Дисертації: "Architectures cognitives"

1

Djerroud, Halim. "Architecture robotique pour la navigation parmi les obstacles amovibles pour un robot mobile." Electronic Thesis or Diss., Paris 8, 2021. http://www.theses.fr/2021PA080050.

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Dans cette thèse, nous abordons la navigation autonome d'un robot mobile en milieu domiciliaire congestionné. Cette problématique relève du domaine de la navigation parmi les obstacles amovibles. Nous proposons une architecture robotique permettant la navigation parmi des obstacles fixes, amovibles et interactifs. L'objectif du robot est de rejoindre une position, tout en évitant les obstacles fixes, déplacer les obstacles amovibles s'ils gênent le passage ou bien demander à des obstacles interactifs (humain, robots, etc.) de céder le passage.Dans notre première contribution, nous proposons une architecture robotique hiérarchique baptisée VICA (VIcarious Cognitive Architecture), dont le niveau décisionnel est couplé à une architecture cognitive. Nous nous sommes inspiré des travaux sur la simplixité de Alain Berthoz qui décrivent comment le vivant prépare l'action et anticipe les réactions. L'architecture robotique se compose d'un planificateur global permettant la navigation dans un environnement inconnu et d'un planificateur local dédié à la gestion des obstacles.La seconde met en œuvre un planificateur global dont le but est de rapprocher autant que possible le robot de son objectif, en utilisant l’algorithme H* que nous avons développé.La troisième propose un planificateur local pour la gestion des obstacles. La solution proposée consiste à utiliser la simulation multi-agents dans le but d'anticiper le comportement des obstacles.L'implémentation de cette solution est réalisée dans l'architecture VICA développée sous ROS (Robot Operating System). En parallèle, nous avons développé un robot expérimental pour valider nos résultats
In this thesis, we address the autonomous navigation of a mobile robot in a congested indoor environment. This problem is related to navigation among movable obstacles (NAMO). We propose a robotic architecture allowing navigation among: fixed, removable and interactive obstacles. The objective of the robot is to reach a position, while avoiding fixed obstacles, to move removable obstacles if they obstruct the path or to ask interactive obstacles (human, robots, etc.) to give way.In our first contribution, we propose a hierarchical robotic architecture named VICA (VIcarious Cognitive Architecture), whose decisional level is coupled to a cognitive architecture. We are inspired by Alain Berthoz's work on simplexity, which describes how living organisms prepare actions and anticipate reactions. The robotic architecture is composed of a global planner allowing navigation in an unknown environment and a local planner dedicated to obstacle management.The second one implements a global planner whose goal is to bring the robot as close as possible to its goal, using the H* algorithm we have developed.The third one proposes a local planner for obstacle management. The proposed solution consists in using multi-agent simulation in order to anticipate the behavior of obstacles.The implementation of this solution is realized in the VICA architecture developed under ROS (Robot Operating System). In parallel, we have developed an experimental robot to validate our results

2

Bay, Joo-Hwa. "Cognitive biases in design the case of tropical architecture /." Delft, the Netherlands : Design Knowledge System Research Centre, Faculteit Bouwkunde, Technische Universiteit Delft, 2001. http://catalog.hathitrust.org/api/volumes/oclc/49528245.html.

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3

Bouhali, Florence. "Processing symbols in the ventral visual cortex : functional architecture and anatomical constraints." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB080.

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Le cortex visuel ventral chez l’homme se compose d’une mosaïque de régions spécialisées dans la reconnaissance de différentes catégories d’objets. Selon une organisation reproductible, certaines régions répondent préférentiellement aux visages, alors que d’autres sont plus activées par les maisons et les lieux, par les outils, ou encore par les parties du corps. Plusieurs facteurs ont été invoqués pour expliquer la préférence d’une région pour une catégorie donnée, tels que des biais pour le traitement de certaines caractéristiques visuelles (préférence pour la position fovéale ou périphérique des stimuli, pour leur fréquence spatiale haute ou basse), le degré d’exposition et d’expertise (expertise pour les voitures par exemple), ou la connectivité anatomique vers des réseaux cérébraux spécialisés dans le traitement d’un domaine particulier. Chez les enfants, l’apprentissage de la lecture de mots ou d’autres systèmes symboliques culturels provoque le développement de régions corticales dédiées, telles que l’aire de la forme visuelle des mots (VWFA), au sein d’une voie ventrale en partie déjà stabilisée. Ce développement ontologique tardif pour la reconnaissance de symboles, indépendamment de contraintes phylogénétiques propres à la lecture, facilite l’étude de ce qui façonne la spécialisation fonctionnelle au sein de la voie ventrale. Dans cette thèse, nous avons étudié la représentation des mots et des partitions de musique au sein du cortex visuel ventral en combinant des méthodes d’imagerie par résonance magnétique fonctionnelle et de diffusion, à des taches comportementales. D’abord, nous montrons que la localisation de la VWFA chez les adultes correspond, en comparaison à des régions voisines du cortex ventral, à une région connectée de manière optimale à celles du langage qui traitent le contenu sémantique et phonologique. Ensuite, nous montrons que les régions ventrales qui sous-tendent le décodage orthographique sont fonctionnellement hétérogènes selon un axe latero-médial. Les régions médianes semblent encoder les graphèmes de façon sérielle, sous le contrôle de régions pariétales, pour les convertir en phonèmes. A l’inverse, les régions latérales traitent les mots de façon plus flexible pour accéder au lexique. Ces études mettent en évidence le rôle majeur de la connectivité anatomique dans le développement d’une spécialisation fonctionnelle pour les mots, avec la contribution de connectivités diverses qui participent à l’hétérogénéité fonctionnelle du système de la forme visuelle des mots. Enfin, nous observons que la maîtrise de la lecture musicale a d’importantes conséquences sur la latéralisation ventrale d’autres catégories. D’une part, la latéralisation à gauche augmente dans des régions latérales ventrales pour toutes les catégories. D’autre part, la latéralisation à droite augmente dans des régions fusiformes postérieures, notamment pour le traitement des visages et des maisons. Ces conséquences, similaires à celles provoquées par l’apprentissage de la lecture de mots, révèlent des processus à la fois de compétition et de transfert entre catégories. Ainsi, nos résultats suggèrent que des mécanismes communs pourraient expliquer comment une expertise culturelle peut recycler et modifier le cortex visuel
The human ventral visual cortex hosts a mosaic of areas specialized in the recognition of different categories of objects. According to a reproducible pattern, some areas respond preferentially to faces, while others are more activated by places and buildings, by tools, or by body parts. Several factors have been proposed as major determinants of the preferred category of a given region, such as visual feature biases (preference for peripheral vs. foveal stimuli, or for high vs. low spatial frequencies), experience (e.g., car expertise) and white-matter connectivity to domain-specific brain networks. In children, learning to read words and other cultural symbols triggers the emergence of dedicated cortical areas, such as the visual word form area (VWFA), within a partially settled ventral pathway. This late ontological development for symbol recognition, free from reading-specific evolutionary constraints, facilitates the investigation of what shapes functional specialization in the ventral pathway. In the current work, we studied in particular the representation of words and musical scores in the ventral visual cortex, using functional magnetic resonance imaging (fMRI), diffusion-weighted imaging and behavioral tasks. First, we show that the location of the VWFA in adults corresponds to a region optimally connected to language regions supporting semantics and phonology, as compared to adjacent ventral cortex regions. Second, we demonstrate that ventral regions supporting orthographic decoding are heterogeneous along a medial-to-lateral axis. Medial regions seem to encode graphemes serially for phonological decoding, under the control of parietal regions. In contrast, lateral regions process words more flexibly for lexical access. These studies reveal a major role of white-matter connectivity in shaping functional specialization for words, with differential connections participating in the functional heterogeneity of the VWFA. Third, we observe that musical literacy has a large impact on lateralization patterns in the ventral stream. A domain general enhancement of leftward lateralization takes place in lateral ventral regions, together with a rightward shift in fusiform regions notably for the processing of faces and houses. These consequences probably reflect both competition between visual categories and transfer across them, and resemble the impact of reading acquisition. Together, our results show that common processes may explain how cultural expertise recycles and modifies the visual cortex

4

Popescu, Alexandru. "Cognitive Radio Networks : Elements and Architectures." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00575.

