Problem In Many Applications Research Articles

QuadriFlow: A Scalable and Robust Method for Quadrangulation

AbstractQuadriFlow is a scalable algorithm for generating quadrilateral surface meshes based on the Instant Field‐Aligned Meshes of Jakob et al. (ACM Trans. Graph. 34(6):189, 2015). We modify the original algorithm such that it efficiently produces meshes with many fewer singularities. Singularities in quadrilateral meshes cause problems for many applications, including parametrization and rendering with Catmull‐Clark subdivision surfaces. Singularities can rarely be entirely eliminated, but it is possible to keep their number small. Local optimization algorithms usually produce meshes with many singularities, whereas the best algorithms tend to require non‐local optimization, and therefore are slow. We propose an efficient method to minimize singularities by combining the Instant Meshes objective with a system of linear and quadratic constraints. These constraints are enforced by solving a global minimum‐cost network flow problem and local boolean satisfiability problems. We have verified the robustness and efficiency of our method on a subset of ShapeNet comprising 17,791 3D objects in the wild. Our evaluation shows that the quality of the quadrangulations generated by our method is as good as, if not better than, those from other methods, achieving about four times fewer singularities than Instant Meshes. Other algorithms that produce similarly few singularities are much slower; we take less than ten seconds to process each model. Our source code is publicly available.

Accurate Synchronization of Digital and Analog Chaotic Systems by Parameters Re-Identification

The verification of the digital models of chaotic systems and processes is a valuable problem in many practical applications, such as nonlinear control and communications. In our study, we propose a hybrid technique for chaotic systems’ identification, based on the chaotic synchronization of digital and analog counterparts and a numerical optimization method used for the fine tuning of parameters. An analog circuit implementing the Rössler oscillator with digitally controlled parameters was chosen as an identification object, and the FPGA model was used as a digital counterpart for coupling and parameter retrieval. The synchronization between analog and digital chaotic models can be used to estimate the quality of an identification procedure. The results of this study clarify the practical bounds of digital and analog systems’ equivalence. They also contribute to the problem of designing technical systems possessing advantages of both analog and digital chaotic generators (e.g., a high accuracy and protection from quasi-chaotic oscillation modes).

Deep Region Hashing for Generic Instance Search from Images

Instance Search (INS) is a fundamental problem for many applications, while it is more challenging comparing to traditional image search since the relevancy is defined at the instance level. Existing works have demonstrated the success of many complex ensemble systems that are typically conducted by firstly generating object proposals, and then extracting handcrafted and/or CNN features of each proposal for matching. However, object bounding box proposals and feature extraction are often conducted in two separated steps, thus the effectiveness of these methods collapses. Also, due to the large amount of generated proposals, matching speed becomes the bottleneck that limits its application to large-scale datasets. To tackle these issues, in this paper we propose an effective and efficient Deep Region Hashing (DRH) approach for large-scale INS using an image patch as the query. Specifically, DRH is an end-to-end deep neural network which consists of object proposal, feature extraction, and hash code generation. DRH shares full-image convolutional feature map with the region proposal network, thus enabling nearly cost-free region proposals. Also, each high-dimensional, real-valued region features are mapped onto a low-dimensional, compact binary codes for the efficient object region level matching on large-scale dataset. Experimental results on four datasets show that our DRH can achieve even better performance than the state-of-the-arts in terms of mAP, while the efficiency is improved by nearly 100 times.

XM-tree: data driven computational model by using metric extended nodes with non-overlapping in high-dimensional metric spaces

Finding similar objects based on a query and a distance, remains a fundamental problem for many applications. The general problem of many similarity measures is to focus the search on as few elements as possible to find the answer. The index structures divides the target dataset into subsets. With large amounts of data, the volumes of the subspaces grow exponentially, that will affect the search algorithms. This problem is caused by inherent deficiencies of space partitioning, and also, the overlap factor between regions. This methods have proven to be unreliable, it becomes hard to store, manage, and analyze these quantities. The research tends to degenerate into a complete analysis of the data set. In this paper, we propose a new indexing technique called XM-tree, that partitions the space using spheres. The idea is to combine two structures, arborescent and sequential, in order to limit the volume of the outer regions of the spheres, by creating extended regions and inserting them into linked lists named extended regions, and also by excluding of the empty sets—separable partitions—that do not contain objects. The goal is to eliminate some objects without the need to compute their relative distances to a query object. Therefore, we proposed a parallel version of the structure on a set of real machine. We also discuss the efficiency of the construction and querying phases, and the quality of our index by comparing it with recent techniques.

Separating snow and forest temperatures with thermal infrared remote sensing

Thermal infrared sensing from space is a well-developed field, but mixed pixels pose a problem for many applications. We present a field study in Dana Meadows, Yosemite National Park, California to scale from point (~2-m resolution) to aerial (~5-m resolution gridded, 1 km × 6 km extent) to satellite (MODIS, ~1000-m resolution, global extent) observations. We demonstrate how multiple thermal bands on MODIS can be used to separate snow and forest temperatures and determine the fractional snow-covered area (fSCA) over a 3 km × 3 km array of 9 MODIS grid cells. During the day, visible, near-infrared, and shortwave-infrared bands provide a first guess of fSCA and help to constrain the solution. This technique, which has estimated errors <2 °C and 10% fSCA for many expected conditions, enables better understanding of the snowpack energy balance, atmospheric inversions and cold air pools, and forest health.

