This dissertation explored the way in which bumblebees' visual system helps them discover their first flower. Previous studies found bees have unlearned preferences for parts of a flower, such as its colour and shape. The first study pitted two variables against each other: pattern type: sunburst or bull's eye, versus the location of the pattern: shapes appeared peripherally or centrally. We observed free-flying bees in a flight cage using Radio-Frequency Identification (RFID) tracking. The results show two distinct behavioural preferences: Pattern type predicts landing: bees prefer radial over concentric patterns, regardless of whether the radial pattern is on the perimeter or near the centre of the flower. Pattern location predicts exploration: bees were more likely to explore the inside of artificial flowers if the shapes were displayed near the centre of the flower, regardless of whether the pattern was radial or concentric. As part of the second component, we implemented a mathematical model aimed at explaining how bees come to prefer radial patterns, leafy backgrounds and symmetry. The model was based on unsupervised neural networks used to describe cognitive mechanisms. The results captured with the results of multiple behavioural experiments. The model suggests that bees choose computationally "cheaper" stimuli, those that contain less information. The third study tested the computational load hypothesis generated by the artificial neural networks. Visual properties of symmetry, and spatial frequency were tested. Studying free-flying bees in a flight cage using motion-sensitive video recordings, we found that bees preferred 4-axis symmetrical patterns in both low and high frequency displays.

Orientador: Martin Tygel / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Cientifica / Made available in DSpace on 2018-08-22T06:13:33Z (GMT). No. of bitstreams: 1 CostaFilho_CarlosAlbertoda_M.pdf: 3131395 bytes, checksum: f8687abfc7e346fdd8e6dc40746526e8 (MD5) Previous issue date: 2013 / Resumo: As reflexões de ondas sísmicas na subsuperfície terrestre podem ser colocadas em duas categorias disjuntas: reflexões primárias e múltiplas. Reflexões primárias carregam informações pontuais sobre um refletor específico, enquanto reflexões múltiplas carregam informações sobre interfaces e pontos de reflexão variados. Consequentemente é usual tentar atenuar reflexões múltiplas e trabalhar somente com reflexões primárias. Neste trabalho, a teoria de ondas acústicas é desenvolvida somente a partir da equação da onda. Um resultado que demonstra como a propagação de ondas acústicas pode ser descrita somente com uma única multiplicação por matriz é exposta. Este resultado permite que um algoritmo seja desenvolvido que, em teoria, pode ser usado para remover todas as reflexões múltiplas que refletiram na superfície pelo menos uma vez. Uma implementação prática deste algoritmo é mostrada. Por conseguinte, a teoria de análise de componentes independentes é apresentada. Suas considerações teóricas e práticas são abordadas. Finalmente, ela é usada em conjunção com o método de eliminação de múltiplas de superfície para atenuar múltiplas de quatro dados diferentes. Estes resultados são então analisados e a eficácia do método é avaliada / Abstract: The reflections of seismic waves in the subsurface of the Earth can be placed under two disjoint categories: primary and multiple reflections. Primary reflections carry pointwise information about a specific reflector while multiple reflections carry informations about various interfaces and reflection points. Consequently, it is customary to attempt to attenuate multiple reflections and work solely with primary reflections. In this work, the theory of acoustic waves is developed solely from the wave equation. A result that shows how acoustic wave propagation can be described as a single matrix multiplication is exposed. This result enables one to develop an algorithm that, in theory, can be used to remove all multiple reflections that have reflected on the surface at least once. The practical implementation of this algorithm is shown. Thereafter, the theory of independent component analysis is presented. Its theoretical and practical considerations are addressed. Finally, it is used in conjunction with the surface-related multiple elimination method to attenuate multiples in four different datasets. These results are then analyzed and the efficacy of the method is evaluated / Mestrado / Matematica Aplicada / Mestre em Matemática Aplicada

