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Calandra, Joséphine. "L'algorithmie cognitive et ses applications musicales." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUL148.
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Cette thèse présente la formalisation et le développement d’un logiciel d'analyse musicale appelé "Multiscale Oracle Representations For Organized Sounds" (MORFOS). Ce logiciel vise à mettre en œuvre un modèle multi-échelle de la forme musicale basé sur l’Algorithme Cognitif de Jean-Marc Chouvel. Les travaux de cette thèse s'inscrivent dans la continuité de l'analyse cognitive en musicologie, visant à comprendre les processus cognitifs qui interviennent lors de l'écoute musicale. Nous étudions une représentation hiérarchisée de la musique et explorons l’influence de cette hiérarchisation dans l’organisation des événements musicaux dans le temps et la compréhension de la musique. Nous formalisons ainsi les concepts de matériau, d’objet et de diagramme formel, et nous introduisons le Diagramme Formel Multi-échelle qui décrit la structure musicale à différentes échelles temporelles et niveaux d'analyse. Celui-ci est composé de trois plans que nous introduisons : la forme, la structure et l’organisation. L'implémentation informatique de MORFOS a été réalisée en Python et accepte des représentations audio, symboliques ou vectorielles. Ce logiciel présente une architecture modulaire intégrant différents modules de traitement audio, de classification et de segmentation : nous présentons ainsi différentes mesures implémentées sous forme d’un ensemble de règles et discutons des contraintes associées à l’étude de la classification et de la segmentation à partir d’une représentation audio. Nous présentons ainsi la notion d’Agenda, qui correspond au choix par l’utilisateur d’un ensemble de règles permettant de représenter un modèle « d’écoute » pour l’analyse d’une œuvre musicale par le logiciel. La thèse explore également la question de la complexité de la structure musicale : nous proposons l’expression d’un coût associé à la description de l’objet musical acquis en fonction de son contexte, selon la définition de Kolmogorov. Nous cherchons également à comparer le comportement du logiciel MORFOS avec les phénomènes d'attention et la charge cognitive lors de l'écoute musicale. Une expérience visant à mesurer la charge cognitive pendant la tâche de segmentation musicale a ainsi été conçue. Cette thèse présente par ailleurs des réflexions sur la visualisation des diagrammes formels multi-échelles. A cette occasion, nous avons développé une interface permettant de rendre le logiciel accessible à tous les utilisateurs. Enfin, des exemples d'analyses musicales réalisées avec MORFOS sont présentées, sur une base de données musicales pop ainsi qu’un corpus d'œuvres classiques<br>This thesis presents the formalization and development of a music analysis software called "Multiscale Oracle Representations For Organized Sounds" (MORFOS). This software aims to implement a multi-scale model of musical form based on Jean-Marc Chouvel's Cognitive Algorithm. The work in this thesis is part of the cognitive analysis in musicology, aimed at understanding the cognitive processes involved in listening to music. We study a hierarchical representation of music and explore the influence of this hierarchy on the organization of musical events over time and on musical comprehension. We formalize the concepts of material, object, and formal diagram, and introduce the Multi-scale Formal Diagram, which describes musical structure at different temporal scales and levels of analysis. This comprises three planes, which we introduce: form, structure, and organization. MORFOS has been implemented in Python and accepts audio, symbolic, and vector representations. This software features a modular architecture integrating different modules for audio processing, classification, and segmentation: we present different measures implemented in the form of a set of rules and discuss the constraints associated with the study of classification and segmentation based on an audio representation. We also introduce the notion of Agenda, which corresponds to the user's choice of a set of rules to represent a "listening" model for the software's analysis of a musical work. The thesis also explores the question of the complexity of the musical structure: we propose the expression of a cost associated with the description of the acquired musical object depending on its context, according to Kolmogorov's definition. We also seek to compare the behavior of MORFOS software with attentional phenomena and cognitive load during musical listening. An experiment designed to measure cognitive load during the musical segmentation task has thus been devised. This thesis also presents reflections on the visualization of multi-scale formal diagrams. To this end, we have developed an interface to make the software accessible to all users. Finally, examples of musical analyses carried out with MORFOS are presented, on a pop music database and a corpus of classical works
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Li, Jun. "Genetic Granular Cognitive Fuzzy Neural Networks and Human Brains for Comparative Cognition." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/cs_theses/7.
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In this thesis, Genetic Granular Cognitive Fuzzy Neural Networks (GGCFNN), combining genetic algorithms (GA) and granular cognitive fuzzy neural networks (GCFNN), is proposed for pattern recognition problems. According to cognitive patterns, biological neural networks in the human brain can recognize different patterns. Since GA and neural networks represent two learning methods based on biological science, it is indispensable and valuable to investigate how biological neural networks and artificial neural networks recognize different patterns. The new GGCFNN, based on granular computing, soft computing and cognitive science, is used in the pattern recognition problems. The hybrid forward-wave-backward-wave learning algorithm, as a main learning technology in GCFNN, is used to enhance learning quality. GA optimizes parameters to make GGCFNN get better learning results. Both pattern recognition results generated by human persons and those by GGCFNN are analyzed in terms of computer science and cognitive science.
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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.
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El-Nainay, Mustafa Y. "Island Genetic Algorithm-based Cognitive Networks." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28297.
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The heterogeneity and complexity of modern communication networks demands coupling network nodes with intelligence to perceive and adapt to different network conditions autonomously. Cognitive Networking is an emerging networking research area that aims to achieve this goal by applying distributed reasoning and learning across the protocol stack and throughout the network. Various cognitive node and cognitive network architectures with different levels of maturity have been proposed in the literature. All of them adopt the idea of coupling network devices with sensors to sense network conditions, artificial intelligence algorithms to solve problems, and a reconfigurable platform to apply solutions. However, little further research has investigated suitable reasoning and learning algorithms.
In this dissertation, we take cognitive network research a step further by investigating the reasoning component of cognitive networks. In a deviation from previous suggestions, we suggest the use of a single flexible distributed reasoning algorithm for cognitive networks. We first propose an architecture for a cognitive node in a cognitive network that is general enough to apply to future networking challenges. We then introduce and justify our choice of the island genetic algorithm (iGA) as the distributed reasoning algorithm.
Having introduced our cognitive node architecture, we then focus on the applicability of the island genetic algorithm as a single reasoning algorithm for cognitive networks. Our approach is to apply the island genetic algorithm to different single and cross layer communication and networking problems and to evaluate its performance through simulation. A proof of concept cognitive network is implemented to understand the implementation challenges and assess the island genetic algorithm performance in a real network environment. We apply the island genetic algorithm to three problems: channel allocation, joint power and channel allocation, and flow routing. The channel allocation problem is a major challenge for dynamic spectrum access which, in turn, has been the focal application for cognitive radios and cognitive networks. The other problems are examples of hard cross layer problems.
We first apply the standard island genetic algorithm to a channel allocation problem formulated for the dynamic spectrum cognitive network environment. We also describe the details for implementing a cognitive network prototype using the universal software radio peripheral integrated with our extended implementation of the GNU radio software package and our island genetic algorithm implementation for the dynamic spectrum channel allocation problem. We then develop a localized variation of the island genetic algorithm, denoted LiGA, that allows the standard island genetic algorithm to scale and apply it to the joint power and channel allocation problem. In this context, we also investigate the importance of power control for cognitive networks and study the effect of non-cooperative behavior on the performance of the LiGA.
The localized variation of the island genetic algorithm, LiGA, is powerful in solving node-centric problems and problems that requires only limited knowledge about network status. However, not every communication and networking problems can be solved efficiently in localized fashion. Thus, we propose a generalized version of the LiGA, namely the K-hop island genetic algorithm, as our final distributed reasoning algorithm proposal for cognitive networks. The K-hop island genetic algorithm is a promising algorithm to solve a large class of communication and networking problems with controllable cooperation and migration scope that allows for a tradeoff between performance and cost. We apply it to a flow routing problem that includes both power control and channel allocation. For all problems simulation results are provided to quantify the performance of the island genetic algorithm variation. In most cases, simulation and experimental results reveal promising performance for the island genetic algorithm.
We conclude our work with a discussion of the shortcomings of island genetic algorithms without guidance from a learning mechanism and propose the incorporation of two learning processes into the cognitive node architecture to solve slow convergence and manual configuration problems. We suggest the cultural algorithm framework and reinforcement learning techniques as candidate leaning techniques for implementing the learning processes. However, further investigation and implementation is left as future work.<br>Ph. D.
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Butterfield, Aaron S. "Using Synthetic Cognits and The Combined Cumulative Squared Deviation as Tools to Quantify the Performance of Cognitive Radar Algorithms." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461242979.
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Béler, Cédrick. "Modélisation générique d'un retour d'expérience cognitif : application à la prévention des risques." Phd thesis, Toulouse, INPT, 2008. http://oatao.univ-toulouse.fr/7249/1/beler.pdf.
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Nous avons défini dans cette thèse une architecture logicielle générique permettant de réaliser des applications de retour d'expérience cognitif. Ces derniers intègrent une formalisation de l'analyse experte et sont une alternative aux Systèmes à Bases de Connaissance. Les applications sont opérationnalisées à partir de la définition du modèle de l'expérience qui est basé sur une structure objet simple couplée avec le modèle des croyances transférables pour prendre en compte les intertitudes. Nous avons développé des algorithmes génériques de recherche adaptés à la formalisation retenue de l'entité expérience ainsi qu'un alogorithme d'extraction d'un indicateur du risque. Ces algorithmes sont basés sur une proposition de similarité ensembliste particulière. Le modèle générique est basé sur un modèle adaptatif (Adaptive Object Model). Nous avons appliqué une partie des résultats de la thèse dans le cadre d'un projet Européen INTERREG SUP (Sécurité Urgence Pyrénées).
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Mäkeläinen, M. (Marko). "Algorithms for opportunistic load balancing cognitive engine." Master's thesis, University of Oulu, 2013. http://urn.fi/URN:NBN:fi:oulu-201303011071.
