Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Emergent Intelligence Based QoS Routing in MANET

Mobile Ad hoc Networks (MANETs) are self-organized, infrastructureless, decentralized wireless networks consist of a group of heterogeneous mobile devices. Due to the inherent characteristics of MANE -Ts, such as frequent change of topology, nodes mobility, resource scarcity, lack of central control, etc., makes QoS routing is the hardest task. QoS routing is the task of routing data packets from source to destination depending upon the QoS resource constraints, such as bandwidth, delay, packet loss rate, cost, etc. In this paper, we proposed a novel scheme of providing QoS routing in MANETs by using Emergent Intelligence (El). The El is a group intelligence, which is derived from the periodical interaction among a group of agents and nodes. We logically divide MANET into clusters by centrally located static agent, and in each cluster a mobile agent is deployed. The mobile agent interacts with the nodes, neighboring mobile agents and static agent for collection of QoS resource information, negotiations, finding secure and reliable nodes and finding an optimal QoS path from source to destination. Simulation and analytical results show that the effectiveness of the scheme. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.ore/licenscs/by-nc-nd/4.0/). Peer-review under responsibility of the Conference Program Chairs

Combined useage statistics as a basis for Research intelligence

Knowledge Exchange hosted a workshop in March 2010 with the aim to bring together technical experts working in partner projects collecting usage statistics including PIRUS2, OAstatistik and SURFsure projects. Experts from other related projects (RePec and NeeO) were also involved. The workshop produced a briefing paper on combined usage statistics as a basis for research intelligence. In this paper, the experts make a cause for collecting and exchanging usage statistics as this can provide valuable insight in how research is being used, not only by the research community, but also by business and society in general. This would provide a basis for 'Research Intelligence', an intelligent use of reliable numerical data which can be used as a basis for decision making in higher education and research. Usage statistics are a clear example of data which can offer a valuable contribution to the information required to make informed decisions. To allow for the meaningful collection, exchange and analysis of usage statistics, a number of challenges and opportunities need to be addressed and further steps need to be taken.

Software como un Servicio: Solución Business Intelligence

El propóosito del proyecto aquíı descrito radica en, por una parte, sentar una base de un sistema de Business Inteligence adaptable a diversos casos de negocio, y por otra, diseñar e implementar una solución completa para una empresa especíıfica fácilmente adaptable a otro caso, incluyendo desde los procesos de Extracción, Transformación y Carga, pasando por el data warehouse hasta el Business Analysis y la Minería de Datos.

Efficient electronic implementations of adaptive systems for ambient intelligence environments

183 p.