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As mobility and computing becomes ever more pervasive in society and business, the non-optimal use of radio resources has created many new challenges for telecommunication operators. Usage patterns of modern wireless handheld devices, such as smartphones and surfboards, have indicated that the signaling traffic generated is many times larger than at a traditional laptop. Furthermore, in spite of approaching theoretical limits by, e.g., the spectral efficiency improvements brought by 4G, this is still not sufficient for many practical applications demanded by end users. Essentially, users located at the edge of a cell cannot achieve the high data throughputs promised by 4G specifications. Worst yet, the Quality of Service bottlenecks in 4G networks are expected to become a major issue over the next years given the rapid growth of mobile devices. The main problems are because of rigid mobile systems architectures with limited possibilities to reconfigure terminals and base stations depending on spectrum availability. Consequently, new solutions must be developed that coexist with legacy infrastructures and more importantly improve upon them to enable flexibility in the modes of operation. To control the intelligence required for such modes of operation, cognitive radio technology is a key concept suggested to be part of the so-called beyond 4th generation mobile networks. The basic idea is to allow unlicensed users access to licensed spectrum, under the condition that the interference perceived by the licensed users is minimal. This can be achieved with the help of devices capable of accurately sensing the spectrum occupancy, learning about temporarily unused frequency bands and able to reconfigure their transmission parameters in such a way that the spectral opportunities can be effectively exploited. Accordingly, this indicates the need for a more flexible and dynamic allocation of the spectrum resources, which requires a new approach to cognitive radio network management. Subsequently, a novel architecture designed at the application layer is suggested to manage communication in cognitive radio networks. The goal is to improve the performance in a cognitive radio network by sensing, learning, optimization and adaptation.

5

Ratko-Dehnert, Emil. "Distributional constraints on cognitive architecture." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-159387.

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Mental chronometry is a classical paradigm in cognitive psychology that uses response time and accuracy data in perceptual-motor tasks to elucidate the architecture and mechanisms of the underlying cognitive processes of human decisions. The redundant signals paradigm investigates the response behavior in Experimental tasks, where an integration of signals is required for a successful performance. The common finding is that responses are speeded for the redundant signals condition compared to single signals conditions. On a mean level, this redundant signals effect can be accounted for by several cognitive architectures, exhibiting considerable model mimicry. Jeff Miller formalized the maximum speed-up explainable by separate activations or race models in form of a distributional bound – the race model inequality. Whenever data violates this bound, it excludes race models as a viable account for the redundant signals effect. The common alternative is a coactivation account, where the signals integrate at some stage in the processing. Coactivation models have mostly been inferred on and rarely explicated though. Where coactivation is explicitly modeled, it is assumed to have a decisional locus. However, in the literature there are indications that coactivation might have at least a partial locus (if not entirely) in the nondecisional or motor stage. There are no studies that have tried to compare the fit of these coactivation variants to empirical data to test different effect generating loci. Ever since its formulation, the race model inequality has been used as a test to infer the cognitive architecture for observers’ performance in redundant signals Experiments. Subsequent theoretical and empirical analyses of this RMI test revealed several challenges. On the one hand, it is considered to be a conservative test, as it compares data to the maximum speed-up possible by a race model account. Moreover, simulation studies could show that the base time component can further reduce the power of the test, as violations are filtered out when this component has a high variance. On the other hand, another simulation study revealed that the common practice of RMI test can introduce an estimation bias, that effectively facilitates violations and increases the type I error of the test. Also, as the RMI bound is usually tested at multiple points of the same data, an inflation of type I errors can reach a substantial amount. Due to the lack of overlap in scope and the usage of atheoretic, descriptive reaction time models, the degree to which these results can be generalized is limited. State-of-the-art models of decision making provide a means to overcome these limitations and implement both race and coactivation models in order to perform large scale simulation studies. By applying a state-of-the-art model of decision making (scilicet the Ratcliff diffusion model) to the investigation of the redundant signals effect, the present study addresses research questions at different levels. On a conceptual level, it raises the question, at what stage coactivation occurs – at a decisional, a nondecisional or a combined decisional and nondecisional processing stage and to what extend? To that end, two bimodal detection tasks have been conducted. As the reaction time data exhibits violations of the RMI at multiple time points, it provides the basis for a comparative fitting analysis of coactivation model variants, representing different loci of the effect. On a test theoretic level, the present study integrates and extends the scopes of previous studies within a coherent simulation framework. The effect of experimental and statistical parameters on the performance of the RMI test (in terms of type I errors, power rates and biases) is analyzed via Monte Carlo simulations. Specifically, the simulations treated the following questions: (i) what is the power of the RMI test, (ii) is there an estimation bias for coactivated data as well and if so, in what direction, (iii) what is the effect of a highly varying base time component on the estimation bias, type I errors and power rates, (iv) and are the results of previous simulation studies (at least qualitatively) replicable, when current models of decision making are used for the reaction time generation. For this purpose, the Ratcliff diffusion model was used to implement race models with controllable amount of correlation and coactivation models with varying integration strength, and independently specifying the base time component. The results of the fitting suggest that for the two bimodal detection tasks, coactivation has a shared decisional and nondecisional locus. For the focused attention experiment the decisional part prevails, whereas in the divided attention task the motor component is dominating the redundant signals effect. The simulation study could reaffirm the conservativeness of the RMI test as latent coactivation is frequently missed. An estimation bias was found also for coactivated data however, both biases become negligible once more than 10 samples per condition are taken to estimate the respective distribution functions. A highly varying base time component reduces both the type I errors and the power of the test, while not affecting the estimation biases. The outcome of the present study has theoretical and practical implications for the investigations of decisions in a multisignal context. Theoretically, it contributes to the locus question of coactivation and offers evidence for a combined decisional and nondecisional coactivation account. On a practical level, the modular simulation approach developed in the present study enables researchers to further investigate the RMI test within a coherent and theoretically grounded framework. It effectively provides a means to optimally set up the RMI test and thus helps to solidify and substantiate its outcomes. On a conceptual level the present study advocates the application of current formal models of decision making to the mental chronometry paradigm and develops future research questions in the field of the redundant signals paradigm.

6

Fawcett, Angela. "A cognitive architecture of dyslexia." Thesis, University of Sheffield, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295122.

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7

Antony, Michael Verne. "Consciousness, content, and cognitive architecture." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/13729.

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8

Novikova, Jekaterina. "Generic Cognitive Architecture for Real-Time, Embedded Cognitive Systems." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3889.

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The problem of integrated cognition , analyzed in the thesis, belongs to a multi-disciplinary area of cognitive engineering. The multi-disciplinary focusing on cognitive models and real-time embedded systems, such as mobile robots, helps to reveal a broader and deeper understanding of robotics as part of everyday life and society. Over the past decades many cognitive architectures have been proposed and steadily developed, based on different approaches and methodologies, but still current cognitive architectures are far from the goal of covering the requirements for general intelligence. Recent research in the area of evolutionary algorithms and genetic programming is used in this study as an inspiration for developing the new version of integrated cognitive architecture, and the knowledge of human brain structure and functions is applied to the architecture as well. In this study a survey of cognitive architectures is performed, a version of biologically inspired hybrid cognitive architecture is developed. This architecture is influenced by a contemporary research in evolutionary algorithms and genetic programming. Some modules of the architecture are applied to a mobile robot in a simulated environment.

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Buc, Calderon Cristian. "Temporal dynamics and neural architecture of action selection." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/229408.

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In this thesis we pitted two views of action selection. On the one hand, a traditional view suggesting that action selection emerges from a sequential process whereby perception, cognition and action proceed serially and are subtended by distinct brain areas. On the other hand, an ecological view (formalized in the affordance competition hypothesis) advocating that action selection stems from the parallel implementation of potential action plans. In parallel, the competition between these action plans would be biased by relevant task factors. We first addressed the issue of the temporal dynamics of action selection processes in Chapter 2. We built a reaching task design that crucially gave equal opportunities for serial and parallel processing of cognitive and motor processes to occur. In our study, we first cued participants with probabilities associated to upcoming potential reaches. After several hundreds of milliseconds, participants were given a deterministic go signal indicating which target to reach for. They had to reach for the signaled target as fast as possible. Importantly, our design tries to cope with the biases involved in previous reaching tasks, allowing for a much more informative way to tackle the issue of serial versus parallel processing in action selection. We show that effects of action probability are not only present in the initiation time (i.e. the time it takes to initiate the movement), but crucially also in the movement time (i.e. the time interval between movement initiation and target reaching). Furthermore, an analysis of the movement trajectories showed that reach probability influenced the trajectories according to the predicted pattern. Thus, these results back up a system where cognitive and motor processes continuously interact with one another to come up with a decision. After clarifying the temporal dynamics, we concentrate our efforts on exposing the neural architecture of processes subtending action selection in Chapter 3. In a two-choice button press task, participants were first cued with predictive information regarding upcoming button presses. Crucially, we experimentally manipulated the amount of information in favor of specific button presses whilst adopting a design as similar as possible to those used in monkey neurophysiology (e.g. Cisek & Kalaska, 2005). Using fMRI, our results showed that as information in favor a button press increases, so does activity in the contralateral primary motor cortex, while activity in the ipsilateral primary motor cortex decreases. Moreover, we observed that primary motor regions are more tightly coupled with fronto-parietal areas in a condition involving a decision compared with a situation not implicating a decision between two button presses. Our results are compatible with an account predicting that decision-making emerges from motor areas, and therefore suggest that the architecture presented in the affordance competition hypothesis is not only valid in monkeys but also humans. In Chapter 4, we combine the findings acquired in the studies of chapter 2 and 3 with recent neurophysiological insights to develop a neuro-computational model capable of grasping the continuous interaction between cognitive and motor processes, responsible for the behavioral pattern in reach selection tasks. Our model functions on the principles of cascade forward models whereby activation at one stage of processing systematically spills to the next one, thereby substantially blurring the boundaries between perceptive, cognitive and motor processes. Contrary to most computational models confining action selection processes prior to action execution, our model allows for these processes to leak into action execution. Moreover, the threshold for action execution is not fixed, but rather dynamic and crucially depends on the activity pattern of the model’s primary motor neurons. We propose that the modification of the threshold is governed by the subthalamic nucleus, receiving direct input signals from the primary motor cortex and in turn imposing a dynamical brake on action execution. By including this dynamical threshold, our model has the advantage that it can release movement execution either rapidly or slowly depending on the context. Our model accounts not only for initiation times, but also movement times in reaching task studies. Furthermore, it can grasp the qualitative pattern of movement trajectories. This study suggests that to explain unfolding actions a classical fixed threshold is not sufficient, but rather an execution threshold level that is continuously being updated depending on the context is required.
Doctorat en Sciences psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished

