Background synaptic activity in rat entorhinal cortical neurones:differential control of transmitter release by presynaptic receptors

The entorhinal cortex (EC) is a key brain area controlling both hippocampal input and output via neurones in layer II and layer V, respectively. It is also a pivotal area in the generation and propagation of epilepsies involving the temporal lobe. We have previously shown that within the network of the EC, neurones in layer V are subject to powerful synaptic excitation but weak inhibition, whereas the reverse is true in layer II. The deep layers are also highly susceptible to acutely provoked epileptogenesis. Considerable evidence now points to a role of spontaneous background synaptic activity in control of neuronal, and hence network, excitability. In the present article we describe results of studies where we have compared background release of the excitatory transmitter, glutamate, and the inhibitory transmitter, GABA, in the two layers, the role of this background release in the balance of excitability, and its control by presynaptic auto- and heteroreceptors on presynaptic terminals. © The Physiological Society 2004.

Comparison between conventional and stochastic pinning control

Theoretical developments on pinning control of complex dynamical networks have mainly focused on the deterministic versions of the model dynamics. However, the dynamical behavior of most real networks is often affected by stochastic noise components. In this paper the pinning control of a stochastic version of the coupled map lattice network with spatiotemporal characteristics is studied. The control of these complex dynamical networks have functional uncertainty which should be considered when calculating stabilizing control signals. Two feedback control methods are considered: the conventional feedback control and modified stochastic feedback control. It is shown that the typically-used conventional control method suffers from the ignorance of model uncertainty leading to a reduction and potentially a collapse in the control efficiency. Numerical verification of the main result is provided for a chaotic coupled map lattice network. © 2011 IEEE.

Stochastic control strategies and adaptive critic methods

Adaptive critic methods have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, nonlinear and nonstationary environments. In this study, a novel probabilistic dual heuristic programming (DHP) based adaptive critic controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) adaptive critic method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterized by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the critic network is then calculated and shown to be equal to the analytically derived correct value.

Generalised Riccati solution and pinning control of complex stochastic networks

This paper considers the global synchronisation of a stochastic version of coupled map lattices networks through an innovative stochastic adaptive linear quadratic pinning control methodology. In a stochastic network, each state receives only noisy measurement of its neighbours' states. For such networks we derive a generalised Riccati solution that quantifies and incorporates uncertainty of the forward dynamics and inverse controller in the derivation of the stochastic optimal control law. The generalised Riccati solution is derived using the Lyapunov approach. A probabilistic approximation type algorithm is employed to estimate the conditional distributions of the state and inverse controller from historical data and quantifying model uncertainties. The theoretical derivation is complemented by its validation on a set of representative examples.

Adaptive power and rate control for QoS guarantee of DSRC based collaborative road safety applications

In this paper we propose an adaptive power and message rate control method for safety applications at road intersections. The design objectives are to firstly provide guaranteed QoS support to both high priority emergency safety applications and low priority routine safety applications and secondly maximize channel utilization. We use an offline simulation based approach to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network with certain safety QoS requirements. The identified configurations are then used online by roadside access points (AP) adaptively according to estimated number of vehicles. Simulation results show that this adaptive method could provide required QoS support to safety applications and it significantly outperforms a fixed control method. © 2013 International Information Institute.

Performance evaluation of adaptive context aware rate selection algorithm (ACARS) for road safety applications in vehicular network

One of the major drawbacks for mobile nodes in wireless networks is power management. Our goal is to evaluate the performance power control scheme to be used to reduce network congestion, improve quality of service and collision avoidance in vehicular network and road safety application. Some of the importance of power control (PC) are improving spatial reuse, and increasing network capacity in mobile wireless communications. In this simulation we have evaluated the performance of existing rate algorithms compared with context Aware Rate selection algorithm (ACARS) and also seen the performance of ACARS and how it can be applied to road safety, improve network control and power management. Result shows that ACARS is able to minimize the total transmit power in the presence of propagation processes and mobility of vehicles, by adapting to the fast varying channels conditions with the Path loss exponent values that was used for that environment which is shown in the network simulation parameter. Our results have shown that ACARS is a very robust algorithm which performs very well with the effect of propagation processes that is prone to every transmitted signal in mobile networks. © 2013 IEEE.