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Due to the increasing use of more and more powerful smart devices demands on the scarce radio spectrum are becoming more intense. One way to cope with increasing demands on radio spectrum is to apply innovative and flexible authorization schemes like spectrum sharing. Under the spectrum sharing paradigm, multiple users and/or systems are authorized to utilize the same spectrum band in a defined sharing agreement. A technology that is generally recommended for the implementation of spectrum sharing is called cognitive radio (CR). In this thesis, we design and implement a cognitive engine (CE) that intelligently and dynamically allocates spectrum resources to users. We first consider a scenario where a network has an exclusive access to a spectrum band and the CE accepts or rejects the arrival user requests based on an algorithm that takes into account a user’s priority and its bandwidth demand. We then consider a spectrum sharing scenario where along with the exclusive utilization to its own spectrum band a network also can opportunistically utilize another network’s spectrum band. For this scenario, we design and implement a CE that performs two main tasks: 1) Accepts or rejects arrival user requests based on a priority based algorithm; and 2) it intelligently load balances the user traffic between the two available network resources, while taking into account the primary user activity in the shared spectrum band. We provide a load balancing algorithm and evaluate its performance under different primary and secondary user traffic scenarios. We show that the proposed load balancing algorithm increases average throughput of the network and it also reduces the average number of users rejected by the network<br>Yhä tehokkaampien älykkäiden langattomien päätelaitteiden nopea lisääntyminen johtaa niukan radiospektrin yhä kiihtyvään käyttöön. Eräs menetelmä radiospektrin lisääntyvän kysynnän tyydyttämiseen on hyödyntää innovatiivista ja joustavaa resurssin käytönjakoa kuten spektrin jakamista. Spektrinjakamismalli mahdollistaa useiden käyttäjien ja/tai järjestelmien yhtäaikaisen käytön samalla taajuuskaistalla hyödyntämällä sovittua käytäntöä resurssien jakamisesta. Radiospektrin jakaminen on tänä päivänä yleisesti suositeltu toteuttamaan hyödyntämällä kognitiivista radioteknologiaa. Tässä työssä suunnittellaan ja toteutetaan kognitiivinen päätöksentekokone, joka jakaa radiospektriresursseja käyttäjille älykkäästi ja dynaamisesti. Kognitiivista päätöksentekokonetta radioresurssien jakamisessa hyödynnetään kahdessa skenaariossa. Ensimmäisessä skenaariossa radioverkolla on yksinomainen pääsy taajuuskaistalle, jonka käyttöä kognitiivinen päätöksentekokone säätelee joko hyväksymällä tai hylkäämällä verkkoon liittyviä käyttäjiä. Kognitiivinen päätöksentekokoneen päätökset perustuu algoritmiin, joka ottaa huomioon käyttäjien määritetyn tärkeyden ja käyttäjän vaatiman kaistanleveyden. Seuraavassa skenaariossa radioverkko voi oman yksinomaisen taajuuskaistan lisäksi hyödyntää opportunisesti toisen radioverkon taajuuskaistaa silloin, kun siellä ei ole liikennettä. Tätä skenaariota varten suunnitteltiin kognitiivinen päätöksentekokone, jolla on kaksi päätehtävää: 1) hyväksyä tai hylätä verkkoon liittyviä käyttäjiä edellämainitun tärkeysperusteisen algoritmin avulla; ja 2) jakaa käyttäjien liikennettä kahden tarjolla olevan verkon välillä samalla ottaen huomioon opportunistisen resurssin pääkäyttäjien liikenteen jaetulla taajuuskaistalla. Tässä työssä esitellään toteutettu kuormantasausalgoritmi, jonka suorituskykyä tarkastellaan erilaisissa pääkäyttäjien ja toissijaisien käyttäjien liikenneskenaarioissa. Simulaatiotulokset osoittavat, että esitellyn kuormanjakoalgoritmin hyödyntäminen kognitiivisessa päätöksentekokoneessa parantaa verkon keskimääräistä siirtonopeutta, sekä vähentää keskimääräistä käyttäjien hylkäysastetta verkossa. Algoritmimme parantaa opportunistisen taajuuskaistan käyttöastetta. Algoritmimme ottaa myös huomioon käyttäjille asetetut prioriteetit ja parantaa korkeampi prioriteettisten käyttäjien asemaa verkossa. Tämä tulee ilmi muun muassa korkeampi prioriteettisten käyttäjien pienemmässä hylkäysasteessa
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Mariani, Andrea <1984>. "Spectrum Sensing Algorithms for Cognitive Radio Applications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5615/2/Mariani_Andrea_SpectrumSensingforCognitiveRadio.pdf.
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Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission.
In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies.
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Mariani, Andrea <1984>. "Spectrum Sensing Algorithms for Cognitive Radio Applications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5615/.
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Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission.
In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies.
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Reje, Franzén Fanny, and Saga Gardelin. "Hide and seek with algorithm : En intervjustudie av cosplay-kreatörers "folk" teorier i förhållande till TikToks algoritm." Thesis, Linnéuniversitetet, Institutionen för medier och journalistik (MJ), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104833.
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This essay aims to study the relationship between cosplay content creators and TikTok’s algorithm. To study this relationship the essay will conduct a qualitative semi-structured interviews with creators from the cosplay community on TikTok. Since the rise of digital plattforms the media and the role of producer as well as consumer has changed drastically. TikTok has been growing rapidly in popularity since its entry on the market, and by 2020 it had 500 million active users. Since many of today's digital platforms have consumer produced content, the consumer of today has taken on a mixed role between consuming and creating content, which creates a new relationship. The content consumers produce vary vastly on TikTok but one kind that has been present in much of TikTok’s existence is cosplay content. Cosplayers are creators who design costumes to already established characters or franchises. Since a discourse has started in the cosplay community on TikTok about the algorithm suppressing their content the study found it to be a good way to start examining content creators as individuals and how they behave towards an algorithm in their content creation process. The study aims to use algorithmic “folk” theory to examine what theories have been created in the community and how the theories affect the creators. The study also applies gatekeeping theory and social cognitive theory (SCT) to paint a clearer picture in how these creators view the algorithm. Seven interviews with cosplay content creators were conducted and with the help of a thematic analysis method the study found several themes in how the creators view and behave in relation to TikTok and its algorithm. The results of our study shows that there’s a definite present of “folk” theories created inside of the community. The most distinct behaviour relating to “folk” theory among the creators was that they can’t use the hashtag cosplay in the belief that the algorithm would suppress the content. This study concludes that the creators are more aware of the algorithm then they themself know and have different ways of working with and around it.
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Gad, Mahmoud M. "Connectivity-Aware Routing Algorithms for Cognitive Radio Networks." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32353.
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The increased demand on wireless applications, coupled with the current inefficiency in spectrum usage, mandate a new communication paradigm shift from fixed spectrum assignment to dynamic spectrum sharing which can be achieved using the cognitive radio technology. Cognitive radio allows unlicensed secondary nodes to form communication links over licensed spectrum bands on an opportunistic basis which increases the spectrum management efficiency. Cognitive radio networks (CRN), however, impose unique challenges due to the fluctuation in the available spectrum as well as the diverse quality of service requirements. One of the main challenges is the establishment and maintenance of routes in multi-hop CRNs. In this thesis, we critically investigate the problem of routing in multi-hop CRNs. The main objective of this research is to maximize network connectivity while limiting routing delay. We developed a general connectivity metric for single-band and multi-band CRNs based on the properties of the Laplacian matrix eigenvalues spectrum. We show through analytical and simulation results that the developed metric is more robust and has lower computational complexity than the previously proposed metrics. Furthermore, we propose a new position-based routing algorithm for large scale CRNs which significantly reduces the routing computational complexity with negligible performance degradation compared to the traditional full node search algorithm. In addition, the connectivity metric developed in this thesis is used to develop a connectivity-aware distributed routing protocol for CRNs. Finally, we use a commodity cognitive radio testbed to demonstrate the concept of CR Wi-Fi networks.
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Teguig, Djamel. "Cooperative Spectrum Sensing Algorithms For Cognitive Radio Networks." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/219942.
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The work presented in this thesis concerns one of the key enabling techniques related to cognitive radio functionalities which is spectrum sensing as well as cooperative spectrum sensing. As cooperative spectrum sensing (CSS) approaches are commonly used for combating fading and improving detection performance, their performances using different combining rules have been analyzed. Due to the low implementation complexity, Goodness of Fit based spectrum sensing has been studied for cognitive radio applications. Motivated by its nice features of local sensing, a distributed consensus spectrum sensing for CR, has been presented, integrating a Goodness of Fit based spectrum sensing scheme.<br>Le travail présenté dans cette thèse concerne l'une des techniques clés dans les fonctionnalités de la radio cognitive qui est la détection du spectre ainsi que la détection coopérative du spectre. La détection coopérative est couramment utilisée pour la lutte contre l’évanouissement du canal à fin d'améliorer les performances de la détection. Les performances de la détection coopérative en utilisant différentes règles de fusion ont été analysées. En raison sa simplicité, la détection du spectre par les testes d’adéquation a été étudiée pour les applications de la radio cognitive. Motivé par la caractéristique d’être indépendant de bruit, ces testes d’adéquation ont été utilisés pour la détection locale, pour la détection coopérative distribuée.<br>Doctorat en Sciences de l'ingénieur et technologie<br>info:eu-repo/semantics/nonPublished
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Stuhlmüller, Andreas. "Modeling cognition with probabilistic programs : representations and algorithms." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100860.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 167-176).<br>This thesis develops probabilistic programming as a productive metaphor for understanding cognition, both with respect to mental representations and the manipulation of such representations. In the first half of the thesis, I demonstrate the representational power of probabilistic programs in the domains of concept learning and social reasoning. I provide examples of richly structured concepts, defined in terms of systems of relations, subparts, and recursive embeddings, that are naturally expressed as programs and show initial experimental evidence that they match human generalization patterns. I then proceed to models of reasoning about reasoning, a domain where the expressive power of probabilistic programs is necessary to formalize our intuitive domain understanding due to the fact that, unlike previous formalisms, probabilistic programs allow conditioning to be represented in a model, not just applied to a model. I illustrate this insight with programs that model nested reasoning in game theory, artificial intelligence, and linguistics. In the second half, I develop three inference algorithms with the dual intent of showing how to efficiently compute the marginal distributions defined by probabilistic programs, and providing building blocks for process-level accounts of human cognition. First, I describe a Dynamic Programming algorithm for computing the marginal distribution of discrete probabilistic programs by compiling to systems of equations and show that it can make inference in models of "reasoning about reasoning" tractable by merging and reusing subcomputations. Second, I introduce the setting of amortized inference and show how learning inverse models lets us leverage samples generated by other inference algorithms to compile probabilistic models into fast recognition functions. Third, I develop a generic approach to coarse-to-fine inference in probabilistic programs and provide evidence that it can speed up inference in models with large state spaces that have appropriate hierarchical structure. Finally, I substantiate the claim that probabilistic programming is a productive metaphor by outlining new research questions that have been opened up by this line of investigation.<br>by Andreas Stuhlmüller.<br>Ph. D.
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Chen, Ye. "Fuzzy Cognitive Maps: Learning Algorithms and Biomedical Applications." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1423581705.
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Awe, Olusegun P. "Machine learning algorithms for cognitive radio wireless networks." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19609.