10

McHugh, Brendan Thomas. "Architecture as a cognitive teaching device." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/23206.

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11

Thompson, Jill Maria. "Cognitive architecture in euthymic bipolar disorder." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417560.

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12

Harrison, David J. "Connectionism, folk psychology and cognitive architecture." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322924.

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13

Miri, Hossein. "CernoCAMAL : a probabilistic computational cognitive architecture." Thesis, University of Hull, 2012. http://hydra.hull.ac.uk/resources/hull:6887.

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This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes. The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally. The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows: - The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically. - The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems. - The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL. A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis.

14

Joshi, Anshul. "WPCA| The Wreath Product Cognitive Architecture." Thesis, The University of Utah, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10242991.

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We propose to examine a representation which features combined action and perception signals, i.e., instead of having a purely geometric representation of the perceptual data, we include the motor actions, e.g., aiming a camera at an object, which are also actions that generate the particular shape. This generative perception-action representation uses Leyton’s cognitive representation based on wreath products. The wreath product is a special kind of group which captures information through symmetries on the sensorimotor data. The key insight is the bundling of actuation and perception data together in order to capture the cognitive structure of interactions with the world. This involves developing algorithms and methods: (1) to perform symmetry detection and parsing, (2) to represent and characterize uncertainties in the data and representations, and (3) to provide an overall cognitive architecture for a robot agent. We demonstrate these functions in 2D text classification, as well as on 3D data, on a real robot operating according to a well-defined experimental protocol for benchmarking indoor navigation, along with capabilities for multirobot communication and knowledge sharing. A cognitive architecture called the Wreath Product Cognitive Architecture is developed to support this approach.

15

Jonas, Eric Michael. "Stochastic architectures for probabilistic computation." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87457.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 107-111).
The brain interprets ambiguous sensory information faster and more reliably than modern computers, using neurons that are slower and less reliable than logic gates. But Bayesian inference, which is at the heart of many models for sensory information processing and cognition, as well as many machine intelligence systems, appears computationally challenging, even given modern transistor speeds and energy budgets. The computational principles and structures needed to narrow this gap are unknown. Here I show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude. By connecting stochastic digital components according to simple mathematical rules, it is possible to rapidly, reliably and accurately solve many Bayesian inference problems using massively parallel, low precision circuits. I show that our circuits can solve problems of depth and motion perception, perceptual learning and causal reasoning via inference over 10,000+ latent variables in real time - a 1,000x speed advantage over commodity microprocessors - by exploiting stochasticity. I will show how this natively stochastic approach follows naturally from the probability algebra, giving rise to easy-to-understand rules for abstraction and composition. I have developed a compiler that automatically generate circuits for a wide variety of problems fixed-structure problems. I then present stochastic computing architectures for models that are viable even when constrained by silicon area and dynamic creation and destruction of random variables. These results thus expose a new role for randomness and Bayesian inference in the engineering and reverse-engineering of computing machines.
by Eric Jonas.
Ph. D.

16

Zheng, Xijia Ph D. Massachusetts Institute of Technology. "Cognitive optical network architecture in dynamic environments." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/126997.

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Thesis: Ph. D. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 149-154).
Emerging network traffic requires a more agile network management and control system to deal with the dynamic network environments than today's networks use. The bursty and large data transactions introduced by new technological applications can cause both high costs and extreme congestion in networks. The prohibitive cost of massive over-provisioning will manifest as huge congestions during peak demand periods. The network management and control system must be able to sense the traffic changes and reconfigure in a timely manner (in tens of milliseconds instead of minutes or hours) to use network resources efficiently. We propose the use of cognitive techniques for fast and adaptive network management and control of future optical networks. The goal of this work is to provide timely network reconfigurations in response to dynamic traffic environments and prevent congestion from building up.
We make a simplified model of the expected traffic arrival rate changes as a multistate Markov process based on the characteristics of the dynamic, bursty, and high granularity traffic. The traffic is categorized into different network traffic environments by the length of the network coherence time, which is the time that the traffic is unvarying. The tunneled network architecture is adopted due to its supremacy in reducing the control complexity when the traffic volume is at least one wavelength. In the long coherence time regime where traffic changes very slowly, the traffic detection performances of two Bayesian estimators and a stopping-trial (sequential) estimator are examined, based on the transient behaviors of networks. The stopping trial estimator has the fastest response time to the changes of traffic arrival statistics. We propose a wavelength reconfiguration algorithm with continuous assessment where the system reconfigures whenever it deems necessary.
The reconfiguration can involve addition or subtraction of multiple wavelengths. Using the fastest detection and reconfiguration algorithm can reduce queueing delays during traffic surges without over-provisioning and thus can reduce network capital expenditure and prevent wasting resources on erroneous decisions when surges occur. For traffic with moderate coherence time (where traffic changes at a moderate rate) and the short coherence time (where traffic changes quickly), the stopping-trial estimator still responds to the traffic changes with a short detection time. As long as the inter-arrival times of traffic transactions are independent, the algorithm is still optimum. The algorithm provides no prejudice on the exact network traffic distribution, avoiding having to sense and estimate detailed arrival traffic statistics.
To deal with fast-changing traffic, we model the transient convergent behaviors of network traffic drift as a result of traffic transition rate changes and validate the feasibility and utility of the traffic prediction. In a simple example when the network traffic rate changes monotonically in a linear model, the sequential maximum likelihood estimator will capture the traffic trend with a small number of arrivals. The traffic trend prediction can help to provide fast reconfiguration, which is very important for maintaining quality of service during large traffic shifts. We further investigate the design of an efficient rerouting algorithm to maintain users' quality of service when the incremental traffic cannot be accommodated on the primary path. The algorithm includes the fast reconfiguration of wavelengths in the existing lit and spatially routed fibers, and the setting up and lighting of new fibers.
Rerouting is necessary to maintain users' quality of service when the queueing delay on the primary path (determined by shortest path routing) exceeds the requirement. Our algorithm triggers reconfiguration when a queueing delay threshold is crossed on the primary path. The triggering by a threshold on the queueing delay is used due to its simplicity, and it is directly measurable by the exact traffic transaction sizes and the queue size, which reflect both the current network traffic environment and the network configurations. A dynamic rerouting algorithm implemented with a shortest path algorithm is proposed to find the secondary paths for rerouting. We make the conjecture that it is desirable that the alternate paths for rerouting have small numbers of hops and are disjoint with other busy paths when the hops on the path are independent. In addition, the conjecture suggests that a good candidate network topology should have high edge-connectivity.
Wavelength reservation for rerouted traffic does not maximize wavelength utilization. We make the conjecture that traffic with different sizes should be broken up into multi-classes with dedicated partitioned resources and the queueing delay should be normalized by the transmission time for rerouting triggering to realize better network utilization.
by Xijia Zheng.
Ph. D. in Computer Science and Engineering
Ph.D.inComputerScienceandEngineering Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

17

Fox, Nathan Josephe. "Cognitive architecture and the function of human cognition." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/25027.

