Denoising for 3D Point Cloud Based on Regularization of a Statistical Low-Dimensional Manifold.

A point cloud obtained by stereo matching algorithm or three-dimensional (3D) scanner generally contains much complex noise, which will affect the accuracy of subsequent surface reconstruction or visualization processing. To eliminate the complex noise, a new regularization algorithm for denoising was proposed. In view of the fact that 3D point clouds have low-dimensional structures, a statistical low-dimensional manifold (SLDM) model was established. By regularizing its dimensions, the denoising problem of the point cloud was expressed as an optimization problem based on the geometric constraints of the regularization term of the manifold. A low-dimensional smooth manifold model was constructed by discrete sampling, and solved by means of a statistical method and an alternating iterative method. The performance of the denoising algorithm was quantitatively evaluated from three aspects, i.e., the signal-to-noise ratio (SNR), mean square error (MSE) and structural similarity (SSIM). Analysis and comparison of performance showed that compared with the algebraic point-set surface (APSS), non-local denoising (NLD) and feature graph learning (FGL) algorithms, the mean SNR of the point cloud denoised using the proposed method increased by 1.22 DB, 1.81 DB and 1.20 DB, respectively, its mean MSE decreased by 0.096, 0.086 and 0.076, respectively, and its mean SSIM decreased by 0.023, 0.022 and 0.020, respectively, which shows that the proposed method is more effective in eliminating Gaussian noise and Laplace noise in common point clouds. The application cases showed that the proposed algorithm can retain the geometric feature information of point clouds while eliminating complex noise.

Chemical heterogeneity in interfacial layers of polymer nanocomposites.

It is well-known that particle-polymer interactions strongly control the adsorption and conformations of adsorbed chains. Interfacial layers around nanoparticles consisting of adsorbed and free matrix chains have been extensively studied to reveal their rheological contribution to the behavior of nanocomposites. This work focuses on how chemical heterogeneity of the interfacial layers around the particles governs the microscopic mechanical properties of polymer nanocomposites. Low glass-transition temperature composites consisting of poly(vinyl acetate) coated silica nanoparticles in poly(ethylene oxide) and poly(methyl acrylate) matrices, and of poly(methyl methacrylate) silica nanoparticles in a poly(methyl acrylate) matrix are examined using rheology and X-ray photon correlation spectroscopy. We demonstrate that miscibility between the adsorbed and matrix chains in the interfacial layers led to the observed unusual reinforcement. We suggest that packing of chains in the interfacial regions may also contribute to the reinforcement in the polymer nanocomposites. These features may be used in designing mechanically adaptive composites operating at varying temperature.

An improved sliding mode approach for trajectory following control of nonholonomic mobile AGV.

This paper attempts to address the trajectory following control problem of nonholonomic mobile AGV by proposing an improved sliding mode control approach in which, based on the kinematics and attitude deviations established for AGV, the motion characteristics are analyzed and a backstepping sliding mode control with a novel reaching law is designed. This reaching law integrates the merits of the power and exponential reaching laws and promotes the convergence rates of tracking errors. Moreover, with the improved sliding mode controller, the asymptotic stability of tracking deviations can be strictly guaranteed. The simulations have demonstrated the effectiveness and superiority of the proposed approach for mobile AGV.

Design of piecewise deformed elliptical gear with closed pitch curve and its conjugate pair.

The family of elliptical gears with closed pitch curves includes elliptical gears and deformed elliptical gears. Elliptical gears are not only the most widely used non-circular gears but also the research hotspot in the field of non-uniform transmission. However, adjusting the shape of its pitch curve is difficult, which seriously restricts its popularization. This article proposes a piecewise deformed elliptical gear with a closed pitch curve, which realizes the unity of all kinds of elliptical gears in the mathematical model, makes the shape of the pitch curve and gear ratio easier to adjust, and expands the connotation and application scope of the family of elliptical gears. Moreover, the design approach of transmission with piecewise deformed elliptical gear was studied, and the inspection method of transmission performance was provided. The internal relationship between piecewise deformed elliptical gears and the family of elliptical gears was then analyzed. Finally, the method of computer-aided design (CAD) system developed was also provided. Additionally, a case was provided to verify the above theories. The designed conjugate pair can realize a correct meshing and adjust the gear ratio more flexibly, laying a theoretical foundation to expand the application field of non-uniform transmission.

