This research addresses the problem of temporal pattern discovery in real-valued, multivariate sensor data. Several algorithms were developed, and subsequent evaluation demonstrates that they can efficiently and accurately discover unknown recurring patterns in time series data taken from many different domains. Different data representations and motif models were investigated in order to design an algorithm with an improved balance between run-time and detection accuracy. The different data representations are used to quickly filter large data sets in order to detect potential patterns that form the basis of a more detailed analysis. The representations include global discretization, which can be efficiently analyzed using a suffix tree, local discretization with a corresponding random projection algorithm for locating similar pairs of subsequences, and a density-based detection method that operates on the original, real-valued data. In addition, a new variation of the multivariate motif discovery problem is proposed in which each pattern may span only a subset of the input features. An algorithm that can efficiently discover such "subdimensional" patterns was developed and evaluated. The discovery algorithms are evaluated by measuring the detection accuracy of discovered patterns relative to a set of expected patterns for each data set. The data sets used for evaluation are drawn from a variety of domains including speech, on-body inertial sensors, music, American Sign Language video, and GPS tracks.

Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo IGMN é uma rede neural online incremental que aprende a partir de uma única passada através de dados por meio de uma versão incremental do algoritmo Expectation-Maximization (EM) combinado com regressão localmente ponderada (Locally Weighted Regression, LWR). Quatro abordagens diferentes são usadas para dar capacidade de processamento temporal para o algoritmo IGMN: linhas de atraso (Time-Delay IGMN), uma camada de reservoir (Echo-State IGMN), média móvel exponencial do vetor de entrada reconstruído (Merge IGMN) e auto-referência (Recursive IGMN). Isso resulta em algoritmos que são online, incrementais, agressivos e têm capacidades temporais e, portanto, são adequados para tarefas com memória ou estados internos desconhecidos, caracterizados por fluxo contínuo ininterrupto de dados, e que exigem operação perpétua provendo previsões sem etapas separadas para aprendizado e execução. Os algoritmos propostos são comparados a outras redes neurais espaço-temporais em 8 tarefas de previsão de séries temporais. Dois deles mostram desempenhos satisfatórios, em geral, superando as abordagens existentes. Uma melhoria geral para o algoritmo IGMN também é descrita, eliminando um dos parâmetros ajustáveis manualmente e provendo melhores resultados. / This work introduces novel neural networks algorithms for online spatio-temporal pattern processing by extending the Incremental Gaussian Mixture Network (IGMN). The IGMN algorithm is an online incremental neural network that learns from a single scan through data by means of an incremental version of the Expectation-Maximization (EM) algorithm combined with locally weighted regression (LWR). Four different approaches are used to give temporal processing capabilities to the IGMN algorithm: time-delay lines (Time-Delay IGMN), a reservoir layer (Echo-State IGMN), exponential moving average of reconstructed input vector (Merge IGMN) and self-referencing (Recursive IGMN). This results in algorithms that are online, incremental, aggressive and have temporal capabilities, and therefore are suitable for tasks with memory or unknown internal states, characterized by continuous non-stopping data-flows, and that require life-long learning while operating and giving predictions without separated stages. The proposed algorithms are compared to other spatio-temporal neural networks in 8 time-series prediction tasks. Two of them show satisfactory performances, generally improving upon existing approaches. A general enhancement for the IGMN algorithm is also described, eliminating one of the algorithm’s manually tunable parameters and giving better results.

Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo IGMN é uma rede neural online incremental que aprende a partir de uma única passada através de dados por meio de uma versão incremental do algoritmo Expectation-Maximization (EM) combinado com regressão localmente ponderada (Locally Weighted Regression, LWR). Quatro abordagens diferentes são usadas para dar capacidade de processamento temporal para o algoritmo IGMN: linhas de atraso (Time-Delay IGMN), uma camada de reservoir (Echo-State IGMN), média móvel exponencial do vetor de entrada reconstruído (Merge IGMN) e auto-referência (Recursive IGMN). Isso resulta em algoritmos que são online, incrementais, agressivos e têm capacidades temporais e, portanto, são adequados para tarefas com memória ou estados internos desconhecidos, caracterizados por fluxo contínuo ininterrupto de dados, e que exigem operação perpétua provendo previsões sem etapas separadas para aprendizado e execução. Os algoritmos propostos são comparados a outras redes neurais espaço-temporais em 8 tarefas de previsão de séries temporais. Dois deles mostram desempenhos satisfatórios, em geral, superando as abordagens existentes. Uma melhoria geral para o algoritmo IGMN também é descrita, eliminando um dos parâmetros ajustáveis manualmente e provendo melhores resultados. / This work introduces novel neural networks algorithms for online spatio-temporal pattern processing by extending the Incremental Gaussian Mixture Network (IGMN). The IGMN algorithm is an online incremental neural network that learns from a single scan through data by means of an incremental version of the Expectation-Maximization (EM) algorithm combined with locally weighted regression (LWR). Four different approaches are used to give temporal processing capabilities to the IGMN algorithm: time-delay lines (Time-Delay IGMN), a reservoir layer (Echo-State IGMN), exponential moving average of reconstructed input vector (Merge IGMN) and self-referencing (Recursive IGMN). This results in algorithms that are online, incremental, aggressive and have temporal capabilities, and therefore are suitable for tasks with memory or unknown internal states, characterized by continuous non-stopping data-flows, and that require life-long learning while operating and giving predictions without separated stages. The proposed algorithms are compared to other spatio-temporal neural networks in 8 time-series prediction tasks. Two of them show satisfactory performances, generally improving upon existing approaches. A general enhancement for the IGMN algorithm is also described, eliminating one of the algorithm’s manually tunable parameters and giving better results.

Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo IGMN é uma rede neural online incremental que aprende a partir de uma única passada através de dados por meio de uma versão incremental do algoritmo Expectation-Maximization (EM) combinado com regressão localmente ponderada (Locally Weighted Regression, LWR). Quatro abordagens diferentes são usadas para dar capacidade de processamento temporal para o algoritmo IGMN: linhas de atraso (Time-Delay IGMN), uma camada de reservoir (Echo-State IGMN), média móvel exponencial do vetor de entrada reconstruído (Merge IGMN) e auto-referência (Recursive IGMN). Isso resulta em algoritmos que são online, incrementais, agressivos e têm capacidades temporais e, portanto, são adequados para tarefas com memória ou estados internos desconhecidos, caracterizados por fluxo contínuo ininterrupto de dados, e que exigem operação perpétua provendo previsões sem etapas separadas para aprendizado e execução. Os algoritmos propostos são comparados a outras redes neurais espaço-temporais em 8 tarefas de previsão de séries temporais. Dois deles mostram desempenhos satisfatórios, em geral, superando as abordagens existentes. Uma melhoria geral para o algoritmo IGMN também é descrita, eliminando um dos parâmetros ajustáveis manualmente e provendo melhores resultados. / This work introduces novel neural networks algorithms for online spatio-temporal pattern processing by extending the Incremental Gaussian Mixture Network (IGMN). The IGMN algorithm is an online incremental neural network that learns from a single scan through data by means of an incremental version of the Expectation-Maximization (EM) algorithm combined with locally weighted regression (LWR). Four different approaches are used to give temporal processing capabilities to the IGMN algorithm: time-delay lines (Time-Delay IGMN), a reservoir layer (Echo-State IGMN), exponential moving average of reconstructed input vector (Merge IGMN) and self-referencing (Recursive IGMN). This results in algorithms that are online, incremental, aggressive and have temporal capabilities, and therefore are suitable for tasks with memory or unknown internal states, characterized by continuous non-stopping data-flows, and that require life-long learning while operating and giving predictions without separated stages. The proposed algorithms are compared to other spatio-temporal neural networks in 8 time-series prediction tasks. Two of them show satisfactory performances, generally improving upon existing approaches. A general enhancement for the IGMN algorithm is also described, eliminating one of the algorithm’s manually tunable parameters and giving better results.

