Mechanism for propagation of rate signals through a 10-layer feedforward neuronal network

Using numerical simulations, we explore the mechanism for propagation of rate signals through a 10-layer feed-forward network composed of Hodgkin-Huxley （HH） neurons with sparse connectivity. When white noise is afferent to the input layer, neuronal firing becomes progressively more synchronous in successive layers and synchrony is well developed in deeper layers owing to the feedforward connections between neighboring layers. The synchrony ensures the successful propagation of rate signals through the network when the synaptic conductance is weak. As the synaptic time constant Tsyn varies, coherence resonance is observed in the network activity due to the intrinsic property of HH neurons. This makes the output firing rate single-peaked as a function of Tsyn, suggesting that the signal propagation can be modulated by the synaptic time constant. These results are consistent with experimental results and advance our understanding of how information is processed in feedforward networks.

Dynamic multimedia stream adaptation and rate control for heterogeneous networks

Dynamic adaptation of multimedia content is seen as an important feature of next generation networks and pervasive systems enabling terminals and applications to adapt to changes in e.g. context, access network, and available Quality-of-Service(QoS) due to mobility of users, devices or sessions. We present the architecture of a multimedia stream adaptation service which enables communication between terminals having heterogeneous hardware and software capabilities and served by heterogeneous networks. The service runs on special content adaptation nodes which can be placed at any location within the network. The flexible structure of our architecture allows using a variety of different adaptation engines. A generic transcoding engine is used to change the codec of streams. An MPEG-21 Digital Item Adaptation (DIA) based transformation engine allows adjusting the data rate of scalable media streams. An intelligent decision-taking engine implements adaptive flow control which takes into account current network QoS parameters and congestion information. Measurements demonstrate the quality gains achieved through adaptive congestion control mechanisms under conditions typical for a heterogeneous network.

Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.

Fair rate control for wireless ad-hoc networks with time varying channel capacities

We consider the problem of fair rate control for wireless ad-hoc networks with time varying channel capacities. The interaction between links in wireless ad-hoc networks introduces additional constraints on the flow rate. A primal-dual algorithm that guarantees fair rate control is proved to be trajectory stable. Various fairness indexes are obtained by choosing the specified form of the utility functions, and the numerical results validate the effectiveness of the proposed algorithm.

New Synchronization Algorithm and Analysis of Its Convergence Rate for Clock Oscillators in Dynamical Network with Time-Delays

New synchronization algorithm and analysis of its convergence rate for clock oscillators in dynamical network with time-delays are presented.A network of nodes equipped with hardware clock oscillators with bounded drift is considered.Firstly,a dynamic synchronization algorithm based on consensus control strategy,namely fast averaging synchronization algorithm （FASA）,is presented to find the solutions to the synchronization problem.By FASA,each node computes the logical clock value based on its value of hardware clock and message exchange.The goal is to synchronize all the nodes＇ logical clocks as closely as possible.Secondly,the convergence rate of FASA is analyzed that proves it is related to the bound by a nondecreasing function of the uncertainty in message delay and network parameters.Then,FASA＇s convergence rate is proven by means of the robust optimal design.Meanwhile,several practical applications for FASA,especially the application to inverse global positioning system （IGPS） base station network are discussed.Finally,numerical simulation results demonstrate the correctness and efficiency of the proposed FASA.Compared FASA with traditional clock synchronization algorithms （CSAs）,the convergence rate of the proposed algorithm converges faster than that of the CSAs evidently.

Power Splitting Based SWIPT in Network-Coded Two-Way Networks with Data Rate Fairness:An Information-Theoretic Perspective

This paper investigates the simultaneous wireless information and powertransfer(SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an SWIPT-aware energy harvesting(EH) relay. We present a power splitting(PS)-based two-way relaying(PS-TWR) protocol by employing the PS receiver architecture. To explore the system sum rate limit with data rate fairness, an optimization problem under total power constraint is formulated. Then, some explicit solutions are derived for the problem. Numerical results show that due to the path loss effect on energy transfer, with the same total available power, PS-TWR losses some system performance compared with traditional non-EH two-way relaying, where at relatively low and relatively high signalto-noise ratio(SNR), the performance loss is relatively small. Another observation is that, in relatively high SNR regime, PS-TWR outperforms time switching-based two-way relaying(TS-TWR) while in relatively low SNR regime TS-TWR outperforms PS-TWR. It is also shown that with individual available power at the two sources, PS-TWR outperforms TS-TWR in both relatively low and high SNR regimes.

