Admission control schemes for TCP elastic traffic in class-based networks

En aquesta tesi proposem dos esquemes de xarxa amb control d'admissió per al trànsit elàstic TCP amb mecanismes senzills. Ambdós esquemes són capaços de proporcionar throughputs diferents i aïllament entre fluxos, on un "flux" es defineix com una seqüència de paquets relacionats dins d'una connexió TCP. Quant a l'arquitectura, ambdós fan servir classes de paquets amb diferents prioritats de descart, i un control d'admissió implícit, edge-to-edge i basat en mesures. En el primer esquema, les mesures són per flux, mentre que en el segon, les mesures són per agregat. El primer esquema aconsegueix un bon rendiment fent servir una modificació especial de les fonts TCP, mentre que el segon aconsegueix un bon rendiment amb fonts TCP estàndard. Ambdós esquemes han estat avaluats satisfactòriament a través de simulació en diferents topologies de xarxa i càrregues de trànsit.

Enhanced convolution approach for CAC in ATM networks, an analytical study and implementation

The characteristics of service independence and flexibility of ATM networks make the control problems of such networks very critical. One of the main challenges in ATM networks is to design traffic control mechanisms that enable both economically efficient use of the network resources and desired quality of service to higher layer applications. Window flow control mechanisms of traditional packet switched networks are not well suited to real time services, at the speeds envisaged for the future networks. In this work, the utilisation of the Probability of Congestion (PC) as a bandwidth decision parameter is presented. The validity of PC utilisation is compared with QOS parameters in buffer-less environments when only the cell loss ratio (CLR) parameter is relevant. The convolution algorithm is a good solution for CAC in ATM networks with small buffers. If the source characteristics are known, the actual CLR can be very well estimated. Furthermore, this estimation is always conservative, allowing the retention of the network performance guarantees. Several experiments have been carried out and investigated to explain the deviation between the proposed method and the simulation. Time parameters for burst length and different buffer sizes have been considered. Experiments to confine the limits of the burst length with respect to the buffer size conclude that a minimum buffer size is necessary to achieve adequate cell contention. Note that propagation delay is a no dismiss limit for long distance and interactive communications, then small buffer must be used in order to minimise delay. Under previous premises, the convolution approach is the most accurate method used in bandwidth allocation. This method gives enough accuracy in both homogeneous and heterogeneous networks. But, the convolution approach has a considerable computation cost and a high number of accumulated calculations. To overcome this drawbacks, a new method of evaluation is analysed: the Enhanced Convolution Approach (ECA). In ECA, traffic is grouped in classes of identical parameters. By using the multinomial distribution function instead of the formula-based convolution, a partial state corresponding to each class of traffic is obtained. Finally, the global state probabilities are evaluated by multi-convolution of the partial results. This method avoids accumulated calculations and saves storage requirements, specially in complex scenarios. Sorting is the dominant factor for the formula-based convolution, whereas cost evaluation is the dominant factor for the enhanced convolution. A set of cut-off mechanisms are introduced to reduce the complexity of the ECA evaluation. The ECA also computes the CLR for each j-class of traffic (CLRj), an expression for the CLRj evaluation is also presented. We can conclude that by combining the ECA method with cut-off mechanisms, utilisation of ECA in real-time CAC environments as a single level scheme is always possible.

Real-Time software control of neural stimulation in a closed loop Animat system

A recent area for investigation into the development of adaptable robot control is the use of living neuronal networks to control a mobile robot. The so-called Animat paradigm comprises a neuronal network (the ‘brain’) connected to an external embodiment (in this case a mobile robot), facilitating potentially robust, adaptable robot control and increased understanding of neural processes. Sensory input from the robot is provided to the neuronal network via stimulation on a number of electrodes embedded in a specialist Petri dish (Multi Electrode Array (MEA)); accurate control of this stimulation is vital. We present software tools allowing precise, near real-time control of electrical stimulation on MEAs, with fast switching between electrodes and the application of custom stimulus waveforms. These Linux-based tools are compatible with the widely used MEABench data acquisition system. Benefits include rapid stimulus modulation in response to neuronal activity (closed loop) and batch processing of stimulation protocols.

Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK

We developed a stochastic simulation model incorporating most processes likely to be important in the spread of Phytophthora ramorum and similar diseases across the British landscape (covering Rhododendron ponticum in woodland and nurseries, and Vaccinium myrtillus in heathland). The simulation allows for movements of diseased plants within a realistically modelled trade network and long-distance natural dispersal. A series of simulation experiments were run with the model, representing an experiment varying the epidemic pressure and linkage between natural vegetation and horticultural trade, with or without disease spread in commercial trade, and with or without inspections-with-eradication, to give a 2 x 2 x 2 x 2 factorial started at 10 arbitrary locations spread across England. Fifty replicate simulations were made at each set of parameter values. Individual epidemics varied dramatically in size due to stochastic effects throughout the model. Across a range of epidemic pressures, the size of the epidemic was 5-13 times larger when commercial movement of plants was included. A key unknown factor in the system is the area of susceptible habitat outside the nursery system. Inspections, with a probability of detection and efficiency of infected-plant removal of 80% and made at 90-day intervals, reduced the size of epidemics by about 60% across the three sectors with a density of 1% susceptible plants in broadleaf woodland and heathland. Reducing this density to 0.1% largely isolated the trade network, so that inspections reduced the final epidemic size by over 90%, and most epidemics ended without escape into nature. Even in this case, however, major wild epidemics developed in a few percent of cases. Provided the number of new introductions remains low, the current inspection policy will control most epidemics. However, as the rate of introduction increases, it can overwhelm any reasonable inspection regime, largely due to spread prior to detection. (C) 2009 Elsevier B.V. All rights reserved.

Modelling disease spread and control in networks: implications for plant sciences

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed.

Self-assembly of two-component gels: stoichiometric control and component selection

Two-component systems capable of self-assembling into soft gel-phase materials are of considerable interest due to their tunability and versatility. This paper investigates two-component gels based on a combination of a L-lysine-based dendron and a rigid diamine spacer (1,4-diaminobenzene or 1,4-diaminocyclohexane). The networked gelator was investigated using thermal measurements, circular dichroism, NMR spectroscopy and small angle neutron scattering (SANS) giving insight into the macroscopic properties, nanostructure and molecular-scale organisation. Surprisingly, all of these techniques confirmed that irrespective of the molar ratio of the components employed, the "solid-like" gel network always consisted of a 1:1 mixture of dendron/diamine. Additionally, the gel network was able to tolerate a significant excess of diamine in the "liquid-like" phase before being disrupted. In the light of this observation, we investigated the ability of the gel network structure to evolve from mixtures of different aromatic diamines present in excess. We found that these two-component gels assembled in a component-selective manner, with the dendron preferentially recognising 1,4-diaminobenzene (>70%). when similar competitor diamines (1,2- and 1,3-diaminobenzene) are present. Furthermore, NMR relaxation measurements demonstrated that the gel based oil 1,4-diaminobenzene was better able to form a selective ternary complex with pyrene than the gel based oil 1,4-diaminocyclohexane, indicative of controlled and selective pi-pi interactions within a three-component assembly. As such, the results ill this paper demonstrate how component selection processes in two-component gel systems call control hierarchical self-assembly.

A novel technique for differentiation of proteins in the development of acid gel structure from control and heat treated milk using confocal scanning laser microscopy

The incorporation of caseins and whey proteins into acid gels produced from unheated and heat treated skimmed milk was studied by confocal scanning laser microscopy (CSLM) using fluorescent labelled proteins. Bovine casein micelles were labelled using Alexa Fluor 594, while whey proteins were labelled using Alexa Fluor 488. Samples of the labelled protein solutions were introduced into aliquots of pasteurised skim milk, and skim milk heated to 90 degrees C for 2 min and 95 degrees C for 8 min. The milk was acidified at 40 degrees C to a final pH of 4.4 using 20 g gluconodelta-lactone/l (GDL). The formation of gels was observed with CSLM at two wavelengths (488 nm and 594 nm), and also by visual and rheological methods. In the control milk, as pH decreased distinct casein aggregates appeared, and as further pH reduction occurred, the whey proteins could be seen to coat the casein aggregates. With the heated milks, the gel structure was formed of continuous strands consisting of both casein and whey protein. The formation of the gel network was correlated with an increase in the elastic modulus for all three treatments, in relation to the severity of heat treatment. This model system allows the separate observation of the caseins and whey proteins, and the study of the interactions between the two protein fractions during the formation of the acid gel structure, on a real-time basis. The system could therefore be a valuable tool in the study of structure formation in yoghurt and other dairy protein systems.