Supporting huge population, megacities are definitely the hot spots of production, consumption, and waste generation. Without careful investment and planning, megacities will be overwhelmed by burgeoning negative impacts on the environment, natural resources, and human health, as well as a host of social and economic issues. The unprecedented combination of economic and population growth since the Reform and Open Policy has led China into transition from a largely rural society to a predominantly urban one. Chinese cities, without question, have not escaped the danger of the series of problems during the rapid progress of urbanization. Therefore, monitoring the spatial-temporal patterns of urban sprawl and their impact on the environment is of critical importance for urban planning and sustainable development, especially in developing Chinese cities such as Shanghai. To date, few studies have focused on the urban trajectories of Shanghai over the past 30 years from a remote sensing perspective. Most of the studies were concentrated on the technical issues of image processing and classification. Moreover, research on spatial metrics has focused on analyzing remote sensing classification results rather than on the use of interpreting, assessing, and verifying urban simulation results. Furthermore, many researches merely focused on baseline projection and very few studies took into consideration urban growth scenarios so far. As yet there have been no reported scenario simulations of future Shanghai growth with several land-use categories within urban areas. The overall objective of this research is to investigate the integration of remote sensing, spatial metrics, and spatial-temporal models in the monitoring, analysis, and simulation of urban growth in Shanghai, China. The specific objectives are to: 1). monitor urban dynamics over time with multi-sensor remote sensing images; 2). quantify spatial-temporal properties of urban growth and representing the urban morphological structures by means of spatial metrics; and 3). simulate the geographic extent, patterns, and detailed catalogs of urban growth under different scenarios using Markov-Cellular Automata (Markov-CA) model to support decision making for a more sustainable Shanghai. Through this study, the combined approach using remotely sensed data with change detection techniques, spatial metrics, and a scenarios-based simulation model proved to be effective to understand, represent, and predict the spatial-temporal dynamics of urban growth. In detail, the segmented-based hierarchy classification and visual interpretation were effective methods to extract urban and industrial land with high-resolution remotely sensed images. Direct change detection using variables derived from tasseled cap transformation was efficient for monitoring impervious surface sprawl. Spatial metrics is a quick and executable way to assessing the impact of urban sprawl on landscape dynamic. Markov-CA model is a useful tool to simulate the scenarios of future urban developments and therefore provides the policy options for sustainable urban planning. The research results of urban trajectories and impervious surface sprawl showed that Shanghai experienced high-speed urban sprawl and the rate of urban expansion, however, was not homogeneous spatially and temporally. The general annual urban expansion speed was 34.8 km2 per year; nevertheless, it reached 80.2 km2 per year recent six years from 2001 to 2007, while it touched the bottom speed around 14.3 km2 per year during 1979-1989. The expanded area in the Puxi region was 5.23 times of its original area while that of Pudong region was 19.94 times of its original area during 1979-2007. The research results of landscape analysis demonstrated that greenbelt becomes fractured while infrastructural and commercial area is more and more aggregated in the central Shanghai area, and satellite images such as SPOT Pan, XS and Landsat TM with 10-30 meter resolution are sufficient for the landscape dynamic research in central Shanghai area. The results of scenarios-based simulation indicated that built-up areas in Shanghai will increase significantly in 2025 and Shanghai will experience less urban sprawl and retain a better environment in 2025 under service-oriented center (SOC) than under baseline (NS) or manufacturing-dominant center (MDC) scenario. If favorable policy for MDC scenario is adopted, however, there will be a lot of manufacturing industries gathering in Shanghai and more agricultural lands will be encroached. The present research focused on the analysis of physical and morphological aspects of urban growth. Urban land-use dynamics are, however, intrinsically linked with socio-economic, political, or demographic drivers. Trying to fill in the missing link between traditional urban geography and urban remote sensing & urban simulation and to improve understanding of the interactions between human and natural aspects in the urban socio-ecosystem is the major focus in the next phase of the Ph.D. research. Keywords: Urban growth, Spatial-temporal pattern, Remote sensing, Spatial metrics, Scenarios-based simulation, Shanghai / QC 20110224