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A number of models of cognitive architecture have been advanced with the intention of providing some sense of the psychological processes that subserve a range of behaviours. For instance, Sober & Wilson (1998), C. Daniel Batson (1988) and Robert Frank (1988 and 1990) attempt to account for contrasting (if not contradictory) behaviours respectively, hedonistic and altruistic behaviour, self-oriented behaviour and other-oriented behaviour marked by empathetic reactions, and behaviour that reflects rational self-interest in material incentives and behaviour that tends to produce long-term benefits in social interactions. However, the approaches that I have examined encounter difficulties. One difficulty in basing psychological models on empirical data is that the mental states that precede and accompany motivations may be ambiguous or obscure. Those states may be composite states consisting of components that are inextricably linked. For instance, it is not clear whether an altruistic act has some desire for pleasure lurking in the shadows. In Sober & Wilsons approach, cognitive structure is predicted largely on the basis of general factors in the natural selection of cognitive devices, e.g., their availability for selection, energetic efficiency, and reliability. However, the particular factors that play a role in the aetiology of traits depend upon the function that those traits evolved to perform. For instance, while the reliability of a physical system component may certainly be an important general factor in natural selection, it may be a detriment for a device that has as a particular biological function the production of phenotypic flexibility. To avoid the problems that I identified in these approaches, I derived a model of cognitive architecture that is intended to predict motivations and actions that are consistent with aspects of evolutionary theory about the function of cognition. The theory upon which I depended is advanced in Peter Godfrey-Smiths book Complexity and the Function of Mind if Nature. He proposes that there is a single overarching adaptive function for the mind: to subserve adaptive plasticity. Accordingly, my model suggests a general pattern in the sequencing of human mental states that would tend to maximize behavioural flexibility as a means of maximizing inclusive fitness.
Graduate

18

Foundalis, Harry E. "Phaeaco a cognitive architecture inspired by Bongard's problems /." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3215222.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2006.
Source: Dissertation Abstracts International, Volume: 67-04, Section: B, page: 2251. Adviser: Douglas R. Hofstadter. "Title from dissertation home page (viewed June 20, 2007)."

19

McGhee, Jeremiah Lane. "Using a Cognitive Architecture in Incremental Sentence Processing." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3499.

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XNL-Soar is a specialized implementation of the Soar cognitive architecture. The version of XNL-Soar described in this thesis builds upon and extends prior research (Lewis, 1993; Rytting,2000) using Soar for natural language processing. This thesis describes the updates made to operators creating syntactic structure and the improved coverage of syntactic phenomena. It describes the addition of semantic structure building capability. This thesis also details the implementation of semantic memory and describes two experiments utilizing semantic memory in structural disambiguation. This thesis shows that XNL-Soar, as currently instantiated, resolves ambiguities common in language using strategies and resources including: reanalysis via snip operators, use of data-driven techniques with annotated corpora, and complex part-of-speech and word sense processing based on WordNet.

20

LANZA, Francesco. "Human-Robot Teaming Interaction: a Cognitive Architecture Solution." Doctoral thesis, Università degli Studi di Palermo, 2021. http://hdl.handle.net/10447/479089.

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21

Perdikis, Dionysios. "Functionnal organization of complex behavioral processes." Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX22050/document.

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Selon des études comportementales, les comportements complexes sont des processus multi-échelles, souvent composés de sous-éléments (unités fonctionnelles ou primitives). Cette thèse propose des architectures fonctionnelles afin de représenter la structure dynamique des unités fonctionnelles ainsi que celle des comportements multi-échelles résultants. Dans un premier temps, des unités fonctionnelles sont modélisées comme des flux structurés de faible dimension dans l'espace de phase (modes de fonctionnement). Des dynamiques supplémen-taires (signaux opérationnels) opèrent sur ces modes de fonctionnement faisant émerger des comportements complexes et sont classifiés selon la séparation entre leur échelle temporelle et celle des modes. Ensuite, des mesures de complexité, appliquées sur des architectures dis-tinctes composant un mouvement simple, révèlent un compromis entre la complexité des modes de fonctionnement et celle des signaux opérationnels. Celui-ci dépend de la séparation entre leurs échelles temporelles et soutient l'efficacité des architectures utilisant des modes non triviaux. Dans un deuxième temps, une architecture pour le comportement séquentiel (ici l'écriture) est construite via le couplage des modes de fonctionnement (réalisant des lettres) et des signaux opérationnels, ceux-ci beaucoup plus lents ou beaucoup plus rapides. Ainsi, l'importance des interactions entre les échelles temporelles pour l'organisation du comporte-ment est illustrée. Enfin, les contributions des modes et des signaux sur la sortie de l'architec-ture sont déterminées. Ceci semble être uniquement possible grâce à l'analyse du flux de phase (c'est-à-dire, non pas à partir des trajectoires dans l'espace de phase ni des séries temporelles)
Behavioural studies suggest that complex behaviours are multiscale processes, which may be composed of elementary ones (units or primitives). Traditional approaches to cognitive mod-elling generally employ reductionistic (mostly static) representations and computations of simplistic dynamics. The thesis proposes functional architectures to capture the dynamical structure of both functional units and the composite multiscale behaviours. First, a mathe-matical formalism of functional units as low dimensional, structured flows in phase space is introduced (functional modes). Second, additional dynamics (operational signals), which act upon functional modes for complex behaviours to emerge, are classified according to the separation between their characteristic time scale and the one of modes. Then, complexity measures are applied to distinct architectures for a simple composite movement and reveal a trade off between the complexities of functional modes and operational signals, depending on their time scale separation (in support of the control effectiveness of architectures employing non trivial modes). Subsequently, an architecture for serial behaviour (along the example of handwriting) is demonstrated, comprising of functional modes implementing characters, and operational signals much slower (establishing a mode competition and ‘binding’ modes into sequences) or much faster (as meaningful perturbations). All components being coupled, the importance of time scale interactions for behavioural organization is illustrated. Finally, the contributions of modes and signals to the output are recovered, appearing to be possible only through analysis of the output phase flow (i.e., not from trajectories in phase space or time)

22

Stepanov, Evgueni A. "Implementing Cognitive Grammar On A Cognitive Architecture: A Case Study With Act-r." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605536/index.pdf.

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Cognitive Grammar is a theory within the framework of Cognitive Linguistics that gives an account of human linguistic ability based entirely on general cognitive abilities. Because of the general complexity and open-endedness of the theory, there is not much computational work associated with it. This thesis proposes that ACT-R cognitive architecture can provide the basic primitives for the cognitive abilities required for a better implementation of Cognitive Grammar. Thus, a language model was developed on the ACT-R architecture. The model processes active and passive sentences, constructs their propositional representations, and tests the representation on a sentence verification task of the experiment of Anderson (1974).

23

Kurup, Unmesh. "Design and use of a bimodal cognitive architecture for diagrammatic reasoning and cognitive modeling." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1198526352.

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24

Dreany, Harry Hayes. "Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10688677.

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This paper presents the integration of an intelligent decision support model (IDSM) with a cognitive architecture that controls an autonomous non-deterministic safety-critical system. The IDSM will integrate multi-criteria, decision-making tools via intelligent technologies such as expert systems, fuzzy logic, machine learning, and genetic algorithms.

Cognitive technology is currently simulated within safety-critical systems to highlight variables of interest, interface with intelligent technologies, and provide an environment that improves the system’s cognitive performance. In this study, the IDSM is being applied to an actual safety-critical system, an unmanned surface vehicle (USV) with embedded artificial intelligence (AI) software. The USV’s safety performance is being researched in a simulated and a real-world, maritime based environment. The objective is to build a dynamically changing model to evaluate a cognitive architecture’s ability to ensure safe performance of an intelligent safety-critical system. The IDSM does this by finding a set of key safety performance parameters that can be critiqued via safety measurements, mechanisms, and methodologies. The uniqueness of this research lies in bounding the decision-making associated with the cognitive architecture’s key safety parameters (KSPs). Other real-time applications (RTAs) that would benefit from advancing cognitive science associated with safety are unmanned platforms, transportation technologies, and service robotics. Results will provide cognitive science researchers with a reference for the safety engineering of artificially intelligent safety-critical systems.

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MERRYFIELD, JESSICA L. "LET'S PLAY: DESIGNING SPACES FOR COGNITIVE DEVELOPMENT." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1148070735.

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26

Ong, Chin Chuan. "Analysis of Cognitive Architecture in the Cultural Geography Model." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17432.

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Approved for public release; distribution is unlimited
The Cultural Geography (CG) Model is a multi-agent discrete event simulation developed by TRAC-Monterey. It provides a framework to study the effects of operations in Irregular Warfare, by modeling behavior and interactions of populations. The model is based on social science theories; in particular, agent decision-making algorithms are built on Exploration Learning (EL) and Recognition-Primed Decision (RPD), and trust between entities is modeled to increase realism of interactions. This study analyzed the effects of these components on behavior and scenario outcome. It aimed to identify potential approaches for simplification of the model, and improve traceability and understanding of entity actions. The effect of using EL/RPD with/without trust was tested in basic stand-alone scenarios to assess its impact in isolation on entities perception of civil security. Further testing also investigated the influence on entity behavior in the context of obtaining resources from infrastructure nodes. The findings indicated that choice of decision-making methods did not significantly change scenario outcome, but variance across replications was greater when both EL and RPD were used. Trust was found to delay the rate of change in population stance due to interactions, but did not affect overall outcome if given sufficient time to reach steady state.

