Abstract Wireless sensor networks (WSNs) is emerging as a key tool for data acquisition in many applications, and localization algorithms of the sensors have a vital importance due to the intrinsic characteristics of this type of networks. The objective of this paper is to propose an algorithm of range-free localization for WSNs using an unmanned aerial vehicle (UAV) equipped with a... Wireless sensor networks (WSNs) is emerging as a key tool for data acquisition in many applications, and localization algorithms of the sensors have a vital importance due to the intrinsic characteristics of this type of networks. The objective of this paper is to propose an algorithm of range-free localization for WSNs using an unmanned aerial vehicle (UAV) equipped with a rotary directional antenna. This algorithm, denominated as the azimuthally defined area localization algorithm (ADAL), is executed in each sensor node and is based only on the analysis of the information received from the UAV, therefore is energy efficient and contributes to extend the lifetime of the sensor network. Additionally the proposed algorithm has the advantage of being simple and economical. The simulation results show that the proposed algorithm is a practical, effective and accurate method for sensor nodes location in a WSN. Read More Read Less

Abstract This research proposes an alternative representation of documents and a new neural network using the proposed representation as its input and weight vectors, for more reliable text clustering. Almost traditional neural networks dictate representation of raw data into numerical vectors for their application to real tasks including text clustering. The traditional representation may lead to difficulties depend on the type... This research proposes an alternative representation of documents and a new neural network using the proposed representation as its input and weight vectors, for more reliable text clustering. Almost traditional neural networks dictate representation of raw data into numerical vectors for their application to real tasks including text clustering. The traditional representation may lead to difficulties depend on the type of raw data. For example, in text clustering, encoding textual data given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. This research addresses the two problems at same time by proposing the alternative representation and a new neural network, called NTSO. The experiments in this research show that the proposed neural network is more practical than k-means algorithm, Kohonen Networks, and single pass algorithm, with respect to clustering performance and clustering speed. Read More Read Less

Abstract In this paper, an IPOMS (Internet Public Opinion Monitoring System) is proposed. This system can collect web pages with some certain key words from Internet news, topics on forum and BBS, and then cluster these web pages according to different ‘event’ groups. Furthermore, this system provides the function of automatically tracking the progress of one event. With this system, supervisors... In this paper, an IPOMS (Internet Public Opinion Monitoring System) is proposed. This system can collect web pages with some certain key words from Internet news, topics on forum and BBS, and then cluster these web pages according to different ‘event’ groups. Furthermore, this system provides the function of automatically tracking the progress of one event. With this system, supervisors can know what is exactly happening and what has happened from different views, which can improve their work efficiency a lot. This system is composed of web crawler, html parser and topic detection and tracking tool. Because of the existence of numerous data in web pages, in order to improve efficiency of Internet public opinion analysis, the technologies of web page cleansing and k-d tree algorithm in topic tracking are adopted. Read More Read Less

Abstract The purpose of this paper is to increase the efficiency of functionality and reliability of Complex Recycling Technical Systems (CRTS) community, by improving the control quality of their life cycle. Automated Control System (ACS) on the basis of Neural Super-Network learning for forecasting damages and ensuring its information representation for learning was proposed. In the paper it was suggested an... The purpose of this paper is to increase the efficiency of functionality and reliability of Complex Recycling Technical Systems (CRTS) community, by improving the control quality of their life cycle. Automated Control System (ACS) on the basis of Neural Super-Network learning for forecasting damages and ensuring its information representation for learning was proposed. In the paper it was suggested an architecture and a method of learning of a neural super-network for forecasting the progress of CRTS community life cycle. The structure of the ACS life cycle of vehicles environment in the form of service “center” that service groups of “client”, exploiting the same type of objects, was developed. Read More Read Less

Abstract Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative of evolutionary algorithms was invented: quantum genetic algorithms (QGA). In this paper, we outline the approach of QGA by giving a comparison with conventional genetic algorithm (CGA). We have executed... Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative of evolutionary algorithms was invented: quantum genetic algorithms (QGA). In this paper, we outline the approach of QGA by giving a comparison with conventional genetic algorithm (CGA). We have executed our algorithm for 100 generations. Each CA in the population was iterated over M = 150 iterations. We have compared our best fitness obtained with the best that exists in the literature. Our parallel algorithm was run on a local network of 10 sites: a master site and nine slaves. The results have shown that QGA can be a very promising tool for exploring search spaces with a high performance and efficiency. Read More Read Less