Intelligent Car Parking Locator Service

This paper presents an InfoStation-based multi-agent system facilitating a Car Parking Locator service provision within a University Campus. The system network architecture is outlined, illustrating its functioning during the service provision. A detailed description of the Car Parking Locator service is given and the system entities’ interaction is described. System implementation approaches are also considered.

InfoStation-based Adaptable Provision of m Learning Services: Main Scenarios

This paper presents an adaptable InfoStation-based multi-agent system facilitating the mobile eLearning (mLearning) service provision within a University Campus. A horizontal view of the network architecture is presented. Main communications scenarios are considered by describing the detailed interaction of the system entities involved in the mLearning service provision. The mTest service is explored as a practical example. System implementation approaches are also considered.

Filtered Networks of Evolutionary Processors

* Supported by INTAS 00-626 and TIC 2003-09319-c03-03.

Neural Control of Chaos and Aplications

Signal processing is an important topic in technological research today. In the areas of nonlinear dynamics search, the endeavor to control or order chaos is an issue that has received increasing attention over the last few years. Increasing interest in neural networks composed of simple processing elements (neurons) has led to widespread use of such networks to control dynamic systems learning. This paper presents backpropagation-based neural network architecture that can be used as a controller to stabilize unsteady periodic orbits. It also presents a neural network-based method for transferring the dynamics among attractors, leading to more efficient system control. The procedure can be applied to every point of the basin, no matter how far away from the attractor they are. Finally, this paper shows how two mixed chaotic signals can be controlled using a backpropagation neural network as a filter to separate and control both signals at the same time. The neural network provides more effective control, overcoming the problems that arise with control feedback methods. Control is more effective because it can be applied to the system at any point, even if it is moving away from the target state, which prevents waiting times. Also control can be applied even if there is little information about the system and remains stable longer even in the presence of random dynamic noise.

Fourier Neural Networks: An Approach with Sinusoidal Activation Functions

* Supported by INTAS 2000-626, INTAS YSF 03-55-1969, INTAS INNO 182, and TIC 2003-09319-c03-03.

The Cascade Orthogonal Neural Network

In the paper new non-conventional growing neural network is proposed. It coincides with the Cascade- Correlation Learning Architecture structurally, but uses ortho-neurons as basic structure units, which can be adjusted using linear tuning procedures. As compared with conventional approximating neural networks proposed approach allows significantly to reduce time required for weight coefficients adjustment and the training dataset size.

Outliers Resistant Learning Algorithm for Radial-basis-fuzzy-wavelet-neural Network in Stomach Acute Injury Diagnosis Tasks

In this paper an outliers resistant learning algorithm for the radial-basis-fuzzy-wavelet-neural network based on R. Welsh criterion is proposed. Suggested learning algorithm under consideration allows the signals processing in presence of significant noise level and outliers. The robust learning algorithm efficiency is investigated and confirmed by the number of experiments including medical applications.

WLAN / WMAN Integration Architecture and Traffic Control for Voice Transmission

The popular technologies Wi-Fi and WiMAX for realization of WLAN and WMAN respectively are much different, but they could compliment each other providing competitive wireless access for voice traffic. The article develops the idea of WLAN/WMAN (Wi-Fi/WiMAX) integration. WiMAX is offering a backup for the traffic overflowing from Wi-Fi cells located into the WiMAX cell. Overflow process is improved by proposed rearrangement control algorithm applied to the Wi-Fi voice calls. There are also proposed analytical models for system throughput evaluation and verification of the effectiveness using WMAN as a backup for WLAN overflow traffic and the proposed call rearrangement algorithm as well.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

The Cascade Neo-Fuzzy Architecture and its Online Learning Algorithm

In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.

A New Charging and Billing Model and Architecture for the Ubiquitous Consumer Wireless World

In a Ubiquitous Consumer Wireless World (UCWW) environment the provision, administration and management of the authentication, authorization and accounting (AAA) policies and business services are provided by third-party AAA service providers (3P-AAA-SPs) who are independent of the wireless access network providers (ANPs). In this environment the consumer can freely choose any suitable ANP, based on his/her own preferences. This new AAA infrastructural arrangement necessitates assessing the impact and re-thinking the design, structure and location of ‘charging and billing’ (C&B) functions and services. This paper addresses C&B issues in UCWW, proposing potential architectural solutions for C&B realization. Implementation approaches of these novel solutions together with a software testbed for validation and performance evaluation are addressed.