Extensible Markup Language (XML) is a simple, very flexible text format derived from SGML (ISO 8879), which is a well defined, public standard. It uses plain text to encode a hierarchical set of information using verbose tags to allow the XML document to be understood without any special reader. The use of schemas in XML also allows a well defined contract describing what a single XML document means. The self-contained nature of XML and the strong contract provided by its schemas makes it useful as an archival storage format and as a means of communicating across system or organizational boundaries. As such XML is being increasingly used by businesses throughout the world. These businesses use XML as a means of storing, transmitting and (with the use of style sheets) displaying information. The simple, well defined structure of XML does present some problems when it is used by businesses and similar organizations. As it is an open, plain text based standard care must be taken when looking at security. The use of plain text with verbose tags also results in XML documents that are far larger than other means of storing the same information. This thesis focuses on the affect of the large size of XML when it is used to communicate across a network. This large size can often increase the time taken to transmit the document and we were interested to see how it could be minimized. we investigated the ways that are used to control the size of XML documents and how they are transmitted. We carefully investigated by implementing solutions on how to transmit the XML document. We then first presented a new method, called dynamic adaptive threshold transmission (DATT), in comparisons with other existing similar methods, which, under the discussed conditions, offers significant improvements in transmission times and network transmission efficiencies.

Energy consumption in Wireless Sensor Networks (WSN) involving multiple sensor nodes is a crucial parameter in many applications like smart healthcare systems, home automation, environmental monitoring, and industrial use. Hence, an energy-efficient cluster-head (CH) selection strategy is imperative in a WSN to improve network performance. So to balance the harsh conditions in the network with fast changes in the energy dynamics, a novel energy-efficient adaptive fuzzy-based CH selection approach is projected. Extensive simulations exploited various real-time scenarios, such as varying the optimal position of the location of the base station and network energy. Additionally, the results showed an improved performance in the throughput (46%) and energy consumption (66%), which demonstrated the robustness and efficacy of the proposed model for the future designs of WSN applications.

This thesis aims at providing a new approach for detecting R-waves in the ECG signal and generating the corresponding R-wave impulses with the delay between the original R-waves and the R-wave impulses being lesser than 100 ms. The algorithm was implemented in Matlab and tested with good results against 90 different ECG recordings from the MIT-BIH database. The Discrete Wavelet Transform (DWT) forms the heart of the algorithm providing a multi-resolution analysis of the ECG signal. The wavelet transform decomposes the ECG signal into frequency scales where the ECG characteristic waveforms are indicated by zero crossings. The adaptive threshold algorithms discussed in this thesis search for valid zero crossings which characterize the R-waves and also remove the Preventricular Contractions (PVC's). The adaptive threshold algorithms allow the decision thresholds to adjust for signal quality changes and eliminate the need for manual adjustments when changing from patient to patient. The delay between the R-waves in the original ECG signal and the R-wave impulses obtained from the algorithm was found to be less than 100 ms.

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The evaluation of discrete processes are performed for industrial and healthcare processes. Count data may be used to measure the number of defective items in industrial applications or the incidence of a certain disease at a health facility. Another classification of a discrete random variable is for binary data, where information on an item can be classified as conforming or nonconforming in a manufacturing context, or a patient's status of having a disease in health-related applications. The first phase of this research uses discrete count data modeled from the Poisson and negative binomial distributions in a healthcare setting. Syndromic counts are currently monitored by the BioSense program within the Centers for Disease Control and Prevention (CDC) to provide real-time biosurveillance. The Early Aberration Reporting System (EARS) uses recent baseline information comparatively with a current day's syndromic count to determine if outbreaks may be present. An adaptive threshold method is proposed based on fitting baseline data to a parametric distribution, then calculating an upper-tailed p-value. These statistics are then converted to an approximately standard normal random variable. Monitoring is examined for independent and identically distributed data as well as data following several seasonal patterns. An exponentially weighted moving average (EWMA) chart is also used for these methods. The effectiveness of these methods in detecting simulated outbreaks in several sensitivity analyses is evaluated. The second phase of research explored in this dissertation considers information that can be classified as a binary event. In industry, it is desirable to have the probability of a nonconforming item, p, be extremely small. Traditional Shewhart charts such as the p-chart, are not reliable for monitoring this type of process. A comprehensive literature review of control chart procedures for this type of process is given. The equivalence between two cumulative sum (CUSUM) charts, based on geometric and Bernoulli random variables is explored. An evaluation of the unit and group--runs (UGR) chart is performed, where it is shown that the in--control behavior of this chart is quite misleading and should not be recommended for practitioners. / Ph. D.