27

DONNART, JEAN-YVES. "Architecture cognitive et proprietes adaptatives d'un animat motivationnellement autonome." Paris 6, 1998. http://www.theses.fr/1998PA066099.

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L'objectif de cette these est de decrire monalysa, l'architecture de controle d'un animat motivationnellement autonome. Cette architecture constitue un systeme motivationnel selectionnant les actions et les buts d'un agent artificiel en fonction de son etat interne, des stimuli de l'environnent et de l'evaluation des consequences a long terme de ses choix de comportement. Cette architecture utilise un systeme de classeurs hierarchique original, capable de generer et de memoriser de maniere efficace differents plans d'actions ainsi que d'elaborer une representation interne de l'environnement de l'animat. Les fonctionnalites de monalysa sont presentees dans le cadre de la navigation d'un animat simule et d'un robot reel. Dans une premiere partie de ce travail, l'animat doit rejoindre un but en evitant les obstacles qu'il peut rencontrer sur sa trajectoire : nous montrons que monalysa a la faculte d'apprendre efficacement un comportement reactif tres general, en particulier parce qu'elle peut dynamiquement changer les buts de l'animat en fonction des obstacles rencontres. De plus, monalysa peut tirer profit de son experience dans l'environnement pour apprendre des plans de trajectoires et en deduire le plan optimal ; elle peut aussi modifier l'organisation de ses plans pour s'adapter aux changements intervenant dans l'environnement. Dans une seconde partie de ce travail, l'animat doit explorer son environnement alors que du bruit a ete ajoute au fonctionnement normal de ses senseurs odometriques. Dans ce contexte, nous montrons que monalysa sait apprendre une representation spatiale fidele de son environnement tout en maintenant une estimation correcte de sa position. Cette representation spatiale presente une grande robustesse au bruit et s'adapte a des environnements quelconques. La grande generalite de l'approche presentee ici permet d'envisager de nombreuses applications de recherche, qui sont evoquees en conclusion de ce travail.

28

Davis, Robert G. "Cognitive and perceptual factors in lighted architectural environments." Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3239430.

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29

Lex, Heiko [Verfasser]. "Granularity of cognitive representations in actions - Advances to the cognitive architecture of actions / Heiko Lex." Bielefeld : Universitätsbibliothek Bielefeld, 2015. http://d-nb.info/1070981370/34.

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30

Hemion, Nikolas [Verfasser]. "Building Blocks for Cognitive Robots: Embodied Simulation and Schemata in a Cognitive Architecture / Nikolas Hemion." Bielefeld : Universitätsbibliothek Bielefeld, 2013. http://d-nb.info/1046174266/34.

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31

Norris, Mary Ann. "The cognitive mapping of musical intention to performance." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/69274.

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32

Mohsenin, Mahsan (SeyedehMahsan). "The impact of urban geometry on cognitive maps." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65743.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 86-87).
This thesis investigates the relationship between urban geometry and cognitive maps. It is focused on the question of how human cognition of the built environment is affected by urban geometry. Building on the foundations of Kevin Lynch's studies of environmental perception (Lynch, 1960) and recent configurational measurement techniques of the built environment, it addresses an important question that Lynch has left unresolved: Why do people have more complete recollections of some parts of the urban environment, and not others? This thesis proposes an analytical measurement framework based on graph theory to compare the results of cognitive maps with objective spatial properties of the corresponding built environment. In order to test our hypothesis, first I measure and define urban geometry based on graph theory in two selected areas with different geometries in Kenmore, Boston and Kendall Sq., Cambridge, MA I will then collect cognitive maps based on specifically designed map drawing surveys. Finally, I examine the relationship between graph results and cognitive maps in order to identify the ways that urban geometry affects human perception. The findings inform urban designers and scholars of the city of how the configuration of the built environment can affect people's memory of a place, thus shaping one's experience of a city. Keywords: configurational patterns, urban geometry, cognitive maps, graph theory.
by Mahsan Mohsenin.
S.M.

33

Sklivanitis, Georgios. "Software-Defined Architectures for Spectrally Efficient Cognitive Networking in Extreme Environments." Thesis, State University of New York at Buffalo, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10744705.

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The objective of this dissertation is the design, development, and experimental evaluation of novel algorithms and reconfigurable radio architectures for spectrally efficient cognitive networking in terrestrial, airborne, and underwater environments. Next-generation wireless communication architectures and networking protocols that maximize spectrum utilization efficiency in congested/contested or low-spectral availability (extreme) communication environments can enable a rich body of applications with unprecedented societal impact. In recent years, underwater wireless networks have attracted significant attention for military and commercial applications including oceanographic data collection, disaster prevention, tactical surveillance, offshore exploration, and pollution monitoring. Unmanned aerial systems that are autonomously networked and fully mobile can assist humans in extreme or difficult-to-reach environments and provide cost-effective wireless connectivity for devices without infrastructure coverage.

Cognitive radio (CR) has emerged as a promising technology to maximize spectral efficiency in dynamically changing communication environments by adaptively reconfiguring radio communication parameters. At the same time, the fast developing technology of software-defined radio (SDR) platforms has enabled hardware realization of cognitive radio algorithms for opportunistic spectrum access. However, existing algorithmic designs and protocols for shared spectrum access do not effectively capture the interdependencies between radio parameters at the physical (PHY), medium-access control (MAC), and network (NET) layers of the network protocol stack. In addition, existing off-the-shelf radio platforms and SDR programmable architectures are far from fulfilling runtime adaptation and reconfiguration across PHY, MAC, and NET layers. Spectrum allocation in cognitive networks with multi-hop communication requirements depends on the location, network traffic load, and interference profile at each network node. As a result, the development and implementation of algorithms and cross-layer reconfigurable radio platforms that can jointly treat space, time, and frequency as a unified resource to be dynamically optimized according to inter- and intra-network interference constraints is of fundamental importance.

In the next chapters, we present novel algorithmic and software/hardware implementation developments toward the deployment of spectrally efficient terrestrial, airborne, and underwater wireless networks. In Chapter 1 we review the state-of-art in commercially available SDR platforms, describe their software and hardware capabilities, and classify them based on their ability to enable rapid prototyping and advance experimental research in wireless networks. Chapter 2 discusses system design and implementation details toward real-time evaluation of a software-radio platform for all-spectrum cognitive channelization in the presence of narrowband or wideband primary stations. All-spectrum channelization is achieved by designing maximum signal-to-interference-plus-noise ratio (SINR) waveforms that span the whole continuum of the device-accessible spectrum, while satisfying peak power and interference temperature (IT) constraints for the secondary and primary users, respectively. In Chapter 3, we introduce the concept of all-spectrum channelization based on max-SINR optimized sparse-binary waveforms, we propose optimal and suboptimal waveform design algorithms, and evaluate their SINR and bit-error-rate (BER) performance in an SDR testbed. Chapter 4 considers the problem of channel estimation with minimal pilot signaling in multi-cell multi-user multi-input multi-output (MIMO) systems with very large antenna arrays at the base station, and proposes a least-squares (LS)-type algorithm that iteratively extracts channel and data estimates from a short record of data measurements. Our algorithmic developments toward spectrally-efficient cognitive networking through joint optimization of channel access code-waveforms and routes in a multi-hop network are described in Chapter 5. Algorithmic designs are software optimized on heterogeneous multi-core general-purpose processor (GPP)-based SDR architectures by leveraging a novel software-radio framework that offers self-optimization and real-time adaptation capabilities at the PHY, MAC, and NET layers of the network protocol stack. Our system design approach is experimentally validated under realistic conditions in a large-scale hybrid ground-air testbed deployment. Chapter 6 reviews the state-of-art in software and hardware platforms for underwater wireless networking and proposes a software-defined acoustic modem prototype that enables (i) cognitive reconfiguration of PHY/MAC parameters, and (ii) cross-technology communication adaptation. The proposed modem design is evaluated in terms of effective communication data rate in both water tank and lake testbed setups. In Chapter 7, we present a novel receiver configuration for code-waveform-based multiple-access underwater communications. The proposed receiver is fully reconfigurable and executes (i) all-spectrum cognitive channelization, and (ii) combined synchronization, channel estimation, and demodulation. Experimental evaluation in terms of SINR and BER show that all-spectrum channelization is a powerful proposition for underwater communications. At the same time, the proposed receiver design can significantly enhance bandwidth utilization. Finally, in Chapter 8, we focus on challenging practical issues that arise in underwater acoustic sensor network setups where co-located multi-antenna sensor deployment is not feasible due to power, computation, and hardware limitations, and design, implement, and evaluate an underwater receiver structure that accounts for multiple carrier frequency and timing offsets in virtual (distributed) MIMO underwater systems.

34

Gaines, David Alexander. "INVESTIGATIONS INTO THE COGNITIVE ABILITIES OF ALTERNATE LEARNING CLASSIFIER SYSTEM ARCHITECTURES." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_diss/250.

