Reconhecimento de padrões em imagens é uma área de grande interesse no mundo científico. Os chamados métodos de extração de características, possuem as habilidades de extrair características das imagens e também de reduzir a dimensionalidade dos dados gerando assim o chamado vetor de características. Considerando uma imagem de consulta, o foco de um sistema de reconhecimento de imagens de faces humanas é pesquisar em um banco de imagens, a imagem mais similar à imagem de consulta, de acordo com um critério dado. Este trabalho de pesquisa foi direcionado para a geração de vetores de características para um sistema de reconhecimento de imagens, considerando bancos de imagens de faces humanas, para propiciar tal tipo de consulta. Um vetor de características é uma representação numérica de uma imagem ou parte dela, descrevendo seus detalhes mais representativos. O vetor de características é um vetor n-dimensional contendo esses valores. Essa nova representação da imagem propicia vantagens ao processo de reconhecimento de imagens, pela redução da dimensionalidade dos dados. Uma abordagem alternativa para caracterizar imagens para um sistema de reconhecimento de imagens de faces humanas é a transformação do domínio. A principal vantagem de uma transformação é a sua efetiva caracterização das propriedades locais da imagem. As wavelets diferenciam-se das tradicionais técnicas de Fourier pela forma de localizar a informação no plano tempo-freqüência; basicamente, têm a capacidade de mudar de uma resolução para outra, o que as fazem especialmente adequadas para análise, representando o sinal em diferentes bandas de freqüências, cada uma com resoluções distintas correspondentes a cada escala. As wavelets foram aplicadas com sucesso na compressão, melhoria, análise, classificação, caracterização e recuperação de imagens. Uma das áreas beneficiadas onde essas propriedades tem encontrado grande relevância é a área de visão computacional, através da representação e descrição de imagens. Este trabalho descreve uma abordagem para o reconhecimento de imagens de faces humanas com a extração de características baseado na decomposição multiresolução de wavelets utilizando os filtros de Haar, Daubechies, Biorthogonal, Reverse Biorthogonal, Symlet, e Coiflet. Foram testadas em conjunto as técnicas PCA (Principal Component Analysis) e IMPCA (Image Principal Component Analysis), sendo que os melhores resultados foram obtidos utilizando a wavelet Biorthogonal com a técnica IMPCA / Image pattern recognition is an interesting area in the scientific world. The features extraction method refers to the ability to extract features from images, reduce the dimensionality and generates the features vector. Given a query image, the goal of a features extraction system is to search the database and return the most similar to the query image according to a given criteria. Our research addresses the generation of features vectors of a recognition image system for human faces databases. A feature vector is a numeric representation of an image or part of it over its representative aspects. The feature vector is a n-dimensional vector organizing such values. This new image representation can be stored into a database and allow a fast image retrieval. An alternative for image characterization for a human face recognition system is the domain transform. The principal advantage of a transform is its effective characterization for their local image properties. In the past few years researches in applied mathematics and signal processing have developed practical wavelet methods for the multi scale representation and analysis of signals. These new tools differ from the traditional Fourier techniques by the way in which they localize the information in the time-frequency plane; in particular, they are capable of trading on type of resolution for the other, which makes them especially suitable for the analysis of non-stationary signals. The wavelet transform is a set basis function that represents signals in different frequency bands, each one with a resolution matching its scale. They have been successfully applied to image compression, enhancement, analysis, classification, characterization and retrieval. One privileged area of application where these properties have been found to be relevant is computer vision, especially human faces imaging. In this work we describe an approach to image recognition for human face databases focused on feature extraction based on multiresolution wavelets decomposition, taking advantage of Biorthogonal, Reverse Biorthogonal, Symlet, Coiflet, Daubechies and Haar. They were tried in joint the techniques together the PCA (Principal Component Analysis) and IMPCA (Image Principal Component Analysis)

