Des techniques statistiques de détection d'objet ponctuel dans le ciel ou résolu dans la mer dans des images infrarouges de veille panoramique sont développées. Ces techniques sont adaptées aux inhomogénéités présentes dans ce type d'image. Elles ne sont fondées que sur l'analyse de l'information spatiale et ont pour objectif de maîtriser le taux de fausse alarme sur chaque image. Pour les zones de ciel, une technique conjointe de segmentation et détection adaptée aux variations spatiales de la luminosité moyenne est mise en œuvre et l'amélioration des performances auxquelles elle conduit est analysée. Pour les zones de mer, un détecteur de bord à taux de fausse alarme constant en présence d'inhomogénéités et de corrélations spatiales des niveaux de gris est développé et caractérisé. Dans chaque cas, la prise en compte des inhomogénéités dans les algorithmes statistiques s'avère essentielle pour maîtriser le taux de fausse alarme et améliorer les performances de détection. / Statistical detection techniques of point target in the sky or resolved target in the sea in infrared surveillance system images are developed. These techniques are adapted to inhomogeneities present in this kind of images. They are based on the spatial information analysis and allow the control of the false alarm rate in each image.For sky areas, a joint segmentation detection technique adapted to spatial variations of the mean luminosity is developed and its performance improvement is analyzed. For sea areas, an edge detector with constant false alarm rate when inhomogeneities and grey level spatial correlations are present is developed and characterized. In each case, taking into account the inhomogeneities in these statistical algorithms is essential to control the false alarm rate and to improve the detection performance.

The content of this dissertation is divided into two main topics: 1) nonlinear profile monitoring and 2) an improved approximate distribution for the T² statistic based on the successive differences covariance matrix estimator. Part 1: Nonlinear Profile Monitoring In an increasing number of cases the quality of a product or process cannot adequately be represented by the distribution of a univariate quality variable or the multivariate distribution of a vector of quality variables. Rather, a series of measurements are taken across some continuum, such as time or space, to create a profile. The profile determines the product quality at that sampling period. We propose Phase I methods to analyze profiles in a baseline dataset where the profiles can be modeled through either a parametric nonlinear regression function or a nonparametric regression function. We illustrate our methods using data from Walker and Wright (2002) and from dose-response data from DuPont Crop Protection. Part 2: Approximate Distribution of T² Although the T² statistic based on the successive differences estimator has been shown to be effective in detecting a shift in the mean vector (Sullivan and Woodall (1996) and Vargas (2003)), the exact distribution of this statistic is unknown. An accurate upper control limit (UCL) for the T² chart based on this statistic depends on knowing its distribution. Two approximate distributions have been proposed in the literature. We demonstrate the inadequacy of these two approximations and derive useful properties of this statistic. We give an improved approximate distribution and recommendations for its use. / Ph. D.

Advances in digital signal processing (DSP) and computing technology have resulted in the emergence of increasingly adaptive radar systems. It is clear that the Electronic Attack (EA), or jamming, of such radar systems is expected to become a more difficult task. The reason for this research was to address the issue of jamming adaptive radar systems. This required consideration of adaptive jamming systems and the development of a methodology for outlining the features of such a system is proposed as the key contribution of this thesis. For the first time, game-based optimization methods have been applied to a maritime counter-surveillance/counter-targeting scenario involving conventional, as well as so-called ‘smart’ noise jamming.Conventional noise jamming methods feature prominently in the origins of radar electronic warfare, and are still widely implemented. They have been well studied, and are important for comparisons with coherent jamming techniques.Moreover, noise jamming is more readily applied with limited information support and is therefore germane to the problem of jamming adaptive radars; during theearly stages when the jammer tries to learn about the radar’s parameters and its own optimal actions.A radar and a jammer were considered as informed opponents ‘playing’ in a non-cooperative two-player, zero-sum game. The effects of jamming on the target detection performance of a radar using Constant False Alarm Rate (CFAR)processing were analyzed using a game theoretic approach for three cases: (1) Ungated Range Noise (URN), (2) Range-Gated Noise (RGN) and (3) False-Target (FT) jamming.Assuming a Swerling type II target in the presence of Rayleigh-distributed clutter, utility functions were described for Cell-Averaging (CA) and Order Statistic (OS) CFAR processors and the three cases of jamming. The analyses included optimizations of these utility functions, subject to certain constraints, with respectto control variables (strategies) in the jammer, such as jammer power and spatial extent of jamming, and control variables in the radar, such as threshold parameter and reference window size. The utility functions were evaluated over the players’ strategy sets and the resulting matrix-form games were solved for the optimal or ‘best response’ strategies of both the jammer and the radar.