Hierarchical Content Caching in Fog Radio Access Networks:Ergodic Rate and Transmit Latency

In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.

Term Structure of Interest Rates Based on Artificial Neural Network

In light of the nonlinear approaching capability of artificial neural networks （ ANN）, the term structure of interest rates is predicted using The generalized regression neural network （GRNN） and back propagation （BP） neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek＇s model and Cox-Ingersoll-Ross （CIR） model. The comparison reveals that neural network models outperform Vasicek＇s model and CIR model, which are more precise and closer to the real market situation.

Performance analysis of a class of networked control systems under data rate constraints

In networked control system (NCS) where control loop is closed over communication network, limited data rate may deteriorate control performance even destabilize the control system. In this paper, performance analysis of a typical second-order control system with data rate constraints is conducted, and the concept of critical data rate (CDR) is presented. In order to find the CDR in NCS, an approximate searching method is proposed to guarantee acceptable control performance.

Joint routing and rate allocation for multiple video streams in ad-hoc wireless networks

The support for multiple video streams in an ad-hoc wireless network requires appropriate routing and rate allocation measures ascertaining the set of links for transmitting each stream and the encoding rate of the video to be delivered over the chosen links. The routing and rate allocation procedures impact the sustained quality of each video stream measured as the mean squared error (MSE) distortion at the receiver, and the overall network congestion in terms of queuing delay per link. We study the trade-off between these two competing objectives in a convex optimization formulation, and discuss both centralized and dis- tributed solutions for joint routing and rate allocation for multiple streams. For each stream, the optimal allocated rate strikes a balance between the selfish motive of minimizing video distortion and the global good of minimizing network congestions, while the routes are chosen over the least-congested links in the network. In addition to detailed analysis, network simulation results using ns-2 are presented for studying the optimal choice of parameters and to confirm the effectiveness of the proposed measures.

AVariablebitrateVideoCodingSchemeBasedonMorphologicalOperationsforATMNetworks

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Global exponential stability for delayed cellular neural networks and estimate of exponential convergence rate

Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.

VBR WAVELET VIDEO CODING AND ITS RATE CONTROL IN ATM NETWORKS

This paper presents a new video coding system based on wavelet transform and its rate control scheme over ATM networks. First, three dimensional wavelet transform is performed for the original image sequence, and an extension of set partitioning in hierarchical trees algorithm is employed to quantize the wavelet coefficients. Then, the output rate of the coder is controlled at group of frame scale, ensuring that it conforms to the parameters of a leaky bucket controller. Several leaky buckets with different sizes are discussed too. Simulation shows the efficiency of this codec and the effectiveness of the proposed rate control scheme.

Volume Flow Rate Optimization of an Axial Fan by Artificial Neural Network and Genetic Algorithm

The present study is to improve the volume flow rate of an axial fan through optimizing the blade shape under the demand for a specified static pressure. Fourteen design variables were selected to control the blade camber lines and the stacking line and the values of these variables were determined by using the experimental design method of the Latin Hypercube Sampling (LHS) to generate forty designs. The optimization was carried out using the genetic algorithm (GA) coupled with the artificial neural network (ANN) to increase the volume flow rate of the axial fan under the constraint of a specific motor power and a required static pressure. Differences in the aerodynamic performance and the flow characteristics between the original model and the optimal model were analyzed in detail. The results showed that the volume flow rate of the optimal model increased by 33%. The chord length, the installation angle and the cascade turning angle changed considerably. The forward leaned blade was beneficial to improve the volume flow rate of the axial fan. The axial velocity distribution and the static pressure distribution on the blade surface were improved after optimization.

