105 research outputs found

    Passivity and synchronization of coupled different dimensional delayed reaction-diffusion neural networks with dirichlet boundary conditions

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    Two types of coupled different dimensional delayed reaction-diffusion neural network (CDDDRDNN) models without and with parametric uncertainties are analyzed in this paper. On the one hand, passivity and synchronization of the raised network model with certain parameters are studied through exploiting some inequality techniques and Lyapunov stability theory, and some adequate conditions are established. On the other hand, the problems of robust passivity and robust synchronization of CDDDRDNNs with parameter uncertainties are solved. Finally, two numerical examples are given to testify the effectiveness of the derived passivity and synchronization conditions

    Fourth SIAM Conference on Applications of Dynamical Systems

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    A graph automorphic approach for placement and sizing of charging stations in EV network considering traffic

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    This paper proposes a novel graph-based approach with automorphic grouping for the modelling, synthesis, and analysis of electric vehicle (EV) networks with charging stations (CSs) that considers the impacts of traffic. The EV charge demands are modeled by a graph where nodes are positioned at potential locations for CSs, and edges represent traffic flow between the nodes. A synchronization protocol is assumed for the network where the system states correspond to the waiting time at each node. These models are then utilized for the placement and sizing of CSs in order to limit vehicle waiting times at all stations below a desirable threshold level. The main idea is to reformulate the CS placement and sizing problems in a control framework. Moreover, a strategy for the deployment of portable charging stations (PCSs) in selected areas is introduced to further improve the quality of solutions by reducing the overshooting of waiting times during peak traffic hours. Further, the inherent symmetry of the graph, described by graph automorphisms, are leveraged to investigate the number and positions of CSs. Detailed simulations are performed for the EV network of Perth Metropolitan in Western Australia to verify the effectiveness of the proposed approach

    On the performance and programming of reversible molecular computers

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    If the 20th century was known for the computational revolution, what will the 21st be known for? Perhaps the recent strides in the nascent fields of molecular programming and biological computation will help bring about the ‘Coming Era of Nanotechnology’ promised in Drexler’s ‘Engines of Creation’. Though there is still far to go, there is much reason for optimism. This thesis examines the underlying principles needed to realise the computational aspects of such ‘engines’ in a performant way. Its main body focusses on the ways in which thermodynamics constrains the operation and design of such systems, and it ends with the proposal of a model of computation appropriate for exploiting these constraints. These thermodynamic constraints are approached from three different directions. The first considers the maximum possible aggregate performance of a system of computers of given volume, V, with a given supply of free energy. From this perspective, reversible computing is imperative in order to circumvent the Landauer limit. A result of Frank is refined and strengthened, showing that the adiabatic regime reversible computer performance is the best possible for any computer—quantum or classical. This therefore shows a universal scaling law governing the performance of compact computers of ~V^(5/6), compared to ~V^(2/3) for conventional computers. For the case of molecular computers, it is shown how to attain this bound. The second direction extends this performance analysis to the case where individual computational particles or sub-units can interact with one another. The third extends it to interactions with shared, non-computational parts of the system. It is found that accommodating these interactions in molecular computers imposes a performance penalty that undermines the earlier scaling result. Nonetheless, scaling superior to that of irreversible computers can be preserved, and appropriate mitigations and considerations are discussed. These analyses are framed in a context of molecular computation, but where possible more general computational systems are considered. The proposed model, the א-calculus, is appropriate for programming reversible molecular computers taking into account these constraints. A variety of examples and mathematical analyses accompany it. Moreover, abstract sketches of potential molecular implementations are provided. Developing these into viable schemes suitable for experimental validation will be a focus of future work

    Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications

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    With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and communication technologies. While the implementation of AI systems using computer algorithms of neural networks is emerging rapidly, scientists are just taking the very first steps in the development of the hardware elements of an artificial brain, specifically neuromorphic microchips. In this review article, we present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes. We address, both experimentally and theoretically, the key dynamic properties of recently developed artificial solid-state neuron microchips with delayed perturbations and describe their role in the study of neural activity and regenerative memory. This review covers our recent research work on excitable and delay dynamic characteristics of both single and autaptic (delayed) artificial neurons including all-or-none response, spike-based data encoding, storage, signal regeneration and signal healing. Furthermore, the neural responses of these neuromorphic microchips display all the signatures of extended spatio-temporal localized structures (LSs) of light, which are reviewed here in detail. By taking advantage of the dissipative nature of LSs, we demonstrate potential applications in optical data reconfiguration and clock and timing at high-speeds and with short transients. The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing. (C) 2017 Author(s).Fundacao para a Ciencia e a Tecnologia (FCT) [UID/Multi/00631/2013]European Structural and Investment Funds (FEEI) through the Competitiveness and Internationalization Operational Program - COMPETE 2020National Funds through FCT [ALG-01-0145-FEDER-016432/POCI-01-0145-FEDER-016432]European Commission under the project iBROW [645369]project COMBINA [TEC2015-65212-C3-3-PAEI/FEDER UE]Ramon y Cajal fellowshipinfo:eu-repo/semantics/publishedVersio

    5th EUROMECH nonlinear dynamics conference, August 7-12, 2005 Eindhoven : book of abstracts

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    5th EUROMECH nonlinear dynamics conference, August 7-12, 2005 Eindhoven : book of abstracts

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    Book of abstracts

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    Disconnection, network dysfunction and cognitive impairment after traumatic brain injury

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    It is now widely accepted that cognitive functions depend on the integrated operation of large-scale distributed brain networks. Recent methodological advances allow both structural and functional connectivity within these networks to be studied non-invasively in vivo. These approaches hold the promise of dramatically extending our understanding of the impact of traumatic brain injury (TBI) on cognition, which should help determine strategic targets for the rehabilitation of individuals with TBI. In this thesis, I present three studies that combine structural and functional magnetic resonance imaging to test the general hypothesis that cognitive deficits after TBI arise from structural disconnection within brain networks that mediate cognitive functions. In the first study, I demonstrate that sustained attention deficits in TBI patients are related to a failure to regulate activity within a ‘default-mode’ network (DMN) thought to be involved, among others, in internally directed processes such as self-referential thought. In addition, these deficits can be predicted by the functional and structural connectivity within the DMN. Next, I present a study investigating the neural basis for inhibitory control in healthy subjects using a modified version of the Stop Signal Task (SST). This study allows a clear distinction between attentional and response inhibition processes, and paves the way for my last study, which investigates inhibitory deficits after TBI. In this study, I demonstrate that a failure of DMN deactivation during response inhibition is associated with impaired inhibitory performance in TBI patients. The ability to efficiently regulate the DMN can be predicted by the structural integrity within a remote brain network previously proposed to be involved in switching between internally and externally directed attention. This work identifies DMN dysfunction as underlying various cognitive deficits after TBI, and confirms the relevance of white matter damage in the development of brain dysfunctions after TBI
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