A taxonomia vegetal atualmente exige um grande esforço dos botânicos, desde o processo de aquisição do espécime até a morosa comparação com as amostras já catalogadas em um herbário. Nesse contexto, o projeto TreeVis surge como uma ferramenta para a identificação de vegetais por meio da análise de atributos foliares. Este trabalho é uma ramificação do projeto TreeVis e tem o objetivo de identificar vegetais por meio da análise do corte transversal de uma folha ampliado por um microscópio. Para tanto, foram extraídas assinaturas da cutícula, epiderme superior, parênquima paliçádico e parênquima lacunoso. Cada assinatura foi avaliada isoladamente por uma rede neural pelo método leave-one-out para verificar a sua capacidade de discriminar as amostras. Uma vez selecionados os vetores de características mais importantes, os mesmos foram combinados de duas maneiras. A primeira abordagem foi a simples concatenação dos vetores selecionados; a segunda, mais elaborada, reduziu a dimensionalidade (três atributos apenas) de algumas das assinaturas componentes antes de fazer a concatenação. Os vetores finais obtidos pelas duas abordagens foram testados com rede neural via leave-one-out para medir a taxa de acertos alcançada pelo sinergismo das assinaturas das diferentes partes da folha. Os experimentos consitiram na identificação de oito espécies diferentes e na identificação da espécie Gochnatia polymorpha nos ambientes Cerrado e Mata Ciliar, nas estações Chuvosa e Seca, e sob condições de Sol e Sombra / Currently, taxonomy demands a great effort from the botanists, ranging from the process of acquisition of the sample to the comparison with the species already classified in the herbarium. For this reason, the TreeVis is a project created to identify vegetal species using leaf attributes. This work is a part of the TreeVis project and aims at identifying vegetal species by analysing cross-sections of leaves amplified by a microscope. Signatures were extract from cuticle, adaxial epiderm, palisade parenchyma and sponge parenchyma. Each signature was analysed by a neural network with the leave-one-out method to verify its ability to identify species. Once the most important feature vectors were selected, two different approachs were adopted. The first was a simple concatenation of the selected feature vectors. The second, and more elaborated approach, consisted of reducing the dimensionality (three attributes only) of some component signatures before the feature vector concatenation. The final vectors obtained by these two approaches were tested by a neural network with leave-one-out to measure the correctness rate reached by the synergism of the signatures of different leaf regions. The experiments resulted in the identification of eight different species and the identification of the Gochnatia polymorpha species in Cerradão and Gallery Forest environments, Wet and Dry seasons, and under Sun and Shadow constraints

<p> </p><p>The today quarry planning at Klinthagen is rough, which provides an opportunity to introduce new exciting methods to improve the quarry gain and efficiency. Nordkalk AB, active at Klinthagen, wishes to start a new quarry at a nearby location. To exploit future quarries in an efficient manner and ensure production quality, multivariate statistics may help gather important information.</p><p>In this thesis the possibilities of the multivariate statistical approaches of Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were evaluated on the Klinthagen bore data. PCA data were spatially interpolated by Kriging, which also was evaluated and compared to IDW interpolation.</p><p>Principal component analysis supplied an overview of the variables relations, but also visualised the problems involved when linking geophysical data to geochemical data and the inaccuracy introduced by lacking data quality.</p><p>The PLS regression further emphasised the geochemical-geophysical problems, but also showed good precision when applied to strictly geochemical data.</p><p>Spatial interpolation by Kriging did not result in significantly better approximations than the less complex control interpolation by IDW.</p><p>In order to improve the information content of the data when modelled by PCA, a more discrete sampling method would be advisable. The data quality may cause trouble, though with sample technique of today it was considered to be of less consequence.</p><p>Faced with a single geophysical component to be predicted from chemical variables further geophysical data need to complement existing data to achieve satisfying PLS models.</p><p>The stratified rock composure caused trouble when spatially interpolated. Further investigations should be performed to develop more suitable interpolation techniques.</p>

Varje studie av en verklig process eller ett verkligt system är baserat på mätdata. Förr var den tillgängliga datamängden vid undersökningar ytterst begränsad, men med dagens teknik är mätdata betydligt mer lättillgängligt. Från att tidigare enbart haft få och ofta osammanhängande mätningar för någon enstaka variabel, till att ha många och så gott som kontinuerliga mätningar på ett större antal variabler. Detta förändrar möjligheterna att förstå och beskriva processer avsevärt. Multivariat analys används ofta när stora datamängder med många variabler utvärderas. I det här projektet har de multivariata analysmetoderna PCA (principalkomponentanalys) och PLS (partial least squares projection to latent structures) använts på data över avloppsvatten insamlat på Hammarby Sjöstadsverk. På reningsverken ställs idag allt hårdare krav från samhället för att de ska minska sin miljöpåverkan. Med bland annat bättre processkunskaper kan systemen övervakas och styras så att resursförbrukningen minskas utan att