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The importance of navigation support for people with disability has been demonstrated by convention on the Rights of Persons with Disabilities. This paper presents the navigation infrastructure for people with disabilities. The infrastructure includes map generation service, web interface, mobile services. Also we purpose method of target area examination by volunteers. Using a modified Openstreetmap...
e-Tourism covers a wide niche of the digital services market. The existing services, although being presented in the large amount in today's Internet, do not achieve high intelligence level. The user still needs to perform a lot of operations manually: to solve a given problem she/he finds and accesses appropriate Internet services or uses mobile applications. A lot of information fragments are linked...
Nowadays the amount of “smart” services in e-Tourism grows rapidly. This is due to widespread using mobile devices with new input methods and large amount of digital data. Together with that, the smart services require complex methods and high cost of their creation. Thereby we have relevant problem of estimating smart service's efficiency. This paper presents evaluation of Cultural trip planning...
This paper describes approach and methods used for the development of navigation services for people with disabilities. Our previous papers contained the description of informational environment development for disabled people. The environment includes several applications utilizing unified database and providing the information support to disabled people. The service “Accessibility Passports” was...
The paper describes development process of information environment for persons with disabilities. Introduction describes the aim and objectives of development, the results of evaluation of typical demands and user scenarios for persons with disabilities. The first section of the main part describes common applications architecture and utilized technologies. Both services use the same data model, which...
The paper deals with the problem of faster optimal coverage of a Growing Neural Gas algorithm for random signals appearing with non-stationary distributions. A modification of the algorithm that successfully solves this problem will be presented with simulations in a 2-D environment and statistical results that will show its efficiency. A comparison with a previous solution for the same problem using...
The perception of the local environment is a crucial issue in mobile robot position estimation. Generating a control sequence for achieving a certain goal is also important. Our approach to solving the problem of environment learning and position estimation uses percept - action - percept graphs based on ultrasound sensor readings. We use a fuzzyART (fuzzy adaptive resonance theory) neural network...
In this paper we present our experimental results in data management for wireless sensor networks when an adapted Fuzzy ART model of neural networks is applied. Our system provides high dimension reduction and transfer savings, sending only classified identification number instead of whole sample. The system implementation is based on MicaZ sensor motes and adapted Fuzzy ART model. It was applied...
An adaptation of one popular model of neural-networks algorithm (ART model) in the field of wireless sensor networks is demonstrated in this paper. The important advantages of the ART class algorithms such as simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings are confirmed within the proposed architecture consisting of one clusterhead...
The general problem of data management in wireless sensor networks (WSNs) is to provide efficient aggregation of different sensor's data taking into account the problems of the limited energy of the nodes and their unpredictable failures. Generally, this is solved by reducing the communication among nodes. In order to have an efficient data aggregation performance, a pre-processing is needed which...
Most of the problems for data management in today's wireless sensor networks were already dealt with during the past thirty years of the artificial neural-networks tradition and that kind of algorithms can be easily implemented to wireless sensor network platforms. These problems include the need for simple parallel distributed computation, possibility for distributed storage, fault-tolerance and...
The development of wireless sensor networks is accompanied by several algorithms for data processing which are modified regression techniques from the field of multidimensional data series analysis in other scientific fields, with examples like nearest neighbor search, principal component analysis and multidimensional scaling (Guestrin, C. et al., Proc. IPSN'04, 2004). We argue that some algorithms,...
In this paper it is demonstrated how some of the algorithms developed within the artificial neural-networks tradition can be simply adopted to wireless sensor network platforms and still meet most of the requirements for sensor networks. Neural-networks clustering algorithms also provide dimensionality reduction which further leads to lower communication costs and thus bigger energy savings. Two different...
The purpose of this paper is to present a new method that combines statistical techniques and neural networks in one method for the better time series prediction. In this paper we presented single exponential smoothing method (statistical technique) merged with feed forward back propagation neural network in one method named as smart single exponential smoothing method (SSESM). The basic idea of the...
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