A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Neural Computing and Applications - Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people...     

Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

Neural Computing and Applications - This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with...     

In Vitro Characterization of Human CD24hiCD38hi Regulatory B Cells Shows CD9 Is Not a Stable Breg Cell Marker

Regulatory B (Breg) cells are endowed with immune suppressive functions. Various human and murine Breg subtypes have been reported. While interleukin (IL)-10 intracellular staining remains the most reliable way to identify Breg cells, this technique hinders further essential functional studies. Recent findings suggest that CD9 is an effective surface marker of murine IL-10 competent Breg cells. However, the stability of CD9 and its relevance as a unique marker for human Breg cells, which have been widely characterized as CD24^hiCD38^hi, have not been investigated. Here, we demonstrate that CD9 expression is sensitive to in vitro B cell stimulations. CD9 expression could either be re-expressed or downregulated in purified CD9-negative B cells and CD9-positive B cells, respectively. We found no significant differences in the Breg differentiation capacity of the CD9-negative and CD9-positive B cells. Furthermore, CD9-positive B cells co-express CD40 and CD86, suggesting their nature as B cell activation or co-stimulatory molecules, rather than regulatory ones. Therefore, we report the relatively unstable CD9 as a distinct surface molecule, indicating the need for further research for a more reliable marker to purify human Breg cells.     

Monitoring web services’ quality of service: a literature review

Monitoring Quality of Service (QoS) compliance is an important procedure in web service environment. It determines whether users’ expectations are met, and becomes the vital factor for them to decide whether to continue paying for the service or not. The monitoring is performed by checking the actual services performance against the QoS stated in Service Level Agreement (SLA). In relation to that, the need for monitoring vague QoS specifications in SLA has become more apparent nowadays. This paper reviews the published literature on web services QoS monitoring. A total of 60 selected articles were systematically analyzed. There were 23 of the articles selected through restrictive search criteria while the other 37 were selected based on unrestrictive search criteria. The review shows that little evidence exists on monitoring vague QoS specifications of web services. Providing ability for monitoring QoS that is specified vaguely in SLA could give new insights and implications to web services field. This paper concludes with some recommended future works to construct the theory and perform the empirical research.     

A quadratic spline approximation using detail multi-layer for soft shadow generation in augmented reality

Implementation of shadows is crucial to enhancement of images in AR environments. Without shadows, virtual objects would look floating over the scene resulting in unrealistic rendering of AR environments. Casting hard shadows would provide only spatial information while soft shadows help improve realism of AR environments. Several algorithms have been proposed to render realistic shadows which often incurred high computational costs. Little attention has been directed towards the balanced trade-off between shadow quality and computational costs. In this study, two approaches are proposed: Quadratic Spline Interpolation (QSI) to soften the outline of the shadow and Detail Multi-Layer (DML) technique to optimize the volume of computations for the generation of soft shadows based on real light sources. QSI estimates boarder hard shadow samples while DML involves three main phases: real light sources estimation, soft shadow production and reduction of the complexity of 3-Dimensional objects’ shadows. To be more precise, a reflective hemisphere is used to capture real light and to create an environment map. The Median Cut algorithm is implemented to locate the direction of real light sources on the environment map. Subsequently, the original hard shadows are retrieved and a sample of multilayer hard shadows is produced where each layer has its unique size and colour. These layers overlap to produce soft shadows based on the real light sources’ directions. Finally, the Level of Details (LOD) algorithm is implemented to increase the efficiency of soft shadows by decreasing the complexity of vertex transformations. The proposed technique is tested using three samples of multilayer hard shadows with varying numbers of light sources generated from the Median Cut algorithm. The experimental results show that the proposed technique successfully produces realistic soft shadows at low computational costs.     

Variational learning for Dirichlet process mixtures of Dirichlet distributions and applications

In this paper, we propose a Bayesian nonparametric approach for modeling and selection based on a mixture of Dirichlet processes with Dirichlet distributions, which can also be seen as an infinite Dirichlet mixture model. The proposed model uses a stick-breaking representation and is learned by a variational inference method. Due to the nature of Bayesian nonparametric approach, the problems of overfitting and underfitting are prevented. Moreover, the obstacle of estimating the correct number of clusters is sidestepped by assuming an infinite number of clusters. Compared to other approximation techniques, such as Markov chain Monte Carlo (MCMC), which require high computational cost and whose convergence is difficult to diagnose, the whole inference process in the proposed variational learning framework is analytically tractable with closed-form solutions. Additionally, the proposed infinite Dirichlet mixture model with variational learning requires only a modest amount of computational power which makes it suitable to large applications. The effectiveness of our model is experimentally investigated through both synthetic data sets and challenging real-life multimedia applications namely image spam filtering and human action videos categorization.     

Rheological,thermal, and morphological properties of blends based on poly(propylene), ethylene propylene rubber,and ethylene-1-octene copolymer that could result from end of life vehicles: Effect of maleic anhydride grafted poly(propylene)

The objective of this work is to study the properties of blends that could result from the recycling of end-of-life vehicles (ELV). While ethylene propylene rubber (EPR) and ethylene propylene diene monomer (EPDM) have been used extensively as elastomeric additives in poly(propylene) (PP), they can be substituted by ethylene-1-octene copolymer (EOC). As a consequence, the matter resulting from the sorting of ELV might be more complex and made of PP, EPR, and EOC. The effect of incorporating EOC [that is a polyethylene elastomer (PEE)] and maleic anhydride grafted polypropylene (PP-g-MAH) on the rheological, thermal, and morphological properties of PP/EPR blends has been investigated. Blends of various compositions (with and without compatibilizer) were prepared using a corotating twin-screw extruder. The results were compared to the ones presented by a commercial (PP/EPR) blend. The EPR phase is dispersed in the form of spherical particles in (PP/EPR). The EOC phase is dispersed in the form of aggregated particles. Dynamic viscoelastic and differential scanning calorimetry properties of (PP/EPR)/EOC blends shows the incompatibility of the components even in presence of PP-g-MAH copolymer. POLYM. ENG. SCI., 47:1009–1015, 2007. © 2007 Society of Plastics Engineers     

Robust face recognition against expressions and partial occlusions

Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis(ICA) features. These local features are acquired using locally lateral subspace(LLS) strategy.Then, through linear discriminant analysis(LDA) we investigate the intraclass and interclass representation of each local ICA feature and express each feature s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding(LLE) along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE,FERET and CK+ is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects.