Image analysis applications on assessing static stability and flowability of self-consolidating concrete

A digital image processing (DIP) method associated with a MATLAB algorithm is used to evaluate cross sectional images of self-consolidating concrete (SCC). Two new parameters, such as inter-particle spacing of coarse aggregate and average mortar-to-coarse aggregate ratio, defined as average mortar thickness index (MTI), were proposed to quantitatively evaluate the static stability of SCC. Statistical models were developed to predict flowability of SCC mixtures. Test results revealed that the proposed DIP method and MATLAB algorithm can be successfully used to derive inter-particle spacing and MTI and quantitatively evaluate the static stability on hardened SCC samples. A probability density of 60% from histogram analysis appears to be a reasonable threshold for indicating a uniformly distributed SCC mixture. For a given mortar yield stress, a critical mortar viscosity of 1.30 Pa s tends to significantly affect the trend of slump flow changing with MTI. The investigated relationship between parameters measured from DIP method and existing theoretical frames is well correlated. The outcome of this study can be of practical value for providing an efficient and useful tool in designing mixture proportions of SCC.     

Kernel-based fourth-order diffusion for image noise removal

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourth-order diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.     

Assessment of local strain field in adhesive layer of an unsymmetrically repaired CFRP panel using digital image correlation

The present study focuses on experimental investigation of through the thickness displacement and strain field in thin adhesive layer in single sided (unsymmetrical) patch repaired CFRP (carbon fiber reinforced polymer) panel under tensile load. Digital image correlation (DIC) technique is employed to acquire the displacement and strain (longitudinal, peel and shear) field. Experimental determination of shear transfer length based on shear strain field obtained from DIC is introduced to estimate the optimum overlap length which is an essential parameter in patch design for the repair of CFRP structures. Further, DIC experiment with magnified optics is performed to get an insight into complex and localized strain field over thin adhesive layer especially at critical zones leading to damage initiation. The failure mechanism, load displacement behavior, damage initiation and propagation are closely monitored using DIC. The influence of patch edge tapering on strain distribution in adhesive layer is also investigated. The DIC successfully captures the global and localized strain field at critical zones over thin adhesive layer and further helps in monitoring the damage based on strain anomalies. Strains are found to have maximum magnitude at the patch overlap edge and the shear strain level in adhesive layer is higher than the peel strain. Normal tapering increases the peel strain and has negligible influence on shear strain level in adhesive layer. The recommended overlap length is found to be consistent with the recommendation in the literature. Whole field strain pattern and the overlap length obtained from experiment are further compared with the finite element analysis results and they appear to be in good coherence.     

A study on the possibility of implementing a real-time stereoscopic 3D rendering TV system

3D video has recently seen a massive increase in exposure in our lives. However, differences between the viewing and shooting conditions for a film lead to disparities between the reformed media and the original three-dimensional effect, which cause severe visual fatigue to viewers and result in headaches and dizziness. In this paper, a series of image processing algorithms are introduced to overcome these problems. The image processing pipeline is composed of four steps, eye-pupil detection, stereo correspondence computation, saliency map generation, and 3D warping. Each step is implemented in an S3DS-3D rendering system and its time complexity is measured. From the results, it was found that real-time stereoscopic 3D rendering is impossible using only a software implementation because SIFT and optical flow calculation requires a significant amount of time. Therefore, these two algorithm blocks should be implemented with hardware acceleration. Fortunately, active research is being conducted on these issues and real-time processing is expected to become available soon for applications beyond full-HD TV screens. In addition, it was found that saliency map generation and 3D warping blocks also need to be implemented in hardware for full-HD display although they do not have significant time complexity compared to SIFT and optical flow algorithm blocks.     

Non-chaotic and chaotic propagation of stationary and dynamic images through MVKS turbulence

Anisoplanatic electromagnetic (EM) propagation across a turbulent atmosphere has been recently examined for an unmodulated carrier propagating over an image-bearing transparency through optical lensing, and for the embedded information inside a carrier recovered using heterodyning and digital demodulation. Carrier modulation yielded better recovery than simple lens-based imaging. A possible mitigation strategy is proposed whereby the image information is encrypted on an RF chaotic carrier, thereafter secondarily embedded onto an optical carrier. Results based on the modified von Karman (MVKS) and the Hufnagel-Valley (H-V) models showed that the signal/image recovery under turbulence is improved compared with non-chaotic propagation. The case of time-varying/dynamic images is also taken up; it is demonstrated via cross-correlation products that turbulence is mitigated by the use of chaotic carrier encryption. Overall, transmission via chaos offers mitigation against distortions due to turbulence along with the security feature inherent via the chaos keys which prevent signal recovery without key-matching.     