RETRACTED ARTICLE: Application and research of clustering fusion algorithm in communication network prediction

In order to solve the problem of clustering fusion algorithm, such as key parameter setting, fusion of “soft” hard clusters, design and selection of consensus functions, we optimize the K-means algorithm. However, this method has many problems in practical application. It requires professionals to specify the number of clusters and make empirical judgments on the results. The improved algorithm of clustering fusion is introduced into the customer segmentation. Based on the data mining of the mobile phone business of a telecom company in a certain city, customer segmentation is carried out, according to the characteristics of customer calls, SMS and other attributes. The results show that the improved clustering fusion algorithm can effectively solve the above problems and get a reasonable clustering result. At the same time, by analyzing the CO association matrix, we can obtain each customer’s belonging class. The purpose of dividing the results is achieved, which makes the data mining more intelligent.

Approximating Power by Weights

Determining the power distribution of the members of a shareholder meeting or a legislative committee is a well-known problem for many applications. In some cases it turns out that power is nearly proportional to relative voting weights, which is very beneficial for both theoretical considerations and practical computations with many members. We present quantitative approximation results with precise error bounds for several power indices as well as impossibility results for such approximations between power and weights.

Singular perturbations in noisy dynamical systems

Consider a deterministic dynamical system in a domain containing a stable equilibrium, e.g., a particle in a potential well. The particle, independent of initial conditions, eventually reaches the bottom of the well. If however, the particle is subjected to white noise, due, e.g., to collisions with a population of smaller, lighter particles comprising the medium through which the particle travels, a dramatic difference in the behaviour of the Brownian particle occurs. The particle will exit the well. The natural questions then are how long will it take for it to exit and from where on the boundary of the domain of attraction of the deterministic equilibrium (the rim of the well) will it exit. We compute the mean first passage time to the boundary and the mean probabilities of the exit positions. When the noise is small each quantity satisfies a singularly perturbed deterministic boundary value problem. We treat the problem by the method of matched asymptotic expansions (MAE) and generalizations thereof. MAE has been used successfully to solve problems in many applications. However, there exist problems for which MAE does not suffice. Among these are problems exhibiting boundary layer resonance, i.e., the problem of ‘spurious solutions’, which led some to conclude that this was ‘the failure of MAE’. We present a physical argument and four mathematical arguments to modify or augment MAE to make it successful. Finally, we discuss applications of the theory.

Fast Top-K Graph Similarity Search Via Representative Matrices

Graph similarity search is a crucial problem in many applications, such as cheminformatics, data mining, and pattern recognition. Top-k graph similarity search aims to find the most similar $k$ graphs to a query graph in graph databases. In this paper, we present a fast top-k graph similarity search algorithm with high classification accuracy. We introduce a new graph similarity measure based upon the number of occurrences of subtree patterns in graphs. In order to accelerate search, we also construct hierarchical representative matrices for graph databases, where each row of the matrices represents a graph set. Using representative matrices, we can derive a similarity upper bound of a query graph and the graph set so as to reduce search space. Comprehensive experiments on real data sets demonstrate that our algorithm has a better performance than compared methods on classification accuracy and query time, and it also can scale to large data sets including 15 million chemical structure graphs.

Two Efficient Hashing Schemes for High-Dimensional Furthest Neighbor Search

The $c$ -Approximate Furthest Neighbor ( $c$ -AFN) search is a fundamental problem in many applications. However, existing hashing schemes for $c$ -AFN search are designed for internal memory. The old techniques for external memory, such as furthest point Voronoi diagram and the tree-based methods, are only suitable for the low-dimensional case. In this paper, we introduce a novel concept of the Reverse Locality-Sensitive Hashing (RLSH) family which is directly designed for $c$ -AFN search. Accordingly, we propose a new reverse query-aware LSH function, which is a random projection coupled with query-aware interval identification. Based on the reverse query-aware LSH functions, we introduce a novel Reverse Query-Aware LSH scheme named RQALSH for high-dimensional $c$ -AFN search over external memory. Our theoretical studies show that RQALSH enjoys a guarantee on query quality. In addition, in order to further speed up RQALSH, we propose a heuristic variant named RQALSH $^*$ which applies a data-dependent objects selection to largely reduce the number of data objects. In the experiment, we compare with two state-of-the-art hashing schemes QDAFN and DrusillaSelect which have been adapted for external memory. Extensive experiments on four real datasets show that our proposed RQALSH and RQALSH $^*$ schemes significantly outperform these two methods.

Suppression of the Coffee-Ring Effect and Evaporation-Driven Disorder to Order Transition in Colloidal Droplets.