försämra reningsgraden. Vissa variabler är lätta att mäta direkt i vattnet medan andra kräver mer omfattande laboratorieanalyser. Några parametrar i den senare kategorin som är viktiga för reningsgraden är avloppsvattnets innehåll av fosfor och kväve, vilka bland annat kräver resurser i form av kemikalier till fosforfällning och energi till luftning av det biologiska reningssteget. Halterna av dessa ämnen i inkommande vatten varierar under dygnet och är svåra att övervaka. Syftet med den här studien var att undersöka om det är möjligt att utifrån lättmätbara variabler erhålla information om de mer svårmätbara variablerna i avloppsvattnet genom att utnyttja multivariata analysmetoder för att skapa modeller över variablerna. Modellerna kallas ofta för mjukvarusensorer (soft sensors) eftersom de inte utgörs av fysiska sensorer. Mätningar på avloppsvattnet i Linje 1 gjordes under tidsperioden 11 – 15 mars 2013 på flera ställen i processen. Därefter skapades flera multivariata modeller för att försöka förklara de svårmätbara variablerna. Resultatet visar att det går att erhålla information om variablerna med PLS-modeller som bygger på mer lättillgänglig data. De framtagna modellerna fungerade bäst för att förklara inkommande kväve, men för att verkligen säkerställa modellernas riktighet bör ytterligare validering ske. / Studies of real processes are based on measured data. In the past, the amount of available data was very limited. However, with modern technology, the information which is possible to obtain from measurements is more available, which considerably alters the possibility to understand and describe processes. Multivariate analysis is often used when large datasets which contains many variables are evaluated. In this thesis, the multivariate analysis methods PCA (principal component analysis) and PLS (partial least squares projection to latent structures) has been applied to wastewater data collected at Hammarby Sjöstadsverk WWTP (wastewater treatment plant). Wastewater treatment plants are required to monitor and control their systems in order to reduce their environmental impact. With improved knowledge of the processes involved, the impact can be significantly decreased without affecting the plant efficiency. Several variables are easy to measure directly in the water, while other require extensive laboratory analysis. Some of the parameters from the latter category are the contents of phosphorus and nitrogen in the water, both of which are important for the wastewater treatment results. The concentrations of these substances in the inlet water vary during the day and are difficult to monitor properly. The purpose of this study was to investigate whether it is possible, from the more easily measured variables, to obtain information on those which require more extensive analysis. This was done by using multivariate analysis to create models attempting to explain the variation in these variables. The models are commonly referred to as soft sensors, since they don’t actually make use of any physical sensors to measure the relevant variable. Data were collected during the period of March 11 to March 15, 2013 in the wastewater at different stages of the treatment process and a number of multivariate models were created. The result shows that it is possible to obtain information about the variables with PLS models based on easy-to-measure variables. The best created model was the one explaining the concentration of nitrogen in the inlet water.

The today quarry planning at Klinthagen is rough, which provides an opportunity to introduce new exciting methods to improve the quarry gain and efficiency. Nordkalk AB, active at Klinthagen, wishes to start a new quarry at a nearby location. To exploit future quarries in an efficient manner and ensure production quality, multivariate statistics may help gather important information. In this thesis the possibilities of the multivariate statistical approaches of Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were evaluated on the Klinthagen bore data. PCA data were spatially interpolated by Kriging, which also was evaluated and compared to IDW interpolation. Principal component analysis supplied an overview of the variables relations, but also visualised the problems involved when linking geophysical data to geochemical data and the inaccuracy introduced by lacking data quality. The PLS regression further emphasised the geochemical-geophysical problems, but also showed good precision when applied to strictly geochemical data. Spatial interpolation by Kriging did not result in significantly better approximations than the less complex control interpolation by IDW. In order to improve the information content of the data when modelled by PCA, a more discrete sampling method would be advisable. The data quality may cause trouble, though with sample technique of today it was considered to be of less consequence. Faced with a single geophysical component to be predicted from chemical variables further geophysical data need to complement existing data to achieve satisfying PLS models. The stratified rock composure caused trouble when spatially interpolated. Further investigations should be performed to develop more suitable interpolation techniques.