[EN] This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability densities, greatly simplifying the modeling of the data. ICA mixture models (ICAMM) provide further flexibility by alleviating the independency requirement of ICA, thus allowing the model to obtain local projections of the data without compromising its generalization capabilities. Here are explored new possibilities of ICAMM for the purposes of estimation and classification of signals. The thesis makes several contributions to the research in non-Gaussian mixtures: (i) a method for maximum-likelihood estimation of missing data, based on the maximization of the PDF of the data given the ICAMM; (ii) a method for Bayesian estimation of missing data that minimizes the mean squared error and can obtain the confidence interval of the prediction; (iii) a generalization of the sequential dependence model for ICAMM to semi-supervised or supervised learning and multiple chains of dependence, thus allowing the use of multimodal data; and (iv) introduction of ICAMM in diverse novel applications, both for estimation and for classification. The developed methods were validated via an extensive number of simulations that covered multiple scenarios. These tested the sensitivity of the proposed methods with respect to the following parameters: number of values to estimate; kinds of source distributions; correspondence of the data with respect to the assumptions of the model; number of classes in the mixture model; and unsupervised, semi-supervised, and supervised learning. The performance of the proposed methods was evaluated using several figures of merit, and compared with the performance of multiple classical and state-of-the-art techniques for estimation and classification. Aside from the simulations, the methods were also tested on several sets of real data from different types: data from seismic exploration studies; ground penetrating radar surveys; and biomedical data. These data correspond to the following applications: reconstruction of damaged or missing data from ground-penetrating radar surveys of historical walls; reconstruction of damaged or missing data from a seismic exploration survey; reconstruction of artifacted or missing electroencephalographic (EEG) data; diagnosis of sleep disorders; modeling of the brain response during memory tasks; and exploration of EEG data from subjects performing a battery of neuropsychological tests. The obtained results demonstrate the capability of the proposed methods to work on problems with real data. Furthermore, the proposed methods are general-purpose and can be used in many signal processing fields. / [ES] Esta tesis considera nuevas aplicaciones de las mezclas no Gaussianas dentro del marco de trabajo del procesado estadístico de señal y del reconocimiento de patrones. Las mezclas no Gaussianas fueron implementadas mediante mezclas de analizadores de componentes independientes (ICA). La hipótesis fundamental de ICA es que las señales observadas pueden expresarse como una transformación lineal de un grupo de variables ocultas, normalmente llamadas fuentes, que son estadísticamente independientes. Esta independencia permite factorizar la función de densidad de probabilidad (PDF) original M-dimensional de los datos como un producto de densidades unidimensionales, simplificando ampliamente el modelado de los datos. Los modelos de mezclas ICA (ICAMM) aportan una mayor flexibilidad al relajar el requisito de independencia de ICA, permitiendo que el modelo obtenga proyecciones locales de los datos sin comprometer su capacidad de generalización. Aquí se exploran nuevas posibilidades de ICAMM para los propósitos de estimación y clasificación de señales. La tesis realiza varias contribuciones a la investigación en mezclas no Gaussianas: (i) un método de estimación de datos faltantes por máxima verosimilitud, basado en la maximización de la PDF de los datos dado el ICAMM; (ii) un método de estimación Bayesiana de datos faltantes que minimiza el error cuadrático medio y puede obtener el intervalo de confianza de la predicción; (iii) una generalización del modelo de dependencia secuencial de ICAMM para aprendizaje supervisado o semi-supervisado y múltiples cadenas de dependencia, permitiendo así el uso de datos multimodales; y (iv) introducción de ICAMM en varias aplicaciones novedosas, tanto para estimación como para clasificación. Los métodos desarrollados fueron validados mediante un número extenso de simulaciones que cubrieron múltiples escenarios. Éstos comprobaron la sensibilidad de los métodos propuestos con respecto a los siguientes parámetros: número de valores a estimar; tipo de distribuciones de las fuentes; correspondencia de los datos con respecto a las suposiciones del modelo; número de