In the past decade, characterizing spiking neuron models has been extensively researched as an essential issue in computational neuroscience. In this thesis, we examine the estimation problem of two different neuron models. In Chapter 2, We propose a modified Izhikevich model with an adaptive threshold. In our two-stage estimation approach, a linear least squares method and a linear model of the threshold are derived to predict the location of neuronal spikes. However, desired results are not obtained and the predicted model is unsuccessful in duplicating the spike locations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptive threshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equipped with an adaptive threshold, a constrained iterative linear least squares method is implemented to fit the model to the reference data. Through manipulation of the system dynamics, the threshold voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parametrized realizable model is then utilized inside a prediction error based framework to identify the threshold parameters with the purpose of predicting single neuron precise firing times. This estimation scheme is evaluated using both synthetic data obtained from an exact model as well as the experimental data obtained from in vitro rat somatosensory cortical neurons. Results show the ability of this approach to fit the MAT model to different types of reference data.

In general, PN code synchronization consists of two steps: PN code acquisition (coarse alignment) and PN code tracking (fine alignment), to estimate the delay offset between received and locally generated codes. Recently, the schemes with a joint adaptive process of PN code acquisition and the weight coefficients of smart antenna have been proposed for improving the received signal-to-interference-plus-noise ratio (SINR) and simultaneously achieving better mean-acquisition-time (MAT) performance in direct-sequence code-division multiple access (DS-CDMA) systems. In which, the setting of the threshold plays an important role on the MAT performance. Often, the received SINR is varying, using the fixed threshold acquisition algorithms may result in undesirable performance. To improve the above problem, in this thesis, a new adaptive threshold scheme is devised in a joint adaptive code acquisition and beam-forming DS-CDMA receiver for code acquisition under a fading multipath and additive white Gaussian-noise (AWGN) channels. The basic idea of this new adaptive threshold scheme is to estimate the averaged output power of smart antenna to scale a reference threshold for each observation interval, such that it can approximately achieve a constant false alarm rate (CFAR) criteria. The system probabilities of the proposed scheme are derived for evaluating MAT under a slowly fading two-paths channels. Numerical analyses and simulation results demonstrate that the proposed adaptive threshold scheme does achieve better performance, in terms of the output SINR, the detection probability and the MAT, compared to a fixed threshold method.

La bioélectronique est un domaine transdisciplinaire qui oeuvre, entre autres, àl’interconnexion entre des systèmes biologiques présentant une activité électrique et le mondede l’électronique. Cette communication avec le vivant implique l’observation de l’activitéélectrique des cellules considérées et nécessite donc une chaine d’acquisition électronique.L’utilisation de Multi/Micro Electrodes Array débouche sur des systèmes devantacquérir un grand nombre de canaux en parallèle, dès lors la consommation etl’encombrement des circuits d’acquisition ont un impact significatif sur la viabilité dusystème destiné à être implanté.Cette thèse propose deux réflexions à propos de ces circuits d’acquisition. Une ces desréflexions a trait aux circuits d’amplification, à leur impédance d’entrée et à leurconsommation ; l’autre concerne un détecteur de potentiels d’action analogique, samodélisation et son optimisation.Ces travaux théoriques ayant abouti à des résultats concrets, un ASIC a été conçu,fabriqué, testé et caractérisé au cours de cette thèse. Cet ASIC à huit canaux comporte doncdes amplificateurs et des détecteurs de potentiels d’action analogiques et constitue le principalapport de ce travail de thèse. / Bioelectronics is a transdisciplinary field which develops interconnection devicesbetween biological systems presenting electrical activity and the world of electronics. Thiscommunication with living tissues implies to observe the electrical activity of the cells andtherefore requires an electronic acquisition chain.The use of Multi / Micro Electrode Array leads to systems that acquire a large numberof parallel channels, thus consumption and congestion of acquisition circuits have asignificant impact on the viability of the system to be implanted.This thesis proposes two reflections about these acquisition circuits. One of thesereflections relates to amplifier circuits, their input impedance and consumption; the otherconcerns an analogue action potentials detector, its modeling and optimization.These theoretical work leading to concrete results, an ASIC was designed,manufactured, tested and characterized in this thesis. This eight-channel ASIC thereforeincludes amplifiers and analogue action potentials detector and is the main contribution of thisthesis.

