Some problems in the stability of networked-control systems with periodic scheduling

This article addresses three stability problems related to networked-control systems (NCSs) with periodic scheduling, where control systems may have multiple samplings in a hyperperiod (a hyperperiod is a periodically repeated scheduling sequence for all tasks in an NCS). As expected, the analysis of a system with multiple samplings is much richer than the case with single sampling. For example, a system with two samplings may be stable (unstable) even if it is unstable (stable) when sampled by either sampling. In this context, it is important to understand how network-induced delays and multiple samplings affect the system's stability. In this article, three particular stability problems involving constant and/or time-varying parameters are investigated, and the corresponding stability regions are derived. Numerical examples and various discussions complete the presentation.     

Global stabilization of oscillators with bounded delayed input

The problem of globally asymptotically stabilizing by bounded feedback of an oscillator with an arbitrary large delay in the input is solved. A first solution follows from a general result on the global stabilization of null controllable linear systems with delay in the input by bounded control laws with a distributed term. Next, it is shown through a Lyapunov analysis that the stabilization can be achieved as well when neglecting the distributed terms. It turns out that this main result is intimately related to the output feedback stabilization problem.     

Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can successfully be applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understanding of how computations can be done with spike trains.     

An adaptive framework for 3D graphics over networks

Access to and transmission of 3D models over networks becomes increasingly popular. However, the performance and quality of access to remote 3D models strongly depends on system load conditions and the capabilities of the various system components, such as clients, servers, and interconnect. The network graphics framework (NGF) integrates various transmission methods for downloading 3D models in a client–server environment. The NGF automatically selects the optimal transmission method for a given pair of client and server, taking into account characteristics of the model to be transmitted, critical environment conditions, user preferences and the capabilities of the client and the server. The NGF aims to provide constant quality of service across different clients and under varying environment conditions.     

On Data Distributions in the Construction of Parallel Programs

Data distributions have a serious impact on time complexity of parallel programs, developed based on domain decomposition. A new kind of distributions—set distributions, based on set-valued mappings, is introduced. These distributions assign a data object to more than one process. The set distributions can be used especially when the number of processes is greater than the data input size, but, sometimes using set distributions can lead to efficient general parallel algorithms. The work-load properties of these distributions and their impact on the number of communications are discussed. In order to illustrate the implications of data distributions in the construction of parallel programs, some examples are presented. Two parallel algorithms for computation of Lagrange interpolation polynomial are developed, starting from simple distributions and set distributions.     

On the analysis of a dynamical model representing gene regulatory networks under negative feedback

In this work, stability analysis is performed for a cyclic dynamical model of gene regulatory networks involving time delays, under negative feedback. The model considered has nonlinearities with negative Schwarzian derivatives. Sufficient conditions implying global stability of these types of GRNs are obtained. The special case of homogenous gene regulatory networks is also studied; in this case, the proposed stability conditions depend only on the parameters of the nonlinearity function. Illustrative numerical examples complete the presentation.Copyright © 2013 John Wiley & Sons, Ltd.     

Double decarbonylation of phthalimide revisited: A facile cathodic synthesis of isoindoline

Electroreduction of phthalimide in water-acetonitrile solution acidified with H₂SO₄ or HCl under controlled acidity conditions results in removal of two carbonyl groups and allows obtaining isoindoline selectively with high yield and current efficiency.     

Neuronal desynchronization as a trigger for seizure generation.

Experimental reports have appeared which challenge the dogma that epileptic seizures arise as a consequence of neuronal hypersynchronization. We sought to explore what mechanisms that desynchronize neuronal firing could induce epileptic seizures. A computer model of connections in a mammalian hippocampal slice preparation was constructed including two recently-reported distinct inhibitory feedback circuits. When inhibition by interneurons that synapse on pyramidal dendrites was decreased, highly localized seizure-like bursting was observed in the CA3 region similar to that which occurs experimentally under GABAergic blockade. In contrast, when inhibition by interneurons that synapse in the axosomatic region was similarly decreased, no such bursting was observed. However, when this transient inhibition was increased, normal coordinated spread of excitation was interrupted by high-frequency localized seizure-like bursting. The increase of this inhibitory input resulted in decreased cell coupling of pyramidal neurons. A decrease in phase coherence was initially observed until seizure-like activity initiated causing a net increase in coherence as has been observed in epileptic patients. These results provide a possible pathway in which a decrease in synchronization could provide the trigger for inducing epileptiform activity.     

Lyapunov stability analysis for nonlinear delay systems

Sufficient conditions ensuring that a nonlinear system with disturbances having a delay is globally asymptotically stable independent of delay are given. The proof carried out relies extensively on a characterization of the stability property in terms of Lyapunov function. The result is applied to some biological systems and neural networks. A stabilizing memoryless controller for a second-order system with state-delay is also proposed.