Electrochemical determination of niobium cathode as an efficient electrocatalyst for hydrogen generation in acidic media

Hydrogen gas (H₂) is notified as a renewable energy carrier. It is wanted to discover a low-cost electrocatalyst for the hydrogen evolution reaction (HER) to substitute the high-cost Pt in electrolysis cell. Niobium electrocatalyst nominated to substitute noble materials for electrocatalytic H₂ production and its electrochemical manner was estimated in H₂SO₄ acid of various concentrations utilizing a steady-state polarization and electrochemical impedance spectroscopy (EIS). The influences of acid concentration, cathodic potential and temperature on the H₂ creation were examined. The outcomes display that HER on Nb electrode proceeds by the Volmer-Heyrovsky mechanism. EIS tests, under open circuit and under cathodic polarization, were performed and the fitting has been done utilizing a suggested model for the electrode/electrolyte interface. Apparent activation energies (E_a) were estimated to be ca. 10.5 kJ mol^?1 for the HER on Nb. Thus, Nb is a good electrocatalyst for the cathodic H₂ manufacturing.     

The effect of maturity and tissue on the ability of mid infrared spectroscopy to predict the geographical origin of banana (Musa Cavendish)

The objective of this study was to evaluate the effect of sample presentation (tissue type) and maturity (ripe and unripe) on the classification of banana (Musa Cavendish) samples sourced from two different geographical regions and analysed using mid infrared (MIR) spectroscopy. The coefficient of determination (R²) and the standard error of cross-validation (SECV) obtained using partial least squares discriminant analysis were 0.83 (0.33), 0.75 (0.25) and 0.94 (0.19) for the prediction of maturity, geographical origin and tissue type, respectively. No effect of either of type of tissue (e.g. pulp or peel) or maturity was observed. The results of this study demonstrated that MIR spectroscopy might be used to classify the origin of the banana samples at different degrees of ripeness. However, one of the limitations of this study is on the number of samples analysed and further validation must be recommended using samples from other sources, regions and harvest seasons.     

A Multiscale-Porous Anion Exchange Membrane for Convenient and Scalable Electrokinetic Concentration of Cationic Species

A device able to electrokinetically concentrate cationic samples has many potential medical and industrial applications, but until now has remained undeveloped due to the lack of a commercial anion-permselective material leading to a prohibitively complex fabrication procedure. Herein, a novel multiscale-porous anion exchange membrane (MP-AEM) that enables the convenient and scalable electrokinetic concentration of cationic species is proposed. A mechanically enhanced multiscale-porous structure with a solid framework is realized by adopting polyester resin as an additive to overcome the intrinsic limitations of the AEM material. The scalable MP-AEM-embedded electrokinetic concentrator is devised based on the peculiar properties of the MP-AEM that for allow both ion and fluid transport. With the MP-AEM, the concentrator is fabricated in a highly streamlined manner consisting only of a simple insertion and assembly. The concentration performance of the MP-AEM-embedded electrokinetic concentrator is demonstrated with a positively charged fluorescent dye and a fluorescein-labeled protein, and the results show enrichment factors of 250 and 500, respectively. The MP-AEM makes cationic electrokinetic concentration more accessible and scalable, thereby enabling further progress in a wide range of fields.     

Discriminative graph convolution networks for hyperspectral image classification

Recently, the proposal of graph convolutional networks (GCN) has successfully implemented into hyperspectral image data representation and analysis. In spite of the great success, there are still several major challenges in hyperspectral image classification, including within-class diversity, and between-class similarity, which generally degenerate hyperspectral image classification performance. To address the problems, we propose a discriminative graph convolution networks (DGCN) for hyperspectral image classification. This method introduces the concepts of within-class scatter and between-class scatter, which respectively reflect the global geometric structure and discriminative information of the input space. The experimental results on the hyperspectral data sets show that the proposed method has good classification performance.     

基于混合远景和近景区域暗通道算法的图像去雾研究

为了提高图像去雾效果,提出混合远景和近景区域暗通道算法。首先建立远景和近景区域暗通道,为近景区域最小值通道和远景区域最小值通道之和,不同区域设置不同的调节系数;接着估计大气光值,通过混合暗通道远景区域的前50个像素点颜色值的平均值获得,增设校正系数;然后导向滤波,采用自适应滑动窗口优化透射率;最后给出了算法流程。实验仿真显示所提算法去雾图像视觉效果较好,定量分析指标对比度、可见边对比度值较优。