Hand gesture recognition from depth and infrared Kinect data for CAVE applications interaction

This paper presents a real-time framework that combines depth data and infrared laser speckle pattern (ILSP) images, captured from a Kinect device, for static hand gesture recognition to interact with CAVE applications. At the startup of the system, background removal and hand position detection are performed using only the depth map. After that, tracking is started using the hand positions of the previous frames in order to seek for the hand centroid of the current one. The obtained point is used as a seed for a region growing algorithm to perform hand segmentation in the depth map. The result is a mask that will be used for hand segmentation in the ILSP frame sequence. Next, we apply motion restrictions for gesture spotting in order to mark each image as a ‘Gesture’ or ‘Non-Gesture’. The ILSP counterparts of the frames labeled as “Gesture” are enhanced by using mask subtraction, contrast stretching, median filter, and histogram equalization. The result is used as the input for the feature extraction using a scale invariant feature transform algorithm (SIFT), bag-of-visual-words construction and classification through a multi-class support vector machine (SVM) classifier. Finally, we build a grammar based on the hand gesture classes to convert the classification results in control commands for the CAVE application. The performed tests and comparisons show that the implemented plugin is an efficient solution. We achieve state-of-the-art recognition accuracy as well as efficient object manipulation in a virtual scene visualized in the CAVE.     

Plumes of discolored water of volcanic origin and possible implications for algal communities. The case of the Home Reef eruption of 2006 (Tonga, Southwest Pacific Ocean)

The Home Reef volcano (Tonga, Southwest Pacific Ocean) erupted in August 2006. Initially a submarine eruption it quickly evolved into a subaerial event upon the formation of an ephemeral island.Remote sensing data from different sensors including MODIS, ASTER, EO-1 ALI and Landsat-7 ETM+ were used to analyze the event, focusing on the plumes of discolored water, ocean chlorophyll-a concentration (OCC) and sea surface temperature.An early classification system for the plumes was devised based on spectral properties and point of origin. Plumes originated at the volcano were named Type-I and those associated to the pumice rafts Type-II.Anomalies in ocean chlorophyll-a concentration, measured using MODIS data, were analyzed and a large bloom, presumably dominated by Trichodesmium sp. was identified. The bloom, which contributed to OCC values 17 times higher than the background, was spatially and temporally coincident with a Type-I plume of discolored water. The OCC increase appears to have been caused by a combined effect of both ocean fertilizations by the subsurface volcanic plume and rising sea surface temperatures.The Home Reef event offers a good candidate for a case of ocean enrichment by a submarine volcano and highlights the need for continuous monitoring of the eruptions even after the end of the explosive, more spectacular stages.     

3D porosity prediction from seismic inversion and neural networks

In this work, we address the problem of transforming seismic reflection data into an intrinsic rock property model. Specifically, we present an application of a methodology that allows interpreters to obtain effective porosity 3D maps from post-stack 3D seismic amplitude data, using measured density and sonic well log data as constraints. In this methodology, a 3D acoustic impedance model is calculated from seismic reflection amplitudes by applying an L₁-norm sparse-spike inversion algorithm in the time domain, followed by a recursive inversion performed in the frequency domain. A 3D low-frequency impedance model is estimated by kriging interpolation of impedance values calculated from well log data. This low-frequency model is added to the inversion result which otherwise provides only a relative numerical scale. To convert acoustic impedance into a single reservoir property, a feed-forward Neural Network (NN) is trained, validated and tested using gamma-ray and acoustic impedance values observed at the well log positions as input and effective porosity values as target. The trained NN is then applied for the whole reservoir volume in order to obtain a 3D effective porosity model. While the particular conclusions drawn from the results obtained in this work cannot be generalized, such results suggest that this workflow can be applied successfully as an aid in reservoir characterization, especially when there is a strong non-linear relationship between effective porosity and acoustic impedance.     

An adaptive learning approach for 3-D surface reconstruction from point clouds.