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In this thesis new methods are presented for achieving spectrum sensing in cognitive radio wireless networks. In particular, supervised, semi-supervised and unsupervised machine learning based spectrum sensing algorithms are developed and various techniques to improve their performance are described. Spectrum sensing problem in multi-antenna cognitive radio networks is considered and a novel eigenvalue based feature is proposed which has the capability to enhance the performance of support vector machines algorithms for signal classification. Furthermore, spectrum sensing under multiple primary users condition is studied and a new re-formulation of the sensing task as a multiple class signal detection problem where each class embeds one or more states is presented. Moreover, the error correcting output codes based multi-class support vector machines algorithms is proposed and investigated for solving the multiple class signal detection problem using two different coding strategies. In addition, the performance of parametric classifiers for spectrum sensing under slow fading channel is studied. To address the attendant performance degradation problem, a Kalman filter based channel estimation technique is proposed for tracking the temporally correlated slow fading channel and updating the decision boundary of the classifiers in real time. Simulation studies are included to assess the performance of the proposed schemes. Finally, techniques for improving the quality of the learning features and improving the detection accuracy of sensing algorithms are studied and a novel beamforming based pre-processing technique is presented for feature realization in multi-antenna cognitive radio systems. Furthermore, using the beamformer derived features, new algorithms are developed for multiple hypothesis testing facilitating joint spatio-temporal spectrum sensing. The key performance metrics of the classifiers are evaluated to demonstrate the superiority of the proposed methods in comparison with previously proposed alternatives.
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TESHOME, ABIY TEREFE. "FPGA based Eigenvalue Detection Algorithm for Cognitive Radio." Thesis, Högskolan i Gävle, Radio Center Gävle, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-7855.
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Radio Communication technologies are undergoing drastic demand over the past two decades. The precious radio resource, electromagnetic radio spectrum, is in vain as technology advances. It is required to come up with a solution to improve its wise uses. Cognitive Radio enabled by Software-Defined Radio brings an intelligent solution to efficiently use the Radio Spectrum. It is a method to aware the radio communication system to be able to adapt to its radio environment like signal power and free spectrum holes. The approach will pose a question on how to efficiently detect a signal. In this thesis different spectrum sensing algorithm will be explained and a special concentration will be on new sensing algorithm based on the Eigenvalues of received signal. The proposed method adapts blind signal detection approach for applications that lacks knowledge about signal, noise and channel property. There are two methods, one is ratio of the Maximum Eigenvalue to Minimum Eigenvalue and the second is ratio of Signal Power to Minimum Eigenvalue. Random Matrix theory (RMT) is a branch of mathematics and it is capable in analyzing large set of data or in a conclusive approach it provides a correlation points in signals or waveforms. In the context of this thesis, RMT is used to overcome both noise and channel uncertainties that are common in wireless communication. Simulations in MATLAB and real-time measurements in LabVIEW are implemented to test the proposed detection algorithms. The measurements were performed based on received signal from an IF-5641R Transceiver obtained from National Instruments.
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Faizan, Shah Ali. "SDN based security using cognitive algorithm against DDOS." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29880.
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The internet and communication industry continue to develop new technologies rapidly, which has caused a boom in smart and networking device manufacturing. With new trends, operators are constantly battling towards deploying multiple systems to cater for the need of all users. The higher bandwidth utilization and flexibility demanded new networking solutions which paved way for Software Defined Network (SDN). SDN is centralized platform which works with other technologies such as Network Function Virtualization (NFV) to offer reliable, flexible and centrally controllable network solutions. It offers remote access control with logical design of the system, security and resource management. Traditional and new developing networks despite their advantages present numerous security challenges. With growing users worldwide, bandwidth related security risks such as Distributed Denial of Service (DDOS) are of grave concern. This encourages towards reliable and rapid response solutions such as Cognitive Algorithms (CA) which can adapt to a threat in real time environment. This dissertation proposes the use of CA to deploy security and mitigation measures against potential DDOS flooding attack to avoid network failure and memory depletion in SDN. The experiment done in proof of concept (PoC) provided proof of greater network resource utilization by limiting the attack while mitigation policies are implemented. It also shows that CA can adapt to growing and evolving network attack strength to counter as much as possible without the intervention of the operator. The work for future solutions based on CA and Artificial Intelligence (AI) for security have been established.
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Ellis, Kevin Ph D. (Kevin M. )Massachusetts Institute of Technology. "Algorithms for learning to induce programs." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/130184.
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Thesis: Ph. D. in Cognitive Science, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, September, 2020<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 213-224).<br>The future of machine learning should have a knowledge representation that supports, at a minimum, several features: Expressivity, interpretability, the potential for reuse by both humans and machines, while also enabling sample-efficient generalization. Here we argue that programs-i.e., source code-are a knowledge representation which can contribute to the project of capturing these elements of intelligence. This research direction however requires new program synthesis algorithms which can induce programs solving a range of AI tasks. This program induction challenge confronts two primary obstacles: the space of all programs is infinite, so we need a strong inductive bias or prior to steer us toward the correct programs; and even if we have that prior, effectively searching through the vast combinatorial space of all programs is generally intractable. We introduce algorithms that learn to induce programs, with the goal of addressing these two primary obstacles. Focusing on case studies in vision, computational linguistics, and learning-to-learn, we develop an algorithmic toolkit for learning inductive biases over programs as well as learning to search for programs, drawing on probabilistic, neural, and symbolic methods. Together this toolkit suggests ways in which program induction can contribute to AI, and how we can use learning to improve program synthesis technologies.<br>by Kevin Ellis.<br>Ph. D. in Cognitive Science<br>Ph.D.inCognitiveScience Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
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Hashmi, Ziaul Hasan. "Dynamic resource allocation for cognitive radio systems." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/961.
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Cognitive Radio (CR) is considered to be a novel approach to improve the underutilization of precious radio resources by exploiting the unused licensed spectrum in dynamically changing environments. Designing efficient resource allocation algorithms for dynamic spectrum sharing and for power allocation in OFDM-CR networks is still a challenging problem. In this thesis, we specifically deal with these two problems.
Dynamic spectrum sharing for the unlicensed secondary users (SU)s with device coordination could minimize the wastage of the spectrum. But this is a feasible approach only if the network considers the fairness criterion. We study the dynamic spectrum sharing problem for device coordinated cognitive radio networks with respect to fairness. We propose a simple modified proportional fair algorithm for a dynamic spectrum sharing scenario with two constraints, time and utility. Utility is measured by the amount of data processed and time is measured as the duration of a slot. This algorithm could result in variable or fixed length time slots. We will discuss the several controls possible on the algorithm and the possible extension of this algorithm for multicarrier OFDM based CR systems.
Traditional water-filling algorithm is inefficient for OFDM-CR networks due to the interaction with primary users (PU)s. We consider reliability/availability of subcarriers or primary user activity for power allocation. We model this aspect mathematically with a risk-return model by defining a general rate loss function. We then propose optimal and suboptimal algorithms to allocate power under a fixed power budget for such a system with linear rate loss. These algorithms as we will see allocate more power to more reliable subcarriers in a water-filling fashion with different water levels. We compare the performance of these algorithms for our model with respect to water-filling solutions. Simulations show that suboptimal schemes perform closer to optimal scheme although they could be implemented with same complexity as water-filling algorithm. We discuss the linearity of loss function and guidelines to choose its coefficients by obtaining upper bounds on them. Finally we extend this model for interference-limited OFDM-CR systems.
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Bouzegzi, Abdelaziz. "Algorithmes de discrimination de signaux pour la radio cognitive." Paris, Télécom ParisTech, 2009. http://www.theses.fr/2009ENST0048.
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Dans le contexte de la Radio Cognitive, la reconnaissance des systèmes radio relève d’une grande importance (e. G. , Wifi, Wimax, 3GPP/LTE, DVB-T). Nous proposons d’étudier ce problème en se focalisant sur les systèmes basés sur la modulation OFDM largement utilisés actuellement. Nous avons noté que les standards existants diffèrent dans la valeur de l’espacement inter-porteuses utilisée. Par conséquent, nous avons développé des algorithmes qui se basent sur l’estimation de ce paramètre pour réaliser la reconnaissance aveugle du système intercepté. Les approches issues de la littérature et basées essentiellement sur l’utilisation du préfixe cyclique se retrouvent inefficaces dans le cas d’un très court préfixe ou d’un canal de propagation fortement sélectif en fréquence. Ce travail de thèse propose alors des solutions alternatives pour estimer les paramètres d’un signal OFDM en faisant appel à différentes approches : i) le kurtosis normalisé, ii) le principe du maximum de vraisemblance, iii) le filtrage adapté et iv) la cyclostationnarité d’ordre deux. Nous avons démontré la grande efficacité de ces algorithmes avec des conditions de fonctionnement très sévères ( un préfixe cyclique court, un canal de propagation très sélectif en fréquence, non sychronisation temporelle et/ou fréquentielle)<br>In the context of cognitive radio it is a crucial task to distinguish blindly various wireless systems (e. G. , Wifi, Wimax, 3GPP/LTE, DVB-T) from each others. We focus on the OFDM based systems which differ from their subcarrier spacing used in OFDM modulation. One can thus carry out recognition algorithms based on the value of the subcarrier spacing. Standard approaches developed in the literature rely on the detection of the cyclic prefix which enables to exhibit the value of the used subcarrier spacing. Nevertheless, these approaches fail when either the cyclic prefix duration is small or the channel impulse response is almost as large as the cyclic prefix. Therefore, this thesis proposes new algorithms to estimate the parameters of OFDM modulated signal (especially the subcarrier spacing) relying on i) the normalized kurtosis, ii) the maximum-likelihood principle, iii) the matched filter, and iv) the second-order cyclostationary property. We have shown the strong robustness of proposed algorithms to short cyclic prefix, multipath channel, time offset, and frequency offset
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Kit, Chun Yu. "Unsupervised lexical learning as inductive inference." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340205.
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Cabrejos, David. "Implementation of a channel selection algorithm using cognitive radios." Thesis, Wichita State University, 2011. http://hdl.handle.net/10057/3945.
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With the increase of wireless devices, the wireless spectrum is becoming overloaded
causing users to experience delays and performance degradation. Typically, a device will start
transmitting data on a frequency and continue transmitting on that frequency regardless of the
channel being overloaded or not. Some smarter devices such as routers are able to sense when
their channel is becoming overloaded by observing delays and amount of devices transmitting on
that frequency. Spectrum analyzers are usually very expensive and usually do not provide many
functionalities other than analysis. Utilizing newer alternatives for sensing the spectrum such as
Software Defined Radios (SDR) can address frequency allocation problems and allow users to
decide the best frequency to use for communication. A promising SDR such as GNU Radio will
be covered in this thesis, as well as the hardware components needed for its functionality. In this
thesis, a cognitive radio approach is taken in designing a channel selection algorithm by scanning
and monitoring the wireless spectrum on IEEE 802.11 b/g through the use of GNU Radio and
USRP. Tests are performed as a proof of concept and to help future research with the use of
cognitive radios.<br>Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering.