Impact of electric vehicles on GB electricity demand and associated benefits for system control

This paper investigates the impact that electric vehicle uptake will have on the national electricity demand of Great Britain. Data from the National Travel Survey, and the Coventry and Birmingham Low Emissions Demonstration (CABLED) are used to model an electrical demand profile in a future scenario of significant electric vehicle market penetration. These two methods allow comparison of how conventional cars are currently used, and the resulting electrical demand with simple substitution of energy source, with data showing how electric vehicles are actually being used at present. The report finds that electric vehicles are unlikely to significantly impact electricity demand in GB. The paper also aims to determine whether electric vehicles have the potential to provide ancillary services to the grid operator, and if so, the capacity for such services that would be available. Demand side management, frequency response and Short term Operating Reserve (STOR) are the services considered. The report finds that electric cars are unlikely to provide enough moveable demand peak shedding to be worthwhile. However, it is found that controlling vehicle charging would provide sufficient power control to viably act as frequency response for dispatch by the transmission system operator. This paper concludes that electric vehicles have technical potential to aid management of the transmission network without adding a significant demand burden. © 2013 IEEE.

Semantic enabled sensor network design

Wireless Sensor Network (WSN) systems have become more and more popular in our modern life. They have been widely used in many areas, such as smart homes/buildings, context-aware devices, military applications, etc. Despite the increasing usage, there is a lack of formal description and automated verification for WSN system design. In this paper, we present an approach to support the rigorous verification of WSN modeling using the Semantic Web technology We use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) to define a meta-ontology for the modeling of WSN systems. Furthermore, we apply ontology reasoners to perform automated verification on customized WSN models and their instances. We demonstrate and evaluate our approach through a Light Control System (LCS) as the case study.

Multiple roles for executive control in belief-desire reasoning:distinct neural networks are recruited for self perspective inhibition and complexity of reasoning

Belief-desire reasoning is a core component of 'Theory of Mind' (ToM), which can be used to explain and predict the behaviour of agents. Neuroimaging studies reliably identify a network of brain regions comprising a 'standard' network for ToM, including temporoparietal junction and medial prefrontal cortex. Whilst considerable experimental evidence suggests that executive control (EC) may support a functioning ToM, co-ordination of neural systems for ToM and EC is poorly understood. We report here use of a novel task in which psychologically relevant ToM parameters (true versus false belief; approach versus avoidance desire) were manipulated orthogonally. The valence of these parameters not only modulated brain activity in the 'standard' ToM network but also in EC regions. Varying the valence of both beliefs and desires recruits anterior cingulate cortex, suggesting a shared inhibitory component associated with negatively valenced mental state concepts. Varying the valence of beliefs additionally draws on ventrolateral prefrontal cortex, reflecting the need to inhibit self perspective. These data provide the first evidence that separate functional and neural systems for EC may be recruited in the service of different aspects of ToM.

Bounds on end-to-end performance of networks employing erasure control coding

Erasure control coding has been exploited in communication networks with an aim to improve the end-to-end performance of data delivery across the network. To address the concerns over the strengths and constraints of erasure coding schemes in this application, we examine the performance limits of two erasure control coding strategies, forward erasure recovery and adaptive erasure recovery. Our investigation shows that the throughput of a network using an (n, k) forward erasure control code is capped by r =k/n when the packet loss rate p ≤ (te/n) and by k(l-p)/(n-te) when p > (t e/n), where te is the erasure control capability of the code. It also shows that the lower bound of the residual loss rate of such a network is (np-te)/(n-te) for (te/n) < p ≤ 1. Especially, if the code used is maximum distance separable, the Shannon capacity of the erasure channel, i.e. 1-p, can be achieved and the residual loss rate is lower bounded by (p+r-1)/r, for (1-r) < p ≤ 1. To address the requirements in real-time applications, we also investigate the service completion time of different schemes. It is revealed that the latency of the forward erasure recovery scheme is fractionally higher than that of the scheme without erasure control coding or retransmission mechanisms (using UDP), but much lower than that of the adaptive erasure scheme when the packet loss rate is high. Results on comparisons between the two erasure control schemes exhibit their advantages as well as disadvantages in the role of delivering end-to-end services. To show the impact of the bounds derived on the end-to-end performance of a TCP/IP network, a case study is provided to demonstrate how erasure control coding could be used to maximize the performance of practical systems. © 2010 IEEE.

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.