Based on the targets outlined by the EU Horizon 2020 (H2020) framework, it is expected that heterogeneous networking will play a crucial role in delivering seamless end-to-end ubiquitous Internet access for users. In due course, the current GSM-Railway (GSM-R) will be deemed unsustainable, as the demand for packet-oriented services continues to increase. Therefore, the opportunity to identify a plausible replacement system conducted in this research study is timely and appropriate. In this research study, a hybrid satellite and terrestrial network for enabling ubiquitous Internet access in a high-speed train environment is investigated. The study focuses on the mobility management aspect of the system, primarily related to the handover management. A proposed handover strategy, employing the RACE II MONET and ITU-T Q.65 design methodology, will be addressed. This includes identifying the functional model (FM) which is then mapped to the functional architecture (FUA), based on the Q.1711 IMT-2000 FM. In addition, the signalling protocols, information flows and message format based on the adopted design methodology will also be specified. The approach is then simulated in OPNET and the findings are then presented and discussed. The opportunity of exploring the prospect of employing neural networks (NN) for handover is also undertaken. This study focuses specifically on the use of pattern classification neural networks to aid in the handover process, which is then simulated in MATLAB. The simulation outcomes demonstrated the effectiveness and appropriateness of the NN algorithm and the competence of the algorithm in facilitating the handover process.

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The thesis deals with the use of neuronal networks for the control of telecommunication network elements. The aim of the thesis is to create a simulation model of network element with switching array with memory, in which the optimization kontrol switching array is solved by means of the neural network. All source code is created in integrated environment MATLAB. To training are used feed-forward backpropagation network. Miss achieve satisfactory result mistakes. Work apposite decision procedure given to problem and it is possible on ni tie up in an effort to find optimum solving.

Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. A general T2FNN is computational intensive due to the complexity of type 2 to type 1 reduction. Therefore the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates can not be both negative. Further, due to variation of the initial MF parameters, i.e. the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search better-fit spread rate for uncertain means and near optimal learnings for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.

由於時間序列在不同範疇的廣泛應用，許多實證結果已明白指出時間序列 資料普遍地存在非線性(nonlinearity)，使得非線型方法在最近幾年受到 極大的重視。然而，對於某些特定的非線型模式，縱然現在已有學者提出 模式選取之檢定方法，但是它們的模式階數確認問題至今卻仍無法有效率 地解決，更遑論得到最佳的模式配適與預測結果了。所以，我們試圖利用 一已於其他科學領域成功應用之新技術──神經網路，來解決非線型時間 序列之預測問題，而我們之所以利用神經網路的原因是其多層前輸網路是 泛函數的近似器(functional approximator)，對任意函數均有極佳之逼 近能力，使我們免除對時間序列資料之屬性(線性或非線性)作事先檢定或 假設的必要。在本篇論文中，我們首先建構15組雙線型時間序列資料，然 後對於這些數據分別以神經網路與自我迴歸整合移動平均(ARIMA) 模式配 適。藉著比較兩者間的配適與預測結果，我們發現神經網路對於預測非線 型時間序列是較具有穩健性。最後，我們以台幣對美元之即期匯率作為我 們的實證資料，結果亦證實了神經網路對於預測一般經濟時間序列亦較具 穩健性。 / With rapid development at the study of time series, the nonlinear approaches have attracted great attention in recent years. However, there are no efficient processes for the problem of identification to many specifically nonlinear models . Even if many testing methods have been proposed, we still can not find the best fitted model and obtain the best forecast performance. Hence, we try to solve the forecast problems by a new technique -- neurocomputing, which has been successfully applied in many scientific fields. The reason why we apply the neural networks is that the multilayer feedforward networks are functional approximators for the unknown function. In this paper, we will first construct several sets of bilinear time series and then fit these series by neural networks and ARIMA models. In this simulation study, we have found that the neural networks perform the robust forecast for some nonlinear time series. Finally, forecasting performance with favorable models will also be compared through the empirical realization of Taiwan.

