Improving data fusion in personal positioning systems for outdoor environments

A fault detection and correction methodology for personal positioning systems for outdoor environments is presented. We demonstrate its successful use in a system consisting of a global positioning system receiver and an inertial measurement unit. Localization is based on the dead reckoning algorithm. In order to obtain more reliable information from data fusion, which is carried out with Kalman filtering, the proposed methodology involves: (1) evaluation of the information provided by the sensors and (2) adaptability of the filtering. By carefully analyzing these factors we accomplish fault detection in different sources of information and in filtering. This allows us to apply corrections whenever the system requires it. Hence, our methodology consists of two stages. In the first stage, the evaluation is conducted. We apply the principles of causal diagnosis using possibility theory by defining states for normal behavior and for fault states. When a fault occurs, corrective measures are applied according to empirical knowledge. In the second stage, the consistency test of the filtering is performed. If this is inconsistent, principles of adaptive Kalman filtering are applied, which means the process and measurement noise matrices are tuned. Our results indicate a reasonable improvement of the trajectory obtained. At the same time, we can achieve consistent filtering, to obtain a more robust system and reliable information.     

Variable Structure Control with chattering elimination and guaranteed stability for a generalized T-S model

In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven infront of disturbances and noise effects.     

Coreference resolution: an empirical study based on SemEval-2010 shared Task 1

This paper presents an empirical evaluation of coreference resolution that covers several interrelated dimensions. The main goal is to complete the comparative analysis from the SemEval-2010 task on Coreference Resolution in Multiple Languages. To do so, the study restricts the number of languages and systems involved, but extends and deepens the analysis of the system outputs, including a more qualitative discussion. The paper compares three automatic coreference resolution systems for three languages (English, Catalan and Spanish) in four evaluation settings, and using four evaluation measures. Given that our main goal is not to provide a comparison between resolution algorithms, these are merely used as tools to shed light on the different conditions under which coreference resolution is evaluated. Although the dimensions are strongly interdependent, making it very difficult to extract general principles, the study reveals a series of interesting issues in relation to coreference resolution: the portability of systems across languages, the influence of the type and quality of input annotations, and the behavior of the scoring measures.     

Privacy-enhanced architecture for smart metering

The recent deployment of smart grids promises to bring numerous advantages in terms of energy consumption reduction in both homes and businesses. A more transparent and instantaneous measurement of electricity consumption through smart meters utilization leads to an enhancement in the ability of monitoring, controlling and predicting energy usage. Nevertheless, it also has associated drawbacks related to the privacy of customers, since such management might reveal their personal habits, which electrical appliances they are using at each moment, whether they are at home or not, etc. In this work, we present a privacy-enhanced architecture for smart metering aimed at tackling this threat by means of encrypting individual measurements while allowing the electricity supplier to access the aggregation of the corresponding decrypted values.     

Evaluation of messaging middleware for high-performance cloud computing

Cloud computing is posing several challenges, such as security, fault tolerance, access interface singularity, and network constraints, both in terms of latency and bandwidth. In this scenario, the performance of communications depends both on the network fabric and its efficient support in virtualized environments, which ultimately determines the overall system performance. To solve the current network constraints in cloud services, their providers are deploying high-speed networks, such as 10 Gigabit Ethernet. This paper presents an evaluation of high-performance computing message-passing middleware on a cloud computing infrastructure, Amazon EC2 cluster compute instances, equipped with 10 Gigabit Ethernet. The analysis of the experimental results, confronted with a similar testbed, has shown the significant impact that virtualized environments still have on communication performance, which demands more efficient communication middleware support to get over the current cloud network limitations.     

