WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

Structural silicone sealants after exposure to laboratory test for durability assessment

During the service life of structural sealant glazing (SSG) facades, the load-bearing capacity of the silicone bonds needs to be guaranteed. Laboratory tests can assess the durability of SSG-systems based on mechanical characteristics of the bond after simultaneous exposure to both climatic and mechanical loads. This article studies how the material characteristics of two common structural sealants are affected by laboratory and field exposure. Dynamic mechanical analysis (DMA) confirms a reduction in the dynamic modulus of exposed silicone samples. Results from thermogravimetric analysis, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and small-angle X-ray scattering/wide-angle X-ray scattering show differences between the two sealants and indicate no/minor changes in the composition and morphology of the laboratory and field exposed sealants. Mechanical characterization methods, such as DMA, and tensile and shear testing of the structural bond, are shown to be sensitive toward the combined climatic and mechanical loadings, and are hence suitable for studying degradation mechanisms of structural sealants.     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions

Numerous brain diseases are associated with abnormalities in morphology and density of dendritic spines, small membranous protrusions whose structural geometry correlates with the strength of synaptic connections. Thus, the quantitative analysis of dendritic spines remodeling in microscopic images is one of the key elements towards understanding mechanisms of structural neuronal plasticity and bases of brain pathology. In the following article, we review experimental approaches designed to assess quantitative features of dendritic spines under physiological stimuli and in pathological conditions. We compare various methodological pipelines of biological models, sample preparation, data analysis, image acquisition, sample size, and statistical analysis. The methodology and results of relevant experiments are systematically summarized in a tabular form. In particular, we focus on quantitative data regarding the number of animals, cells, dendritic spines, types of studied parameters, size of observed changes, and their statistical significance.     

Anisotropy Effects in the Shape-Memory Performance of Polymer Foams

Isotropic and anisotropic shape-memory polymer foams are prepared by supercritical carbon dioxide foaming from a multiblock copolymer (PDLCL) consisting of poly(ω-pentadecalactone) and poly(ε-caprolactone) segments. Analysis by micro-computed tomography reveals for the anisotropic PDLCL foam cells a high shape anisotropy ratio of R = 1.72 ± 0.62 with a corresponding Young's compression moduli ratio between longitudinal and transversal direction of 4.3. The experimental compression data in the linear elastic range can be well described by the anisotropic open foam model of Gibson and Ashby. A micro-morphological analysis for single pores using scanning electron microscopy images permits the correlation between the macroscopic stress-compression behavior and microscale structural changes.     

海蜇Ⅰ型胶原蛋白的提取及结构特性研究

以海蜇为原料，采用盐酸-胃蛋白酶法提取胶原蛋白，并通过聚丙烯酰胺凝胶电泳（SDS-Polyacrylamide gel electrophoresis，SDS-PAGE）、紫外光谱（UV-visible spectroscopy，UV）、氨基酸分析、红外光谱（Fourier-transform infrared spectroscopy，FTIR）、圆二色谱（Circular dichroism，CD）以及扫描电镜（Scanning electron microscope，SEM）对其结构特性进行了系统全面的研究。结果显示，海蜇胶原蛋白在135 kDa左右有一条α-链，在245 kDa以上有一条β-链和γ-链，紫外吸收峰在233 nm处，符合I型胶原蛋白的特征，其亚基组成可能为[α₁(I)]₃。海蜇胶原蛋白中含量最高的氨基酸为甘氨酸，占氨基酸总量的25.99%，且含有15.94%的亚氨基酸。红外光谱和圆二色谱均表明海蜇胶原蛋白分子排布紧凑，具有主要由氢键构成的完整三螺旋结构。扫描电镜结果显示海蜇胶原蛋白具有多层聚集、以纤维为主的无规则网状结构。     

Structure,mechanical and thermal properties of Y-doped CrAlN coatings

CrAlYN coatings with different Y contents (0, 5 and 12 at.%) were deposited by cathodic arc evaporation to investigate the influence of Y-addition on the structure, mechanical and thermal properties of CrAlN coatings by using X-ray diffraction, scanning electron microscopy, differential scanning calorimetry, thermal gravimetric analysis and nanoindentation. The structural transformation of single phase cubic Cr_0.42Al_0.58N and Cr_0.39Al_0.56Y_0.05N coatings to cubic–wurtzite mixed Cr_0.32Al_0.56Y_0.12N coating leads to a drop in hardness from (30.2±0.7) GPa of Cr_0.42Al_0.58N and (32.0±1.0) GPa of Cr_0.39Al_0.56Y_0.05N to (25.2±0.7) GPa of Cr_0.32Al_0.56Y_0.12N. The incorporation of 5 at.% Y retards the thermal decomposition of CrAlN, verified by the postponed precipitation of w-AlN and N-loss upon annealing. Correspondingly, Cr_0.39Al_0.56Y_0.05N coating consistently exhibits the highest hardness value during thermal annealing. Nevertheless, alloying with Y exerts an adverse effect on the oxidation resistance of CrAlN.