Baleias jubarte usam a costa leste do Brasil como área de reprodução e cria. As águas ao redor do Arquipélago dos Abrolhos são importantes devido a grande concentração de grupos com filhotes. Um estudo de 7 anos (entre 1998 e 2004) foi realizado, a partir de um ponto fixo de observação em terra, a fim de se investigar padrões temporais na estruturação de grupos e no comportamento de baleias jubarte. Dependendo das condições climáticas e de visibilidades eram realizadas varreduras com duração de 1 hora e na seqüência, observações de grupo ou indivíduo focal. Concomitante com o aumento da população brasileira de baleia jubarte, o número de avistagens de baleias adultas ao redor do Arquipélago aumentou, especialmente de 2002 a 2004, porém avistagens de filhotes somente aumentaram durante a temporada 2004. De uma forma gradual baleias chegam, se concentram e partem de Abrolhos, refletindo a migração segregada e as alterações de status sociais dos indivíduos. A medida que a temporada progride, ocorre uma mudança na freqüência das diferentes categorias de grupo de baleia jubarte, de grupos sem filhotes para grupos com filhotes, assim como os comportamentos, os quais, dentro de cada categoria de grupo, parecem estar adequado ao estágio de desenvolvimento do filhote (para aqueles grupos com filhote) e refletem o que parece estar relacionado a busca por oportunidades de acasalamentos e interações sociais. Ao longo das 7 temporadas estudadas, não houve mudanças na estruturação de grupos de baleias jubarte, nem houve alterações comportamentais marcantes. Porém, verificou-se que na presença de barcos num raio de 100-300 metros, fêmeas (mães) permanecem menos tempo em repouso e filhotes ficam menos tempo em comportamento de provável amamentação. Existe a preocupação de que repetidas mudanças comportamentais decorrentes de fatores antrópicos possam levar a população a risco, já que em espécies como baleias, as alterações a nível populacional podem levar muitos anos para ser detectadas. Logo é sugerido que estudos seguindo a mesma metodologia sejam continuados para que comparações sejam possíveis. Um estudo de longo-prazo permitiria uma investigação continua dos padrões (ou alterações deles) com que as baleias jubarte utilizam a área e como vêm respondendo as pressões antrópicas. / Humpback whales use the east coast of Brazil as a breeding and calving area. The waters surrounding Abrolhos Archipelago are important because of the high concentration of humpback whale groups with calves. A seven-year study (1998 - 2004) was conducted, from a land base station, to investigate temporal patterns of group structure and behavior of humpback whales. Depending on weather and sightability conditions, one-hour-scans were done followed by observations of animal or group focal follows. Concomitant with the observation of an increasing Brazilian humpback whale population, the number of adult whales sighted around the Archipelago increased, especially from 2002 to 2004. However, sightings of calves only increased during 2004 season. The humpback whales gradually arrive, concentrate and leave the region, reflecting segregated migration and individual social changes. As the season progresses, the frequency of different groups categories changes, from groups without calf to groups with calf, as well as the behaviors, which, within each group category, seems to be appropriate to calf development stages (for groups with calf) and reflect what seems to be related to the search for mating opportunities and other social interactions. During those 7 seasons, there were no changes in humpback whale group structure, nor were there strong behavioral changes. However, it was observed that, in the presence of vessels around 100 to 300 meters, mothers spent less time resting and calves spent less time in activities probably related to suckling. There is a concern that behavioral changes caused by anthropogenic factors may put the population at risk and changes at population level may take several years to be detected. So, it is suggested that studies following the same methodology continues, allowing further future comparisons. A long-term study would permit continued investigation of humpback whale use patterns (or theirs alterations) showing their responses to anthropogenic pressures.

