Chitosan hydrogel‐coated cotton fabric: Antibacterial,pH‐responsiveness,and physical properties

In this work, chitosan hydrogel has been synthesized and used to impart pH‐sensitivity and antimicrobial finish to cotton fabric. In order to enhance the incorporation rate of hydrogel, anionic, and cationic activation of the textile surface was applied and then compared. The antibacterial activity of the fabric was then studied. The results revealed an enhancement of the antibacterial activities of the modified fabrics against Escherichia coli, Listeria monocytogene, and Staphylococcus aureus bacteria's. The capacity of material to respond to pH change was studied and confirmed using contact angle method. The anionic fabric treated with hydrogel showed a better pH‐responsiveness. Scanning electron microscopic testing results has also confirmed that the deposition of hydrogel was clearly better with the anionic activation. The characteristics of breathability of the fabrics were analyzed. The results show that the moisture management behavior of the finished materials is significantly better than the control one. Although the permeability to air has reduced by 10%, the permeability to water vapor remained practically unchanged. Furthermore, the effects of the antibacterial finishing on the physical properties of the cotton fabrics were also investigated. It was established that the functionalized samples have changed structure parameters, thickness, air permeability, tensile strength, and resistance to wrinkles. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46645.     

Robust Articulated‐ICP for Real‐Time Hand Tracking

We present a robust method for capturing articulated hand motions in realtime using a single depth camera. Our system is based on a realtime registration process that accurately reconstructs hand poses by fitting a 3D articulated hand model to depth images. We register the hand model using depth, silhouette, and temporal information. To effectively map low‐quality depth maps to realistic hand poses, we regularize the registration with kinematic and temporal priors, as well as a data‐driven prior built from a database of realistic hand poses. We present a principled way of integrating such priors into our registration optimization to enable robust tracking without severely restricting the freedom of motion. A core technical contribution is a new method for computing tracking correspondences that directly models occlusions typical of single‐camera setups. To ensure reproducibility of our results and facilitate future research, we fully disclose the source code of our implementation.     

Re-examination of the β→γ transformation of Ca2SiO4

Dicalcium silicate finds applications in different fields (cement, bio-ceramics, refractories). In the case of Portland cement, its interest is its lower sintering temperature compared to tricalcium silicate and therefore an interesting compound for low CO₂ cements. Dicalcium silicate goes through different polymorphic forms, namely α, α'_H, α'_L, β and γ, as the temperature decreases. In theory, only the γ-phase is stable at room temperature. In a polycrystalline material, the different polymorphs can co-exist depending on several factors (sintering conditions, presence of impurities, grain size) and the interpretations given in the literature are sometimes contradictory. For cement applications, only the α, α' or β polymorphs react with water to give hydrates while γ does not. Therefore, this paper focuses specifically on the effect of grain size on the β to γ transformation. We also propose that the transformation is semi-reconstructive and not martensitic as suggested by some authors.     

Semi-supervised dimensionality reduction for analyzing high-dimensional data with constraints

In this paper, we present a novel semi-supervised dimensionality reduction technique to address the problems of inefficient learning and costly computation in coping with high-dimensional data. Our method named the dual subspace projections (DSP) embeds high-dimensional data in an optimal low-dimensional space, which is learned with a few user-supplied constraints and the structure of input data. The method projects data into two different subspaces respectively the kernel space and the original input space. Each projection is designed to enforce one type of constraints and projections in the two subspaces interact with each other to satisfy constraints maximally and preserve the intrinsic data structure. Compared to existing techniques, our method has the following advantages: (1) it benefits from constraints even when only a few are available; (2) it is robust and free from overfitting; and (3) it handles nonlinearly separable data, but learns a linear data transformation. As a conclusion, our method can be easily generalized to new data points and is efficient in dealing with large datasets. An empirical study using real data validates our claims so that significant improvements in learning accuracy can be obtained after the DSP-based dimensionality reduction is applied to high-dimensional data.     

Sparse Iterative Closest Point

Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point computations to establish correspondences between two data sets, and solving for the optimal transformation that brings these correspondences into alignment. A major difficulty for this approach is the sensitivity to outliers and missing data often observed in 3D scans. Most practical implementations of the ICP algorithm address this issue with a number of heuristics to prune or reweight correspondences. However, these heuristics can be unreliable and difficult to tune, which often requires substantial manual assistance. We propose a new formulation of the ICP algorithm that avoids these difficulties by formulating the registration optimization using sparsity inducing norms. Our new algorithm retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data. The complete source code of our implementation is provided at http://lgg.epfl.ch/sparseicp .     

Guided Mesh Normal Filtering

The joint bilateral filter is a variant of the standard bilateral filter, where the range kernel is evaluated using a guidance signal instead of the original signal. It has been successfully applied to various image processing problems, where it provides more flexibility than the standard bilateral filter to achieve high quality results. On the other hand, its success is heavily dependent on the guidance signal, which should ideally provide a robust estimation about the features of the output signal. Such a guidance signal is not always easy to construct. In this paper, we propose a novel mesh normal filtering framework based on the joint bilateral filter, with applications in mesh denoising. Our framework is designed as a two‐stage process: first, we apply joint bilateral filtering to the face normals, using a properly constructed normal field as the guidance; afterwards, the vertex positions are updated according to the filtered face normals. We compute the guidance normal on a face using a neighboring patch with the most consistent normal orientations, which provides a reliable estimation of the true normal even with a high‐level of noise. The effectiveness of our approach is validated by extensive experimental results.     

Role of dopants (B,P and S) on the stabilization of β-Ca2SiO4

The interest in belite (Ca₂SiO₄ + impurities or dopants) phase increases significantly since new types of clinkers based essentially on this phase become promising alternatives to Portland clinker for reducing CO₂ emission. Belite is also of interest as a biomedical cement. For the cement industry, stabilization of the β-polymorph is essential since the γ-polymorph has no hydraulic activity. In order to understand the mechanism of β-polymorph stabilization, this paper explores the addition of three dopants, namely P, B and S. It turns out that these dopants can modify the lattice parameters of Ca₂SiO₄ and induce specific twinning morphologies and surface relief, as well as grain boundaries deformations. A link between the addition of dopant leading to significant microstructural changes and the stabilization of β-polymorph has been established.     

Shape‐Up: Shaping Discrete Geometry with Projections

We introduce a unified optimization framework for geometry processing based on shape constraints. These constraints preserve or prescribe the shape of subsets of the points of a geometric data set, such as polygons, one‐ring cells, volume elements, or feature curves. Our method is based on two key concepts: a shape proximity function and shape projection operators. The proximity function encodes the distance of a desired least‐squares fitted elementary target shape to the corresponding vertices of the 3D model. Projection operators are employed to minimize the proximity function by relocating vertices in a minimal way to match the imposed shape constraints. We demonstrate that this approach leads to a simple, robust, and efficient algorithm that allows implementing a variety of geometry processing applications, simply by combining suitable projection operators. We show examples for computing planar and circular meshes, shape space exploration, mesh quality improvement, shape‐preserving deformation, and conformal parametrization. Our optimization framework provides a systematic way of building new solvers for geometry processing and produces similar or better results than state‐of‐the‐art methods.