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Gardiye, Punchihewage Anjana. "Advanced transceiver algorithm design for cognitive radio physical layer." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/30557.
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With the ever increasing demand for wireless applications, current wireless systems are challenged to meet the higher data rate and higher reliability requirements. Although the current and future technological developments allow making these requirements reachable, some other resources remain limited. The radio spectrum is one such natural resource. Previous studies have shown that the radio spectrum is not efficiently utilized. Therefore, recent studies are focused on fully utilizing this unexpandable radio spectrum. Cognitive radio (CR) has emerged as a possible solution to improve the spectrum utilization by opportunistically exploiting the licenced users transmit spectrum in dynamically changing environments. On the other hand, the development of CR technology raises new challenges of proper design of transmission and receive schemes for CR to facilitate high data rate access and better performance along with high spectral efficiency. To achieve these objectives, in this thesis, advanced transceiver algorithms for CR physical layer are designed to improve the throughput and the error rate performance in hostile wireless channels.
We first designed a linear precoder for orthogonal space-time block coded, orthogonal frequency division multiplexing (OFDM)-based multiple-input multiple-output antenna CR when operating in correlated Rayleigh fading channels. The linear precoder is designed by minimizing an upper bound on the average pairwise error probability, constrained to a set of per subcarrier power constraints at CR transmitter and a set of primary users interference power thresholds. An efficient algorithm is proposed to obtain the optimal precoder matrices. We then proposed a power allocation policy to achieve a lower-bound on the ergodic sum capacity of single-input single-output opportunistic spectrum sharing multiple access channel with imperfect channel estimates. An efficient algorithm is proposed to obtain the optimal power allocation for each CR transmitter. Finally, we proposed a blind parameter estimation algorithm for OFDM signal affected by a time-dispersive channel, carrier phase, timing offset, carrier frequency offset and additive Gaussian noise. The cyclostationarity properties of received OFDM signal in time-dispersive channel is exploited to estimate the OFDM parameters. These parameters includes OFDM symbol period, useful symbol period, cyclic prefix factor, number of subcarriers and carrier frequency offset.
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Blot, Guillaume. "Élaboration, parcours et automatisation de traces et savoirs numériques." Thesis, Paris 4, 2017. http://www.theses.fr/2017PA040089.
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Comment l'accès au savoir peut-il être impacté par la technologie ? Il suffit d'observer le virage intenté par les outils de communication au début des années 2000 pour se rendre compte : convergence des médias, pratiques participatives et numérisation massive des données. Dans ce contexte, on imagine que l'accès au savoir tend à se démocratiser. En effet, les individus semblent se réapproprier les espaces de vie, en inversant le modèle de transmission top-down, qui va du producteur vers le consommateur, au profit de processus de transfert basés sur l'intelligence collective. Pourtant, on aurait tort de réduire cette réorganisation à un simple renversement du modèle. Car l'intelligence collective est encline à divers biais cognitifs et socio-cognitifs, amenant parfois vers des situations irrationnelles. Autrefois, on s’accommodait de ces mécaniques sociales aux conséquences limitées, aujourd'hui les savoirs numérisés constituent des ensembles massivement communiquant, donnant naissance à de nouvelles voies d'accès et à de nouveaux clivages. Pourquoi ce savoir qui n'a jamais été aussi massif et ouvert, se révèle-t-il si sélectif ? Je propose d'explorer ce paradoxe. L'enregistrement massif et constant de nos traces numériques et l'hyper-connexion des individus, participent à la construction de structures organisationnelles, où se retrouvent numérisées de manière complexe, une partie des dynamiques sociales. En formalisant de la sorte les voies navigables, ces structures organisationnelles façonnent nos trajectoires. Sur cette base, les informaticiens ont mis au point des algorithmes de parcours individualisés, ayant pour objectifs de prédire et de recommander. Ainsi, on propose d'automatiser l'accès au savoir. Se pose alors la question de la gouvernance des individus, dans un contexte où l'intelligence collective est soumise à l'infrastructure : enregistrement des traces, composition des structures organisationnelles et algorithmes de parcours<br>How access to knowledge can be impacted by Information Technology? In the earlier 2000s, communication tools caused a significant turn : media convergence, participative practices and massive data. In this way, free access to knowledge might tend to be democratized. People seem to regain spaces, reversing traditional top-down model, going from producer to consumer, for the benefit of an horizontal model based on collective intelligence. However, it should not automatically be assumed that this leads to a simple model reversing. Collective intelligence is subject to cognitive biases, leading to potential irrational situations. Formerly, those social mechanisms had limited consequences. Nowadays, digital knowledge are massive communicating spaces, giving birth to new access paths and new cleavages. Why this massive and open knowledge, is actually so selective? I propose to explore this paradox. Massive and constant tracking of traces and individuals hyper-connection, these two facts help organizational structures design, where social dynamics are digitalized in a complex way. These structures formalize human trajectories. On this basis, computer scientists set up prediction algorithms and recommender engines. This way, knowledge access is automatized. It can then be asked about people governance, in this context of infrastructure submission: recording traces, designing knowledge structure and automating algorithms
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Thomas, Ryan William. "Cognitive Networks." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28319.
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For complex computer networks with many tunable parameters and network performance objectives, the task of selecting the ideal network operating state is difficult. To improve the performance of these kinds of networks, this research proposes the idea of the cognitive network. A cognitive network is a network composed of elements that, through learning and reasoning, dynamically adapt to varying network conditions in order to optimize end-to-end performance. In a cognitive network, decisions are made to meet the requirements of the network as a whole, rather than the individual network components.
We examine the cognitive network concept by first providing a definition and then outlining the difference between it and other cognitive and cross-layer technologies. From this definition, we develop a general, three-layer cognitive network framework, based loosely on the framework used for cognitive radio. In this framework, we consider the possibility of a cognitive process consisting of one or more cognitive elements, software agents that operate somewhere between autonomy and cooperation.
To understand how to design a cognitive network within this framework we identify three critical design decisions that affect the performance of the cognitive network: the selfishness of the cognitive elements, their degree of ignorance, and the amount of control they have over the network. To evaluate the impact of these decisions, we created a metric called the price of a feature, defined as the ratio of the network performance with a certain design decision to the performance without the feature.
To further aid in the design of cognitive networks, we identify classes of cognitive networks that are structurally similar to one another. We examined two of these classes: the potential class and the quasi-concave class. Both classes of networks will converge to Nash Equilibrium under selfish behavior and in the quasi-concave class this equilibrium is both Pareto and globally optimal. Furthermore, we found the quasi-concave class has other desirable properties, reacting well to the absence of certain kinds of information and degrading gracefully under reduced network control.
In addition to these analytical, high level contributions, we develop cognitive networks for two open problems in resource management for self-organizing networks, validating and illustrating the cognitive network approach. For the first problem, a cognitive network is shown to increase the lifetime of a wireless multicast route by up to 125\%. For this problem, we show that the price of selfishness and control are more significant than the price of ignorance. For the second problem, a cognitive network minimizes the transmission power and spectral impact of a wireless network topology under static and dynamic conditions. The cognitive network, utilizing a distributed, selfish approach, minimizes the maximum power in the topology and reduces (on average) the channel usage to within 12\% of the minimum channel assignment. For this problem, we investigate the price of ignorance under dynamic networks and the cost of maintaining knowledge in the network.
Today's computer networking technology will not be able to solve the complex problems that arise from increasingly bandwidth-intensive applications competing for scarce resources. Cognitive networks have the potential to change this trend by adding intelligence to the network. This work introduces the concept and provides a foundation for future investigation and implementation.<br>Ph. D.
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Artero, Sylvaine. "Détection des troubles cognitifs légers (MCI) : algorithmes diagnostiques, dépistage et validité prédictive." Montpellier 1, 2004. http://www.theses.fr/2004MON1T004.
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Le vieillissement de la population a provoque une augmentation de la prevalence des troubles cognitifs et de la demence. Ces troubles ont un impact important au niveau de la sante publique a cause de leur haute prevalence et du niveau eleve de pertes fonctionnelles qui les accompagnent. La question s'est posee de savoir si l'utilisation des anticholinesterasiques tres tot, a des etapes precliniques de la demence pouvait augmenter leur efficacite. Dans ce contexte, c'est le concept de mci (mild cognitive impairment : troubles cognitifs legers) qui est devenu le principal centre d'interet. L'algorithme du mci permet la detection chez les personnes agees de potentiels changements cognitifs pathologiques evoluant vers une demence, dans un contexte de plainte cognitive. Les objectifs de cette these etaient d'evaluer la validite de ce concept en population generale et de considerer le statut de ce concept en tant qu'entite nosologique. Mais egalement de suggerer des modifications aux criteres actuels afin d'ameliorer l'identification des sujets mci dans un contexte de medecine generale. Pour l'essentiel, notre travail montre que le concept de mci tel qu'il a ete defini par petersen et son equipe en 1999, pose un certain nombre de problemes. Ces auteurs supposent qu'un dysfonctionnement de la memoire, associe a un fonctionnement cognitif general normal et a des capacites conservees a effectuer les taches quotidiennes sont hautement predictifs d'une maladie neurodegenerative. Mais ce concept apparait a la fois heterogene et instable sur le plan evolutif. En pratique, il s'avere donc difficilement utilisable en population generale. Nous avons demontre qu'une amelioration des criteres nosologiques doit etre envisagee sur le plan neuropsychologique et psychocomportemental. Nous avons egalement souligne l'interet potentiel de l'imagerie cerebrale dans le cadre du mci en vue d'une amelioration du pouvoir predictif. A partir de nos resultats, nous avons pu proposer un nouvel algorithme ayant une forte valeur predictive pour la demence. Ce nouvel algorithme plus performant est applicable en epidemiologie et devrait permettre d'identifier les sujets a risque dans de futures etudes mais egalement dans un cadre de medecine generale afin de les orienter vers un service specialise de neurologie.
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Wang, Nan. "Threshold setting algorithms for spectrum sensing in cognitive radio networks." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/9064.
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As the demand for wireless communication services grows quickly, spectrum scarcity has been on the rise sharply. In this context, cognitive radio (CR) is being viewed as a new intelligent technology to solve the deficiency of fixed spectrum assignment policy in wireless communications. Spectrum sensing is one of the most fundamental technologies to realise dynamic spectrum access in cognitive radio networks. It requires high accuracy as well as low complexity. In this thesis, a novel adaptive threshold setting algorithm is proposed to optimise the trade-off between detection and false alarm probability in spectrum sensing while satisfying sensing targets set by the IEEE 802.22 standard. The adaptive threshold setting algorithm is further applied to minimise the error decision probability with varying primary users' spectrum utilisations. A closed-form expression for the error decision probability, satisfied SNR value, number of samples and primary users' spectrum utilisation ratio are derived in both fixed and the proposed adaptive threshold setting algorithms. By implementing both Welch and wavelet based energy detectors, the adaptive threshold setting algorithm demonstrates a more reliable and robust sensing result for both primary users (PUs) and secondary users (SUs) in comparison with the conventional fixed one. Furthermore, the wavelet de-noising method is applied to improve the sensing performance when there is insu cient number of samples. Finally, a novel database assisted spectrum sensing algorithm is proposed for a secondary access of the TV White Space (TVWS) spectrum. The proposed database assisted sensing algorithm is based on the developed database assisted approach for detecting incumbents like Digital Terrestrial Television (DTT) and Programme Making and Special Events (PMSE), but assisted by spectrum sensing to further improve the protection to primary users. Monte-Carlo simulations show a higher SUs' spectrum efficiency can be obtained for the proposed database assisted sensing algorithm than the existing stand-alone database assisted or sensing models.
