Two-dimensional approximately harmonic projection for gait recognition

This paper presents a two-dimensional approximately harmonic projection (2DAHP) algorithm for gait recognition. 2DAHP is originated from the approximately harmonic projection (AHP), while 2DAHP offers some advantages over AHP. 1) 2DAHP can preserve the local geometrical structure and cluster structure of image data as AHP. 2) 2DAHP encodes images as matrices or second-order tensors rather than one-dimensional vectors, so 2DAHP can keep the correlation among different coordinates of image data. 3) 2DAHP avoids the singularity problem suffered by AHP. 4) 2DAHP runs faster than AHP. Extensive experiments on gait recognition show the effectiveness and efficiency of the proposed method

Disorder and metal-insulator transitions in Weyl semimetals

The Weyl semimetal (WSM) is a newly proposed quantum state of matter. It has Weyl nodes in bulk excitations and Fermi arcs surface states. We study the effects of disorder and localization in WSMs and find three exotic phase transitions. (I) Two Weyl nodes near the Brillouin zone boundary can be annihilated pairwise by disorder scattering, resulting in the opening of a topologically nontrivial gap and a transition from a WSM to a three-dimensional (3D) quantum anomalous Hall state. (II) When the two Weyl nodes are well separated in momentum space, the emergent bulk extended states can give rise to a direct transition from a WSM to a 3D diffusive anomalous Hall metal. (III) Two Weyl nodes can emerge near the zone center when an insulating gap closes with increasing disorder, enabling a direct transition from a normal band insulator to a WSM. We determine the phase diagram by numerically computing the localization length and the Hall conductivity, and propose that the exotic phase transitions can be realized on a photonic lattice.Comment: 7 pages with appendix, 6 figure

A microfluidic study of transient flow states in permeable media using fluorescent particle image velocimetry

Velocity fields in flow in permeable media are of great importance to many subsurface processes such as geologic storage of CO2 , oil and gas extraction, and geothermal systems. Steady-state flow is characterized by velocity fields that do not change significantly over time. The flow field transitions to a new steady state once it experiences a disturbance such as a change in flow rate or in pressure gradient. This transition is often assumed to be instantaneous, which justifies the expression of constitutive relations as functions of instantaneous phase saturations. This work examines the evolution of velocity fields in a surrogate quasi-2D permeable medium using a microfluidic device, a microscopy system, and a high-speed camera. Tracer particles are injected into the medium along with Deionized water. The evolution of the velocity field is examined by tracing these particles in the captured images using the standard high-density particle image velocimetry algorithm founded on cross-correlation. The results suggest that the transition between steady states for an incompressible fluid takes a finite and non-negligible amount of time that is independent of the magnitude of the change in pressure gradient. The existence of transient states and the nature of the response during these states are readily interpreted by the principle of least action where flow gradually establishes an optimal configuration such that energy dissipation is minimized. The findings provide evidence against the applicability of the assumption that flowing phases relax instantaneously to their steady states and, hence, against the accuracy of the classical multiphase extension of Darcy’s law.Cited as: Sun, J., Li, Z., Furtado, F., Aryana, S. A. A microfluidic study of transient flow states in permeable media using fluorescent particle image velocimetry. Capillarity, 2021, 4(4): 76-86, doi: 10.46690/capi.2021.04.0

Environmental impact assessment of wastewater discharge with multi-pollutants from iron and steel industry

The iron and steel industry discharges large quantities of wastewater. The environmental impact of the wastewater is traditionally assessed from the quantitative aspect. However, the water quality of discharged wastewater plays a more significant role in damaging the natural environment. Moreover, comprehensive assessment of multi-pollutants in wastewater from both quality and quantity is still a gap. In this work, a total environmental impact score (TEIS) is defined to assess the environmental impact of wastewater discharge, by considering the volume of wastewater and the quality of main processes. To implement the comprehensively qualitative and quantitative assessment, a field monitoring and measurement of wastewater discharge volume and the quality is conducted to acquire pH, suspend solids (SS), chemical oxygen demand (COD), total nitrogen (TN), total iron (TFe), and hexavalent chromium (Cr(VI)). The sequence of TEIS values is obtained as steelmaking > ironmaking > sintering > hot rolling > coking > cold rolling and TN > COD > SS > pH > Cr(VI) > TFe. The TEIS of the investigated steel plant is 26.27. The leading process lies in steelmaking with a TEIS of 19.98. The dominant pollutant is TN with a TEIS of 15.00. Finally, a sensitivity analysis is performed to validate the feasibility and generalisability of the TEIS

Multiview Discriminative Geometry Preserving Projection for Image Classification

In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for multiple view data is to concatenate these feature vectors as a new feature vector. However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with “curse of dimensionality.” To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP) for feature extraction and classification. MDGPP can not only preserve the intraclass geometry and interclass discrimination information under a single view, but also explore the complementary property of different views to obtain a low-dimensional optimal consensus embedding by using an alternating-optimization-based iterative algorithm. Experimental results on face recognition and facial expression recognition demonstrate the effectiveness of the proposed algorithm

Semisupervised Kernel Marginal Fisher Analysis for Face Recognition

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm