Empirical mode decomposition-based facial pose estimation inside video sequences

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions

Solvability for second-order nonlocal boundary value problems with a p-Laplacian at resonance on a half-line

This paper investigates the solvability of the second-order boundary value problems with the one-dimensional $p$-Laplacian at resonance on a half-line $$\left\{\begin{array}{llll} (c(t)\phi_{p}(x'(t)))'=f(t,x(t),x'(t)),~~~~0<t<\infty,\\ x(0)=\sum\limits_{i=1}^{n}\mu_ix(\xi_{i}), ~~\lim\limits_{t\rightarrow +\infty}c(t)\phi_{p}(x'(t))=0 \end{array}\right.$$ and $$\left\{\begin{array}{llll} (c(t)\phi_{p}(x'(t)))'+g(t)h(t,x(t),x'(t))=0,~~~~0<t<\infty,\\ x(0)=\int_{0}^{\infty}g(s)x(s)ds,~~\lim\limits_{t\rightarrow +\infty}c(t)\phi_{p}(x'(t))=0 \end{array}\right.$$ with multi-point and integral boundary conditions, respectively, where $\phi_{p}(s)=|s|^{p-2}s$, $p>1$. The arguments are based upon an extension of Mawhin's continuation theorem due to Ge. And examples are given to illustrate our results

Estimation of Viscoelastic Properties of Cells Using Acoustic Tweezing Cytometry

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135346/1/jum201635122537.pd

Deep Operator Learning Lessens the Curse of Dimensionality for PDEs

Deep neural networks (DNNs) have achieved remarkable success in numerous domains, and their application to PDE-related problems has been rapidly advancing. This paper provides an estimate for the generalization error of learning Lipschitz operators over Banach spaces using DNNs with applications to various PDE solution operators. The goal is to specify DNN width, depth, and the number of training samples needed to guarantee a certain testing error. Under mild assumptions on data distributions or operator structures, our analysis shows that deep operator learning can have a relaxed dependence on the discretization resolution of PDEs and, hence, lessen the curse of dimensionality in many PDE-related problems including elliptic equations, parabolic equations, and Burgers equations. Our results are also applied to give insights about discretization-invariant in operator learning

Optimization of Induction Quenching Processes for HSS Roll Based on MMPT Model

To improve the comprehensive performance of high speed steel (HSS) cold rolls, the induction hardening processes were analyzed by numerical simulation and experimental research. Firstly, a modified martensitic phase transformation (MMPT) model of the tested steel under stress constraints was established. Then, the MMPT model was fed into DEFORM to simulate the induction quenching processes of working rolls based on an orthogonal test design and the optimal dual frequency of the induction quenching process was obtained. The results indicate that the depth of the roll’s hardened layer increases by 32.5% and the axial residual tensile stress also becomes acceptable under the optimized process. This study provides guidance for studying phase transformation laws under stress constraints and the optimization of complex processes in an efficient manner

4,4′-[Piperazine-1,4-diylbis(propyl­ene­nitrilo­methyl­idyne)]diphenol

In the title mol­ecule, C24H32N4O2, the piperazine ring adopts a chair conformation and the dihedral angle between the two benzene rings is 35.4 (1)°. In the crystal structure, inter­molecular O—H⋯N hydrogen bonds link mol­ecules into chains along [001]

Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently been proposed and attracted immediate attention. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To combat this effect, the concept of dynamic learning rate (DLR) is proposed in this work. We begin our discussion by considering multiple-input-single-output (MISO) scenario, since the underlying optimization problem is convex and has closed-form solution. We then extend our studies to more general multiple-input-multiple-output (MIMO) case and an iterative method is derived. Extensive simulation results demonstrate the effectiveness of the proposed scheme in reducing the aggregate distortion and guaranteeing the testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present the asymptotic analysis and give a near-optimal receive beamforming design solution in closed form, which is verified by numerical simulations

Formability of a HSAS Based on Hot Processing Maps and Texture Analyses

Aiming to improve the formability of a HSAS Docol 1500 Bor, hot processing maps were obtained based on Prasad, Babu and Murty instability criteria. The hot processing maps based on the above instability criteria are similar and the explanation of its similarity is given. Recrystallization and misorientation in typical quenched specimens were observed by using SEM with a EBSD system. It was found that the fraction values of HAGBs in quenched martensite are all below 0.4 under experimental conditions. Flow location bands occurs at lower deformation temperatures and higher strain rates. The textures in martensite mainly include ⟨110⟩ / / ND and ⟨110⟩ / / RD components. Based on N-W OR, the textures in deformed austenite are mostly ⟨111⟩ / / ND and ⟨112⟩ / / RD⟩ components. Prasad and Babu instability criteria are more conservative than Murty instability criterion in obtaining the processing maps of the tested steel. To reduce the anisotropy of quenched workpieces because of the textures at room temperature, the quenching temperature in the stamping process of the tested steel should be lower

Visible hyperspectral imaging for lamb quality prediction

Three factors, including tenderness, juiciness and flavour, are found to have an impact on lamb eating quality, which determines the repurchase behaviour of customers. In addition to these factors, the surface colour of lamb can also influence the purchase decision of consumers. From a long time ago, meat industries have been looking for fast and non-invasive objective quality evaluation approaches, where near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) have shown great promises in assessing beef quality compared with conventional methods. However, rare research has been conducted for lamb samples. Therefore, in this paper the feasibility of the HSI system for evaluating lamb quality was tested. In total 80 lamb samples were imaged using a visible range HSI system and the spectral profiles were used for predicting lamb quality related traits. For some traits, noise was further removed from HSI spectra by singular spectrum analysis (SSA) for better performance. Considering support vector machine (SVM) is sensitive to high dimensional data, principal component analysis (PCA) was applied to reduce the dimensionality of HSI spectra before feeding into SVM for constructing prediction equations. The prediction results suggest that HSI is promising in predicting some lamb eating quality traits, which could be beneficial for lamb industries