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Abdel-Rahman, Mohammad Jamal. "Robust Cognitive Algorithms For Fast-Varying Spectrum-Agile Wireless Networks." Diss., The University of Arizona, 2014. http://hdl.handle.net/10150/338872.
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Wireless communications have experienced tremendous growth in the last decade, which has placed significant demand for RF spectrum, leading to spectrum "crunch." Driven by numerous studies that revealed the significant under-utilization of many licensed channels in the VHF and UHF bands, a new paradigm for spectrum sharing has emerged in the past decade. In this paradigm, wireless devices (a.k.a. secondary users) are allowed to operate opportunistically in certain licensed bands without interfering with the licensed users (a.k.a. primary users). The realization of this new communication paradigm necessitates the design of a new generation of smart, adaptable, and programmable radios, called cognitive radios. Enabling opportunistic operation requires addressing various challenges including device coordination, resource allocation, channel monitoring, and various security issues. Specifically, secondary users are particularly vulnerable to node compromise and malicious jamming attacks. In this dissertation, we first develop several rendezvous algorithms for establishing unicast as well as multicast communication links in opportunistic spectrum access networks. The developed rendezvous algorithms are shown to be robust to node compromise attacks. Second, we study the anti-jamming rendezvous problem in the presence of an insider attack. We develop a combinatorial game-theoretic framework to capture the interactions between the rendezvousing nodes and the insider jammer. Third, to account for the dynamism of primary users, we propose novel stochastic resource allocation schemes under channel-quality uncertainty. The proposed schemes support channel bonding and aggregation and account for adjacent channel interference by introducing guard-bands. Fourth, to prevent interference with primary users, we design an optimal spectrum-sensing algorithm that achieves high detection accuracy and low false-alarm rate. Finally, we present an application of using cognitive radios for jamming mitigation in satellite communications. Extensive simulations are conducted to demonstrate the effectiveness and robustness of the proposed algorithms.
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Razavian, Adam A. "Cognitive Based Adaptive Path Planning Algorithm for Autonomous Robotic Vehicles." NSUWorks, 2004. http://nsuworks.nova.edu/gscis_etd/793.
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Processing requirement of a complex autonomous robotic vehicle demands high efficiency in algorithmic and software execution. Today's advanced computer hardware technology provides processing capabilities that were not available a decade ago. There are still major space and time limitations on these technologies for autonomous robotic applications. Increasingly, small to miniature mobile robots are required for reconnaissance, surveillance, and hazardous material detections for military and industrial applications. The small sized autonomous mobile robotic applications have limited power capacity as well as memory and processing resources.
A number of algorithms exist for producing optimal traverses given changing arc costs. One algorithm stands out as the most used algorithm in simple path finding applications such as games, named the A * algorithm. This dissertation investigated the hypothesis that cognitive based adaptive path planning algorithms are efficient. This assumption is based on the observed capability of biological systems, which ignore irrelevant information and quickly process non-optimum but efficient paths. Path planning function for all organisms from insects to humans is a critical function of survival, and living organisms perform it with graceful accuracy and efficiency. This hypothesis was tested by developing a Cognitive Based Adaptive Path Planning Algorithm (CBAPPA) and a limited simulation program to test the theory of the algorithm, and comparing the result with other known approaches.
This dissertation presented a new cognitive based approach in solving the path planning problems for autonomous robotic applications. The goal of this paper was to show that adaptive cognitive based techniques are more efficient by comparing this paper's path planning approach to analytical and heuristic algorithms. This study presented a two-step methodology of Primary Path and Refined Path. Each step was implemented by a number of heuristic algorithms.
This paper illustrated that the CBAPPA’s path-finding efficiency exceeds the efficiency of some popular analytical and heuristic approaches. This research paper concluded that the hypothesis was verified and cognitive based path planning algorithm is efficient and is a viable approach for autonomous robotic applications.
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Filippou, Miltiades. "Performance et coordination dans les réseaux radios cognitifs multi-antennes." Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0047.
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Dans cette thèse, nous avons d'abord réalisé une analyse de la performance analytique des deux plus populaires systèmes de la radio cognitif (CR), à savoir les réseaux de radio cognitive (CRN) interweaved et underlay. Il a été montré que numériquement le comportement de chacun des approches CRN examinés est fortement dépendant des paramètres du système de base. En outre, nous avons étudié le problème de taux optimale de recevoir BF et la sélection de l'utilisateur, compte tenu de la liaison montante d'un multi-utilisateur, CRN sans priorité. Comme l'hypothèse d'une information d'état de canal (CSI) réglage, par lequel les chaînes concernées ne seraient que instantanément (resp. statistiquement) connu est, en grande partie, optimiste (resp. pessimiste), nous avons considéré un scénario de CSI mixte. Ensuite, le problème de taux des optimale de transmission BF pour un MISO underlay CRN, en supposant l'existence de CSI mixte, a ensuite été formulée. Se concentrer sur la communication de downlink, l'objectif de la conception du système était la maximisation de la capacité ergodique réalisable du système secondaire, soumis à une contrainte de taux moyen imposée sur la communication primaire. Poursuite de l'enquête du problème de précodage dernier avec la connaissance du canal distribute et mixte, nous avons développé un système de coordination, selon lequel, les émetteurs de coordonner sur la base de statistiques (covariance) des informations de la chaîne mondiale. La stratégie de pré-codage proposé a été montré à surperformer les approches classiques tirés de la littérature. Enfin, dans un cadre CRN priorité, nous avons proposé un algorithme d'affectation des pilotes<br>In this thesis, we initially conducted an analytical performance analysis of two of the most popular cognitive radio (CR) schemes, namely the interweaved and the underlay cognitive radio network (CRN) approaches. It was numerically shown that the behavior of each of the examined CRN approaches is highly dependent on basic system parameters. Furthermore, we studied the problem of rate-optimal receive BF and user selection, considering the uplink of a multi-user, unprioritized CRN. As the assumption of a channel state information (CSI) setting, whereby the involved channels would be merely instantaneously (resp. statistically) known is, to a great extent, optimistic (resp. pessimistic), we considered a mixed (combined) CSI scenario. Then, the problem of rate-optimal transmit BF for a MISO underlay CRN, assuming the existence of mixed CSI, was thereafter formulated. Concentrating on downlink communication, the goal of the system’s design was the maximization of the secondary system’s achievable ergodic capacity, subject to an average rate constraint imposed on primary communication. Continuing the investigation of the latter precoding problem with mixed, distributed channel knowledge, we developed a coordination scheme, according to which, the transmitters coordinate on the basis of statistical (covariance) information of the global channel. The proposed precoding strategy was shown to outperform conventional approaches taken from the literature. Finally, within a prioritized CRN framework, we proposed a pilot assignment algorithm
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Stetner, Michael E. (Michael Edward). "Algorithms and circuits for motor control and learning in the songbird." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121829.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 179-192).<br>From riding a bike to brushing our teeth, we learn many of our motor skills through trial and error. Many biologically based trial and error learning models depend on a teaching signal from dopamine neurons. Dopamine neurons increase their firing rates to signal outcomes that are better than expected and decrease their firing rates to signal outcomes that are worse than expected. This dopamine signal is thought to control learning by triggering synaptic changes in the basal ganglia. What are the origins of this dopaminergic teaching signal? How do synaptic changes in the basal ganglia lead to changes in behavior? In this thesis, I study these questions in a model of skill learning - the songbird. In the first part of my thesis, I develop a computational model of song learning. This model incorporates a dopaminergic reinforcement signal in VTA and dopamine-dependent synaptic plasticity in the singing-related part of the basal ganglia.<br>I demonstrate that this model can provide explanations for a variety of experimental results from the literature. In the second part of my thesis, I investigate a potential source of the dopaminergic error signal in VTA. I performed the first recordings from one cortical input to VTA: the dorsal intermediate arcopallium (AId). Previous studies disagree on the role of Ald in behavior. Some studies argue that AId contributes vocal error information to VTA. Other studies suggest that AId is not involved in the computation of error signals, but is instead responsible for controlling head and body movements. I directly tested these hypotheses by recording single neurons in AId during singing and during natural movements. My results support a motor role for AId - AId neurons had highly significant changes in activity during head and body movements. Meanwhile, following vocal errors Aid neurons had small but marginally significant decrease in firing rate.<br>In a more detailed analysis, I developed an automated behavior classification algorithm to categorize zebra finch behavior and related these behavior classes to the activity of single units in Aid. My results support the hypothesis that AId is part of a general-purpose motor control network in the songbird brain.<br>by Michael E. Stetner.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
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Tom, Anas. "Physical Layer Algorithms for Interference Reduction in OFDM-Based Cognitive Radio Systems." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5872.
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Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier transmission scheme used in most of the existing wireless standards such as LTE, WiFi and WiMAX. The popularity of OFDM stems from the multitude of benefits it offers in terms of providing high data rate transmission, robustness against multipath fading and ease of implementation. Additionally, OFDM signals are agile in the sense that any subcarrier can be switched on or off to fit the available transmission bandwidth, which makes it well suited for systems with dynamic spectrum access such as cognitive radio systems. Nonetheless, and despite all the aforementioned advantages, OFDM signals have high spectral sidelobes outside the designated band of transmission, that can create severe interference to users in adjacent transmission bands, particularly when there is no synchronization between users. The focus of this dissertation is to propose baseband solutions at the Physical Layer (PHY) of the communications system to address the interference resulting from the high out-of-band (OOB) emissions of OFDM.
In the first part of this dissertation, we propose a precoder capable of generating mask compliant OFDM signals with low OOB emissions that are always contained under a given spectrum emission mask (SEM) specified by the OFDM standard. The proposed precoder generates transmitted signals with bit error rate (BER) performance similar to that of classical OFDM and does not reduce the spectral efficiency of the system.