Backscatter radio - the broad class of systems that communicate using scattered electromagnetic waves - is the driving technology behind many compelling applications such as radio frequency identification (RFID) tags and passive sensors. These systems can be used in many ways including article tracking, position location, passive temperature sensors, passive data storage, and in many other systems which require information exchange between an interrogator and a small, low-cost transponder with little-to-no transponder power consumption. Although backscatter radio is maturing, such systems have limited communication range and reliability caused, in part, by multipath fading. The research presented in this dissertation investigates how multipath fading can be reduced using multiple antennas at the interrogator transmitter, interrogator receiver, and on the transponder, or RF tag. First, two link budgets for backscatter radio are presented and fading effects demonstrated through a realistic, 915 MHz, RFID-portal example. Each term in the link budget is explained and used to illuminate the propagation and high-frequency effects that influence RF tag operation. Second, analytic envelope distributions for the M x L x N, dyadic backscatter channel - the general channel in which a backscatter system with M transmitter, L RF tag, and N receiver antennas operates - are derived. The distributions show that multipath fading can be reduced using multiple-antenna RF tags and by using separate transmitter and receiver antenna arrays at the interrogator. These results are verified by fading measurements of the M x L x N, dyadic backscatter channel at 5.8 GHz - the center of the 5725-5850 MHz unlicensed industrial, scientific, and medical (ISM) frequency band that offers reduced antenna size, increased antenna gain, and, in some cases, reduced object attachment losses compared to the commonly used 902-928 MHz ISM band. Measurements were taken with a custom backscatter testbed and details of its design are provided. In the end, this dissertation presents both theory and measurements that demonstrate multipath fading reductions for backscatter-radio systems that use multiple antennas.

Ce travail de thèse à été financé par le projet LICORVE (Développement de garnitures légères, innovantes, recyclables et poly-sensorielles pour les applications de coffres de véhicule). Il consiste à développer une méthodologie de mesure pour localiser et caractériser les sources de bruit dans un coffre de voiture. L'environnement de mesure se caractérise par un petit volume où les réflexions de la source d'intérêt et des sources perturbatrices sur les parois ne peuvent être négligées. La méthode doit donc permettre de séparer les différents contributions pour estimer le plus précisément possible les sources étudiées (déconfinement). Dans un premier temps, deux méthodes de séparation : Double Layer SONAH (Statistically Optimal Near el Acoustical Holography) et Field Separation Method (FSM) sont étudiées numériquement. Les limites et avantages de chacune ont été déterminés dans un environnement de mesure confiné. Cela a permis de choisir la méthode la plus adaptée à notre problématique. Dans un deuxième temps les principales conclusions de l'étude numérique sont validées expérimentalement. Dans ce cadre, un ensemble de mesures sont réalisées dans une maquette avec la méthode FSM pour localiser et caractériser des sources maitrisées. Après avoir validée la fiabilité de la méthode de séparation, FSM a été mise en œuvre dans le coffre d'une Peugeot 508 sw en condition de roulement. Les résultats obtenus ont permis d'orienter le choix des garnitures pour un traitement acoustique optimal. / This thesis consists in developing, through the LICORVE project (light garnitures, innovative, recyclable and multi-sensorial for vehicle boots applications), a measurement method for localizing and characterizing noise sources in a vehicle trunk. The measuring environment is distinguished by a small volume where the reflections on the partitions generated by the source of interest and the interfering sources cannot be neglected. Therefore, the method must allow the separation of the different contributions in order to assess accurately the studied sources. As a first step, two separation methods : Double Layer SONAH (Statistically Optimized Near-Field Acoustical Holography) and Field Separation Method (FSM) are numerically studied. The limitations and advantages of each of them are determined in a confined measuring environment; this allowed to select the most appropriate method to tackle our problem. As a second step, the main conclusions of the numerical study are confirmed experimentally. In this context, measurements are performed, using the FSM method, in a trunk mock-up to localize and characterize the controlled sources. So confirmed the reliability of the separation method, it has been tested in the boot of a Peugeot 508 SW on a roller bench. The obtained results allowed guiding the selection of garniture for the acoustic treatment.

Gaze tracking means to detect and follow the direction in which a person looks. This can be used in for instance human-computer interaction. Most existing systems illuminate the eye with IR-light, possibly damaging the eye. The motivation of this thesis is to develop a truly non-intrusive gaze tracking system, using only a digital camera, e.g. a web camera. The approach is to detect and track different facial features, using varying image analysis techniques. These features will serve as inputs to a neural net, which will be trained with a set of predetermined gaze tracking series. The output is coordinates on the screen. The evaluation is done with a measure of accuracy and the result is an average angular deviation of two to four degrees, depending on the quality of the image sequence. To get better and more robust results, a higher image quality from the digital camera is needed.