In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.     

A Framework for Integrating Non-Functional Requirements into Conceptual Models

The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely non-functional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.       

Histology, histochemistry and stereology of the adipose fin of Prochilodus lineatus

The adipose fin is small, nonpared, and usually located medially between the dorsal and caudal fin. Its taxonomic occurrence is very restrict; thus, it represents an important trace for taxon distinction. As it does not play a known vital physiological roll and it is easily removed, it is commonly used in marking and recapture studies. The present study characterizes the adipose fin of Prochilodus lineatus, as it is poorly explored by the literature. The adipose fin consists basically of a loose connective core, covered by a stratified epithelium supported by collagen fibers. At the epithelium, pigmented cells and alarm substance cells are found. Despite the name, adipocytes or lipid droplets are not observed on the structure of the fin.     

Pressureless sintering of in-situ toughened Yb α-SiAlON ceramics by adding seed crystals prepared by combustion synthesis

Single-phase Yb α-SiAlON ceramics in-situ toughened by elongated grains were prepared by pressureless sintering, with the crystalline powder prepared by combustion synthesis used as seed crystals. The effects of seed addition on the densification, phase transformation, microstructure development, and mechanical properties of the samples were investigated in detail. From the experimental results, it was found that the added seed crystals could promote the phase transformation and facilitate the growth of elongated grains. By adding seed crystals the fracture toughness of the sintered α-SiAlON ceramics was considerably improved, with no obvious degradation in the bulk density and hardness at the same time.     

Distributed parameters for low-frequency dielectric characterization of liquids with open-ended coaxial cell

The theory of distributed parameters is applied at low frequencies to the open-ended coaxial-line dielectric characterization technique. A procedure is proposed to determine the permittivity of liquids when the sample is placed between two dielectric beads to ensure tight closure. The technique leads to a complex transcendental equation that the authors solve. An ad hoc sample holder is built to implement the proposed method. Measurements of both polar and nonpolar liquids demonstrate the adequacy of the method and its advantages with respect to the lumped-circuit theory in the case of polar samples.     

A New Distribution System Reconfiguration Approach Using Optimum Power Flow and Sensitivity Analysis for Loss Reduction

This paper presents a new approach for distribution system reconfiguration (DSR) based on optimum power flow (OPF) in which the branch statuses (open/close) are represented by continuous functions. In the proposed approach, all branches are initially considered closed, and from the OPF results, a heuristic technique is used to determine the next loop to be broken by opening one switch. Then the list of switches that are candidates to be opened is updated, and the above process is repeated until all loops are broken, making the distribution system radial. This paper includes results and comparisons on test systems utilized in three classical papers published in the technical literature, as well as in a previous paper by the authors. Results obtained on a real large-scale distribution system are also presented     

Parallel Monte Carlo Driver (PMCD)—a software package for Monte Carlo simulations in parallel

Thanks to the dramatic decrease of computer costs and the no less dramatic increase in those same computer's capabilities and also thanks to the availability of specific free software and libraries that allow the set up of small parallel computation installations the scientific community is now in a position where parallel computation is within easy reach even to moderately budgeted research groups. The software package PMCD (Parallel Monte Carlo Driver) was developed to drive the Monte Carlo simulation of a wide range of user supplied models in parallel computation environments. The typical Monte Carlo simulation involves using a software implementation of a function to repeatedly generate function values. Typically these software implementations were developed for sequential runs. Our driver was developed to enable the run in parallel of the Monte Carlo simulation, with minimum changes to the original code that implements the function of interest to the researcher. In this communication we present the main goals and characteristics of our software, together with a simple study its expected performance. Monte Carlo simulations are informally classified as “embarrassingly parallel”, meaning that the gains in parallelizing a Monte Carlo run should be close to ideal, i.e. with speed ups close to linear. In this paper our simple study shows that without compromising the easiness of use and implementation, one can get performances very close to the ideal.