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The Learning Classifier System (LCS) and its descendant, XCS, are promising paradigms for machine learning design and implementation. Whereas LCS allows classifier payoff predictions to guide system performance, XCS focuses on payoff-prediction accuracy instead, allowing it to evolve "optimal" classifier sets in particular applications requiring rational thought. This research examines LCS and XCS performance in artificial situations with broad social/commercial parallels, created using the non-Markov Iterated Prisoner's Dilemma (IPD) game-playing scenario, where the setting is sometimes asymmetric and where irrationality sometimes pays. This research systematically perturbs a "conventional" IPD-playing LCS-based agent until it results in a full-fledged XCS-based agent, contrasting the simulated behavior of each LCS variant in terms of a number of performance measures. The intent is to examine the XCS paradigm to understand how it better copes with a given situation (if it does) than the LCS perturbations studied.Experiment results indicate that the majority of the architectural differences do have a significant effect on the agents' performance with respect to the performance measures used in this research. The results of these competitions indicate that while each architectural difference significantly affected its agent's performance, no single architectural difference could be credited as causing XCS's demonstrated superiority in evolving optimal populations. Instead, the data suggests that XCS's ability to evolve optimal populations in the multiplexer and IPD problem domains result from the combined and synergistic effects of multiple architectural differences.In addition, it is demonstrated that XCS is able to reliably evolve the Optimal Population [O] against the TFT opponent. This result supports Kovacs' Optimality Hypothesis in the IPD environment and is significant because it is the first demonstrated occurrence of this ability in an environment other than the multiplexer and Woods problem domains.It is therefore apparent that while XCS performs better than its LCS-based counterparts, its demonstrated superiority may not be attributed to a single architectural characteristic. Instead, XCS's ability to evolve optimal classifier populations in the multiplexer problem domain and in the IPD problem domain studied in this research results from the combined and synergistic effects of multiple architectural differences.

35

Raizer, Klaus 1982. "Executive functions for Learning and decision-making in a bio-inspired cognitive architecture = Funções executivas para aprendizado e tomada de decisão em uma arquitetura cognitiva bio-inspirada." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261106.

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Orientador: Ricardo Ribeiro Gudwin
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-27T01:31:15Z (GMT). No. of bitstreams: 1 Raizer_Klaus_D.pdf: 4879759 bytes, checksum: 77716297b6419a3ee55bdf97ac67493d (MD5) Previous issue date: 2015
Resumo: O objetivo deste trabalho é o desenvolvimento de funções executivas para uma arquitetura cognitiva bioinspirada baseada em codelets. Um desafio que toda criatura (seja ela artificial ou biológica) enfrenta é definir qual a próxima ação a ser tomada, a cada instante de tempo, em função da percepção de um determinado ambiente. Essa decisão pode ser definida por um algoritmo que sempre repete as mesmas decisões em função de uma determinada situação, ou pode ser uma decisão adaptativa, que utiliza de mecanismos de aprendizagem para assumir decisões distintas, em função das experiências em situações passadas. Neste trabalho, buscou-se a integração dos processos de tomada de decisão deliberativos e mecanismos de aprendizado por reforço em um mesmo framework. Estas funções são conhecidas na literatura de ciências cognitivas como funções executivas. A solução aqui proposta insere-se dentro do contexto de nosso grupo de pesquisa, onde se busca o desenvolvimento de uma arquitetura cognitiva baseada em codelets. Nesta perspectiva, uma das contribuições deste trabalho é desenvolver algoritmos e implementações computacionais dotando a arquitetura cognitiva desenvolvida pelo grupo de funções executivas diversas, que poderão ser utilizadas para implementar soluções complexas com granularidade arbitrária. As funções de tomada de decisão deliberativa foram implementada na forma de uma rede de comportamentos modificada, enquanto que o componente de aprendizado foi desenvolvido na forma de um novo algoritmo (GLAS - Gated-Learning Action Selection) baseado em stimulus gating e inspirado em modelos de neurociência computacional conhecidos da literatura. Este framework foi validado em problemas de robótica móvel e de seleção de ação por aprendizado por reforço. A arquitetura cognitiva sendo desenvolvida, incrementada com as contribuições deste trabalho, tem o potencial de servir de base para futuros trabalhos de pesquisa nas áreas de inteligência artificial, robótica e cognição artificial
Abstract: This work¿s goal is the development of executive functions for a codelet-based bio-inspired cognitive architecture. One of the major challenges every creature faces, being biological or artificial, is to define the next action to be taken, at each time step, as a function of how it perceives its surrounding environment. This decision can be made by a reactive algorithm, which always repeats the same decisions for a given situation, or by an adaptive process, which is able to make use of learning mechanisms in order to make distinct decisions based on past experience. In this work, deliberative decision-making and reinforcement learning mechanisms have been integrated into a single framework. In cognitive science literature, these functions are known as executive functions. The solution proposed here is part of our group¿s central line of research, which is the investigation and development of a codelet-based cognitive architecture. In this context, a central contribution made by this work is the development and implementation of algorithms capable of providing this cognitive architecture with a group of executive functions, which in turn can be used to implement complex solutions with arbitrary granularity. Functions for deliberative decision-making have been implemented in the form of a modified behavior network, while the learning component was developed in the form of a new algorithm called GLAS (Gated-Learning Action Selection), based on stimulus gating and known computational neuroscience models. This framework has been validated with problems in mobile robotics and in action selection by reinforcement learning. The cognitive architecture under development, when incremented by the contributions presented in this work, has the potential to serve as a base for future work and research in the fields of artificial intelligence, robotics and artificial cognition
Doutorado
Engenharia de Computação
Doutor em Engenharia Elétrica

36

Cahill, Daniel. "Utilising information in architectural design drawings." Thesis, Heriot-Watt University, 2000. http://hdl.handle.net/10399/1143.

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37

Ginhac, Dominique. "Adéquation Algorithme architecture : Aspects logiciels, matériels et cognitifs." Habilitation à diriger des recherches, Université de Bourgogne, 2008. http://tel.archives-ouvertes.fr/tel-00646480.

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Les travaux présentés dans le cadre de cette Habilitation à Diriger des Recherches s'inscrivent principalement dans la problématique dite d'" Adéquation Algorithme Architecture ". Ils ont pour objectif commun la mise en œuvre de systèmes matériels et logiciels dédiés à la vision artificielle à fortes contraintes temporelles. Ils se focalisent sur différents aspects cruciaux tels que l'acquisition d'images par des capteurs dédiés, le développement et la programmation d'architectures optimisées de traitement des images et l'implantation d'algorithmes de traitement du signal et d'images en temps réel sur ces architectures.

38

Bergeron, Vincent. "Cognitive architecture and the brain : beyond domain-specific functional specification." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2711.

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My dissertation applies philosophical analysis to the problem of how we should cognitively characterize brain activity. Let us distinguish between high-level cognitive functions—e.g. decision-making, face recognition—and the lower-level computational operations that are carried out by discrete regions of the brain. One can assume that cognitive functions are assembled from interactions between relatively autonomous computational operations carried out by discrete brain regions. My thesis, stated very broadly, is that in order to be effective, the decomposition of a cognitive function into a set of interactions between localized computational operations may need to be specified domain-neutrally, and not in terms of a particular informational domain or stimulus class. Jerry Fodor’s influential work on modularity has sparked an industry of research that is based on the idea that the mind is, to a large extent, a configuration of domain-specific and relatively autonomous cognitive mechanisms, or modules. My treatment indicates how this modular approach must be modified in order successfully to decompose domain-specific cognitive functions into localizable computational operations. I proceed in two steps. First, I provide an analysis of the kinds of inferences that are used by cognitive scientists to postulate the existence of cognitive modules; I call these the modularity inferences. I offer a new characterization of these inferences, and argue that they can, and do, operate in three distinct modes in cognitive scientific research. Second, I present a general approach to the decomposition of a cognitive function into localizable computational operations. According to this approach, which I call the working zone approach, the contribution of a distinct brain region to a cognitive function is specified in terms of the type of operations that this region performs, and not in terms of a particular informational domain. I demonstrate the value of this approach in several research contexts within the cognitive sciences.

39

Le, Bin. "Building a Cognitive Radio: From Architecture Definition to Prototype Implementation." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28320.