Cyanobakterien bilden zahlreiche bioaktive Substanzen mit zum Teil humantoxischer Relevanz. Nicht selten spielen dabei zyklische Peptide, zu denen unter anderem die Microcystine (MCYST) gehören, eine Schlüsselrolle. MCYST werden u.a. von Microcystis KÜTZING EX LEMMERMANN 1907 gebildet. Erkenntnisse zur ökophysiologischen Funktion der MCYST, die zweifelsfrei bei den Produzenten selbst zu suchen ist, liegen bisher kaum vor. Mit Hilfe von Freilanduntersuchungen sollten im Rahmen der vorliegenden Arbeit Kenntnisse zu einer möglichen ökologischen Funktion der MCYST erweitert und vertieft werden. Grundlage stellte dabei die Phänologie von Microcystis als einer der bedeutendsten limnischen MCYST-Produzenten dar. Microcystis zeigt im Freiland einen charakteristischen annuellen Lebenszyklus mit benthisch-pelagischer Kopplung. Ziel der vorliegenden Arbeit war es, die phänologischen Phasen des Lebenszyklus von Microcystis im Freiland zu differenzieren sowie die Dynamik der MCYST während dieser Phasen kompartimentübergreifend gesamtheitlich zu erfassen. Über eine MCYST-Massenbilanzierung sollen anschließend die dem annuellen Zyklus zugrundeliegenden Teilprozesse quantifiziert und zusammengeführt werden. Vordergründiges Anliegen war es, Phasen einzugrenzen, bei denen MCYST möglicherweise eine ökophysiologische Funktion haben könnte. Der annuelle Lebenszyklus von Microcystis wurde anhand von Biomasseänderungen am Sediment und im Pelagial der TS Quitzdorf in die phänologischen Phasen Überwinterung, Reinvasion, pelagisches Wachstum und Sedimentation unterteilt: Intakte, im Herbst aus dem Freiwasser aussedimentierte, Microcystis-Kolonien überwintern am Sediment und steigen im Frühjahr und Frühsommer zurück ins Freiwasser auf. Dort erfolgt der Wachstumsprozess, dem sich im darauffolgenden Herbst erneut ein Zusammenbruch und die Sedimentation der Freiwassergemeinschaft anschließt. Die benthisch-pelagische Kopplung wirkt dabei als interannuelles Bindeglied. Zwischen dem annuellen Lebenszyklus von Microcystis und der MCYST-Dynamik wurde eine enge Bindung nachgewiesen: Änderungen der absoluten MCYST-Konzentrationen während der Übergangsphasen Aufstieg (Frühjahr) und Sedimentation (Herbst) zeigen, dass MCYST mit den aufsteigenden bzw. aussedimentierenden Microcystis-Kolonien aus dem bzw. in das Sediment ‚transportiert’ werden. Ausschließlich während der pelagischen Phase, die sich dem Reinvasionsprozess anschließt, kommt es in Abhängigkeit vom Wachstum der Produzenten und deren Sukzession zur Neubildung von MCYST. Während den Wintermonaten wurden MCYST am Sediment intrazellulär ‚konserviert’. Der Verlauf der pelagischen MCYST-Konzentration wurde mit Hilfe eines Wachstumsmodells nachgebildet. In dieses Modell wurde die genetische Variabilität der MCYST-Produzenten sowie eine mögliche physiologische Steuerung der MCYST-Synthese über die Verfügbarkeit des anorganischen Kohlenstoffs integriert. Der prinzipielle Verlauf zeigte dabei weitestgehend Koinzidenz zwischen den real gemessenen und den simulierten MCYST-Konzentrationswerten. Abweichungen zwischen beiden konnten mit Hilfe des gesamtheitlich kompartimentübergreifenden MCYST-Bilanzierungsansatzes – in erster Linie über benthisch-pelagische Kopplungsprozesse – plausibel erklärt werden. Der Habitatwechsel ist für Microcystis prinzipiell mit Verlusten (Seneszenz/Lyse oder möglicherweise Apoptose) verbunden, sowohl für MCYST-Produzenten und Nichtproduzenten. Die auffallende Stabilität der benthischen MCYST-Zellquote während der Überwinterung gibt Grund zur Annahme, dass eine Funktion von MCYST am/im Sediment eher unwahrscheinlich ist. Da MCYST über derart lange Zeiträume am Sediment intrazellulär ‚konserviert’ werden, ist eine Bedeutung der MCYST während der Reinvasionsphase und in der frühen pelagischen Phase nicht auszuschließen. Im Speziellen wurde eine mögliche ökologische Funktion von MCYST in Zusammenhang mit der Variation der Koloniegröße bzw. dem epiphytischen Bewuchs von Microcystis-Kolonien mit Pseudanabaena mucicola geprüft: Aus dem Zusammenhang zwischen extra-/intrazellulärer MCYST-Konzentration und der Microcystis-Koloniegrößenverteilung waren keine konsistenten Schlussfolgerungen abzuleiten, welche auf eine Steuerung der Koloniebildung durch MCYST deuten. Vor dem Hintergrund, dass MCYST keinen nachweislich allelopathischen Effekt auf den Epibionten Pseudanabaena mucicola ausüben, wurde postuliert, dass zwischen dem beobachteten epiphytischen Besiedlungs-/Verteilungsmuster und der MCYST-Produktion ein indirekter Zusammenhang besteht, welcher die zeitweise Einnischung von Pseudanabaena mucicola auf Microcystis-Kolonien ermöglicht. Die Ergebnisse der vorliegenden Untersuchung lassen weder unmittelbar noch mittelbar eine Variabilität der ökophysiologischen Bedeutung von MCYST, die im Zusammenhang mit der raum-zeitlichen Verteilung potentieller Produzenten steht, erkennen. Eine divergierende Funktion der MCYST auf intra- bzw. extrazellulärer Ebene kann nicht zwingend ausgeschlossen werden. Die Mehrzahl der aus der MCYST-Phänologie und MCYST-Bilanzierung abzuleitenden Schlussfolgerungen deutet allerdings eher auf eine Funktion auf (intra-)zellulärer Ebene hin, wie etwa die Effizienzsteigerung des Kohlenstoffmetabolismus (d.h. der intrazellulä-ren Akkumulation anorganischen Kohlenstoffs) während der pelagischen (Wachstums-)Phase der Produzenten.

