Generalized Eigenvalue Decomposition Applied to Estimation of Spatial rPPG Distribution of Skin

Journal of Mathematical Imaging and Vision - Remote photoplethysmography (rPPG) has been at the forefront recently, thanks to its capacity in estimating non-contact physiological parameters such as...     

Rheological properties of hyperbranched polymer/poly(ethylene terephthalate) reactive blends

The rheological properties in solution, in shear and in uniaxial elongation of poly(ethylene terephthalate) (PET) reacted together with hyperbranched polymers (HBPs) were investigated. Two different PET grades, of low and high molecular weights, were compounded with sub‐ to over‐stoichiometric concentrations of HBPs of second and fourth pseudo‐generation, and subsequently subjected to a solid‐state polycondensation (SSP). The formation of microgels, which occurs at high HBP concentration, gave rise to a large increase in melt elasticity and a related decrease in melt strength. At low HBP concentrations, the complex viscosity of the unreacted HBP/PET was considerably reduced, thus demonstrating a lubrication effect of the HBP molecules. During SSP, the intrinsic and shear viscosities exhibited a gradual increase, which was similar for both PET and HBP/PET blends, and was correlated to an increase in molecular weight, through linear‐chain extension and branching reactions. The elongational viscosity of the reactive blends was also increased as a function of reaction time, and this increase was much larger in the case of the HBP/PET blends. A 400% increase in melt strength of the PET was obtained by combining SSP and trace amounts of an HBP of second generation, without any decrease in drawability.     

Explicit reference modeling methodology in parametric CAD system

Today parametric associative CAD systems must help companies to create more efficient virtual development processes. While dealing with complex parts (e.g. the number of surfaces of the solid) no CAD modeling methodology is existing. Based on the analysis of industrial designers' practices as well as student practices on CAD, we identified key factors that lead to better performance. Our objective in this article is to propose a practical method for complex parts modeling in parametric CAD system. An illustration of the performances and the results obtained by this method are presented comparing the traditional method with the proposed one while using an academic case and then an industrial case.     

A new test for cleaning efficiency assessment of cleaners for hard surfaces

A new test was developed to assess the efficiency of no-wiping hard-surface cleaning. The test allows cleaner comparisons according to their ability to remove greasy soils. The chosen approach minimizes the mechanical forces applied while cleaning so that the interactions between a detergent solution and the soil to be removed can be characterized. For this, immersion cleaning was chosen, with coated stainless steel as substrate and pigmented oils as the model soil. Several parameters were studied in defined ranges using the Experimental Design method and systematic comparisons. The test shows high reliability on degreasing assessments and is there-fore especially suited to optimization of nonionic surfactant mixes. The originality of the test lies in the possibility of keeping a visual trace of the cleaned substrate appearance by imprinting it on a piece of paper. The validation of the test leads to corroboration of several practical observations. Temperature and agitation play a major role in cleaning efficiency. Detergent solution concentration is a more relevant parameter than pH. Sodium carbonate is shown to have a higher buffering effect than pentahydrated sodium metasilicate. The test is easy to set up, highly sensitive, and can be adapted to solve the problems encountered by formulators of detergent cleaners, such as screening the best ethoxylated fatty alcohol mix for better degreasing properties.     

Ultraviolet photodetectors and readout based on a-IGZO semiconductor technology

In this work, real-time ultraviolet photodetectors are realized through metal–semiconductor–metal (MSM) structures. Amorphous indium gallium zinc oxide (a-IGZO) is used as semiconductor material and gold as metal electrodes. The readout of an individual sensor is implemented by a transimpedance amplifier (TIA) consisting of an all-enhancement a-IGZO thin-film transistor (TFT) operational amplifier and a switched capacitor (SC) as feedback resistance. The photosensor and the transimpedance amplifier are both manufactured on glass substrates. The measured photosensor possesses a high responsivity R , a low response time t _{R E S} , and a good noise equivalent power value NEP .     

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.     

Modeling the behavior of persons with mild cognitive impairment or Alzheimer’s for intelligent environment simulation

User Modeling and User-Adapted Interaction - Intelligent environments may improve the independence and quality of life of persons with mild cognitive impairment (MCI) or Alzheimer’s disease...     

Automatic speech recognition system for Tunisian dialect

Although Modern Standard Arabic is taught in schools and used in written communication and TV/radio broadcasts, all informal communication is typically carried out in dialectal Arabic. In this work, we focus on the design of speech tools and resources required for the development of an Automatic Speech Recognition system for the Tunisian dialect. The development of such a system faces the challenges of the lack of annotated resources and tools, apart from the lack of standardization at all linguistic levels (phonological, morphological, syntactic and lexical) together with the mispronunciation dictionary needed for ASR development. In this paper, we present a historical overview of the Tunisian dialect and its linguistic characteristics. We also describe and evaluate our rule-based phonetic tool. Next, we go deeper into the details of Tunisian dialect corpus creation. This corpus is finally approved and used to build the first ASR system for Tunisian dialect with a Word Error Rate of 22.6%.