In the second part of this dissertation, we introduce a novel and elegant approach, called suppressing alignment (SA), to jointly reduce the OOB interference and peak-to-average power ratio (PAPR) of OFDM systems. SA exploits the unavoidable redundancy provided by the CP as well as the wireless communications channel to generate an OOB/PAPR suppressing signal at the OFDM transmitter. Furthermore, after passing through the wireless channel, the suppressing signal is aligned with the CP duration at the OFDM receiver, essentially causing no interference to the data portion of the OFDM symbol. The proposed approach improves the PAPR of the transmitted OFDM signal and reduces the OOB interference by tens of decibels. Additionally, the proposed approach maintains an error performance similar to that of plain OFDM without requiring any change in the receiver structure of legacy OFDM.
In order to reduce the spectral emissions of OFDM, additional blocks, such as linear precoders, are usually introduced in the transmitter leading to a transmitted signal that is drastically different than that of a classical OFDM signal. This distortion is typically quantified by the error vector magnitude (EVM), a widely used metric specified by the wireless standard and is directly related to the BER performance of the system. The receiver can usually decode the information data with acceptable error probabilities if the distortion introduced to the transmitted signal is below the EVM values specified in the OFDM standard. Linear precoders, while capable of achieving significant reduction in the OOB interference, they typically introduce large distortion to the transmitted signal. As such, the receiver needs to know the precoding done at the transmitter to be able to recover the data which usually entails sending large amount of side information that can greatly reduce the spectral efficiency of the system. In the last part of this dissertation, we target the design of precoders for the purpose reducing the OOB interference, in a transparent manner where the receiver does not need to know the changes introduced in the transmitter. We present two precoders capable of significantly reducing the OOB emissions while producing transmitted signals with EVM values below those specified by the wireless standard, thereby guaranteeing acceptable error performance.
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CANAVITSAS, ANGELO ANTONIO CALDEIRA. "PREDICTION OF WHITE SPACES FOR COGNITIVE RADIOS: METHODOLOGY, ALGORITHMS, SIMULATION AND PERFORMANCE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=27066@1.
Full textAbstract:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>A tecnologia de rádio cognitivo está em pleno desenvolvimento na academia e indústria, sendo apresentada como uma solução para o reduzir o congestionamento do espectro radioelétrico. Dessa forma, diversos estudos têm sido desenvolvidos para obter novas técnicas de compartilhamento do espectro entre usuários ditos primários e secundários. Estas técnicas devem ser robustas o suficiente para minimizar as colisões de ocupação do espectro entre os usuários supracitados, quando o acesso dinâmico ao espectro for aplicado. O presente estudo investigou as soluções de ocupação compartilhada do espectro, em especial nos para serviços de voz na faixa de 450 MHz. A modelagem de ocupação dos canais, a partir de medidas de transmissões reais, permitiu o desenvolvimento de algoritmo robusto que realiza a predição de espaços espectrais (white spaces) dentro de canais destinados a usuários primários. Esse método proposto define, estatisticamente, uma janela de intervalos de tempo futuros que pode ser utilizada por usuários secundários, por apresentar maior probabilidade de possuir espaços espectrais livres, minimizando as possíveis colisões. O emprego do método proposto aumenta a vazão de informações de modo seguro e,com alto desempenho, otimizando,assim,a utilização do espectro radioelétrico.<br>The cognitive radio technology is being developedin universities and industry as a solution to the radio spectrum scarcity. This technology willallow spectrum sharing between primary and secondary telecommunication users. The techniques employed must be robust enough to minimize spectrum occupancy collisions, when the dynamic spectrum access is applied. This study investigates the trends of spectrum usersoccupation, particularly in voice services in the 450 MHz frequency band.An users occupancy model was developed taking into accountmeasured data of real transmissions. It allowed the development of a robust algorithm that predicts spectral vacancy in channels allocated to primary users. The method selects, statistically, a group of future time intervals that can be used by secondary users, due to a higher probability of having a free spectral space. The use of this new technique minimizes possible collisions, increasing the flow of information in secure way and optimizing the radio spectrum use.
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SILVA, MARCELO MOLINA. "EVALUATION OF DETECTION ALGORITHMS OF SPECTRAL WHITE SPACES FOR COGNITIVE RADIO APPLICATIONS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35253@1.
Full textAbstract:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Com o desenvolvimento tecnológico no setor de telecomunicações, o espectro radioelétrico está quase totalmente ocupado com um grande número de múltiplas atribuições para os muitos serviços sem fio de aplicação comercial e, também, não comercial, tais como defesa, controle de tráfego aéreo e exploração científica. O espectro eletromagnético é um recurso natural precioso e escasso, por isso, importantes esforços estão sendo direcionados para o desenvolvimento de rádios cognitivos, com capacidade de sensoriar o uso do espectro e utilizar frequências momentaneamente disponíveis de forma oportunista. O rastreamento e a utilização de intervalos espectrais, ou white spaces, através da tecnologia de rádios cognitivos, permitirá aumentar a eficiência de uso do espectro com a introdução de novos serviços de telecomunicações a serem explorados por usuários secundários, obrigados a não interferir ou a provocar interferência muito limitada nos usuários primários. O objetivo geral deste trabalho é avaliar os principais algoritmos de detecção dos intervalos espectrais (Detector de Energia, Detecção do Valor Absoluto de Covariância, Sensoriamento de Covariância Espectral) por meio de simulações com dados experimentais obtidos em campanhas de medições e testes em laboratório. Os algoritmos foram testados para avaliar o seu desempenho em termos de probabilidade de detecção dada uma probabilidade de falso alarme requerida, complexidade computacional e robustez quanto a relações sinal-a-ruído baixas. Os dados experimentais utilizados provêm de campanhas de medidas realizadas em ambiente urbano na faixa de 3.5 GHz.<br>With the technological development of the telecommunications industry, the radio spectrum is almost fully occupied with a large number of multiple assignments for wireless services for both commercial and non-commercial applications, such as defense, air traffic control and scientific exploration. The electromagnetic spectrum is a precious and scarce natural resource. Therefore, a considerable effort is being directed at the development of cognitive radios, capable of sensoring the spectrum and using momentarily available frequency bands in an opportunistic way. The tracking and using of these spectral intervals, or white spaces, using cognitive radio technology will enhance the efficiency of the spectrum use and allow the introduction of new telecommunications services to be exploited by secondary users, obliged not to interfere or produce very limited interference to primary users. The aim of this study is to evaluate the main algorithms for detection of spectral intervals (Energy Detector, Detection of Covariance Absolute Value, Spectral Covariance Sensing) through simulations with experimental data obtained in field measurements campaigns. The algorithms were tested to evaluate their performance in terms of detection probability given a required false alarm probability, computational complexity and robustness in low signal-to-noise conditions. The experimental data used comes from the measurements campaigns in urban environments at the 3.5 GHz band.
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RIVIELLO, DANIEL GAETANO. "Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2641328.
Full textAbstract:
The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities.
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PINNA, SIMONE. "Extended cognition, dynamics, and algorithms. A turing machine based approach to the study of arithmetical skills." Doctoral thesis, Università degli Studi di Cagliari, 2014. http://hdl.handle.net/11584/266521.
Full textAbstract:
The fields of philosophy of mind and cognitive science have been characterized, in the last few
decades, by a growing interest for explanations of mind's activity in terms of interaction between
brains, bodies and the world. Embodiment, embeddedness, situatededness are key words that most
often can be found in contemporary cognitive studies. However, some cognitive activities seem
recalcitrant to this kind of treatment. Mathematical thinking is one of them. Explanations of human
computational competencies, indeed, focus typically on representational issues, while giving less
importance to the role of mind/body/environment interaction for the performance and development
of algorithmic skills, namely, those capacities which are essential in order to operate with numbers
and carry out symbolic transformation.
The significance of these skills for a general understanding of computational activities is explicitely
recognized in Alan Turing's theory of computation, which is focused on the construction of
idealized models of the mechanisms at work in a real cognitive system, namely the one consisting
of a man performing calculations with paper and pencil. In the present thesis I take seriously Marco
Giunti's proposal to use a Turing machine (TM)-based computational architecture, namely the
Bidimensional Turing Machine (BTM), in order to study human algorithmic skills.
This work consists of two main parts. The first part, philosophically-oriented, deals with Andrew
Wells' ecological interpretation of the TM's architecture and its relations with a set of philosophical
and psychological positions such as classic computationalism, the extended-mind hypothesis and
the dynamical approach to cognition; the second, more technical part, sets up a theoretical and
methodological framework for the development and justification of BTM-based models of human
algorithmic skills.
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Kong, Garry. "An Investigation of Complex Visual Search With The Genetic Algorithm." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16326.
Full textAbstract:
Visual search is an everyday task, defined as the search for a target amongst distractors. Visual search has historically been thought to reflect primarily early perceptual processes, with little influence of cognitive processes. However, recent research suggests that this is an artefact of the simple stimuli typically used in the field. In this thesis, I will use the genetic algorithm to study visual search for complex stimuli. In part 1, I use the genetic algorithm to investigate a display comprised of 119 distractors, each with an orientation, colour and size. Results suggest that distractors differing in colour but sharing orientation with the target facilitated performance. This result is not predicted by common visual search models, posing an external validity problem for experiments using simple stimuli. Part 2 examines the processes behind visual search for colour, using displays that took advantage of colour labelling. The results show that it is only possible to search optimally when the target can be easily encoded in language. Further experimentation shows that priming a colour target that cannot be easily encoded in language then allows for optimal search, indicating that a top-down mechanism not based in language is used in search for colour. In part 3, we study linear separability, where search for a target is difficult if it is presented with distractors with more of a feature and other distractors with less of that same feature. We systematically varied the orientation of the target and the distractors, finding two independent measures of performance. One measure was correlated with bottom-up ability, while the other was correlated with the top-down ability. In the final chapter, I discuss the results of the three parts in the context of search for complex, heterogeneous displays. I speculate on the nature of the top-down mechanism, and offer directions for future research.
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Eamrurksiri, Techin. "Implementation and Analysis of Spectrum Sensing Algorithms for SIMO Links." Thesis, Linköpings universitet, Kommunikationssystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-98211.
Full textAbstract:
Cognitive radio is an autonomous transceiver that is continuously sensing theongoing communication in its environment, it then starts the communication whenever it is appropriate. Therefore, cognitive radio helps improving the spectrum utilization of the overall communication system. However, without suitable spectrum sensing techniques, cognitive radio would fail. Hence, in this thesis we investigate and implement various spectrum sensing algorithms via software defined radio for both single antenna and multiple antenna cases. The main communi-cation scheme that we are using is OFDM. Moreover, both computer simulations and real-world measurements, have also been done for comparison and analysis ofthe detector’s performance. The detectors we are using are based on correlationfunction of the received signal and generalized likelihood ratio test with its eigen-value. The results from the simulations and measurements are then representedas probability of missed detection curves for different signal to noise ratios. Ourresults show that the performance of the generalized likelihood ratio test baseddetectors are at least 2 dB better than the correlation based detector in our mea-surement. Moreover, our simulations show that they are able to outperform thecorrelation function detector by more than 6 dB. Although, generalized likelihoodratio test based detectors seem to be better than the correlation function baseddetector, it requires more computational power which may limit its practical use.