Självskattningsformulär utgör ett viktigt kliniskt redskap för både utredning och intervention av röstproblem men i nuläget är tillgången till olika formulär i Sverige begränsad då antalet validerade svenska översättningar är få. Syfte med studien var att översätta och preliminärt validera The Voice Symptom Scale (VoiSS) som består av 30 frågor tilldelade komponenterna Nedsättning, Emotionellt och Fysiskt. Den svenska versionen av VoiSS framtogs genom ”Forward-backward Translation” med en efterföljande pilotstudie. Sammanlagt deltog 203 vuxna individer som rekryterades via webb- och pappersenkät. Av dessa uppgav 86 deltagare att de upplevde röstbesvär. Resultaten visade på tydliga gruppskillnader där gruppen Med upplevda röstproblem genererade högre genomsnittliga svarspoäng än gruppen Utan upplevda röstproblem. Inga överlappningar kunde konstateras. En principalkomponentanalys (PCA) var i stort sett förenlig med en trekomponentstruktur som tillsammans med gruppseparationen visade på hög konstruktvaliditet. Vidare noterades samstämmighet mellan den svenska versionen och VoiSS-originalet. Sensitivitets- och specificitetsvärden bekräftade en hög diagnostisk validitet. Slutsatsen drogs att formuläret med god validitet förmår att diagnosticera upplevelse av röstproblem. Den preliminära valideringen visade således att den svenska versionen av VoiSS kan användas som ett instrument vid utredning av röstproblem men att ytterligare forskning behövs för att säkerställa formulärets användbarhet i klinisk verksamhet. / Self-assessment questionnaires are important clinical instruments for both investigation and intervention of voice problems but at date access to various questionnaires in Sweden is limited due to few validated translations. The objective of this study was to translate and preliminary validate the Voice Symptom Scale (VoiSS) consisting of 30 questions assigned Impairment, Emotional and Physical. The Swedish version of VoiSS was developed through ”Forward-backward Translation” followed by a pilot study. The questionnaire was completed by a total of 203 adults who were recruited by web and paper survey. Out of these, 86 participants experienced voice problems. Obvious group differences were observed in that the group With experienced voice problems generated higher mean scores than the group Without experienced voice problems. No overlaps were observed. A principal component analysis (PCA) was largely consistent with a three component structure that, combined with the group separation, affirmed high construct validity. Moreover, concurrence between the Swedish version and the VoiSS-original was found. Calculated values of sensitivity and specificity confirmed a high diagnostic validity. The conclusion was made that the self-assessment questionnaire with good validity was able to diagnose experience of voice problems. In conclusion, preliminary validation showed that the Swedish version of VoiSS can be used as a diagnostic tool in assessing voice problems. However, more research needs to be done to ensure the questionnaires adaptation to clinical context.

'Independent component analysis' (ICA) ist ein Werkzeug der statistischen Datenanalyse und Signalverarbeitung, welches multivariate Signale in ihre Quellkomponenten zerlegen kann. Obwohl das klassische ICA Modell sehr nützlich ist, gibt es viele Anwendungen, die Erweiterungen von ICA erfordern. In dieser Dissertation präsentieren wir neue Verfahren, die die Funktionalität von ICA erweitern: (1) Zuverlässigkeitsanalyse und Gruppierung von unabhängigen Komponenten durch Hinzufügen von Rauschen, (2) robuste und überbestimmte ('over-complete') ICA durch Ausreissererkennung, und (3) nichtlineare ICA mit Kernmethoden. / Independent component analysis (ICA) is a tool for statistical data analysis and signal processing that is able to decompose multivariate signals into their underlying source components. Although the classical ICA model is highly useful, there are many real-world applications that require powerful extensions of ICA. This thesis presents new methods that extend the functionality of ICA: (1) reliability and grouping of independent components with noise injection, (2) robust and overcomplete ICA with inlier detection, and (3) nonlinear ICA with kernel methods.

