The role of microcrystalline structure on optical scattering characteristics of semi-crystalline thermoplastics and the accuracy of numerical input for IR-heating modeling

Infrared (IR) heating is widely used for thermoforming of thermoplastic polymers. The key benefit of radiation heating is that a significant amount of the radiative energy penetrates into the polymers thanks to their semi-transparency. For the case of heating unfilled semi-crystalline polymers, the relation between their microcrystalline structure and optical properties is the key to develop a predictive IR-heating model as microcrystalline structure introduces an optically heterogeneous medium. In this study, a relation between the microcrystalline structure of a polyethylene (PE) and its effect on the thermo-optical properties was experimentally analyzed considering a two-step analysis. At very first step, the relation was analyzed considering samples with identical thicknesses and different morphologies, characterized here in terms of degree of crystallinity (X_c (%)). Using Fourier Transform Infrared (FT-IR) spectroscopy and integrating sphere, optical characteristics of the PE samples were analyzed in near-infrared (NIR) and middle-infrared (MIR) spectral ranges. The analyses showed that a slight variation in X_c (%) has a great effect on the optical characteristics of PE, particularly the transmission characteristics in NIR range. The wavelength-dependent effect of X_c (%) on the transmission behaviors opened a discussion about the fact that the microcrystalline structures -in particular spherulites or their substructures such as lamellae- are responsible for optical scattering. Using the optical properties obtained from the two-step experimental analyses, two different thermo-optical properties were calculated, namely extinction and absorption coefficients, and used as a numerical input for the parametric numerical studies. The numerical studies were performed using an in-house developed radiation heat transfer algorithm -RAYHEAT-. Both the experimental and numerical analyses demonstrated the importance of the optical scattering regarding the identification of thermo-optical properties, used as a numerical input for radiation heat transfer models.     

Next generation session management for 3D teleimmersive interactive environments

In the recent past we have seen a boom of distributed interactive multimedia environments which use multiple correlated media sensors, multi-view displays, and advanced haptic-audio-visual user interfaces for teleimmersive gaming, business meetings and other collaborative activities. However, when we investigate the emerging teleimmersive environments closer, we realize that their overall session management, including end-to-end session setup, establishment and run-time management are not appropriate for the new demands that these environments present. These environments are cyber-physical rooms that demand (a) large scale of multi-sensory devices across geographically-distributed locations and (b) interaction with each other in synchronous and real-time manner. To deal with the new teleimmersive demands, we present a new session management design with (a) session initiation protocol(s) that understand media correlations, (b) view-based multi-stream topology establishment among multiple parties, (c) efficient, light-weight and distributed session monitoring with querying and debugging capabilities, (d) flexible view-based session adaptation with efficient topology adjustments, and (e) light-weighted and consistent session tear-down protocols. The presented design of the next generation session management protocols, services, algorithms and data structures is based on our extensive experiences with building 3D teleimmersive interactive systems, experimenting with high impact teleimmersive applications and deploying such environments at various venues.     

The Covering Law Model Applied to Dynamical Cognitive Science: A Comment on Joel Walmsley

In a 2008 paper, Walmsley argued that the explanations employed in the dynamical approach to cognitive science, as exemplified by the Haken, Kelso and Bunz model of rhythmic finger movement, and the model of infant preservative reaching developed by Esther Thelen and her colleagues, conform to Carl Hempel and Paul Oppenheim’s deductive-nomological model of explanation (also known as the covering law model). Although we think Walmsley’s approach is methodologically sound in that it starts with an analysis of scientific practice rather than a general philosophical framework, we nevertheless feel that there are two problems with his paper. First, he focuses only on the deductivenomological model and so neglects the important fact that explanations are causal. Second, the explanations offered by the dynamical approach do not take the deductive-nomological format, because they do not deduce the explananda from exceptionless laws. Because of these two points, Walmsley makes the dynamical explanations in cognitive science appear problematic, while in fact they are not.     

Combining preprocessor slicing with C/C++ language slicing

Of the very few practical implementations of program slicing algorithms, the majority deal with C/C++ programs. Yet, preprocessor-related issues have been marginally addressed by these slicers, despite the fact that ignoring (or only partially handling) these constructs may lead to serious inaccuracies in the slicing results and hence in the program analysis task being performed. Recently, an accurate slicing method for preprocessor-related constructs has been proposed, which-when combined with existing C/C++ language slicers-can provide more complete slices and hence a more successful analysis of programs written in one of these languages. In this paper, we present our approach which combines the two slicing methods and, via practical experiments, describe its benefits in terms of the completeness of the resulting slices.     

Abnormality detection using low-level co-occurring events

We propose in this paper a method for behavior modeling and abnormal events detection which uses low-level features. In conventional object-based approaches, objects are identified, classified, and tracked to locate those with suspicious behavior. We proceed directly with event characterization and behavior modeling using low-level features. We first learn statistics about co-occurring events in a spatio-temporal volume in order to build the normal behavior model, called the co-occurrence matrix. The notion of co-occurring events is defined using mutual information between motion labels sequences. Then, in the second phase, the co-occurrence matrix is used as a potential function in a Markov random field framework to describe, as the video streams in, the probability of observing new volumes of activity. The co-occurrence matrix is thus used for detecting moving objects whose behavior differs from the ones observed during the training phase. Interestingly, the Markov random field distribution implicitly accounts for speed, direction, as well as the average size of the objects without any higher-level intervention. Furthermore, when the spatio-temporal volume is sufficiently large, the co-occurrence distribution contains the average normal path followed by moving objects. Our method has been tested on various indoor and outdoor videos representing various challenges.     

d-Dimensional parametric models for dynamic animation of deformable objects

This paper introduces an accurate, efficient, and unified engine dedicated to dynamic animation of d-dimensional deformable objects. The objects are modelled as d-dimensional manifolds defined as functional combinations of a mesh of 3D control points, weighted by parametric blending functions. This model ensures that, at each time step, the object shape conforms to its manifold definitions. The object motion is deduced from the control points dynamic animation. In fact, control points should be viewed as the degrees of freedom of the continuous object. The chosen dynamic equations (Lagrangian formalism) reflect this generic modelling scheme and yield an exact and computationally efficient linear system.     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

A finite macro-element for corroded reinforced concrete

This paper proposes a model of the mechanical behaviour of corroded reinforced concrete members subjected to bending under service load. The model is based on the formulation of a macro-element to be used in FEM analysis, having a length equal to the distance between two consecutive flexural cracks and a cross-section equal to the member cross-section. The mechanical formulation is directly written in generalized variables (bending moment and curvature) and is based on the concept of the transfer length necessary for the transmission of tensile load from re-bar to tensile concrete thanks to the bond. It is thus possible to take into account the effect of reinforcement corrosion on the bond between re-bar and concrete, by increasing the transfer length versus intensity of corrosion. The variation of the transfer length versus corrosion is expressed using a scalar damage parameter. A first experimental validation is performed on a 17-year-old beam kept in a chloride environment under its service load.