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Neel, James O'Daniell. "Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/29998.
Full textAbstract:
Cognitive radio is frequently touted as a platform for implementing dynamic distributed radio resource management algorithms. In the envisioned scenarios, radios react to measurements of the network state and change their operation according to some goal driven algorithm. Ideally this flexibility and reactivity yields tremendous gains in performance. However, when the adaptations of the radios also change the network state, an interactive decision process is spawned and once desirable algorithms can lead to catastrophic failures when deployed in a network.
This document presents techniques for modeling and analyzing the interactions of cognitive radio for the purpose of improving the design of cognitive radio and distributed radio resource management algorithms with particular interest towards characterizing the algorithms' steady-state, convergence, and stability properties. This is accomplished by combining traditional engineering and nonlinear programming analysis techniques with techniques from game to create a powerful model based approach that permits rapid characterization of a cognitive radio algorithm's properties. Insights gleaned from these models are used to establish novel design guidelines for cognitive radio design and powerful low-complexity cognitive radio algorithms.
This research led to the creation of a new model of cognitive radio network behavior, an extensive number of new results related to the convergence, stability, and identification of potential and supermodular games, numerous design guidelines, and several novel algorithms related to power control, dynamic frequency selection, interference avoidance, and network formation. It is believed that by applying the analysis techniques and the design guidelines presented in this document, any wireless engineer will be able to quickly develop cognitive radio and distributed radio resource management algorithms that will significantly improve spectral efficiency and network and device performance while removing the need for significant post-deployment site management.<br>Ph. D.
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DHAKAL, PAWAN. "Algorithms for 5G physical layer." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2670627.
Full textAbstract:
There is a great activity in the research community towards the investigations of the various aspects of 5G at different protocol layers and parts of the network. Among all, physical
layer design plays a very important role to satisfy high demands in terms of data rates, latency, reliability and number of connected devices for 5G deployment. This thesis addresses
he latest developments in the physical layer algorithms regarding the channel coding, signal detection, frame synchronization and multiple access technique in the light of 5G use
cases. These developments are governed by the requirements of the different use case scenarios that are envisioned to be the driving force in 5G.
All chapters from chapter 2 to 5 are developed around the need of physical layer algorithms dedicated to 5G use cases. In brief, this thesis focuses on design, analysis, simulation and
he advancement of physical layer aspects such as 1. Reliability based decoding of short length Linear Block Codes (LBCs) with very good properties in terms of minimum hamming
istance for very small latency requiring applications. In this context, we enlarge the grid of possible candidates by considering, in particular, short length LBCs (especially extended CH codes) with soft-decision decoding; 2. Efficient synchronization of preamble/postamble in a short bursty frame using modified Massey correlator; 3. Detection of Primary User
activity using semiblind spectrum sensing algorithms and analysis of such algorithms under practical imperfections; 4. Design of optimal spreading matrix for a Low Density Spreading (LDS) technique in the context of non-orthogonal multiple access. In such spreading matrix, small number of elements in a spreading sequences are non zero allowing each user to
spread its data over small number of chips (tones), thus simplifying the decoding procedure using Message Passing Algorithm (MPA).
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Filippou, Miltiades. "Performance et coordination dans les réseaux radios cognitifs multi-antennes." Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0047/document.
Full textAbstract:
Dans cette thèse, nous avons d'abord réalisé une analyse de la performance analytique des deux plus populaires systèmes de la radio cognitif (CR), à savoir les réseaux de radio cognitive (CRN) interweaved et underlay. Il a été montré que numériquement le comportement de chacun des approches CRN examinés est fortement dépendant des paramètres du système de base. En outre, nous avons étudié le problème de taux optimale de recevoir BF et la sélection de l'utilisateur, compte tenu de la liaison montante d'un multi-utilisateur, CRN sans priorité. Comme l'hypothèse d'une information d'état de canal (CSI) réglage, par lequel les chaînes concernées ne seraient que instantanément (resp. statistiquement) connu est, en grande partie, optimiste (resp. pessimiste), nous avons considéré un scénario de CSI mixte. Ensuite, le problème de taux des optimale de transmission BF pour un MISO underlay CRN, en supposant l'existence de CSI mixte, a ensuite été formulée. Se concentrer sur la communication de downlink, l'objectif de la conception du système était la maximisation de la capacité ergodique réalisable du système secondaire, soumis à une contrainte de taux moyen imposée sur la communication primaire. Poursuite de l'enquête du problème de précodage dernier avec la connaissance du canal distribute et mixte, nous avons développé un système de coordination, selon lequel, les émetteurs de coordonner sur la base de statistiques (covariance) des informations de la chaîne mondiale. La stratégie de pré-codage proposé a été montré à surperformer les approches classiques tirés de la littérature. Enfin, dans un cadre CRN priorité, nous avons proposé un algorithme d'affectation des pilotes<br>In this thesis, we initially conducted an analytical performance analysis of two of the most popular cognitive radio (CR) schemes, namely the interweaved and the underlay cognitive radio network (CRN) approaches. It was numerically shown that the behavior of each of the examined CRN approaches is highly dependent on basic system parameters. Furthermore, we studied the problem of rate-optimal receive BF and user selection, considering the uplink of a multi-user, unprioritized CRN. As the assumption of a channel state information (CSI) setting, whereby the involved channels would be merely instantaneously (resp. statistically) known is, to a great extent, optimistic (resp. pessimistic), we considered a mixed (combined) CSI scenario. Then, the problem of rate-optimal transmit BF for a MISO underlay CRN, assuming the existence of mixed CSI, was thereafter formulated. Concentrating on downlink communication, the goal of the system’s design was the maximization of the secondary system’s achievable ergodic capacity, subject to an average rate constraint imposed on primary communication. Continuing the investigation of the latter precoding problem with mixed, distributed channel knowledge, we developed a coordination scheme, according to which, the transmitters coordinate on the basis of statistical (covariance) information of the global channel. The proposed precoding strategy was shown to outperform conventional approaches taken from the literature. Finally, within a prioritized CRN framework, we proposed a pilot assignment algorithm
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Nguyen, Diep Ngoc. "RESOURCE ALLOCATION STRATEGIES FOR COGNITIVE AND COOPERATIVE MIMO COMMUNICATIONS: ALGORITHM AND PROTOCOL DESIGN." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/292674.
Full textAbstract:
Dynamic Spectrum Access (DSA) and multi-input multi-output (MIMO) communications are among the most promising solutions to address the ever-increasing wireless demand. Cognitive radio (CR) is the enabling technology for DSA. In this dissertation, we propose several resource allocation strategies for multiuser and cooperative MIMO communications in the context of DSA/CR systems and wireless sensor networks (WSNs). First, to maximize the Cognitive MIMO (CMIMO) network throughput, we develop a low-complexity distributed algorithm that configures the transmit antenna radiation directions and allocates power to all data streams over both frequency and space/antenna dimensions. We formulate the joint power, spectrum allocation, and MIMO beamforming problem as a noncooperative game. We prove that the game always admits at least one Nash Equilibrium (NE). To improve the efficiency of this NE (i.e., network throughput), we derive user-dependent pricing policies that force MIMO transmitters to steer their beams away from nearby unintended receivers. Second, we propose beamforming games (with and without pricing policies) that jointly improve the power and spectrum efficiency while meeting various rate demands. We derive sufficient conditions under which a given rate-demand profile can be supported. To account for user fairness, we develop a channel assignment and power allocation mechanism based on the Nash Bargaining solution. The proposed scheme allows CMIMO links to first propose their rate demands, and then cooperate and bargain in the process of determining their channel assignment, power allocation, and "precoding" matrices. In the context of WSNs where energy efficiency is a key design metric, we propose a cooperative
MIMO framework. The framework partitions a WSN into various clusters in which several single-antenna sensors cooperate and form a virtual MIMO node so as to conserve power through harvesting MIMO's diversity gain. Extensive simulations show that our proposed schemes achieve significant throughput and energy efficiency improvement compared with state-of-the-art designs.
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Akbari, Masoomeh. "Probabilistic Transitive Closure of Fuzzy Cognitive Maps: Algorithm Enhancement and an Application to Work-Integrated Learning." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41401.
Full textAbstract:
A fuzzy cognitive map (FCM) is made up of factors and direct impacts. In graph
theory, a bipolar weighted digraph is used to model an FCM; its vertices represent the
factors, and the arcs represent the direct impacts. Each direct impact is either positive
or negative, and is assigned a weight; in the model considered in this thesis, each
weight is interpreted as the probability of the impact. A directed walk from factor F
to factor F' is interpreted as an indirect impact of F on F'. The probabilistic transitive
closure (PTC) of an FCM (or bipolar weighted digraph) is a bipolar weighted digraph
with the same set of factors, but with arcs corresponding to the indirect impacts in
the given FCM.
Fuzzy cognitive maps can be used to represent structured knowledge in diverse
fields, which include science, engineering, and the social sciences. In [P. Niesink, K.
Poulin, M. Sajna, Computing transitive closure of bipolar weighted digraphs, Discrete
Appl. Math. 161 (2013), 217-243], it was shown that the transitive closure provides
valuable new information for its corresponding FCM. In particular, it gives the total
impact of each factor on each other factor, which includes both direct and indirect
impacts. Furthermore, several algorithms were developed to compute the transitive
closure of an FCM. Unfortunately, computing the PTC of an FCM is computationally
hard and the implemented algorithms are not successful for large FCMs. Hence, the
Reduction-Recovery Algorithm was proposed to make other (direct) algorithms more
efficient. However, this algorithm has never been implemented before.
In this thesis, we code the Reduction-Recovery Algorithm and compare its running
time with the existing software. Also, we propose a new enhancement on the
existing PTC algorithms, which we call the Separation-Reduction Algorithm. In particular, we state and prove a new theorem that describes how to reduce the input
digraph to smaller components by using a separating vertex. In the application part
of the thesis, we show how the PTC of an FCM can be used to compare different
standpoints on the issue of work-integrated learning.
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Myers, Tracy S. (Tracy Scott). "Reasoning with incomplete probabilistic knowledge : the RIP algorithm for de Finetti's fundamental theorem of probability." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11885.