Das erste gestellte Ziel war es, die histologischen Strukturen eines Gewebedünnschnittes anhand der aufgenommenen FT-IR-Spektren sichtbar zu machen und diese mit dem konventionell gefärbten Schnitt und der autoradiographischen Aufnahme zu vergleichen. Dazu wurde ein Messsystem bestehend aus einem FT-IR-Spektrometer mit Mikroskop und einem computergesteuerten XY-Tisch aufgebaut und die notwendige Steuer- und Auswerte-Software entwickelt. Es konnte gezeigt werden, dass sich die FT-IR-Spektren mit geeigneten Auswerteverfahren zur Darstellung der histologischen Strukturen nutzen lassen. Dazu wurden zwei verschiedene Methoden eingesetzt, die PCA und die Fuzzy-Clusterung (FCM). Im zweiten Teil dieser Arbeit sollte ein Klassifikations-Algorithmus gefunden werden, mit dessen Hilfe sich Spektren von unbekannten Gewebeproben vorher definierten Modellen zuordnen lassen. Dazu wurde eine Spektren-Datenbank aus mehr als einhundert Gewebeproben angelegt. Aus dieser Datenbank wurden einige zehntausend Spektren ausgewählt und zu Modell-Datensätzen für sechs verschiedene Gewebetypen zusammengefasst. Für die Zuordnung unbekannter Spektren zu diesen Modellen wurde ein SIMCA-Klassifikations-Algorithmus entwickelt sowie ein LDA-Algorithmus eingesetzt. Für beide Methoden wurde die Klassi-fikations-Leistung anhand der Spezifität und Sensitivität bestimmt. Beide Klassifikations-Algorithmen führten zu guten Ergebnissen. Der SIMCA-Algorithmus erreichte eine Spezifität zwischen 97 % und 100 %, sowie eine Sensitivität zwischen 62 % und 78 % (bei einem Vertrauensintervall von 97,5 %). Der LDA-Algorithmus ermöglichte eine etwas bessere Sensitivität von 72 % bis 90 %, auf Kosten der Spezifität, welche zwischen 90 % und 98 % lag. Zusammenfassend kann festgestellt werden, dass sich die FT-IR-Mikrospektroskopie und die vorgestellten Auswerte-Algorithmen sehr gut zur Klassifizierung von Gewebedünnschnitten eignen.

Les récepteurs couplés aux protéines G (RCPG) sont une famille très diversifiée de protéines membranaires capables de répondre à un grand nombre de signaux chimiques tels que des photons, des molécules odorantes, ou des hormones. En plus de cette diversité, l’étude des RCPG montre que des associations protéiques spécifiques multiplient les possibilités de signalisation de chacun de ces récepteurs. En permettant d’atténuer, de potentialiser, ou de générer une nouvelle voie de signalisation, l’association des RCPG en oligomères s’avère une importante source de diversité. L’utilisation du transfert d’énergie de résonance de bioluminescence (BRET) qui permet de détecter les interactions protéiques a révélé de nombreuses associations de RCPG. Durant cette thèse, des outils ont été développés pour combiner efficacement le BRET à des essais de complémentation de protéines (PCA) dans le but de savoir si l’oligomérisation des RCPG pouvait impliquer plus de deux récepteurs. Les résultats présentés montrent que les récepteurs de chimiokines CXCR4 et CCR2 forment des homo et hétéro tétramères, et que l’activation d’un dimère CCR2 peut moduler la conformation d’un dimère CXCR4 par un changement conformationnel trans-récepteur. La coopérativité négative de liaison de ligand qui a été démontrée auparavant entre CXCR4 et CCR2 dans des lymphocytes T CD4+ exprimant les récepteurs de manière endogène confirme la validité biologique de cette interaction. Les données présentées suggèrent également que ces complexes peuvent engager les effecteurs Gαi et β-arrestine2, indiquant qu’ils représentent la forme fonctionnelle de ces récepteurs. Enfin, nous avons pu confirmer que chaque récepteur de l’hétérodimère CXCR4-CCR2 est impliqué dans l’engagement des effecteurs lors de l’activation de CCR2. Un autre niveau de complexité dans la signalisation des RCPG est atteint par leur capacité à coupler de multiples protéines G. La liaison du facteur dérivé des cellules stromales (SDF-1) au récepteur CXCR4 permet la migration des lymphocytes T par une voie de signalisation dépendante de la protéine Gαi. Nous avons pu démontrer en revanche que la migration des cellules de cancer du sein était initiée par un couplage de CXCR4 à la voie Gα13-Rho pour former des métastases dans des organes distants. Enfin, un dernier niveau de régulation des RCPG a été abordé par l’étude de la phosphorylation de CXCR4 suite à son activation, qui permet la désensibilisation du récepteur et l’engagement de voies de signalisation dépendantes de la β-arrestine. Il apparaît que la désensibilisation de la voie du calcium serait médiée par la phosphorylation de CXCR4 par les kinases des RCPG (GRK) GRK2 et GRK6 et le recrutement de β- arrestine2, alors GRK3, GRK6 et la β-arrestine1 potentialiseraient l’activation des kinases régulées par les signaux extracellulaires (ERK1/2). Nous suggérons également que c’est la phosphorylation de l’extrémité C-terminale de CXCR4 qui permettrait son association avec la β-arrestine. / G protein-coupled receptors (GPCRs) are a diverse family of membrane proteins capable of responding