[The upconversion luminescence characteristics of Ho3+/Yb3+ codoped ZrO2-ZnO].

Samples of Ho3+/Yb3+ codoped ZrO2-ZnO powder were prepared by a solid-state reaction. Upconversion luminescence of Ho3+ /Yb3+ codoped ZrO2-ZnO powders was reported in the present paper. The excitation spectrum detected at room temperature suggests three excitation peaks centered at 540, 671 and 762 nm respectively, corresponding to 5S2/5F4 --> I8, 5 Fs --> 5 I8, and 5S2/5 F4 --> 5 I7 or 5 I4 --> 5 I8 of Ho3+ ions. The contents of rare-earth ions could influence the property of upconversion lumunescence. When the Ho3+ content was 0.1 mol%, with the increase in Yb3+ content, the intensity of red luminescence and green luminescence decreased first and then increased, but the intensity of infrared luminescence increased first and then decreased. Furthermore, the ratio of the intensity of green emission to red emission decreased. When the Yb3+ content was 4.7 mol%, with the increase in Ho3+ content, the intensity of all emission decreased, but the ratio of the intensity of green emission to red emission was almost constant. The logI - logP curve shows that all the upconversion luminescence is double-photon processes. And the upconversion mechanisms of the samples were introduced. It is shown from the XRD spectrum that the powder of samples is uniform.

Adaptive robust servo constraint tracking control for an underactuated quadrotor UAV with mismatched uncertainties.

In this research, to achieve the altitude and attitude tracking control of an underactuated quadrotor UAV with mismatched uncertainties, based upon Udwadia-Kalaba theory, a novel adaptive robust tracking control approach is proposed and which will be designed in two steps. First, aiming at the uncertain and underactuated quadrotor UAV, regardless of initial constraint deviation and mismatched uncertainties, a nominal control is constructed through transforming the desired trajectories into corresponding servo constraints; second, for the mismatched uncertainties, we decompose them into two parts, i.e. the matched part and mismatched part, and the mismatched part will "vanish" during the stability analysis of proposed adaptive robust controller. Eventually, with such a decomposition technique, the large mismatched uncertainties can be addressed properly and the burden of controller design will be reduced to a certain degree. In addition, two deterministic robust control performances are also guaranteed by our proposed approach. The simulation results have shown a good robustness and tracking precision of our proposed scheme for quadrotor UAV.

A programmable epidermal microfluidic valving system for wearable biofluid management and contextual biomarker analysis.

Active biofluid management is central to the realization of wearable bioanalytical platforms that are poised to autonomously provide frequent, real-time, and accurate measures of biomarkers in epidermally-retrievable biofluids (e.g., sweat). Accordingly, here, a programmable epidermal microfluidic valving system is devised, which is capable of biofluid sampling, routing, and compartmentalization for biomarker analysis. At its core, the system is a network of individually-addressable microheater-controlled thermo-responsive hydrogel valves, augmented with a pressure regulation mechanism to accommodate pressure built-up, when interfacing sweat glands. The active biofluid control achieved by this system is harnessed to create unprecedented wearable bioanalytical capabilities at both the sensor level (decoupling the confounding influence of flow rate variability on sensor response) and the system level (facilitating context-based sensor selection/protection). Through integration with a wireless flexible printed circuit board and seamless bilateral communication with consumer electronics (e.g., smartwatch), contextually-relevant (scheduled/on-demand) on-body biomarker data acquisition/display was achieved.

Deformation of Chemically Heterogeneous Interfacial Layers of Polymer Nanocomposites.