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.

Advances in digital signal processing (DSP) and computing technology have resulted in the emergence of increasingly adaptive radar systems. It is clear that the Electronic Attack (EA), or jamming, of such radar systems is expected to become a more difficult task. The reason for this research was to address the issue of jamming adaptive radar systems. This required consideration of adaptive jamming systems and the development of a methodology for outlining the features of such a system is proposed as the key contribution of this thesis. For the first time, game-based optimization methods have been applied to a maritime counter-surveillance/counter-targeting scenario involving conventional, as well as so-called ‘smart’ noise jamming.Conventional noise jamming methods feature prominently in the origins of radar electronic warfare, and are still widely implemented. They have been well studied, and are important for comparisons with coherent jamming techniques.Moreover, noise jamming is more readily applied with limited information support and is therefore germane to the problem of jamming adaptive radars; during theearly stages when the jammer tries to learn about the radar’s parameters and its own optimal actions.A radar and a jammer were considered as informed opponents ‘playing’ in a non-cooperative two-player, zero-sum game. The effects of jamming on the target detection performance of a radar using Constant False Alarm Rate (CFAR)processing were analyzed using a game theoretic approach for three cases: (1) Ungated Range Noise (URN), (2) Range-Gated Noise (RGN) and (3) False-Target (FT) jamming.Assuming a Swerling type II target in the presence of Rayleigh-distributed clutter, utility functions were described for Cell-Averaging (CA) and Order Statistic (OS) CFAR processors and the three cases of jamming. The analyses included optimizations of these utility functions, subject to certain constraints, with respectto control variables (strategies) in the jammer, such as jammer power and spatial extent of jamming, and control variables in the radar, such as threshold parameter and reference window size. The utility functions were evaluated over the players’ strategy sets and the resulting matrix-form games were solved for the optimal or ‘best response’ strategies of both the jammer and the radar.

Les océans subissent des pressions d'origine anthropique particulièrement fortes comme la surpêche, la pollution physico-chimique, et le bruit rayonné par les activités industrielles et militaires. Cette thèse se place dans un contexte de compréhension de l'impact du bruit rayonné dans les océans sur les mammifères marins. L'acoustique passive joue donc un rôle fondamental dans ce problème. Ce travail aborde la tâche de détection de signatures acoustiques de mammifères marins dans le spectrogramme. Cette tâche est difficile pour deux raisons : 1. le bruit océanique a une structure complexe (non-stationnaire, coloré), 2. les signaux de mammifères marins sont inconnus et possèdent eux aussi une structure complexe (non-stationnaires bande étroite et/ou impulsionnels). Le problème doit donc être résolu de manière locale en temps-fréquence, et ne pas faire d'hypothèse a priori sur le signal. Des détecteurs statistiques basés uniquement sur la connaissance des statistiques du bruit dans le spectrogramme existent, mais souffrent deux lacunes : 1. leurs performances en terme de probabilité de fausse alarme/ probabilité de détection se dégradent fortement à faible rapport signal à bruit, et 2. ils ne sont pas capables de séparer les signaux à bande étroite des signaux impulsionnels. Ce travail apporte des pistes de réflexion sur ces problèmes.L'originalité de ce travail de thèse repose dans la formulation d'un test d'hypothèse binaire prenant explicitement en compte l'organisation spatiale des pics temps-fréquence. Nous introduisons une méthode d'Analyse de la Densité des Fausses Alarmes (FADA) qui permet de discriminer les régions temps-fréquence abritant le signal de celles n'abritant que du bruit. Plus précisément,le nombre de fausses alarmes dans une région du plan est d'abord modélisé par une loi binomiale, puis par