The Sanaga discharge at the Edea Catchment outlet (Cameroon): An example of hydrologic responses of a tropical rain‐fed river system to changes in precipitation and groundwater inputs and to flow regulation

The Sanaga River is one of Sub‐Saharan Africa's largest and greatly regulated rivers. Available flow data for this hydrosystem largely cover the pre‐ and post‐regulation periods. From comparisons between unregulated (hypothetical) and observed scenarios, it has been possible to separate and to quantify hydro‐climatic (groundwater + rainfall) change effects from anthropogenic impacts (especially dam‐related alterations). To appreciate shifts in the river regime, discontinuity detection tests and the IHA model were applied to discharge data series reflecting average and extreme flow conditions, respectively. Results obtained principally from the Hubert segmentation method reveal that a major discontinuity occurred in 1970–1971 separating a surplus phase between 1945–1946 and 1969–1970, and a deficient and much contrasted one, from 1971/1972. This implies that the Sanaga catchment is dominantly affected by hydro‐climatic changes. However, wide land cover/land use changes experienced here since 1988 have resulted in an increase in surface runoff. Additional quickflows linked to these changes may have partly compensated for the substantial decline in the dry season rainfall and groundwater inputs observed from this date. Although at the monthly scale, dam‐related impacts on average flows increase with stage of regulation, the seasonal variability of the river regime remains generally unaffected. A comparison of the IHA statistics, calculated from unregulated and observed streamflow data, show that hydrologic shifts occurring in maximum and minimum discharges are mostly significant from 1971/1972 and are mainly due to the action of dams. Minimum flows appear, however, widely impacted, thus reflecting the prime objective assigned to the existing reservoirs, constructed to supplement flows for hydroelectricity production during the dry season. Copyright © 2010 John Wiley & Sons, Ltd.     

Impoverishment of YOY‐fish assemblages by intense commercial navigation in a large Lowland river

Navigation‐induced physical forces have been suggested to modify the structure of riverine fish assemblages by impeding especially the recruitment of littoral bound species. To investigate the effect of vessel frequency on fish, we compared the composition and seasonal succession of young‐of‐the‐year (YOY) fish assemblages in three similarly degraded river reaches differing in average vessel passages (2, 6 and 41 per day). Fish were caught by electrofishing biweekly between May and September. Multivariate tests were used to analyse differences between YOY‐fish assemblages and hurdle regression models applied to determine abiotic factors predicting fish occurrence and abundance. Roach (Rutilus rutilus) and perch (Perca fluviatilis) densities were compared. Roach larvae remain in the littoral zone while perch larvae shift to the pelagic zone immediately after hatch. YOY‐fish assemblage structure substantially changed along the traffic intensity gradient. In the high traffic intensity reach, species number and total fish density were markedly reduced compared to the other reaches. Roach densities were lowest in the high traffic intensity reach whereas perch densities did not decline along the gradient. Hurdle regressions confirmed a stronger effect of commercial navigation traffic intensity on roach than on perch. The total zooplankton biomass was highest in the high traffic intensity reach. Our results provide empirical evidence that intensive commercial navigation impoverishes fish assemblages in width‐restricted waterways. They underlined that in particular those species that have their first nursery habitats in shoreline areas were more affected by intensive commercial navigation than species whose larvae live predominantly pelagic. The results indicate that the negative effect of intensive navigation on riverine fish results primarily from the navigation‐induced hydraulic disturbances along the banks. Therefore, mitigation of navigation‐induced hydraulic forces is required to prevent degradation of fish communities in waterways. Copyright © 2010 John Wiley & Sons, Ltd.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

Exaggerating Character Motions Using Sub‐Joint Hierarchy

Motion capture cannot generate cartoon‐style animation directly. We emulate the rubber‐like exaggerations common in traditional character animation as a means of converting motion capture data into cartoon‐like movement. We achieve this using trajectory‐based motion exaggeration while allowing the violation of link‐length constraints. We extend this technique to obtain smooth, rubber‐like motion by dividing the original links into shorter sub‐links and computing the positions of joints using Bézier curve interpolation and a mass‐spring simulation. This method is fast enough to be used in real time.     

Raycasting of Light Field Galleries from Volumetric Data

The paper describes a technique to generate high‐quality light field representations from volumetric data. We show how light field galleries can be created to give unexperienced audiences access to interactive high‐quality volume renditions. The proposed light field representation is lightweight with respect to storage and bandwidth capacity and is thus ideal as exchange format for visualization results, especially for web galleries. The approach expands an existing sphere‐hemisphere parameterization for the light field with per‐pixel depth. High‐quality paraboloid maps from volumetric data are generated using GPU‐based ray‐casting or slicing approaches. Different layers, such as isosurfaces, but not restricted to, can be generated independently and composited in real time. This allows the user to interactively explore the model and to change visibility parameters at run‐time.