clases en el modelo de mezclas; y aprendizaje supervisado, semi-supervisado y no supervisado. El rendimiento de los métodos propuestos fue evaluado usando varias figuras de mérito, y comparado con el rendimiento de múltiples técnicas clásicas y del estado del arte para estimación y clasificación. Además de las simulaciones, los métodos también fueron probados sobre varios grupos de datos de diferente tipo: datos de estudios de exploración sísmica; exploraciones por radar de penetración terrestre; y datos biomédicos. Estos datos corresponden a las siguientes aplicaciones: reconstrucción de datos dañados o faltantes de exploraciones de radar de penetración terrestre de muros históricos; reconstrucción de datos dañados o faltantes de un estudio de exploración sísmica; reconstrucción de datos electroencefalográficos (EEG) dañados o artefactados; diagnóstico de desórdenes del sueño; modelado de la respuesta del cerebro durante tareas de memoria; y exploración de datos EEG de sujetos durante la realización de una batería de pruebas neuropsicológicas. Los resultados obtenidos demuestran la capacidad de los métodos propuestos para trabajar en problemas con datos reales. Además, los métodos propuestos son de propósito general y pueden utilizarse en muchos campos del procesado de señal. / [CAT] Aquesta tesi considera noves aplicacions de barreges no Gaussianes dins del marc de treball del processament estadístic de senyal i del reconeixement de patrons. Les barreges no Gaussianes van ser implementades mitjançant barreges d'analitzadors de components independents (ICA). La hipòtesi fonamental d'ICA és que els senyals observats poden ser expressats com una transformació lineal d'un grup de variables ocultes, comunament anomenades fonts, que són estadísticament independents. Aquesta independència permet factoritzar la funció de densitat de probabilitat (PDF) original M-dimensional de les dades com un producte de densitats de probabilitat unidimensionals, simplificant àmpliament la modelització de les dades. Els models de barreges ICA (ICAMM) aporten una major flexibilitat en alleugerar el requeriment d'independència d'ICA, permetent així que el model obtinga projeccions locals de les dades sense comprometre la seva capacitat de generalització. Ací s'exploren noves possibilitats d'ICAMM pels propòsits d'estimació i classificació de senyals. Aquesta tesi aporta diverses contribucions a la recerca en barreges no Gaussianes: (i) un mètode d'estimació de dades faltants per màxima versemblança, basat en la maximització de la PDF de les dades donat l'ICAMM; (ii) un mètode d'estimació Bayesiana de dades faltants que minimitza l'error quadràtic mitjà i pot obtenir l'interval de confiança de la predicció; (iii) una generalització del model de dependència seqüencial d'ICAMM per entrenament supervisat o semi-supervisat i múltiples cadenes de dependència, permetent així l'ús de dades multimodals; i (iv) introducció d'ICAMM en diverses noves aplicacions, tant per a estimació com per a classificació. Els mètodes desenvolupats van ser validats mitjançant una extensa quantitat de simulacions que cobriren múltiples situacions. Aquestes van verificar la sensibilitat dels mètodes proposats amb respecte als següents paràmetres: nombre de valors per estimar; mena de distribucions de les fonts; correspondència de les dades amb respecte a les suposicions del model; nombre de classes del model de barreges; i aprenentatge supervisat, semi-supervisat i no-supervisat. El rendiment dels mètodes proposats va ser avaluat mitjançant diverses figures de mèrit, i comparat amb el rendiments de múltiples tècniques clàssiques i de l'estat de l'art per a estimació i classificació. A banda de les simulacions, els mètodes van ser verificats també sobre diversos grups de dades reals de diferents tipus: dades d'estudis d'exploració sísmica; exploracions de radars de penetració de terra; i dades biomèdiques. Aquestes dades corresponen a les següents aplicacions: reconstrucció de dades danyades o faltants d'estudis d'exploracions de radar de penetració de terra sobre murs històrics; reconstrucció de dades danyades o faltants en un estudi d'exploració sísmica; reconstrucció de dades electroencefalogràfiques (EEG) artefactuades o faltants; diagnosi de desordres de la son; modelització de la resposta del cervell durant tasques de memòria; i exploració de dades EEG de subjectes realitzant una bateria de tests neuropsicològics. Els resultats obtinguts han demostrat la capacitat dels mètodes proposats per treballar en problemes amb dades reals. A més, els mètodes proposats són de propòsit general i poden fer-se servir en molts camps del processament de senyal. / Safont Armero, G. (2015). New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/53913 / TESIS