Folding rate prediction using complex network analysis for proteins with two- and three-state folding kinetics

It is a challenging task to investigate the different in- fluence of long-range and short-range interactions on two-state and three-state folding kinetics of protein. The networks of the 30 two-state proteins and 15 three-state proteins were constructed by complex networks analysis at three length scales: Protein Contact Networks, Long-range Interaction Networks and Short-range Interaction Networks. To uncover the relationship between structural properties and folding kinetics of the proteins, the correlations of protein network parameters with protein folding rate and topology parameters contact order were analyzed. The results show that Protein Contact Networks and Short-range Interaction Networks (for both two-state and three-state proteins) exhibit the “small-world” property and Long-range Interaction networks indicate “scale-free” behavior. Our results further indicate that all Protein Contact Networks and Short- range Interaction networks are assortative type. While some of Long-range Interaction Networks are of assortative type, the others are of disassortative type. For two-state proteins, the clustering coefficients of Short-range Interaction Networks show prominent correlation with folding rate and contact order. The assortativity coefficients of Short-range Interaction Networks also show remarkable correlation with folding rate and contact order. Similar correlations exist in Protein Contact Networks of three-state proteins. For two-state proteins, the correlation between contact order and folding rate is determined by the numbers of local contacts. Short- range interactions play a key role in determining the connecting trend among amino acids and they impact the folding rate of two-state proteins directly. For three-state proteins, the folding rate is determined by short-range and long-range interactions among residues together.

An Artificial Neural Network Model to Forecast Exchange Rates

For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. The objective of the research is to predict the trend of the ex-change rate Euro/USD up to three days ahead of last data available. The variable of output of the ANN designed is then the daily exchange rate Euro/Dollar and the frequency of data collection of variables of input and the output is daily. By the analysis of the data it is possible to conclude that the ANN model developed can largely predict the trend to three days of exchange rate Euro/USD.

A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion

The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.

Variable Chlorine Decay Rate Modeling of the Matsapha Town Water Network Using EPANET Program

A variable chlorine decay rate modeling of the Matsapha town water network was developed based on initial chlorine dosages. The model was adequately described by a second order rate function of the chlorine decay rate with respect to the initial chlorine dose applied. Simulations of chlorine residuals within the Matsapha water distribution network were run using the EPANET 2.0 program at different initial chlorine dosages and using the variable decay rate as described by the second order model. The measurement results indicated that the use of constant decay rate tended to underestimate chlorine residuals leading to potentially excess dosages with the associated chemical cost and side effects. The error between the two rate models varied between 0% and 15%. It is suggested that the use of water quality simulation programs such as EPANET be enhanced through the extension programs that accommodate variable rate modeling of chlorine residuals within distribution systems.

Joint Optimization Strategy for Video Transmission over Distributed Cognitive Radio Networks

A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video transmission consisted of a series of shortterm transmissions, the optimization problem in the video transmission was a composite optimization process. Firstly,considering some factors like primary user's( PU's) collision limitations,non-synchronization between SU and PU,and SU's limited buffer size, the short-term optimization problem was formulated as a mixed integer non-linear program( MINLP) to minimize the block probability of video packets. Secondly,combining the minimum packet block probability obtained in shortterm optimization and SU's constraint on hardware complexity,the partially observable Markov decision process( POMDP) framework was proposed to learn PU's statistic information over DCRNs.Moreover,based on the proposed framework,joint optimization strategy was designed to obtain the minimum packet loss rate in long-term video transmission. Numerical simulation results were provided to demonstrate validity of our strategies.

Optimal Rate for Constant-Fidelity Entanglement in Quantum Communication Networks

In this paper, we propose an entanglement scheme for long-distance, constant-fidelity communication in quantum networks. We discuss the optimal rate of entanglement that allows for constant fidelity in both elementary and muhihop links. We also discuss time complexity and propose the mathematical order of the rate capacity for an entanglement scheme. We propose a recursive entanglement scheme, a simultaneous entanglement scheme, and an adjacent entanglement scheme mathematically analyze these schemes. The rate capacity of the recursive and simultaneous entanglement schemes is Ω（1/e^n）, but the adjacent entanglement scheme performs better, providing a rate of lΩ（1/n）.