une loi binomiale corrélée, afin de prendre en considération la redondance du spectrogramme. Le test d'hypothèse binaire est résolu par une approche de Neyman-Pearson. Nous démontrons numériquement la pertinence de cette approche et nous la validons sur données réelles de mammifères marins disposant d'une grande variété de signaux et de conditions de bruit. En particulier, nous illustrons la capacité de FADA à discriminer efficacement le signal du bruit en milieu fortement impulsionnel. / The oceans experience heavy anthropogenic pressure due to overfishing, physico-chemical pollution, and noise radiated by industrial and military activities. This work focuses on the use of passive acoustic monitoring of the oceans, as a tool to understand the impact of radiated noise on marine ecosystems, and particularly on marine mammals. This work tackles the task of detection of acoustical signals of marine mammals using the spectrogram. This task is uneasy for two reasons : 1. the ocean noise structure is complex (non-stationary and colored) and 2. the signals of interest are unknown and also shows a complex structure (non-stationary narrow band and/or impulsive). The problem therefore must be solved locally without making a priori hypothesis on the signal. Statistical detectors only based on the local analysis of the noise spectrogram coefficients are available, making them suitable for this problem. However, these detectors suffer two disadvantages : 1. the trade-offs false alarm probability/ detection probability that are available for low signal tonoise ratio are not satisfactory and 2. the separation between narrow-band and impulsive signals is not possible. This work brings some answers to these problems.The main contribution of this work is to formulate a binary hypothesis test taking explicitly in account the spatial organization of time-frequency peaks. We introduce the False Alarm Density Analysis (FADA) framework that efficiently discriminates time-frequency regions hosting signal from the ones hosting noise only. In particular the number of false alarms in regions of the binary spectrogram is first modeled by a binomial distribution, and then by a correlated binomial distribution to take in account the spectrogram redundancy. The binary hypothesis test is solved using a Neyman-Pearson criterion.We demonstrate the relevance of this approach on simulated data and validate the FADA detector on a wide variety of real signals. In particular we show the capability of the proposed method to efficiently detect signals in highly impulsive environment.

Abstract Cognitive radio (CR) is becoming a promising tool for solving the problem of the scarce radio resource and spectrum inefficiency. Spectrum sensing (signal detection) enables real-time detection of spectrum holes by unlicensed secondary users (SUs) in cognitive radio networks (CRNs). In this thesis, performance analysis of CRNs and radio resource allocation are considered. A continuous time Markov chain (CTMC) based analytical model taking into account all relevant elements as well as addressing the issue of the false alarm rate (FAR) associated with the continuous sensing is developed. In some cases, the PU can be modeled as time-slotted with constant state (transmitting or not) in each slot. In this case, assuming SU can synchronize to the slots, its intuitive to use beginning of a slot for sensing and rest (possibly) for communication. For this model, M/D/1 priority queueing scheme has been applied in this thesis to find waiting time and queue length for PU and SU. Multiple access among SUs in a time-slotted channel is considered next. A conventional method is e.g. using a channel access probability ψ in each slot similar to the slotted ALOHA. A radically new idea is introduced in this thesis: why not increase the false alarm probability PFA of each SU and use it as a multiple access method? A game theoretic approach to radio resource allocation for the downlink capacity providing fair resource sharing among mobile nodes located along a multihop link is presented. Furthermore, the problem of resource allocations in heterogeneous wireless networks is also studied. Finally, device-to-device (D2D) communication - with localized distribution, where users tend to gather around some areas (clusters/hot-spots) within the cell such as buildings is studied. Theoretical analysis with two dimensional clustering is presented including cases with correlated clusters. Correlation in cluster selection is shown to significantly improve performance. / Tiivistelmä Kognitiivinen radio (CR) on nousemassa lupaavaksi työkaluksi niukkojen radioresurssien ja spektrin käytön tehottomuuden ratkaisemisessa. Spektrin nuuskiminen (signaalin ilmaisu) mahdollistaa spektriaukkojen reaaliaikaisen tunnistamisen toissijaisten käyttäjien (SU) toimesta kognitiivisissa radioverkoissa (CRN). Tässä väitöskirjassa painotus on CRN verkkojen suorituskykyanalyysissa ja radioresurssien hallinnassa. Työssä kehitetään jatkuva-aikaiseen Markov ketjuun (CTMC) perustuva analyyttinen malli joka ottaa huomioon kaikki olennaiset asiat mukaan lukien jatkuva-aikaiseen spektrin nuuskimiseen liittyvän väärien hälytysten tiheyden (FAR). Joissakin tapauksissa PU:ta voidaan mallintaa aikajaoteltuna siten että PU:n tila on vakio kussakin aikavälissä. Olettaen että SU voi synkronoitua aikaväleihin, on intuitiivista käyttää aikavälin alkua nuuskimiselle ja loppuosaa (mahdollisesti) viestintää varten. M/D/1:n ensisijaisuus-jonotus-suunnitelmaa soveltamalla tässä väitöskirjassa saadaan tuloksia odotusajalle ja jonon pituudelle sekä SU:lle että PU:lle. Seuraavaksi käsitellään monikäyttöä SU:den joukossa aikajaotellussa kanavassa. Tavanomainen menetelmä käyttää esimerkiksi kanavapääsytodennäköisyyttä ψ kussakin aikavälissä vastaten aikajaoteltua ALOHA protokollaa. Tässä väitöskirjassa esitetään radikaali uusi idea: miksei lisätä väärän hälytyksen todennäköisyyttä kussakin SU:ssa ja käytetä sitä moniliittymämenetelmänä? Työssä esitetään peliteoreettinen lähestymistapa radioresurssien allokointiin siten että resurssit jaetaan oikeudenmukaisesti monen yhteysvälin linkeissä. Lisäksi tutkitaan myös resursoinnin ongelmaa heterogeenisissa langattomissa verkoissa. Lopuksi tutkitaan laitteiden välistä suoraa viestintää (D2D) paikallisen jakauman kanssa, jossa käyttäjillä on tapana kasaantua solun sisällä esim. rakennuksiin. Esitetään teoreettinen analyysi kaksiulotteisella klusteroinnilla myös korreloitujen ryhmien kanssa. Osoitetaan että korrelaatio ryhmän valinnassa parantavaa merkittävästi suorituskykyä.

Είναι γνωστό από τη Θεωρία της Πληροφορίας, πως η δειγματοληψία σημάτων ακολουθεί το Θεώρημα των Shannon-Nyquist. Σύμφωνα με το θεώρημα αυτό, για την εκτέλεση της δειγματοληψίας ενός σήματος χωρίς απώλεια πληροφορίας, ο ρυθμός δειγματοληψίας αυτού θα πρέπει να είναι τουλάχιστον δύο φορές μεγαλύτερος από τη μεγαλύτερη συχνότητα που εμφανίζεται στο φάσμα του σήματος. Αυτή τη θεωρία κατάφερε – κατά κάποιο τρόπο - να ανατρέψει το 2006 μια νέα, αυτή της Συμπιεσμένης Καταγραφής που ξεκίνησε από δύο επιστημονικές εργασίες των Donoho, Candes, Romberg και Tao και η οποία έρχεται να αλλάξει τα έως σήμερα δεδομένα. Σήμερα, λίγα έτη αργότερα, μια αφθονία θεωρητικών πτυχών της συμπιεσμένης καταγραφής εξερευνάται ήδη σε περισσότερες από 1000 δημοσιεύσεις. Οι εφαρμογές αυτής της τεχνικής εκτείνονται και σε άλλα πεδία όπως η επεξεργασία εικόνας, η μαγνητική τομογραφία, η ανάλυση γεωφυσικών δεδομένων, η επεξεργασία εικόνας radar, η αστρονομία κ.α. Η μέθοδος της συμπιεσμένης καταγραφής ή αλλιώς Compressed Sensing ή Compressed Sampling, όπως αυτή είναι γνωστή στη βιβλιογραφία, στηρίζεται στη δυνατότητα ανακατασκευής αραιών σημάτων από πλήθος δειγμάτων αισθητά κατώτερο από αυτό που προβλέπει το θεωρητικό όριο του Nyquist. Έχει αποδειχθεί ότι, η ανακατασκευή αυτή είναι δυνατή όταν το σήμα ή έστω κάποιος μετασχηματισμός του περιέχει λίγα μη μηδενικά στοιχεία σε σχέση με το μήκος του. Στα πλαίσια αυτής της εργασίας παρουσιάζονται οι βασικές αρχές που διέπουν την ανακατασκευή αραιών σημάτων μέσω της επίλυσης υπο-ορισμένων συστημάτων γραμμικών εξισώσεων. Στη συγκεκριμένη εργασία, γίνεται μία προσπάθεια εφαρμογής της εν λόγω μεθόδου στα ανερχόμενα Cognitive Radio δίκτυα (Cognitive Radio Networks - CRN) τα οποία εμφανίζουν την ιδιότητα Spectrum Sharing. Σύμφωνα με αυτή την ιδιότητα, δηλαδή, το διαμοιρασμό του διαθέσιμου φάσματος, ο πρωταρχικός στόχος, είναι