Feature-domain super-resolution framework for Gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics

Improving deep convolutional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset

Appropriate sample size and effects of microscopic parameters on the shear strength and strain localisation of 2D cohesive-frictional granular assemblies

Granular materials are made up of smaller particles, manifestation of microstructure results in a macroscopic response of granular material. Understanding the overall mechanical behaviour from microscopic parameters is one of the main challenges in many engineering fields including civil engineering. When modelling this kind of material by Discrete Element Model (DEM) using idealized circular grains, the effects of appropriate sample size and microscopic parameter changes have been a crucial subject. Previous research has primarily relied on the case of purely frictional granular materials. In this paper, we use DEM to investigate the appropriate sample size and the relationship between microscopic parameters and the macroscopic responses of cohesive-frictional granular assemblies by performing a series of biaxial tests. Our findings indicate that a minimum number of particles is required to balance between mechanical behaviour and computing time. In addition, through extensive parametric studies, the paper explores the impact of factors such as interparticle bonds, intergranular friction coefficients, and initial void index on the overall shear behaviour of granular assemblies. Also, the result reveals a strong correlation between shear band formation and the break field of cohesive contact (static variable) and the translations and rotations of grains (kinematic variable)

DEM investigation on strain localization in a dense periodic granular assembly with high coordination number

Strain localization is one of key phenomena which have been studied extensively in geomaterials and for different kinds of materials including metals and polymers. This well-known phenomenon appears when structure/material is closed to failure. Theoretical, experimental, and numerical research have been dedicated to this subject for a long while. In the numerical aspects, strain localization inside the periodic granular assembly has not been well studied in the literature. In this paper, we investigate the occurrence and development of strain localization within a dense cohesive-frictional granular assembly with high coordination number under bi-periodic boundary conditions by Discrete Element Modeling (DEM). The granular assembly is composed of 2D circular disks and subjected to biaxial loading with constant lateral pressure. The results show that the formation of shear bands is of periodic type, consistent with the boundary conditions. This formation has the origins of the irreversible losing of cohesive contacts, viewed as micro-crackings which strongly concentrated in the periodic shear zones. This micromechanical feature is therefore strongly related to the strain localization observed at the sample scale. Finally, we also show that the strain localization is in perfect agreement with the sample’s displacement fluctuation fields

Carbon assessment for Robusta coffee production systems in Vietnam: a case study in Dak Lak

Carbon assessments have proliferated to identify climate friendly practices in Arabica producing systems, though little attention is given to Robusta. In this study, we evaluated the climate impact of Robusta production via quantification of carbon stock and greenhouse gas (GHG) emissions in the intensive shaded and unshaded coffee farms of the world’s largest Robusta producing region, Vietnam’s Central Highlands. We find due to the linear relationship between fertilizer use and yields, emissions from input use on a per unit product basis are not significantly different between the intensive and less intensive systems. However, when accounting for carbon sequestered in shade and coffee trees, the less intensive systems are carbon positive (sequestering more than they emit) per unit of green coffee bean produced

Impacts of Economic Development on the Living Conditions of Ethnic Minority People in the Border Region of Northern Vietnam

Purpose:   In this study, we examine the effects of the economic development policies of the Vietnamese government on the living conditions of ethnic minority people in the border region of Northern Vietnam.   Theoretical framework: We examine the impact of economic changes, societal changes, and environmental improvements on the living conditions of ethnic minority people in the border region of Northern Vietnam.   Design/Methodology/Approach: The collected data was subsequently cleaned and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with the aid of SPSS 20 software.     Findings: The results show that the current Vietnamese government’s policies on developing agriculture, industry, trade, and service for ethnic minority people have a diverse impact on their living conditions. In general, agriculture, trade, and service are major fields that benefit the living conditions of ethnic minorities. In contrast, the industry seems to have no effects on improving the standard of living of ethnic minority people in Northern Vietnam.   Research, practical & social implications: The results of this study provide suggestion to Vietnamese government on promoting living conditions of ethnic minority people in the border region of Northern Vietnam.   Originality/Value: This is the first paper evaluates the impact of the economic development policies of the Vietnamese government on the living conditions of ethnic minority people in the border region of Northern Vietnam

Static and vibration analysis of isotropic and functionally graded sandwich plates using an edge-based MITC3 finite elements

Static and vibration analysis of isotropic and functionally graded sandwich plates using a higher-order shear deformation theory is presented in this paper. Lagrangian functional is used to derive the equations of motion. The mixed interpolation of tensorial components (MITC) approach and edge-based-strain technique is used to solve problems. A MITC3 three-node triangle element with 7 degree-of-freedoms per nodes that only requires the C0-type continuity is developed. Numerical results for isotropic and functionally graded sandwich plates with different boundary conditions are proposed to validate the developed theory and to investigate effects of material distribution, side-to-thickness ratio, thickness ratio of layers and boundary conditions on the deflection, stresses and natural frequencies of the plates

Dynamic analysis of prestressed Bernoulli beams resting on two-parameter foundation under moving harmonic load

This paper describes the dynamic analysis of prestressed Bernoulli beams resting on a two-parameter elastic foundation under a moving harmonic load by the finite element method. Using the cubic Hermitian polynomials as interpolation functions for the deflection, the stiffness of the Bernoulli beam element augmented by that of the foundation support and prestress is formulated. The nodal load vector is derived using the polynomials with the abscissa measured from the left-hand node of the current loading element to the position of the moving load. Using the formulated element, the dynamic response of the beams is computed with the aid of the direct integration Newmark method. The effects of the foundation support, prestress as well as excitation frequency, velocity and acceleration on the dynamic characteristics of the beams are investigated in detail and highlighted

FACTORS AFFECTING THE SHARE OF FAKE NEWS ABOUT COVID-19 OUTBREAK ON SOCIAL NETWORKS IN VIETNAM

In recent days in Vietnam, the amount of fake news spreading online about the Covid-19 epidemic has shown signs of increasing, causing information confusion and complicating the situation. This fact has received significant attention from scientists. To supplement the evidence of previous studies, enrich the research literature and make policy recommendations to the Government, this study explores the factors influencing the sharing of fake news on social networks. This study was conducted through a cross-sectional survey using an intentional sampling technique (n = 200) multivariate linear regression analysis technique was applied to prove the hypotheses. Research results show that the factors of altruism, entertainment, socialization, self-promotion, and instant information sharing have a positive and meaningful impact on sharing fake news about Covid_19 on social networks

Deep context modeling for semantic segmentation

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robust- ness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene parsing, face recognition). Adopting graphical models (GMs) to incorporate spatial and contextual information into the DCNNs is expected to improve the performance of DCNN-based computer vision tasks. Recent research has shown that combining DCNNs and Conditional Random Fields (CRFs) can significantly improve scene parsing accuracy. This is achieved either through the combination of their independent outputs or through their application as a cascade. In this work, we propose a novel strategy to incorporate CRFs deeper inside DCNNs by modeling a CRF as a DCNN layer which is pluggable into any layer of a DCNN. This implants spatial and contextual information into the DCNN, allowing end-to-end training, better controlling the spatial constraints and improving segmentation accuracy. The new strategy for coupling graphical models with the state-of-the-art fully convolutional neural network has shown promising results on the PASCAL-Context dataset