Diplomová práce se zabývá hybridním vyšetřením fMRI-EEG. V EEG signálu jsou přítomny dva základní artefakty, gradientní a balistokardiografický. Po odstranění gradientního artefaktu a detekce R vln v kanálu EKG byl navržen postup pro odstranění balistokardiografického artefaktu, pomocí metody odečtu artefaktového vzoru, pomocí metody ICA (metody nezávislých proměnných) a metody spojující obě uvedené. Byla vyhodnocena úspěšnost všech metod a jejich vliv na evokované potenciály v signálu EEG.

The new approach for motion artifact suppression in optical action potential records is presented in this thesis. Presented approach is based on independent component analysis utilization. Efficiency of proposed approach is evaluated here as well as its comparison with state of the art motion artifact suppression approaches.

In the semiconductor industry the reliability of devices is of paramount importance. Therefore, after removing the defective ones, one wants to detect irregularities in measurement data because corresponding devices have a higher risk of failure early in the product lifetime. The paper presents a method to improve the detection of such suspicious devices where the screening is made on transformed measurement data. Thereby, e.g., dependencies between tests can be taken into account. Additionally, a new dimensionality reduction is performed within the transformation, so that the reduced and transformed data comprises only the informative content from the raw data. This simplifies the complexity of the subsequent screening steps. The new approach will be applied to semiconductor measurement data and it will be shown, by means of examples, how the screening can be improved.

Le rétinoblastome est un cancer pédiatrique rare de la rétine en cours de développement. Si dans les pays développés, le taux de survie avoisine 100%, une énucléation de l’oeil atteint est cependant nécessaire dans plus de 70% des cas.En 1971, Knudson émit l’hypothèse des deux “hits”, qui permit de comprendre que le rétinoblastome s’initie généralement après une perte bi-allélique du gène RB1. Cependant, les autres mécanismes moléculaires qui régissent ce cancer restent depuis peu connus. Par exemple, peu d’études génomiques ont été conduites. Ainsi, la nature de la cellule d’origine, ainsi que la présence ou non d’une hétérogénéité intertumorale, font encore débat. Dans cette étude, nous avons dressé un portrait génomique et clinique complet du rétinoblastome; plusieurs observations ont montré qu’il s’agit bien d’une maladie hétérogène, avec deux sous-types distincts. Nous avons d’abord identifié les deux sous-types avec à une approche couplant une analyse en composantes indépendantes (ACI) de transcriptomes tumoraux avec des marquages immunohistochimiques. Les rétinoblastomes du premier sous-type, dits “cone-like” expriment uniformément des marqueurs de cônes, tandis que ceux du second sous-type, dits “bivalent-type”, ont une forte hétérogénéité intratumorale, avec un enchevêtrement de zones de différenciation ganglionnaire ou cône. Grâce à une étude plus approfondie des transcriptomes et de données d’altérations génomiques, nous avons ensuite montré que les sous-types dépendent de voies de signalisation et d’oncogènes différents. Les bivalent-type ont notamment une présence quasi-systématique de gains de MDM4 ou d’amplifications de MYCN. Nous nous sommes ensuite tournés vers les méthylomes des rétinoblastomes, et constaté une forte hétérogénéité entre les sous-types. Nous avons décomposé cette hétérogénéité grâce à une ACI, et constaté qu’elle n’était pas liée uniquement à la différenciation cône ou ganglion. Nous avons ensuite étudié les données cliniques de la cohorte, et constaté que les sous-types avaient des âges au diagnostic et des formes de croissance différents, les tumeurs cone-like se developpant généralement chez des patients jeunes avec des tumeurs exophytiques, et les bivalent-type chez des patients plus âgés avec des tumeurs endophytiques. De plus, les patients avec des inactivations constitutionnelles du gène RB1 développent majoritairement des tumeurs cone-like; les cone-like s’initieraient donc plus tôt durant le développement de la rétine. Nous avons finalement séquencé les exomes de 74 paires tumeur-normal. Les rétinoblastomes avaient un taux de mutations extrêmement faible (0.1 mutations par mégabase), comme beaucoup de cancers pédiatriques. Nous avons identifié des mutations somatiques récurrentes dans RB1, BCOR et ARID1A. Ces gènes se trouvaient de plus dans des régions minimales de pertes chromosomiques. Surtout, les inactivations des deux gènes avaient souvent de fortes fréquences alléliques. Ceci indique que ces inactivations ont lieu précocément dans la tumorigénèse. En conclusion, notre étude a permis de dresser un premier portrait génomique complet du rétinoblastome, a révélé l’existence de deux sous-types distincts, ainsi que fourni des indices quant à la cellule d’origine de chaque sous-type, et les mécanismes moléculaires les régissant. / Retinoblastoma is a rare pediatric cancer of the developing retina. In high-income countries, survival rates near 100%; however, enucleation of the affected eye has to be performed in over 70% of patients. Knudson’s 1971 two-hit hypothesis led to the discovery that this cancer usually initiates after a bi-allelic loss of the RB1 gene. Despite this early finding, little is known about the other molecular underpinnings of retinoblastoma. For instance, few genome-wide studies have described the genetic and epigenetic characteristics of these tumors. Furthermore, there is still no clear consensus regarding this cancer’s cell of origin, or whether or not it is homogenous disease. In this study, we built a comprehensive molecular and clinical portrait of retinoblastoma. Several lines of evidence led us to conclude that retinoblastoma is in fact a heterogeneous disease, with two distinct subtypes. We first uncovered the subtypes through a strategy that coupled an independent component analysis (ICA) of tumor transcriptomes to tumor immunohistochemical stainings. Retinoblastomas of the first subtype, called “cone-like”, homogeneously display cone-like differentiation, while those of the second subtype, called “bivalent-type”, exhibit strong intratumoral heterogeneity, with areas of cone-like differentiation intertwined with areas of ganglion-like differentiation. Further analysis of the transcriptomic data, as well as of copy number alteration data revealed that both subtypes may rely on different pathways and oncogenes. We notably observed a quasi-systematic presence of MDM4 gains or MYCN amplifications in bivalent-type tumors. We next turned to retinoblastomas’ methylomes; these considerably varied between the subtypes. ICA allowed us to decompose this inter-subtype methylomic heterogeneity, which was found to go beyond methylation due to cone-like or ganglion-like differentiation. We next studied the tumors’ clinical data, and found that cone-like tumors are most often diagnosed in very young patients with exophytic tumor growth, while bivalent-type tumors are found in older patients with endophytic tumor growth. Furthermore, patients with germline inactivations of RB1 mostly developed cone-like retinoblastomas, indicating that these tumors may initiate earlier during retinal development. In the final part of our study, we performed whole exome sequencing of 74 tumor-normal pairs. Like many pediatric cancers, the tumors had very low background mutation rates (0.1 mutations per megabase). Recurrent somatic mutations were found in RB1, BCOR and ARID1A, and these genes were also found to be in minimal regions of chromosomal losses. Importantly, both inactivations often had very high allelic frequencies, indicating that these events occur very early on in retinoblastoma tumorigenesis.Taken together, our study outlines a first comprehensive genomic portrait of retinoblastomas, points to the existence of two distinct subtypes, and provides insights into the cells-or-origin and the molecular mechanisms underlying these subtypes.