η ανίχνευση και η αναγνώριση των λεγόμενων spectrum holes σε ασύρματο περιβάλλον. Πιο συγκεκριμένα, παρουσιάζεται μια Distributed (κατανεμημένη) προσέγγιση συμπιεσμένης καταγραφής φάσματος για (τα ultra-) Wideband Cognitive Radio δίκτυα. Η τεχνική Compressed Sensing εφαρμόζεται σε τοπικά CRs του δικτύου, προκειμένου να ανιχνεύσει το υπερ-ευρύ φάσμα (ultra-wideband) με ρεαλιστική πολυπλοκότητα ανάκτησης του αρχικού σήματος. Οι φασματικές εκτιμήσεις από πολλαπλούς τοπικούς CRs του δικτύου «συνενώνονται» για να αποκομίσουν το χωρικό κέρδος ποικιλομορφίας (spatial diversity gain), το οποίο όσο αυξάνεται, βελτιώνει την ποιότητα ανίχνευσης, ειδικά στην περίπτωση των υπό εξασθένιση καναλιών (channel fading effect). Αρχικά, μελετάται ένας κατανεμημένος αλγόριθμος πλειοψηφίας (Distributed Consensus Algorithm) για να επιτευχθεί η συνεργασία κατά το στάδιο της ανίχνευσης της πληροφορίας που μεταφέρεται στο δίκτυο και έπειτα η αποστολή αυτής σε ένα fusion center. Αυτού του είδους ο distributed αλγόριθμος που χρησιμοποιεί μόνο one-hop επικοινωνία, συγκλίνει γρήγορα σε συνολικά βέλτιστες λύσεις που λειτουργούν με χαμηλό φόρτο επικοινωνίας και υπολογισμού που είναι ανάλογο του μεγέθους του δικτύου. Ένα σενάριο που εξετάζεται στο πλαίσιο αυτής της εργασίας, είναι η συγκεντρωτική ανίχνευση φάσματος ευρείας ζώνης με επικαλυπτόμενες συχνότητες ή αλλιώς κανάλια που είναι κοινά (frequency overlapping) σε Cognitive Radio δίκτυα και τα οποία, χρησιμοποιούν την τεχνική Compressed Sensing καθώς επίσης και την από κοινού ανακατασκευή (Joint Reconstruction) του αρχικού σήματος. Τέλος, προτείνεται ένα σενάριο, μιας κατανεμημένης αυτή τη φορά, τεχνικής ανίχνευσης φάσματος, που βασίζεται σε κανόνες πλειοψηφίας. Τα αποτελέσματα της προσομοίωσης, σε περιβάλλον Matlab, επιβεβαιώνουν την αποτελεσματικότητα αυτής της προτεινόμενης προσέγγισης, δηλαδή την ανίχνευση φάσματος, από συνδυασμό Cognitive Radio δικτύων με αραιά επικαλυπτόμενες συχνότητες. / It is well known from Information Theory, that the sampling of signals should be performed as dictated by the celebrated Shannon – Nyquist theorem. According to this theorem, in order to fully recover a signal from its samples, it must be sampled at a sampling rate that should be at least twice the bandwidth of the signal. This theory has been significantly extended over the past few years by the advent of the so-called Compressed Sensing theory, which first appeared in seminal scientific articles of Donoho, Candes, Romberg and Tao in 2006. Nowadays, an abundance of theoretical aspects of compressed sensing is already explored in more than 1000 articles. Τhis technique has been applied in various fields such as image processing, magnetic tomography, analysis of geophysical data, radar image processing, astronomy etc. The method of Compressed Sensing, also known as Compressed Sampling, is related to the reconstruction of sparse signals from far fewer samples or measurements than what the theoretical limit of Nyquist suggests. It has been proved that, this reconstruction is possible when the signal or a transformation of it, contains just a few non-zero elements with respect to its length. In this work, we firstly summarize the basic principles that condition the reconstruction of sparse signals via the solution of underdetermined systems of linear equations. Next, in this Master Thesis we aim at implementing Compressed Sensing method in emerging Cognitive Radio (CR) networks with spectrum sharing. The first cognitive task preceding any dynamic spectrum access is the sensing and identification of spectral holes in wireless environments. In more detail, this work is mainly concerned with a distributed compressed spectrum sensing approach for (ultra-)wideband CR networks. Compressed sensing is performed at local CRs to scan the very wide spectrum at practical signal-acquisition complexity. Meanwhile, spectral estimates from multiple local CR detectors are fused to collect spatial diversity gain, which improves the sensing quality especially under fading channels. Initially, a distributed consensus algorithm is analyzed for collaborative sensing and fusion in a scenario where all nodes are estimating the same spectral bands. Using only one-hop