), which usually fail to meet the established performance standards. Traditionally, PAs are forced to operate deeply in their back-off region, far from their power capacity, in order to pass the mandatory spectrum mask (ACPR requirement) and to achieve acceptable EVM. Despite its simplicity, this solution is increasingly discarded, as it leads to cost and power inefficient radios. Alternatively, several linearization techniques, such as feedback, feed-forward and predistortion, have been devised to tackle PA nonlinearity and, consequently, improve the achievable the linearity versus power efficiency trade-off. Among these linearization techniques, the Digital Pre-Distortion (DPD) technique consists of incorporating an extra nonlinear function before the PA, in order to preprocess the input signal to the PA, so that the overall cascaded systems behave linearly. The overall linearity of the cascaded system (DPD plus PA) relies primarily on the ability of the DPD function to produce nonlinearities that are equal in magnitude and out-of-phase to those generated by the PA. Hence, a good understanding and accurate modeling of PA distortions is a crucial step in the construction of an adequate DPD function. This thesis explores DPD through techniques based on Artificial Neural Networks (ANNs). The choice of ANN as a modeling tool was motivated by its proven strength in modeling dynamic nonlinear systems. This thesis starts by providing a summary of the PA nonlinearity problem background, as well as an overview of the most well-known linearization techniques, with a special focus on DPD techniques. The thesis then discusses ANN structures and the learning parameters. Finally, a novel Two Hidden Layers ANN (2HLANN) model is suggested to predict the dynamic nonlinear behaviour of wideband PAs. An extensive validation of the 2HLANN model demonstrates its excellent modeling accuracy and linearization capability.

This work outlines the theoretical underpinnings, method, results, and implications for constructing a discussion list analysis tool that categorizes online, educational discussion list messages into levels of cognitive effort. Purpose The purpose of such a tool is to provide evaluative feedback to instructors who facilitate online learning, to researchers studying computer-supported collaborative learning, and to administrators interested in correlating objective measures of students’ cognitive effort with other measures of student success. This work connects computer–supported collaborative learning, content analysis, and artificial intelligence. Method Broadly, the method employed is a content analysis in which the data from the analysis is modeled using artificial neural network (ANN) software. A group of human coders categorized online discussion list messages, and inter-rater reliability was calculated among them. That reliability figure serves as a measuring stick for determining how well the ANN categorizes the same messages that the group of human coders categorized. Reliability between the ANN model and the group of human coders is compared to the reliability among the group of human coders to determine how well the ANN performs compared to humans. Findings Two experiments were conducted in which artificial neural network (ANN) models were constructed to model the decisions of human coders, and the experiments revealed that the ANN, under noisy, real-life circumstances codes messages with near-human accuracy. From experiment one, the reliability between the ANN model and the group of human coders, using Cohen’s kappa, is 0.519 while the human reliability values range from 0.494 to 0.742 (M=0.6). Improvements were made to the human content analysis with the goal of improving the reliability among coders. After these improvements were made, the humans coded messages with a kappa agreement ranging from 0.816 to 0.879 (M=0.848), and the kappa agreement between the ANN model and the group of human coders is 0.70.

Šiame darbe yra nagrinėjama kliūčių atpažinimo vaizde problema. Ši problema susideda iš skirtumų vaizdo formavimo, vaizdo paruošimo dirbtiniam neuroniniam tinklui ir objektų klasifikavimo, naudojant dirbtinį neuroninį tinklą, uždavinių. Darbe siekiama išnagrinėti esamus vaizdo formavimo, apdorojimo ir dirbtinio neuroninio tinklo klasifikavimo būdus ir pateikti uždavinių sprendimo variantą kaip tai galima padaryti geriau. Remiantis autoriaus siūlomais sprendimais yra sukurta programinė įrangą, kuri sudaryta iš trijų modulių: skirtumų žemėlapio vaizdo formavimo, sukurtojo skirtumų žemėlapio vaizdo pirminio apdorojimo ir DNT kliūčių identifikavimo apdorotame skirtumų žemėlapio vaizde. / The problem of obstacle recognition on way is analyzed by author in this work. This problem consists of view formation, view preparation for Artificial Neural Network and object classification using neural networks tasks. It is striving to analyze the formation of view, processing of view and ways of ANN classification, and suggest the better way of task solutions in this thesis. It is compiled software using authors suggested solutions which consists of three modules: disparity map formation, filtering preparation of created one and obstacle recognition using ANN.

The dissertation examines the thermal flow detection and prediction prob-lems during an autonomous aircraft flight. The main research object is the thermal flows and artificial neural networks. Thermal flows are a very im-portant source for improving autonomous aircraft flight parameters, such as flight time and duration. The primary aim of the dissertation is to create methodologies and algorithms to detect, identify and to successfully predict the parameters the thermal flows. The application are of the methods and algorithms developed is autonomous aircraft control system synthesis, research on mesoscale meteorological phenomena and synthesis of computing systems using biological models. The following objectives are carried out: thermal flow sensing using aircraft navigational parameters measurement data, thermal flow simulation modeling and data input necessary for modeling. The dissertation consists of an introduction, four chapters, conclusions, bibliography, and list of author publications on the topic as well as three annexes. The introductory chapter discusses the research problem and the relevance of the research described in the thesis, formulates the goal and objectives, describes the research methodology, scientific novelty, the practical significance of the results, hypotheses. In the end of the introduction a list of author's publications on the topic and the structure of the dissertation are presented. The first section provides a review of previous... [to full text] / Disertacijoje nagrinėjamos terminių srautų paieškos ir prognozavimo autonominio orlaivio skrydžio metu problemos. Pagrindinis tyrimų objektas yra terminių srautų aparatinis aptikimas ir prognozavimas. Terminiai srautai yra labai svarbus autonominio orlaivio skrydžio charakteristikų, kaip antai skrydžio laikas ir trukmė, gerinimo šaltinis. Pagrindinis disertacijos tikslas – sukurti metodikas ir algoritmus, leidžiančius aptikti terminį srautą, nustatyti bei sėkmingai prognozuoti jo parametrus. Sukurtų metodų ir algoritmų taikymo sritis – autonominių orlaivių valdymo sistemų sintezė, meteorologiniai mezomastelinių meteorologinių reiškinių tyrimai, biologinius skaičiavimo modelius naudojančių sistemų sintezė. Darbe sprendžiami keli uždaviniai: terminio srauto aptikimas naudojant orlaivio navigacinių parametrų matavimo duomenis, terminio srauto modeliavimas bei modeliui reikalingų duomenų pateikimas. Disertaciją sudaro įvadas, keturi skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų disertacijos tema sąrašai ir tris priedai. Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos ir konferencijų pranešimai bei disertacijos struktūra. Pirmajame skyriuje pateikiama su disertacijos... [toliau žr. visą tekstą]