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...     

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.     

Conjugated effects of the compatibilization and the dynamic vulcanization on the phase inversion behavior in poly(butylene terephthalate)/epoxide-containing rubber reactive polymer blends

The conjugated effects of both reactive compatibilization and dynamic vulcanization on the phase inversion behavior of poly(butylene terephthalate) (PBT)/epoxide-containing rubber blends have been studied in detail. Pure ethylene-methyl acrylate random copolymer (E-MA) and ethylene-methyl acrylate-glycidyl methacrylate random terpolymer (E-MA-GMA) were used as non reactive or reactive rubber phase, respectively. Location of the phase inversion region was studied using several techniques, including transmission electron microscopy (TEM) and dynamical mechanical thermal analysis (DMTA). To evaluate the relative influence of the blend compatibilization and the dynamic vulcanization on the phase inversion behavior, the relative kinetics of the two reactions were modified using different PBT and E-MA-GMA grades. The obtained results show unambiguously that the position and the width of the phase inversion region is essentially governed by the kinetic of the dynamic vulcanization process. The effect of the blend compatibilization remains quite limited even in the case of fast interfacial reaction. The crosslinking of the rubber phase induces an important shift of the phase inversion composition to higher rubber content. For blends containing low molecular weight PBT, up to 60 wt% of rubber can be homogeneously dispersed in the PBT matrix at long mixing time. In this case, development of high performance PBT based thermoplastic vulcanisates can be envisioned.     

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 .     

Mechanical characterization and modeling of the deformation and failure of the highly crosslinked RTM6 epoxy resin

The nonlinear deformation and fracture of RTM6 epoxy resin is characterized as a function of strain rate and temperature under various loading conditions involving uniaxial tension, notched tension, uniaxial compression, torsion, and shear. The parameters of the hardening law depend on the strain-rate and temperature. The pressure-dependency and hardening law, as well as four different phenomenological failure criteria, are identified using a subset of the experimental results. Detailed fractography analysis provides insight into the competition between shear yielding and maximum principal stress driven brittle failure. The constitutive model and a stress-triaxiality dependent effective plastic strain based failure criterion are readily introduced in the standard version of Abaqus, without the need for coding user subroutines, and can thus be directly used as an input in multi-scale modeling of fibre-reinforced composite material. The model is successfully validated against data not used for the identification and through the full simulation of the crack propagation process in the V-notched beam shear test.     

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.     

Close Shadowing Natural Versus Synthetic Speech

Close shadowing experiments involving natural and synthetic stimuli are described. Preliminary results show that speakers are able to follow natural stimuli with an average delay of 70 ms whereas this delay typically exceeds 100 ms for stimuli produced by text-to-speech systems. A complementary experiment shows that this contrast is mainly due to the inappropriate or impoverished prosody generated by actual text-to-speech systems.     

Non-Reproducibility of Oral Rotenone as a Model for Parkinson’s Disease in Mice

Oral rotenone has been proposed as a model for Parkinson’s disease (PD) in mice. To establish the model in our lab and study complex behavior we followed a published treatment regimen. C57BL/6 mice received 30 mg/kg body weight of rotenone once daily via oral administration for 4 and 8 weeks. Motor functions were assessed by RotaRod running. Immunofluorescence studies were used to analyze the morphology of dopaminergic neurons, the expression of alpha-Synuclein (α-Syn), and inflammatory gliosis or infiltration in the substantia nigra. Rotenone-treated mice did not gain body weight during treatment compared with about 4 g in vehicle-treated mice, which was however the only robust manifestation of drug treatment and suggested local gut damage. Rotenone-treated mice had no deficits in motor behavior, no loss or sign of degeneration of dopaminergic neurons, no α-Syn accumulation, and only mild microgliosis, the latter likely an indirect remote effect of rotenone-evoked gut dysbiosis. Searching for explanations for the model failure, we analyzed rotenone plasma concentrations via LC-MS/MS 2 h after administration of the last dose to assess bioavailability. Rotenone was not detectable in plasma at a lower limit of quantification of 2 ng/mL (5 nM), showing that oral rotenone had insufficient bioavailability to achieve sustained systemic drug levels in mice. Hence, oral rotenone caused local gastrointestinal toxicity evident as lack of weight gain but failed to evoke behavioral or biological correlates of PD within 8 weeks.