local communications, this distributed algorithm converges fast to the globally optimal solutions, at low communication and computation load scalable to the network size. Another scenario that has been investigated in this thesis is the joint wideband spectrum sensing in frequency overlapping cognitive radio networks, using centralized compressive sensing techniques. Finally, for the latter scenario, a distributed compressive sensing technique, based on consensus, has been proposed. Simulation results in Matlab environment verify the effectiveness of proposed joint spectrum sensing approach in jointly sparse frequency overlapping cognitive radio networks.

This thesis deals with the problem of newborn seizre detection from the Electroencephalogram (EEG) signals. The ultimate goal is to design an automated seizure detection system to assist the medical personnel in timely seizure detection. Seizure detection is vital as neurological diseases or dysfunctions in newborn infants are often first manifested by seizure and prolonged seizures can result in impaired neuro-development or even fatality. The EEG has proved superior to clinical examination of newborns in early detection and prognostication of brain dysfunctions. However, long-term newborn EEG signals acquisition is considerably more difficult than that of adults and children. This is because, the number of the electrodes attached to the skin is limited by the size of the head, the newborns EEGs vary from day to day, and the newborns are reluctant of being in the recording situation. Also, the movement of the newborn can create artifact in the recording and as a result strongly affect the electrical seizure recognition. Most of the existing methods for neonates are either time or frequency based, and, therefore, do not consider the non-stationarity nature of the EEG signal. Thus, notwithstanding the plethora of existing methods, this thesis applies the discrete wavelet transform (DWT) to account for the non-stationarity of the EEG signals. First, two methods for seizure detection in neonates are proposed. The detection schemes are based on observing the changing behaviour of a number of statistical quantities of the wavelet coefficients (WC) of the EEG signal at different scales. In the first method, the variance and mean of the WC are considered as a feature set to dassify the EEG data into seizure and non-seizure. The test results give an average seizure detection rate (SDR) of 97.4%. In the second method, the number of zero-crossings, and the average distance between adjacent extrema of the WC of certain scales are extracted to form a feature set. The test obtains an average SDR of 95.2%. The proposed feature sets are both simple to implement, have high detection rate and low false alarm rate. Then, in order to reduce the complexity of the proposed schemes, two optimising methods are used to reduce the number of selected features. First, the mutual information feature selection (MIFS) algorithm is applied to select the optimum feature subset. The results show that an optimal subset of 9 features, provides SDR of 94%. Compared to that of the full feature set, it is clear that the optimal feature set can significantly reduce the system complexity. The drawback of the MIFS algorithm is that it ignores the interaction between features. To overcome this drawback, an alternative algorithm, the mutual information evaluation function (MIEF) is then used. The MIEF evaluates a set of candidate features extracted from the WC to select an informative feature subset. This function is based on the measurement of the information gain and takes into consideration the interaction between features. The performance of the proposed features is evaluated and compared to that of the features obtained using the MIFS algorithm. The MIEF algorithm selected the optimal 10 features resulting an average SDR of 96.3%. It is also shown, an average SDR of 93.5% can be obtained with only 4 features when the MIEF algorithm is used. In comparison with results of the first two methods, it is shown that the optimal feature subsets improve the system performance and significantly reduce the system complexity for implementation purpose.

