FM radio for indoor localization with spontaneous recalibration

The position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi–Fi signal strength fingerprinting techniques are widely used in office buildings with an existing Wi–Fi infrastructure. Our previous work has proposed a solution based on exploitation of a FM signal to deal with environments not covered with Wi–Fi signal or environments with only a single Wi–Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibrations of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi–Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. We also address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system. Finally, the results related to device orientation and localization accuracy are discussed.     

Designing adaptive learning itineraries using features modelling and swarm intelligence

In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user??s success on the course. Therefore, the path can change in order to adapt itself to learners?? preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system.     

Real-time collaboration of virtual laboratories through the Internet

Web-based learning environments are becoming increasingly popular in higher education. One of the most important web-learning resources is the virtual laboratory (VL), which gives students an easy way for training and learning through the Internet. Moreover, on-line collaborative communication represents a practical method to transmit the knowledge and experience from the teacher to students overcoming physical distance and isolation. Considering these facts, the authors of this document have developed a new dynamic collaborative e-learning system which combines the main advantages of virtual laboratories and collaborative learning practices. In this system, the virtual laboratories are based on Java applets which have embedded simulations developed in Easy Java Simulations (EJS), an open-source tool for teachers who do not need complex programming skills. The collaborative e-learning is based on a real-time synchronized communication among these Java applets. Therefore, this original approach provides a new tool which integrates virtual laboratories inside a synchronous collaborative e-learning framework. This paper describes the main features of this system and its successful application in a distance education environment among different universities from Spain.     

An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification

Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals.     

FAO’s AVHRR-based Agricultural Stress Index System (ASIS) for global drought monitoring

Agricultural production is highly dependent on climate variability in many parts of the world. In particular, drought may severely reduce crop yields, potentially affecting food availability at local, regional, and global scales. The Food and Agriculture Organization of the United Nations (FAO) operates the Global Early Warning System (GIEWS), which monitors global food supply and demand. One of the key challenges is to obtain synoptic information on a recurrent and timely basis about drought-affected agricultural zones. This is needed to quickly identify areas requiring immediate attention. The Agricultural Stress Index System (ASIS), based on imagery from the Advanced Very High Resolution Radiometer (AVHRR) sensors on board the National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational Satellite (METOP) satellites, was specifically developed to meet this need. The system is based on a methodology developed by Rojas, Vrieling, and Rembold over the African continent. This approach has been modified and adapted to the global scale to produce an agricultural stress index (ASI) representing, per administrative unit, the percentage of cropland (or pasture) areas affected by drought over the growing season. The vegetation health index (VHI), based on normalized difference vegetation index (NDVI) and temperature anomalies, is used as a drought indicator. A fused time series of AVHRR data from METOP and NOAA was used to produce a consistent time series of VHI at 1 km resolution. Global phenology maps, indicating the number of growing seasons and their start and end dates, were derived from a multi-annual image set of SPOT-Vegetation (1999–2011). The VHI time series and phenology maps were then combined to produce the ASI for the years 1984 to the present. This allowed evaluation of the suitability of the ASIS to identify drought using historical reports and ancillary data. As a result of this analysis, ASIS was positively evaluated to support the FAO early warning system.     

Stability of Local On‐Ramp Metering Control Laws

A framework for stability analysis of local on‐ramp metering control strategies based on the cell transmission model is presented. Within this framework, it is possible to formulate Lyapunov and input‐state stability results for on‐ramp metering control strategies in an open section of highway with on‐ramps. Using this analysis, recommendations for the design of on‐ramp metering control laws set points are derived. Two examples on the use of such analysis are presented. One deals with the stability analysis of a local on‐ramp metering control law and the other with the design of a disturbance observer that, used in combination with the local on‐ramp metering control law, provides a more robust response to traffic regulation. Simulation results are included that confirm the possibility of using this framework to test the impact of local on‐ramp metering control strategies.     

Incorporating technology in service-oriented i* business models: a case study

In recent years, considerable attention has been paid to enterprise information systems. This interest is motivated by the need for achieving better integration of new technologies (hardware and software) with the business processes of an organization. Business processes have become more and more dependent on technologies because technology has a direct impact on business processes, changing the way they are performed and thus also affecting the way analysts design the software system. However, at the present time, there are still some gaps between the definition of business processes and the technologies used in the organization. In practice, organizations have carried out their business processes using different technologies; however, it is sometimes not possible to determine how technologies are useful in achieving current business goals. This is because business models do not explicitly consider the technologies in the organizational requirements. The goal of this paper is to present a systematic process for integrating business processes and technologies at the conceptual level. To validate our approach, we present a case study that describes the processes of the inventory management department of a public research center.     

Visualization of level-of-detail meshes on the GPU

Extensive research has been carried out in multiresolution models for many decades. The tendency in recent years has been to harness the potential of GPUs to perform the level-of-detail extraction on graphics hardware. The aim of this work is to present a new level-of-detail scheme based on triangles which is both simple and efficient. In this approach, the extraction process updates vertices instead of indices, thus providing a perfect framework for adapting the algorithms to work completely on GPU shaders. One of the key aspects of our proposal is the need for just a single rendering pass in order to obtain the desired geometry. Moreover, coherence among the different approximations is maximized by means of a symmetric extraction algorithm, which performs the same process when refining and coarsening the mesh. Lastly, we also introduce different uses of the scheme to offer continuous and view-dependent resolution.