Dynamics of entangled interfacial polymer layers around nanoparticles determine the linear rheological properties of polymer nanocomposites. In this study, the nonlinear elastic properties of nanocomposites are examined under large-amplitude oscillatory shear (LAOS) flow to reveal the effect of interfacial chemical heterogeneity on the deformation mechanism of polymer-grafted and polymer-adsorbed nanoparticle composites. Adsorbed-poly(methyl methacrylate) (PMMA) layers presented stronger interfacial stiffening and reinforcement than PMMA-grafted layers. Chemical heterogeneities of interfacial layers, provided by polymer-adsorbed and low graft density particles, deformed at smaller strains than the poly(ethylene oxide) (PEO) matrix. Interfaces of loosely bound PMMA and PEO exhibited stiffening at low strains due to the enhanced chain mixing and entanglements. These results demonstrate that chemical and dynamic heterogeneities in interfacial layers have significant importance in designing adaptive polymer nanocomposites for large shear deformation.

Mode decomposition evolution equations.

Partial differential equation (PDE) based methods have become some of the most powerful tools for exploring the fundamental problems in signal processing, image processing, computer vision, machine vision and artificial intelligence in the past two decades. The advantages of PDE based approaches are that they can be made fully automatic, robust for the analysis of images, videos and high dimensional data. A fundamental question is whether one can use PDEs to perform all the basic tasks in the image processing. If one can devise PDEs to perform full-scale mode decomposition for signals and images, the modes thus generated would be very useful for secondary processing to meet the needs in various types of signal and image processing. Despite of great progress in PDE based image analysis in the past two decades, the basic roles of PDEs in image/signal analysis are only limited to PDE based low-pass filters, and their applications to noise removal, edge detection, segmentation, etc. At present, it is not clear how to construct PDE based methods for full-scale mode decomposition. The above-mentioned limitation of most current PDE based image/signal processing methods is addressed in the proposed work, in which we introduce a family of mode decomposition evolution equations (MoDEEs) for a vast variety of applications. The MoDEEs are constructed as an extension of a PDE based high-pass filter (Europhys. Lett., 59(6): 814, 2002) by using arbitrarily high order PDE based low-pass filters introduced by Wei (IEEE Signal Process. Lett., 6(7): 165, 1999). The use of arbitrarily high order PDEs is essential to the frequency localization in the mode decomposition. Similar to the wavelet transform, the present MoDEEs have a controllable time-frequency localization and allow a perfect reconstruction of the original function. Therefore, the MoDEE operation is also called a PDE transform. However, modes generated from the present approach are in the spatial or time domain and can be easily used for secondary processing. Various simplifications of the proposed MoDEEs, including a linearized version, and an algebraic version, are discussed for computational convenience. The Fourier pseudospectral method, which is unconditionally stable for linearized the high order MoDEEs, is utilized in our computation. Validation is carried out to mode separation of high frequency adjacent modes. Applications are considered to signal and image denoising, image edge detection, feature extraction, enhancement etc. It is hoped that this work enhances the understanding of high order PDEs and yields robust and useful tools for image and signal analysis.

Iterative filtering decomposition based on local spectral evolution kernel.

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms.

Selective Extraction of Entangled Textures via Adaptive PDE Transform.

Texture and feature extraction is an important research area with a wide range of applications in science and technology. Selective extraction of entangled textures is a challenging task due to spatial entanglement, orientation mixing, and high-frequency overlapping. The partial differential equation (PDE) transform is an efficient method for functional mode decomposition. The present work introduces adaptive PDE transform algorithm to appropriately threshold the statistical variance of the local variation of functional modes. The proposed adaptive PDE transform is applied to the selective extraction of entangled textures. Successful separations of human face, clothes, background, natural landscape, text, forest, camouflaged sniper and neuron skeletons have validated the proposed method.

Biomolecular surface construction by PDE transform.