[pt] A probabilidade de alarme falso, alfa, dos gráficos de controle de processos depende dos seus limites de controle, que, por sua vez, dependem de estimativas dos parâmetros do processo. Esta tese apresenta inicialmente uma revisão dos principais trabalhos sobre o efeito dos erros de estimação dos parâmetros do processo sobre alfa quando se utiliza o gráfico de X e S individualmente e em conjunto. O desempenho dos gráficos é medido através de medidas de desempenho (número médio de amostras até o sinal, taxa de alarme falso, distribuição do número de amostras até o sinal, que, em geral, são variáveis aleatórias, função dos erros de estimação. Pesquisas recentes têm focado nas propriedades da distribuição condicional do número de amostras até o sinal, ou ainda, nas propriedades da distribuição da taxa de alarme-falso condicional. Esta tese adota esta abordagem condicional e analisa o efeito da estimação dos parâmetros do processo no desempenho conjunto dos gráficos de X e S em dois casos: Caso KU (Média conhecida – Variância desconhecida) e Caso UU (Média desconhecida – Variância desconhecida). A quase totalidade dos trabalhos anteriores considerou apenas um gráfico, isoladamente; sobre efeito da estimação dos parâmetros sobre o desempenho conjunto conhecemos apenas um trabalho, sobre gráficos de X e R, mas nenhum sobre gráficos de X e S. Os resultados da análise mostram que o desempenho dos gráficos pode ser muito afetado pela estimação de parâmetros e que o número de amostras iniciais requerido para garantir um desempenho desejado é muito maior que os números tradicionalmente recomendados na literatura normativa de controle estatístico de processo (livros texto e manuais). Esse número é, porém, menor que o máximo entre os números requeridos para os gráficos de X e de S individualmente. Questões a serem investigadas como desdobramento dessa pesquisa são também indicadas nas Considerações Finais e Recomendações. / [en] The false-alarm rate of control charts, alpha, depends on the control limits calculated, which depend, in turn, on the estimated process parameters. This dissertation initially presents a review of the main research articles about the effect of the estimation errors of the process parameters upon alpha when X and S charts are used separately and together. The charts performance is evaluated through performance measures (average run-length, false-alarm rate, run-length distribution, etc), which are, in general, random variables, function of the estimation errors. Recent researches focused on the properties of the conditional run-length, or still (in the case of Shewhart charts) on the properties of the conditional false-alarm rate distribution. This dissertation adopts this conditional approach and investigates the effect of parameter estimation on the joint behavior of X and S charts in two cases: KU Case (Known mean – Unknown variance) and UU Case (Unknown mean - Unknown variance). Almost all previous works considered just only one chart separately – just only one joint performance work is known by the author, one about the effect of the estimation errors of the process parameters upon X e R joint performance. The results show that the charts performance can be severely affected by the parameter estimation and the number of initial samples required to ensure the desirable performance is greater than the numbers of initial samples recommended by traditional statistical process control reference texts (books and manuals). This number is, however, smaller than the maximum between the numbers of samples required by the X and the S charts separately. Additional issues for follow-up research are recommended in the concluding section.