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Cognitive radio (CR) technology introduces a revolutionary wireless communication mechanism in terminals and network segments, so that they are able to learn their environment and adapt intelligently to the most appropriate way of providing the service for the user's exact need. By supporting multi-band, mode-mode cognitive applications, the cognitive radio addresses an interactive way of managing the spectrum that harmonizes technology, market and regulation. This dissertation gives a complete story of building a cognitive radio. It goes through concept clarification, architecture definition, functional block building, system integration, and finally to the implementation of a fully-functional cognitive radio node prototype that can be directly packaged for application use. This dissertation starts with a comprehensive review of CR research from its origin to today. Several fundamental research issues are then addressed to let the reader know what makes CR a challenging and interesting research area. Then the CR system solution is introduced with the details of its hierarchical functional architecture called the Egg Model, modular software system called the cognitive engine, and the kernel machine learning mechanism called the cognition cycle. Next, this dissertation discusses the design of specific functional building blocks which incorporate environment awareness, solution making, and adaptation. These building blocks are designed to focus on the radio domain that mainly concerns the radio environment and the radio platform. Awareness of the radio environment is achieved by extracting the key environmental features and applying statistical pattern recognition methods including artificial neural networks and k-nearest neighbor clustering. Solutions for the radio behavior are made according to the recognized environment and the previous knowledge through case based reasoning, and further adapted or optimized through genetic algorithm solution search. New experiences are gained through the practice of the new solution, and thus the CR's knowledge evolves for future use; therefore, the CR's performance continues improving with this reinforcement learning approach. To deploy the solved solution in terms of the radio's parameters, a platform independent radio interface is designed. With this general radio interface, the algorithms in the cognitive engine software system can be applied to various radio hardware platforms. To support and verify designed cognitive algorithms and cognitive functionalities, a complete reconfigurable SDR platform, called the CWT2 waveform framework, is designed in this dissertation. In this waveform framework, a hierarchical configuration and control system is constructed to support flexible, real-time waveform reconfigurability. Integrating all the building blocks described above allows a complete CR node system. Based on this general CR node structure, a fully-functional Public Safety Cognitive Radio (PSCR) node is prototyped to provide the universal interoperability for public safety communications. Although the complete PSCR node software system has been packaged to an official release including installation guide and user/developer manuals, the process of building a cognitive radio from concept to a functional prototype is not the end of the CR research; on-going and future research issues are addressed in the last chapter of the dissertation.
Ph. D.

40

Trübutschek, Darinka. "Characterizing the neuro-cognitive architecture of non-conscious working memory." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS101.

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Depuis plus d'un siècle, les neuroscientifiques considèrent la mémoire de travail comme liée à la conscience, les deux fonctions ayant des caractéristiques et des mécanismes cérébraux similaires. Des travaux récents remettent en question cette interprétation en démontrant que des informations non-conscientes peuvent affecter le comportement pendant plusieurs secondes (« vision aveugle »), et suggérant qu'il existe un véritable système de mémoire de travail non-conscient. Nous combinons ici l’étude du comportement, l’imagerie du cerveau à haute résolution temporelle, et la modélisation computationnelle pour tester ces hypothèses. Nous rejetons d’abord plusieurs explications alternatives à la vision aveugle, montrant que celle-ci résulte d'un processus véritablement non-conscient. Nous montrons également que le maintien non-conscient de l’information ne s’accompagne pas nécessairement d'une activité cérébrale soutenue, mais pourrait dépendre d’états cérébraux «silencieux» qui impliquent des changements transitoires dans la connectivité synaptique. Ensuite, nous explorons systématiquement les propriétés clés de la mémoire de travail consciente dans le contexte de la vision aveugle. Même si plusieurs éléments et leur ordre temporel peuvent être stockés de manière non-consciente, la manipulation des représentations nécessite l’accès conscient et une activité neuronale soutenue. Ces résultats défient les théories qui assimilent le maintien de l'information en mémoire de travail à une activité consciente soutenue. Ils contredisent aussi la notion d'une véritable mémoire «de travail» non-consciente. Nous proposons donc l'existence d'une mémoire à court terme "silencieuse"
Our lives hinge on our ability to hold information online for immediate use. For over a century, cognitive neuroscientists have regarded such working memory as closely related to consciousness, with both functions sharing similar features and brain mechanisms. Recent work has challenged this view, demonstrating that non-conscious information may affect behavior for several seconds, and suggesting that there exists a genuine non-conscious working memory system. I here combine behavioral and modeling approaches with time-resolved magnetoencephalography and multivariate pattern analysis to put this proposal to the test. In a first study, I rule out alternative explanations for the long-lasting blindsight effect, showing that it results from a genuinely non-conscious process. Crucially, this non-conscious maintenance is not accompanied by persistent delay-period activity, but instead stores information in “activity-silent” brain states via transient changes in synaptic weights. In a second set of experiments, I systematically evaluate key properties of conscious working memory in the context of long-lasting blindsight. While even multiple items and their temporal order may be stored non-consciously, manipulating stored representations is associated with consciousness and sustained neural activity. Together, these results challenge theories that equate the maintenance of information in working memory with conscious activity sustained throughout the delay period, but also contradict the notion of a genuine non-conscious “working” memory. Instead, I propose the existence of activity-silent short-term memory

41

Kondrakunta, Sravya. "Implementation and Evaluation of Goal Selection in a Cognitive Architecture." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1503319861179462.

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42

TANEVSKA, ANA. "Towards a Cognitive Architecture for Socially Adaptive Human-Robot Interaction." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/998699.

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People have a natural predisposition to interact in an adaptive manner with others, by instinctively changing their actions, tones and speech according to the perceived needs of their peers. Moreover, we are not only capable of registering the affective and cognitive state of our partners, but over a prolonged period of interaction we also learn which behaviours are the most appropriate and well-suited for each one of them individually. This universal trait that we share regardless of our different personalities is referred to as social adaptation (adaptability). Humans are always capable of adapting to the others although our personalities may influence the speed and efficacy of the adaptation. This means that in our everyday lives we are accustomed to partake in complex and personalized interactions with our peers. Carrying this ability to personalize to human-robot interaction (HRI) is highly desirable since it would provide user-personalized interaction, a crucial element in many HRI scenarios - interactions with older adults, assistive or rehabilitative robotics, child-robot interaction (CRI), and many others. For a social robot to be able to recreate this same kind of rich, human-like interaction, it should be aware of our needs and affective states and be capable of continuously adapting its behaviour to them. Equipping a robot with these functionalities however is not a straightforward task. A robust approach for solving this is implementing a framework for the robot supporting social awareness and adaptation. In other words, the robot needs to be equipped with the basic cognitive functionalities, which would allow the robot to learn how to select the behaviours that would maximize the pleasantness of the interaction for its peers, while being guided by an internal motivation system that would provide autonomy to its decision-making process. The goal of this research was threefold: attempt to design a cognitive architecture supporting social HRI and implement it on a robotic platform; study how an adaptive framework of this kind would function when tested in HRI studies with users; and explore how including the element of adaptability and personalization in a cognitive framework would in reality affect the users - would it bring an additional richness to the human-robot interaction as hypothesized, or would it instead only add uncertainty and unpredictability that would not be accepted by the robot`s human peers? This thesis covers the work done on developing a cognitive framework for human-robot interaction; analyzes the various challenges of implementing the cognitive functionalities, porting the framework on several robotic platforms and testing potential validation scenarios; and finally presents the user studies performed with the robotic platforms of iCub and MiRo, focused on understanding how a cognitive framework behaves in a free-form HRI context and if humans can be aware and appreciate the adaptivity of the robot. In summary, this thesis had the task of approaching the complex field of cognitive HRI and attempt to shed some light on how cognition and adaptation develop from both the human and the robot side in an HRI scenario.

43

Tupe, Sameer Vijay. "A Cognitively Inspired Architecture for Wireless Sensor Networks: A Web Service Oriented Middleware for a Traffic Monitoring System." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/33624.

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We describe CoSMo, a Cognitively Inspired Service and Model Architecture for situational awareness and monitoring of vehicular traffic in urban transportation systems using a network of wireless sensors. The system architecture combines (i) a cognitively inspired internal representation for analyzing and answering queries concerning the observed system and (ii) a service oriented architecture that facilitates interaction among individual modules, of the internal representation, the observed system and the user. The cognitively inspired model architecture allows one to effectively respond to deductive as well as inductive queries by combining simulation based dynamic models with traditional relational databases. On the other hand the service oriented design of interaction allows one to build flexible, extensible and scalable systems that can be deployed in practical settings. To illustrate our concepts and the novel features of our architecture, we have recently completed a prototype implementation of CoSMo. The prototype illustrates advantages of our approach over other traditional approaches for designing scalable software for situational awareness in large complex systems. The basic architecture and its prototype implementation are generic and can be applied for monitoring other complex systems. CoSMo's architecture has a number of features that distinguish cognitive systems. This includes: dynamic internal models of the observed system, inductive and deductive learning and reasoning, perception, memory and adaptation.

This thesis describes the service oriented model and the associated prototype implementation. Two important contributions of this thesis include the following:

The Generic Service Architecture - CoSMo's service architecture is generic and can be applied to many other application domains without much change in underlying infrastructure.
Integration of emerging web technologies - Use of Web Services, UPnP, UDDI and many other emerging technologies have taken CoSMo beyond a prototype implementation and towards a real production system.

Master of Science

44

Gursoy, Benay. "The Cognitive Aspects Of Model-making In Architectural Design." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611677/index.pdf.