Prefrontal social cognition network dysfunction underlying face encoding and social anxiety in fragile X syndrome

Individuals with fragile X syndrome (FXS) commonly display characteristics of social anxiety, including gaze aversion, increased time to initiate social interaction, and difficulty forming meaningful peer relationships. While neural correlates of face processing, an important component of social interaction, are altered in FXS, studies have not examined whether social anxiety in this population is related to higher cognitive processes, such as memory. This study aimed to determine whether the neural circuitry involved in face encoding was disrupted in individuals with FXS, and whether brain activity during face encoding was related to levels of social anxiety. A group of 11 individuals with FXS (5 M) and 11 age-and gender-matched control participants underwent fMRI scanning while performing a face encoding task with onlineeye-tracking. Results indicate that compared to the control group, individuals with FXS exhibited decreased activation of prefrontal regions associated with complex social cognition, including the medial and superior frontal cortex, during successful face encoding. Further, the FXS and control groups showed significantly different relationships between measures of social anxiety (including gaze-fixation) and brain activity during face encoding. These data indicate that social anxiety in FXS may be related to the inability to successfully recruit higher level social cognition regions during the initial phases of memory formation. (C) 2008 Elsevier Inc. All rights reserved.

Neural systems in the visual control of steering

Visual control of locomotion is essential for most mammals and requires coordination between perceptual processes and action systems. Previous research on the neural systems engaged by self-motion has focused on heading perception, which is only one perceptual subcomponent. For effective steering, it is necessary to perceive an appropriate future path and then bring about the required change to heading. Using function magnetic resonance imaging in humans, we reveal a role for the parietal eye fields (PEFs) in directing spatially selective processes relating to future path information. A parietal area close to PEFs appears to be specialized for processing the future path information itself. Furthermore, a separate parietal area responds to visual position error signals, which occur when steering adjustments are imprecise. A network of three areas, the cerebellum, the supplementary eye fields, and dorsal premotor cortex, was found to be involved in generating appropriate motor responses for steering adjustments. This may reflect the demands of integrating visual inputs with the output response for the control device.

Architecture for neuronal cell control of a mobile robot

It is usually expected that the intelligent controlling mechanism of a robot is a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot - thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. In particular, the use of rodent primary dissociated cultured neuronal networks for the control of mobile `animals' (artificial animals, a contraction of animal and materials) is a novel approach to discovering the computational capabilities of networks of biological neurones. A dissociated culture of this nature requires appropriate embodiment in some form, to enable appropriate development in a controlled environment within which appropriate stimuli may be received via sensory data but ultimate influence over motor actions retained. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animal) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This 'closed loop' interaction with the environment through both sensing and effecting will enable investigation of its learning capacity This paper details the components of the overall animat closed loop system and reports on the evaluation of the results from the experiments being carried out with regard to robot behaviour.

Generic control interface for networked haptic virtual environments

As Virtual Reality pushes the boundaries of the human computer interface new ways of interaction are emerging. One such technology is the integration of haptic interfaces (force-feedback devices) into virtual environments. This modality offers an improved sense of immersion to that achieved when relying only on audio and visual modalities. The paper introduces some of the technical obstacles such as latency and network traffic that need to be overcome for maintaining a high degree of immersion during haptic tasks. The paper describes the advantages of integrating haptic feedback into systems, and presents some of the technical issues inherent in a networked haptic virtual environment. A generic control interface has been developed to seamlessly mesh with existing networked VR development libraries.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Application of neural network to hybrid systems with binary inputs

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.