No description available.

In wireless communications, frequency-selective fading is a major source of impairment for wireless communications. In this research, a novel Frequency-Domain Independent Component Analysis (ICA-F) approach is proposed to blindly separate and deconvolve signals traveling through frequency-selective, slow fading channels. Compared with existing time-domain approaches, the ICA-F is computationally efficient and possesses fast convergence properties. Simulation results confirm the effectiveness of the proposed ICA-F. Orthogonal Frequency Division Multiplexing (OFDM) systems are widely used in wireless communications nowadays. However, OFDM systems are very sensitive to Carrier Frequency Offset (CFO). Thus, an accurate CFO compensation technique is required in order to achieve acceptable performance. In this dissertation, two novel blind approaches are proposed to estimate and compensate for CFO within the range of half subcarrier spacing: a Maximum Likelihood CFO Correction approach (ML-CFOC), and a high-performance, low-computation Blind CFO Estimator (BCFOE). The Bit Error Rate (BER) improvement of the ML-CFOC is achieved at the expense of a modest increase in the computational requirements without sacrificing the system bandwidth or increasing the hardware complexity. The BCFOE outperforms the existing blind CFO estimator [25, 128], referred to as the YG-CFO estimator, in terms of BER and Mean Square Error (MSE), without increasing the computational complexity, sacrificing the system bandwidth, or increasing the hardware complexity. While both proposed techniques outperform the YG-CFO estimator, the BCFOE is better than the ML-CFOC technique. Extensive simulation results illustrate the performance of the ML-CFOC and BCFOE approaches.

No description available.