Baleias jubarte usam a costa leste do Brasil como área de reprodução e cria. As águas ao redor do Arquipélago dos Abrolhos são importantes devido a grande concentração de grupos com filhotes. Um estudo de 7 anos (entre 1998 e 2004) foi realizado, a partir de um ponto fixo de observação em terra, a fim de se investigar padrões temporais na estruturação de grupos e no comportamento de baleias jubarte. Dependendo das condições climáticas e de visibilidades eram realizadas varreduras com duração de 1 hora e na seqüência, observações de grupo ou indivíduo focal. Concomitante com o aumento da população brasileira de baleia jubarte, o número de avistagens de baleias adultas ao redor do Arquipélago aumentou, especialmente de 2002 a 2004, porém avistagens de filhotes somente aumentaram durante a temporada 2004. De uma forma gradual baleias chegam, se concentram e partem de Abrolhos, refletindo a migração segregada e as alterações de status sociais dos indivíduos. A medida que a temporada progride, ocorre uma mudança na freqüência das diferentes categorias de grupo de baleia jubarte, de grupos sem filhotes para grupos com filhotes, assim como os comportamentos, os quais, dentro de cada categoria de grupo, parecem estar adequado ao estágio de desenvolvimento do filhote (para aqueles grupos com filhote) e refletem o que parece estar relacionado a busca por oportunidades de acasalamentos e interações sociais. Ao longo das 7 temporadas estudadas, não houve mudanças na estruturação de grupos de baleias jubarte, nem houve alterações comportamentais marcantes. Porém, verificou-se que na presença de barcos num raio de 100-300 metros, fêmeas (mães) permanecem menos tempo em repouso e filhotes ficam menos tempo em comportamento de provável amamentação. Existe a preocupação de que repetidas mudanças comportamentais decorrentes de fatores antrópicos possam levar a população a risco, já que em espécies como baleias, as alterações a nível populacional podem levar muitos anos para ser detectadas. Logo é sugerido que estudos seguindo a mesma metodologia sejam continuados para que comparações sejam possíveis. Um estudo de longo-prazo permitiria uma investigação continua dos padrões (ou alterações deles) com que as baleias jubarte utilizam a área e como vêm respondendo as pressões antrópicas. / Humpback whales use the east coast of Brazil as a breeding and calving area. The waters surrounding Abrolhos Archipelago are important because of the high concentration of humpback whale groups with calves. A seven-year study (1998 - 2004) was conducted, from a land base station, to investigate temporal patterns of group structure and behavior of humpback whales. Depending on weather and sightability conditions, one-hour-scans were done followed by observations of animal or group focal follows. Concomitant with the observation of an increasing Brazilian humpback whale population, the number of adult whales sighted around the Archipelago increased, especially from 2002 to 2004. However, sightings of calves only increased during 2004 season. The humpback whales gradually arrive, concentrate and leave the region, reflecting segregated migration and individual social changes. As the season progresses, the frequency of different groups categories changes, from groups without calf to groups with calf, as well as the behaviors, which, within each group category, seems to be appropriate to calf development stages (for groups with calf) and reflect what seems to be related to the search for mating opportunities and other social interactions. During those 7 seasons, there were no changes in humpback whale group structure, nor were there strong behavioral changes. However, it was observed that, in the presence of vessels around 100 to 300 meters, mothers spent less time resting and calves spent less time in activities probably related to suckling. There is a concern that behavioral changes caused by anthropogenic factors may put the population at risk and changes at population level may take several years to be detected. So, it is suggested that studies following the same methodology continues, allowing further future comparisons. A long-term study would permit continued investigation of humpback whale use patterns (or theirs alterations) showing their responses to anthropogenic pressures.