to a large number of extracellular stimuli including photons, odorant molecules and hormones. In addition to this diversity, it has been shown that GPCRs form specific protein:protein interactions, multiplying the signalling possibilities of each of these receptors. With the ability to diminish, to potentiate or even generate new signalling pathways, the oligomeric association of GPCRs plays an important role in generating this diversity. The use of bioluminescence resonance energy transfer (BRET), which allows the detection of interactions among proteins, has revealed numerous associations between GPCRs. During this thesis, tools have been developed that effectively combine BRET with protein complementation assays (PCA) with the goal of determining if interactions between GPCRs could involve more than two receptors. The results show that the chemokine receptors CXCR4 and CCR2 form both homo and hetero tetramers, and that the activation of a dimer of CCR2 can modulate the conformation of a CXCR4 dimer through a transreceptor conformational change. Negative cooperativity of ligand binding has previously been demonstrated between CXCR4 and CCR2 in CD4+ T lymphocytes endogenously expressing the receptors, confirming the biological validity of this interaction. The data presented also suggests that these complexes can engage the effector proteins Gαi and β- arrestin 2, indicating that they represent a functional form of the receptors. Furthermore, we have confirmed that each receptor of the CXCR4-CCR2 heterodimer is implicated in the engagement of effectors during the activation of CCR2. An additional level of complexity in GPCR-promoted signaling exists in their capacity to couple of multiple G proteins. Binding of stromal cell-derived factor-1 (SDF-1) to CXCR4 is known to promote T lymphocyte migration through a Gαi-dependent signalling pathway. In addition to this mechanism, we have demonstrated that breast cancer cell migration can initiated by a coupling of CXCR4 to the Gα13-Rho pathway, leading to the formation of metastases in distant organs. Finally, a novel level of GPCR regulation was revealed through the study of CXCR4 phosphorylation following its activation, which leads to the desensitization of the receptor and the engagement of β-arrestin-dependent signalling pathways. It appears that the desensitization of calcium signalling is mediated through the phosphorylation of CXCR4 by the GPCR kinases (GRKs) GRK2 and GRK6 and the recruitment of β-arrestin 2, whereas GRK3, GRK6 and β-arrestin 1 potentiate the activation of extracellular regulated kinase (ERK1/2). We also propose that the phosphorylation of the far C-terminal tail of CXCR4 is required for the interaction between the receptor and β-arrestin. / Thèse réalisée en cotutelle avec l'université Montpellier2 dans le laboratoire de pharmacologie moléculaire de Jean-Philippe Pin à l'institut de génomique fonctionnelle (IGF), Montpellier, France.

Esta pesquisa consiste na solução do problema inverso de transferência radiativa para um meio participante (emissor, absorvedor e/ou espalhador) homogêneo unidimensional em uma camada, usando-se a combinação de rede neural artificial (RNA) com técnicas de otimização. A saída da RNA, devidamente treinada, apresenta os valores das propriedades radiativas [&#969;, &#964;0, &#961;1 e &#961;2] que são otimizadas através das seguintes técnicas: Particle Collision Algorithm (PCA), Algoritmos Genéticos (AG), Greedy Randomized Adaptive Search Procedure (GRASP) e Busca Tabu (BT). Os dados usados no treinamento da RNA são sintéticos, gerados através do problema direto sem a introdução de ruído. Os resultados obtidos unicamente pela RNA, apresentam um erro médio percentual menor que 1,64%, seria satisfatório, todavia para o tratamento usando-se as quatro técnicas de otimização citadas anteriormente, os resultados tornaram-se ainda melhores com erros percentuais menores que 0,04%, especialmente quando a otimização é feita por AG. / This research consists in the solution of the inverse problem of radiative transfer for a participating media (emmiting, absorbing and/or scattering) homogeneous one-dimensional in one layer, using the combination of artificial neural network (ANN), with optimization techniques. The output of the ANN, properly trained presents the values of the radiative properties [w, to, p1 e p2] that are optimized through the following techniques: Particle Collision Algorithm (PCA), Genetic Algorithm (GA), Greedy Randomized Adaptive Search Procedure (GRASP) and Tabu Search (TS). The data used in the training are synthetics, generated through the direct problem without the introduction of noise. The results obtained by the (ANN) alone, presents an average percentage error minor than 1,64%, what it would be satisfying, however, for the treatment using the four techniques of optimization aforementioned, the results have become even better with percentage errors minor than 0,03%, especially when the optimization is made by the GA.