La thèse porte sur la conception et l'analyse mathématique de méthodes de Monte Carlo fiables et précises pour l'estimation de la (très petite) probabilité qu'un processus de Markov atteigne une région critique de l'espace d'état avant un instant final déterministe. L'idée sous-jacente aux méthodes de branchement multi-niveaux étudiées ici est de mettre en place une suite emboitée de régions intermédiaires de plus en plus critiques, de telle sorte qu'atteindre une région intermédiaire donnée sachant que la région intermédiaire précédente a déjà été atteinte, n'est pas si rare. En pratique, les trajectoires sont propagées, sélectionnées et répliquées dès que la région intermédiaire suivante est atteinte, et il est facile d'estimer avec précision la probabilité de transition entre deux régions intermédiaires successives. Le biais dû à la discrétisation temporelle des trajectoires du processus de Markov est corrigé en utilisant des régions intermédiaires perturbées, comme proposé par Gobet et Menozzi. Une version adaptative consiste à définir automatiquement les régions intermédiaires, à l’aide de quantiles empiriques. Néanmoins, une fois que le seuil a été fixé, il est souvent difficile voire impossible de se rappeler où (dans quel état) et quand (à quel instant) les trajectoires ont dépassé ce seuil pour la première fois, le cas échéant. La contribution de la thèse consiste à utiliser une première population de trajectoires pilotes pour définir le prochain seuil, à utiliser une deuxième population de trajectoires pour estimer la probabilité de dépassement du seuil ainsi fixé, et à itérer ces deux étapes (définition du prochain seuil, et évaluation de la probabilité de transition) jusqu'à ce que la région critique soit finalement atteinte. La convergence de cet algorithme adaptatif à deux étapes est analysée dans le cadre asymptotique d'un grand nombre de trajectoires. Idéalement, les régions intermédiaires doivent êtres définies en terme des variables spatiale et temporelle conjointement (par exemple, comme l'ensemble des états et des temps pour lesquels une fonction scalaire de l’état dépasse un niveau intermédiaire dépendant du temps). Le point de vue alternatif proposé dans la thèse est de conserver des régions intermédiaires simples, définies en terme de la variable spatiale seulement, et de faire en sorte que les trajectoires qui dépassent un seuil précocement sont davantage répliquées que les trajectoires qui dépassent ce même seuil plus tardivement. L'algorithme résultant combine les points de vue de l'échantillonnage pondéré et du branchement multi-niveaux. Sa performance est évaluée dans le cadre asymptotique d'un grand nombre de trajectoires, et en particulier un théorème central limite est obtenu pour l'erreur d'approximation relative. / The thesis deals with the design and mathematical analysis of reliable and accurate Monte Carlo methods in order to estimate the (very small) probability that a Markov process reaches a critical region of the state space before a deterministic final time. The underlying idea behind the multilevel splitting methods studied here is to design an embedded sequence of intermediate more and more critical regions, in such a way that reaching an intermediate region, given that the previous intermediate region has already been reached, is not so rare. In practice, trajectories are propagated, selected and replicated as soon as the next intermediate region is reached, and it is easy to accurately estimate the transition probability between two successive intermediate regions. The bias due to time discretization of the Markov process trajectories is corrected using perturbed intermediate regions as proposed by Gobet and Menozzi. An adaptive version would consist in the automatic design of the intermediate regions, using empirical quantiles. However, it is often difficult if not impossible to remember where (in which state) and when (at which time instant) did each successful trajectory reach the empirically defined intermediate region. The contribution of the thesis consists in using a first population of pilot trajectories to define the next threshold, in using a second population of trajectories to estimate the probability of exceeding this empirically defined threshold, and in iterating these two steps (definition of the next threshold, and evaluation of the transition probability) until the critical region is reached. The convergence of this adaptive two-step algorithm is studied in the asymptotic framework of a large number of trajectories. Ideally, the intermediate regions should be defined in terms of the spatial and temporal variables jointly (for example, as the set of states and times for which a scalar function of the state exceeds a time-dependent threshold). The alternate point of view proposed in the thesis is to keep intermediate regions as simple as possible, defined in terms of the spatial variable only, and to make sure that trajectories that manage to exceed a threshold at an early time instant are more replicated than trajectories that exceed the same threshold at a later time instant. The resulting algorithm combines importance sampling and multilevel splitting. Its preformance is evaluated in the asymptotic framework of a large number of trajectories, and in particular a central limit theorem is obtained for the relative approximation error.