Made available in DSpace on 2015-04-22T22:00:46Z (GMT). No. of bitstreams: 1 Pamela Campos Levy.pdf: 4863540 bytes, checksum: 820e34768b005399acf73dec3e491ae5 (MD5) Previous issue date: 2012-08-24 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / Tuberculosis (TB) is an infectious disease transmitted by Koch's bacillus, or Mycobacterium tuberculosis. An estimated 1.4 million people died of tuberculosis in 2010. About 95% of these deaths occurred in developing countries, or development. In Brazil, each year are registered more than 68,000 new cases. Currently, Amazon is the Brazilian state with the highest incidence rate of the disease. a of TB diagnostic methods, adopted by the Ministry of Health is examining smear of bright field. The smear is the count of bacilli in slides containing sputum samples of the patient, prepared and stained according to the methodology standard. Over the past five years, research related to the recognition of bacilli tuberculosis, using images obtained by microscopy bright field, has been carried out with a view to automating this diagnostic method, given the fact that the number high smear tests performed by professional induce eyestrain and due to diagnostic errors. This paper presents a new method of recognition and targeting of tubercle bacilli in slides fields of images, containing pulmonary secretions of the patient, stained by Kinyoun method. From these bacilli images of pixels and background samples were extracted for training classifier. Images were automatically broken down into two groups, according with substantial content. The developed method selects an optimal set of color characteristics of the bacillus and of the background, using the method of selection climbing characteristics. These features were used in a pixel classifier, a multilayer perceptron, trained by backpropagation algorithm. The optimal set of features selected, {GI, Y-Cr, La, RG, a}, from the RGB color spaces, HSI, YCbCr and Lab, combined with the network perceptron with eighteen (18) neurons in first layer three (3) and the second one (1) in the third (18-3-1), resulted in an accuracy of 92.47% in the segmentation of bacilli. The image discrimination method in relation to automated background content contributed to affirm that the method described in this paper it is more appropriate to target bacilli images with low content density background (more uniform background). For future work, new techniques to remove noise present in images with high density of background content (containing background many artifacts) should be developed. / A tuberculose (TB) é uma doença infectocontagiosa, transmitida pelo bacilo de Koch, ou Mycobacterium tuberculosis. Estima-se que 1,4 milhões de pessoas morreram de tuberculose em 2010. Cerca de 95% dessas mortes ocorreram em países subdesenvolvidos ou em desenvolvimento. No Brasil, a cada ano são registrados mais de 68 mil novos casos. Atualmente, o Amazonas é o estado brasileiro com a maior taxa de incidência da doença. Um dos métodos de diagnóstico da TB, adotado pelo Ministério da Saúde, é o exame de baciloscopia de campo claro. A baciloscopia consiste na contagem dos bacilos em lâminas contendo amostras de escarro do paciente, preparadas e coradas de acordo com metodologia padronizada. Nos últimos cinco anos, pesquisas relacionadas ao reconhecimento de bacilos da tuberculose, utilizando imagens obtidas por microscopia de campo claro, tem sido realizadas com vistas a automatização desse método diagnóstico, em face do fato de que o número elevado de exames de baciloscopia realizado pelos profissionais induzirem a fadiga visual e em consequência a erros diagnósticos. Esse trabalho apresenta um novo método de reconhecimento e segmentação de bacilos da tuberculose em imagens de campos de lâminas, contendo secreção pulmonar do paciente, coradas pelo método de Kinyoun. A partir dessas imagens foram extraídas amostras de pixels de bacilos e de fundo para treinamento do classificador. As imagens foram automaticamente discriminadas em dois grupos, de acordo com o conteúdo de fundo. O método desenvolvido seleciona um conjunto ótimo de características de cor do bacilo e do fundo da imagem, empregando o método de seleção escalar de características. Essas características foram utilizadas em um classificador de pixels, um perceptron multicamada, treinado pelo algoritmo backpropagation. O conjunto ótimo de características selecionadas, {G-I, Y-Cr, L-a, R-G, a}, proveniente dos espaços de cores RGB, HSI, YCbCr e Lab, combinado com a rede perceptron com 18 (dezoito) neurônios na primeira camada, 3 (três) na segunda e 1 (um) na terceira (18-3-1), resultou em uma acurácia de 92,47% na segmentação dos bacilos. O método de discriminação de imagens em relação ao conteúdo de fundo automatizado contribuiu para afirmar que o método descrito neste trabalho é mais adequado para segmentar bacilos em imagens com baixa densidade de conteúdo de fundo (fundo mais uniforme). Para os trabalhos futuros, novas técnicas para remover os ruídos presentes em imagens com alta densidade de conteúdo de fundo (fundo contendo muitos artefatos) devem ser desenvolvidas.

In this paper we view learning as an unconstrained non-linear minimization problem in which the objective function is defined by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit neural spatial interaction model. We consider Alopex based global search, as opposed to local search based upon backpropagation of gradient descents, each in combination with the bootstrapping pairs approach to solve the maximum likelihood learning problem. Interregional telecommunication traffic flow data from Austria are used as test bed for comparing the performance of the two learning procedures. The study illustrates the superiority of Alopex based global search, measured in terms of Kullback and Leibler's information criterion.