This work proposes a new framework for the surface generation based on the partial differential equation (PDE) transform. The PDE transform has recently been introduced as a general approach for the mode decomposition of images, signals, and data. It relies on the use of arbitrarily high-order PDEs to achieve the time-frequency localization, control the spectral distribution, and regulate the spatial resolution. The present work provides a new variational derivation of high-order PDE transforms. The fast Fourier transform is utilized to accomplish the PDE transform so as to avoid stringent stability constraints in solving high-order PDEs. As a consequence, the time integration of high-order PDEs can be done efficiently with the fast Fourier transform. The present approach is validated with a variety of test examples in two-dimensional and three-dimensional settings. We explore the impact of the PDE transform parameters, such as the PDE order and propagation time, on the quality of resulting surfaces. Additionally, we utilize a set of 10 proteins to compare the computational efficiency of the present surface generation method and a standard approach in Cartesian meshes. Moreover, we analyze the present method by examining some benchmark indicators of biomolecular surface, that is, surface area, surface-enclosed volume, solvation free energy, and surface electrostatic potential. A test set of 13 protein molecules is used in the present investigation. The electrostatic analysis is carried out via the Poisson-Boltzmann equation model. To further demonstrate the utility of the present PDE transform-based surface method, we solve the Poisson-Nernst-Planck equations with a PDE transform surface of a protein. Second-order convergence is observed for the electrostatic potential and concentrations. Finally, to test the capability and efficiency of the present PDE transform-based surface generation method, we apply it to the construction of an excessively large biomolecule, a virus surface capsid. Virus surface morphologies of different resolutions are attained by adjusting the propagation time. Therefore, the present PDE transform provides a multiresolution analysis in the surface visualization. Extensive numerical experiment and comparison with an established surface model indicate that the present PDE transform is a robust, stable, and efficient approach for biomolecular surface generation in Cartesian meshes.

Partial differential equation transform - Variational formulation and Fourier analysis.

Nonlinear partial differential equation (PDE) models are established approaches for image/signal processing, data analysis and surface construction. Most previous geometric PDEs are utilized as low-pass filters which give rise to image trend information. In an earlier work, we introduced mode decomposition evolution equations (MoDEEs), which behave like high-pass filters and are able to systematically provide intrinsic mode functions (IMFs) of signals and images. Due to their tunable time-frequency localization and perfect reconstruction, the operation of MoDEEs is called a PDE transform. By appropriate selection of PDE transform parameters, we can tune IMFs into trends, edges, textures, noise etc., which can be further utilized in the secondary processing for various purposes. This work introduces the variational formulation, performs the Fourier analysis, and conducts biomedical and biological applications of the proposed PDE transform. The variational formulation offers an algorithm to incorporate two image functions and two sets of low-pass PDE operators in the total energy functional. Two low-pass PDE operators have different signs, leading to energy disparity, while a coupling term, acting as a relative fidelity of two image functions, is introduced to reduce the disparity of two energy components. We construct variational PDE transforms by using Euler-Lagrange equation and artificial time propagation. Fourier analysis of a simplified PDE transform is presented to shed light on the filter properties of high order PDE transforms. Such an analysis also offers insight on the parameter selection of the PDE transform. The proposed PDE transform algorithm is validated by numerous benchmark tests. In one selected challenging example, we illustrate the ability of PDE transform to separate two adjacent frequencies of sin(x) and sin(1.1x). Such an ability is due to PDE transform's controllable frequency localization obtained by adjusting the order of PDEs. The frequency selection is achieved either by diffusion coefficients or by propagation time. Finally, we explore a large number of practical applications to further demonstrate the utility of proposed PDE transform.

High rate internal pressurization of the human eye to determine dynamic rupture pressure.

Over 1.9 million people suffer from eye injuries in the United States, occurring from automobile accidents, sports related impacts, and military combat. The purpose of the current study is to analyze the rupture pressure of human eyes using a high rate pressurization system. Internal pressure was dynamically induced into the eye with a drop tower pressurization system. The rupture pressure was measured with a small pressure sensor inserted into the optic nerve. A total of 10 human eye dynamic pressure tests were performed to determine rupture pressure and to compare the results with previous data. It was found that the average high rate rupture pressure of human eyes is 0.89+/- 0.25 MPa. In comparing these data with previous studies, it is concluded that as the loading rate increases the rupture pressure also increases.