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Considerable research has been done by various scholars to assess the significance of sketching in the early stages of the design process. However, sketching in design studies usually corresponds to drawing and the extensive research on the cognitive aspects of sketching does not always include three-dimensional sketching through physical and digital models produced in the early phases of design process. The aim of the presented research is to identify some characteristics of model-making that make it effective in the design process and design cognition as a form of sketching. Departing from key research on sketching which articulates its uncertain nature as a positive drive in early design phases, this thesis looks at whether physical and digital models can also be counted among ambiguous design tools. The inquiry is supported by empirical data from the protocol studies realized with three graduate students of architecture.

45

Rast, Alexander Douglas. "Scalable event-driven modelling architectures for neuromimetic hardware." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/scalable-eventdriven-modelling-architectures-for-neuromimetic-hardware(0c7f08e1-ad35-4cec-94a5-b765e25bab97).html.

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Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Dedicated hardware may thus be more suitable for executing them. Given that there is no clear consensus on the model of computation in the brain, model flexibility is at least as important a characteristic of neural hardware as is performance acceleration. The SpiNNaker chip is an example of the emerging 'neuromimetic' architecture, a universal platform that specialises the hardware for neural networks but allows flexibility in model choice. It integrates four key attributes: native parallelism, event-driven processing, incoherent memory and incremental reconfiguration, in a system combining an array of general-purpose processors with a configurable asynchronous interconnect. Making such a device usable in practice requires an environment for instantiating neural models on the chip that allows the user to focus on model characteristics rather than on hardware details. The central part of this system is a library of predesigned, 'drop-in' event-driven neural components that specify their specific implementation on SpiNNaker. Three exemplar models: two spiking networks and a multilayer perceptron network, illustrate techniques that provide a basis for the library and demonstrate a reference methodology that can be extended to support third-party library components not only on SpiNNaker but on any configurable neuromimetic platform. Experiments demonstrate the capability of the library model to implement efficient on-chip neural networks, but also reveal important hardware limitations, particularly with respect to communications, that require careful design. The ultimate goal is the creation of a library-based development system that allows neural modellers to work in the high-level environment of their choice, using an automated tool chain to create the appropriate SpiNNaker instantiation. Such a system would enable the use of the hardware to explore abstractions of biological neurodynamics that underpin a functional model of neural computation.

46

Musgrave, John. "Cognitive Malice Representation and Identification." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565348664149804.

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47

Lynn, Michael (Michael Benjamin). "Generation and tuning of learned sensorimotor behavior by multiple neural circuit architectures." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100876.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 25-26).
Organisms have a remarkable ability to respond to complex sensory inputs with intricate, tuned motor patterns. How does the brain organize and tune these motor responses, and are certain circuit architectures, or connectivity patterns, optimally suited for certain sensorimotor applications? This thesis presents progress towards this particular problem in three subprojects. The first section re-analyzes a large data set of single-unit recordings in zebra finch area HVC during singing. While HVC is known to be essential for proper expression of adult vocalization, its circuit architecture is contentious. Evidence is presented against the recently postulated gesture-trajectory extrema hypothesis for the organization of area HVC. Instead, the data suggest that the synaptic chain model of HVC organization is a better fit for the data, where chains of RA-projecting HVC neurons are synaptically connected to walk the bird through each time-step of the song. The second section examines how optimal sensorimotor estimation using a Bayesian inference framework could be implemented in a cerebellar circuit. Two novel behavioral paradigms are developed to assess how rats might tune their motor output to the statistics of the sensory inputs, and whether their behavior might be consistent with the use of a Bayesian inference paradigm. While neither behavior generated stable behavior, evidence indicates that rats may use a spinal circuit to rapidly and dynamically adjust motor output. The third section addresses the formation of habitual behaviors in a cortico-striatal network using rats. Stress and depression are known to significantly alter decision-making abilities, but the neural substrate of this is poorly understood. Towards this goal, rats are trained on a panel of decision-making tasks in a forced-choice T-maze, and it is shown that a chronic stress procedure produces a dramatic shift in behavior in a subset of these tasks but not the rest. This behavioral shift is reversed by optogenetic stimulation of prelimbic input to striatum, pinpointing a circuit element which may control stress-induced behavioral changes. Furthermore, a circuit hypothesis is presented to explain why sensitivity to changing reward values diminishes with overtraining.
by Michael Lynn.
S.M.

48

Gallagher, Justin (Justin R. ). "An asylum : design specificity for the spectrum of cognitive conditions." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81654.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Architecture, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 81).
This thesis seeks to re-engage the intimate connection between architecture and the minds of its inhabitants through design that addresses specific cognitive needs. Architecture fundamentally shares a connection with the mind. Through its inhabitants' subjective experience, architecture necessarily interfaces with their cognitive conditions, but to varying extents. This connection was demonstrated most intimately in the architecture and history of the asylum. It was then, when perception was conceived as universal conditions that the built environment participated in the cure of the insane. The result of this attitude was colossal, centralized institutions where those considered insane would be treated. The architectural response to the patients reflected the generalized understanding of the mind at the time--homogenous. Today, the role of architecture has been marginalized as the conception of the mind is strictly chemical and neither environmental nor spatial. As a result, these once colossal institutions are now extinct. Treatment of mental illness is now primarily behavioral therapy and psychoactive drugs, which grow more and more pervasive. Currently, 1 in 4 people have a diagnosable illness. This figure has been used to support the claim for a Mental Illness Crisis in America. And while there maybe be an increase in mental instability, the statistic is more likely a consequence of a new, developing understanding of the mind. That is, through this pursuit to decode our very being into chemical formulas, modern science has revealed a diverse spectrum of cognitive or experiential conditions. The new normal is: there is no normal. The urban condition has already begun to respond to this with the growing network of hospitals, pharmacies, and therapists attending to the mentally ill. However, this thesis projects that soon the mind will be so demystified, that all people will register on a spectrum of cognitive conditions. As a result, architecture will need to respond to not only specific physical requirements such as environment, human body, site, program etc. but to the specific cognitive or experiential needs of the inhabitants. These needs will not longer be recognized as illnesses, but rather as "mindstyles" of the individual. Through the design of three domestic spaces for specific mindstyles--SAD, OCD, and APD--this thesis posits the ability for architecture to behave with the localization and specialization of a pill.
by Justin Gallagher.
S.B.

49

Glodek, Michael [Verfasser]. "Learning in layered multimodal classifier architectures for cognitive technical systems / Michael Glodek." Ulm : Universität Ulm, 2016. http://d-nb.info/1106329902/34.

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50

Song, Zhiguo. "Systèmes de numérisation hautes performances - Architectures robustes adaptées à la radio cognitive." Phd thesis, Supélec, 2010. http://tel.archives-ouvertes.fr/tel-00589826.

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Les futures applications de radio cognitive requièrent des systèmes de numérisation capables de convertir alternativement ou simultanément soit une bande très large avec une faible résolution soit une bande plus étroite avec une meilleure résolution, ceci de manière versatile (i.e. par contrôle logiciel). Pour cela, les systèmes de numérisation basés sur les Bancs de Filtres Hybrides (BFH) sont une solution attractive. Ils se composent d'un banc de filtres analogiques, un banc de convertisseurs analogique-numérique et un banc de filtres numériques. Cependant, ils sont très sensibles aux imperfections analogiques. L'objectif de cette thèse était de proposer et d'étudier une méthode de calibration qui permette de corriger les erreurs analogiques dans la partie numérique. De plus, la méthode devait être implémentable dans un système embarqué. Ce travail a abouti à une nouvelle méthode de calibration de BFH utilisant une technique d'Égalisation Adaptative Multi-Voies (EAMV) qui ajuste les coefficients des filtres numériques par rapport aux filtres analogiques réels. Cette méthode requiert d'injecter un signal de test connu à l'entrée du BFH et d'adapter la partie numérique afin de reconstruire le signal de référence correspondant. Selon le type de reconstruction souhaité (d'une large-bande, d'une sous-bande ou d'une bande étroite particulière), nous avons proposé plusieurs signaux de test et de référence. Ces signaux ont été validés en calculant les filtres numériques optimaux par la méthode de Wiener-Hopf et en évaluant leurs performances de ces derniers dans le domaine fréquentiel. Afin d'approcher les filtres numériques optimaux avec une complexité calculatoire minimum, nous avons implémenté un algorithme du gradient stochastique. La robustesse de la méthode a été évaluée en présence de bruit dans la partie analogique et de en tenant compte de la quantification dans la partie numérique. Un signal de test plus robuste au bruit analogique a été proposé. Les nombres de bits nécessaires pour coder les différentes données dans la partie numérique ont été dimensionnés pour atteindre les performances visées (à savoir 14 bits de résolution). Ce travail de thèse a permis d'avancer vers la réalisation des futurs systèmes de numérisation basés sur les BFH.

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