De nombreux systèmes électrochimiques sont composés d'un grand nombre d'éléments électroactifs en interaction. Si la réaction électrochimique possède une cinétique non linéaire, des comportements coopératifs complexes peuvent émerger suivant la nature et l’intensité des interactions entre les éléments du système. L'objectif de cette thèse est de comprendre l'influence de la taille finie de l’électrode et des interactions entre les microélectrodes sur le comportement coopératif d'un groupe de microélectrodes de platine soumis à un couplage global. Les réactions choisies pour cette étude sont l’électrooxydation du monoxyde de carbone (CO), une réaction avec une cinétique bistable et l’électrooxydation du formaldéhyde (HCHO), qui présente des oscillations de potentiel sous contrôle galvanostatique. Au cours de l’électrooxydation galvanodynamiques du CO sur une seule microélectrode de Pt, la branche S-NDR a pu être mise en évidence contrairement au comportement observé sur une macroélectrode de Pt. En outre, les nouveaux comportements coopératifs comme l'activation séquentielle des microélectrodes, des oscillations de courant et de potentiel spontanées et un régime de commutation dynamique entre les électrodes ont été découverts pour cette réaction lorsque quatre électrodes ont été couplées globalement. Pendant l’électrooxydation de HCHO, l'introduction du couplage global à deux électrodes conduit à des oscillations de courant en opposition de phase. / Many electrochemical systems are composed of a large number of interacting electroactive elements. If the reaction taking place on them has nonlinear kinetics and their interactions allow them to exchange information, complex cooperative behaviors can emerge. The objective of this thesis is to understand the influence of finite-size effects and cooperative phenomena on the global behavior of a group of coupled Pt microelectrodes. The reactions chosen for this study were CO electrooxidation, a reaction with current bistability, and HCHO electrooxidation, which exhibits oscillations under galvanostatic control. During the galvanodynamic electrooxidation of CO on a single microelectrode the S-NDR branch could be evidenced, on macroelectrodes this is not possible due to the formations of stationary domains. Additionally, novel cooperative behaviors (i.e., sequential activation, oscillations and complex switching) were discovered for this reaction when four electrodes were globally coupled. During HCHO electrooxidation the introduction of global coupling to two electrodes led to anti-phase current oscillations.

I extended an artificial life platform called GReaNs (the name stands for Gene Regulatory evolving artificial Networks) to explore the evolutionary abilities of biologically inspired Spiking Neural Network (SNN) model. The encoding of SNNs in GReaNs was inspired by the encoding of gene regulatory networks. As proof-of-principle, I used GReaNs to evolve SNNs to obtain a network with an output neuron which generates a predefined spike train in response to a specific input. Temporal pattern recognition was one of the main tasks during my studies. It is widely believed that nervous systems of biological organisms use temporal patterns of inputs to encode information. The learning technique used for temporal pattern recognition is not clear yet. I studied the ability to evolve spiking networks with different numbers of interneurons in the absence and the presence of noise to recognize predefined temporal patterns of inputs. Results showed, that in the presence of noise, it was possible to evolve successful networks. However, the networks with only one interneuron were not robust to noise. The foraging behaviour of many small animals depends mainly on their olfactory system. I explored whether it was possible to evolve SNNs able to control an agent to find food particles on 2-dimensional maps. Using ring rate encoding to encode the sensory information in the olfactory input neurons, I managed to obtain SNNs able to control an agent that could detect the position of the food particles and move toward it. Furthermore, I did unsuccessful attempts to use GReaNs to evolve an SNN able to control an agent able to collect sound sources from one type out of several sound types. Each sound type is represented as a pattern of different frequencies. In order to use the computational power of neuromorphic hardware, I integrated GReaNs with the SpiNNaker hardware system. Only the simulation part was carried out using SpiNNaker, but the rest steps of the genetic algorithm were done with GReaNs.