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Rakhlin, Alexander. "Applications of empirical processes in learning theory : algorithmic stability and generalization bounds." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34564.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2006.<br>Includes bibliographical references (p. 141-148).<br>This thesis studies two key properties of learning algorithms: their generalization ability and their stability with respect to perturbations. To analyze these properties, we focus on concentration inequalities and tools from empirical process theory. We obtain theoretical results and demonstrate their applications to machine learning. First, we show how various notions of stability upper- and lower-bound the bias and variance of several estimators of the expected performance for general learning algorithms. A weak stability condition is shown to be equivalent to consistency of empirical risk minimization. The second part of the thesis derives tight performance guarantees for greedy error minimization methods - a family of computationally tractable algorithms. In particular, we derive risk bounds for a greedy mixture density estimation procedure. We prove that, unlike what is suggested in the literature, the number of terms in the mixture is not a bias-variance trade-off for the performance. The third part of this thesis provides a solution to an open problem regarding the stability of Empirical Risk Minimization (ERM). This algorithm is of central importance in Learning Theory.<br>(cont.) By studying the suprema of the empirical process, we prove that ERM over Donsker classes of functions is stable in the L1 norm. Hence, as the number of samples grows, it becomes less and less likely that a perturbation of o(v/n) samples will result in a very different empirical minimizer. Asymptotic rates of this stability are proved under metric entropy assumptions on the function class. Through the use of a ratio limit inequality, we also prove stability of expected errors of empirical minimizers. Next, we investigate applications of the stability result. In particular, we focus on procedures that optimize an objective function, such as k-means and other clustering methods. We demonstrate that stability of clustering, just like stability of ERM, is closely related to the geometry of the class and the underlying measure. Furthermore, our result on stability of ERM delineates a phase transition between stability and instability of clustering methods. In the last chapter, we prove a generalization of the bounded-difference concentration inequality for almost-everywhere smooth functions. This result can be utilized to analyze algorithms which are almost always stable. Next, we prove a phase transition in the concentration of almost-everywhere smooth functions. Finally, a tight concentration of empirical errors of empirical minimizers is shown under an assumption on the underlying space.<br>by Alexander Rakhlin.<br>Ph.D.
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Tiwari, Ayush. "Comparison of Statistical Signal Processing and Machine Learning Algorithms as Applied to Cognitive Radios." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1533218513862248.
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Sabih, Ann Faik. "Cognitive smart agents for optimising OpenFlow rules in software defined networks." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15743.
Full textAbstract:
This research provides a robust solution based on artificial intelligence (AI) techniques to overcome the challenges in Software Defined Networks (SDNs) that can jeopardise the overall performance of the network. The proposed approach, presented in the form of an intelligent agent appended to the SDN network, comprises of a new hybrid intelligent mechanism that optimises the performance of SDN based on heuristic optimisation methods under an Artificial Neural Network (ANN) paradigm. Evolutionary optimisation techniques, including Particle Swarm Optimisation (PSO) and Genetic Algorithms (GAs) are deployed to find the best set of inputs that give the maximum performance of an SDN-based network. The ANN model is trained and applied as a predictor of SDN behaviour according to effective traffic parameters. The parameters that were used in this study include round-trip time and throughput, which were obtained from the flow table rules of each switch. A POX controller and OpenFlow switches, which characterise the behaviour of an SDN, have been modelled with three different topologies. Generalisation of the prediction model has been tested with new raw data that were unseen in the training stage. The simulation results show a reasonably good performance of the network in terms of obtaining a Mean Square Error (MSE) that is less than 10−6 [superscript]. Following the attainment of the predicted ANN model, utilisation with PSO and GA optimisers was conducted to achieve the best performance of the SDN-based network. The PSO approach combined with the predicted SDN model was identified as being comparatively better than the GA approach in terms of their performance indices and computational efficiency. Overall, this research demonstrates that building an intelligent agent will enhance the overall performance of the SDN network. Three different SDN topologies have been implemented to study the impact of the proposed approach with the findings demonstrating a reduction in the packets dropped ratio (PDR) by 28-31%. Moreover, the packets sent to the SDN controller were also reduced by 35-36%, depending on the generated traffic. The developed approach minimised the round-trip time (RTT) by 23% and enhanced the throughput by 10%. Finally, in the event where SDN controller fails, the optimised intelligent agent can immediately take over and control of the entire network.
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farooq, Muhammad, and Abdullah Aslam Raja. "Genetic Algorithm for Selecting Optimal Secondary Users to Collaborate in Spectrum sensing." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3418.
Full textAbstract:
Cognitive Radio is an innovative technology that allows the secondary unlicensed users to share the spectrum with licensed primary users to utilize the spectrum. For maximum utilization of spectrum, in cognitive radio network spectrum sensing is an important issue. Cognitive user under extreme shadowing and channel fading can‟t sense the primary licensed user signal correctly and thus to improve the performance of spectrum sensing, collaboration between secondary unlicensed users is required. In collaborative spectrum sensing the observation of each secondary user is received by a base station acting as a central entity, where a final conclusion about the presence or absence of the primary user signal is made using a particular decision and fusion rule. Due to spatially correlated shadowing the collaborative spectrum sensing performance decreases, and thus optimum secondary users must be selected to, not only improve spectrum sensing performance but also lessen the processing overhead of the central entity. A particular situation is depicted in the project where according to some performance parameters, first those optimum secondary users that have enough spatial separation and high average received SNR are selected using Genetic Algorithm, and then collaboration among these optimum secondary users is done to evaluate the performance. The collaboration of optimal secondary user providing high probability of detection and low probability of false alarm, for sensing the spectrum is compared with the collaboration of all the available secondary users in that radio environment. At the end a conclusion has been made that collaboration of selected optimum secondary users provides better performance, then the collaboration of all the secondary users available.<br>Cognitive Radio is an innovative technology that allows the secondary unlicensed users to share the spectrum with licensed primary users to utilize the spectrum. For maximum utilization of spectrum, in cognitive radio network spectrum sensing is an important issue. Cognitive user under extreme shadowing and channel fading can‟t sense the primary licensed user signal correctly and thus to improve the performance of spectrum sensing, collaboration between secondary unlicensed users is required. In collaborative spectrum sensing the observation of each secondary user is received by a base station acting as a central entity, where a final conclusion about the presence or absence of the primary user signal is made using a particular decision and fusion rule. Due to spatially correlated shadowing the collaborative spectrum sensing performance decreases, and thus optimum secondary users must be selected to, not only improve spectrum sensing performance but also lessen the processing overhead of the central entity. A particular situation is depicted in the project where according to some performance parameters, first those optimum secondary users that have enough spatial separation and high average received SNR are selected using Genetic Algorithm, and then collaboration among these optimum secondary users is done to evaluate the performance. The collaboration of optimal secondary user providing high probability of detection and low probability of false alarm, for sensing the spectrum is compared with the collaboration of all the available secondary users in that radio environment. At the end a conclusion has been made that collaboration of selected optimum secondary users provides better performance, then the collaboration of all the secondary users available.
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Lamus, Garcia Herreros Camilo. "Models and algorithms of brain connectivity, spatial sparsity, and temporal dynamics for the MEG/EEG inverse problem." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/103160.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 123-131).<br>Magnetoencephalography (MEG) and electroencephalography (EEG) are noninvasive functional neuroimaging techniques that provide high temporal resolution recordings of brain activity, offering a unique means to study fast neural dynamics in humans. Localizing the sources of brain activity from MEG/EEG is an ill-posed inverse problem, with no unique solution in the absence of additional information. In this dissertation I analyze how solutions to the MEG/EEG inverse problem can be improved by including information about temporal dynamics of brain activity and connectivity within and among brain regions. The contributions of my thesis are: 1) I develop a dynamic algorithm for source localization that uses local connectivity information and Empirical Bayes estimates to improve source localization performance (Chapter 1). This result led me to investigate the underlying theoretical principles that might explain the performance improvement observed in simulations and by analyzing experimental data. In my analysis, 2) I demonstrate theoretically how the inclusion of local connectivity information and basic source dynamics can greatly increase the number of sources that can be recovered from MEG/EEG data (Chapter 2). Finally, in order to include long distance connectivity information, 3) I develop a fast multi-scale dynamic source estimation algorithm based on the Subspace Pursuit and Kalman Filter algorithms that incorporates brain connectivity information derived from diffusion MRI (Chapter 3). Overall, I illustrate how dynamic models informed by neurophysiology and neuroanatomy can be used alongside advanced statistical and signal processing methods to greatly improve MEG/EEG source localization. More broadly, this work provides an example of how advanced modeling and algorithm development can be used to address difficult problems in neuroscience and neuroimaging.<br>by Camilo Lamus Garcia Herreros.<br>Ph. D.
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Apedome, Kouami Seli. "Proposition d’une démarche d’intégration des aspects cognitifs au retour d’expérience statistique : application à la maintenance industrielle." Paris 8, 2012. http://octaviana.fr/document/167322427#?c=0&m=0&s=0&cv=0.
Full textAbstract:
La performance d’une entreprise industrielle réside en partie dans la capacité de son personnel à créer de la valeur à travers son expérience. Des milliers d’heures sont perdues dans les entreprises industrielles, à répéter des tâches déjà réalisées par d’autres, à redéfinir les mêmes actions inefficaces par le passé, et des millions d’euros sont dépensés pour réparer des erreurs. Certains savoirs-faires disparaissent avec le départ des plus anciens, qui n’ont pas forcement transféré leurs connaissances. Parfois bien que, quelques entreprises disposent de base de données, celles ci sont toujours confrontées à des problèmes lors des prises de décision due à des informations incomplètes et imprécises. Le fait que les informations contenues dans les bases de données ne soient pas contextualités et ne soient pas suivi par une formulation de bonnes pratiques, joue grandement sur la qualité de leur exploitation. Ceci représente à notre sens, une partie de la procédure de traitement des expériences qui n’est pas toujours simple à formaliser et pour laquelle plusieurs plus ou moins adaptés sont envisageables. Dans notre, travail nous proposons une démarche de formalisation et d’exploitation des expériences à partir du réseau bayésien. Le réseau bayésien est un modèle graphique, mathématique et statistique qui aide à gérer l’incertitude. Il permet de représenter intuitivement un domaine de connaissance, de gérer un ensemble de données incomplètes, et peut représenter un véritable outil d’aide à la décision<br>Performance of an industrial enterprise resides in part in his staff's capacity to create value through his experience. Thousands of hours are lost in the industrial enterprises, to repeat tasks already achieved by others, to redefine the same inefficient actions in the past, and millions euros are spent to repair some mistakes. Some how- know disappears with the departure of the oldest, that doesn't have forcing transferred their knowledge. Sometimes, some enterprises have data base, those are confronted here always to problems at the time of the decision makings due to incomplete and imprecise information. Fact that information contained in data bases are not contextual and are not followed by a good practice formulation, cheek greatly on quality of their exploitation. It represents to our sense, a part of procedure of treatment of experiences that is not always simple to formalize and for which several more or less adapted are foreseeable. In our, work we propose a gait of formalism and exploitation of experiences from Bayesian network. Bayesian network is a graphic, mathematical and statistical model that helps to manage uncertainty. It permits to represent a domain of knowledge, to manage a set of incomplete data, intuitively and can represent a real tool of help to decision