The formation of a ring-like deposit at the periphery of a drying colloidal droplet is a vexing problem in many applications. We show a complete suppression of such deposits when a droplet of aqueous colloidal suspension, deposited on a glass substrate coated with a thin layer of silicone oil, is evaporated. This coating prevents the periphery of the aqueous droplet from getting pinned to the substrate and helps in suppressing the ring formation. It also decreases the surface area of the droplet, thereby decreasing the evaporation rate. These two factors together, driving the colloidal particles slowly to the center of the droplet, contribute to form an ordered crystallite at the end of the evaporation process. Brownian dynamics simulations performed to study ordering in the aggregate show that the spherical colloidal particles form face-centered cubic structures. Experiments and simulations show that slow rates of droplet evaporation and smaller-sized colloidal particles further lead to high-quality ordered colloidal crystallites.

Lightweight Hexagonal Boron Nitride Foam for CO2 Absorption.

Weak van der Waals forces between inert hexagonal boron nitride (h-BN) nanosheets make it easy for them to slide over each other, resulting in an unstable structure in macroscopic dimensions. Creating interconnections between these inert nanosheets can remarkably enhance their mechanical properties. However, controlled design of such interconnections remains a fundamental problem for many applications of h-BN foams. In this work, a scalable in situ freeze-drying synthesis of low-density, lightweight 3D macroscopic structures made of h-BN nanosheets chemically connected by poly(vinyl alcohol) (PVA) molecules via chemical cross-link is demonstrated. Unlike pristine h-BN foam which disintegrates upon handling after freeze-drying, h-BN/PVA foams exhibit stable mechanical integrity in addition to high porosity and large surface area. Fully atomistic simulations are used to understand the interactions between h-BN nanosheets and PVA molecules. In addition, the h-BN/PVA foam is investigated as a possible CO2 absorption and as laser irradiation protection material.

Simultaneous Calibration: A Joint Optimization Approach for Multiple Kinect and External Cameras.

Camera calibration is a crucial problem in many applications, such as 3D reconstruction, structure from motion, object tracking and face alignment. Numerous methods have been proposed to solve the above problem with good performance in the last few decades. However, few methods are targeted at joint calibration of multi-sensors (more than four devices), which normally is a practical issue in the real-time systems. In this paper, we propose a novel method and a corresponding workflow framework to simultaneously calibrate relative poses of a Kinect and three external cameras. By optimizing the final cost function and adding corresponding weights to the external cameras in different locations, an effective joint calibration of multiple devices is constructed. Furthermore, the method is tested in a practical platform, and experiment results show that the proposed joint calibration method can achieve a satisfactory performance in a project real-time system and its accuracy is higher than the manufacturer’s calibration.

Self-training semi-supervised classification based on density peaks of data

Abstract Having a multitude of unlabeled data and few labeled ones is a common problem in many practical applications. A successful methodology to tackle this problem is self-training semi-supervised classification. In this paper, we introduce a method to discover the structure of data space based on find of density peaks. Then, a framework for self-training semi-supervised classification, in which the structure of data space is integrated into the self-training iterative process to help train a better classifier, is proposed. A series of experiments on both artificial and real datasets are run to evaluate the performance of our proposed framework. Experimental results clearly demonstrate that our proposed framework has better performance than some previous works in general on both artificial and real datasets, especially when the distribution of data is non-spherical. Besides, we also find that the support vector machine is particularly suitable for our proposed framework to play the role of base classifier.

General Point Sampling with Adaptive Density and Correlations

Analyzing and generating sampling patterns are fundamental problems for many applications in computer graphics. Ideally, point patterns should conform to the problem at hand with spatially adaptive density and correlations. Although there exist excellent algorithms that can generate point distributions with spatially adaptive density or anisotropy, the pair-wise correlation model, blue noise being the most common, is assumed to be constant throughout the space. Analogously, by relying on possibly modulated pair-wise difference vectors, the analysis methods are designed to study only such spatially constant correlations. In this paper, we present the first techniques to analyze and synthesize point patterns with adaptive density and correlations. This provides a comprehensive framework for understanding and utilizing general point sampling. Starting from fundamental measures from stochastic point processes, we propose an analysis framework for general distributions, and a novel synthesis algorithm that can generate point distributions with spatio-temporally adaptive density and correlations based on a locally stationary point process model. Our techniques also extend to general metric spaces. We illustrate the utility of the new techniques on the analysis and synthesis of real-world distributions, image reconstruction, spatio-temporal stippling, and geometry sampling.

Numerical Solution of Partial Integrodifferential Equations of Diffusion Type

A collocation method based on linear Legendre multiwavelets is developed for numerical solution of one-dimensional parabolic partial integrodifferential equations of diffusion type. Such equations have numerous applications in many problems in the applied sciences to model dynamical systems. The proposed numerical method is validated by applying it to various benchmark problems from the existing literature. The numerical results confirm the accuracy, efficiency, and robustness of the proposed method.

Perception-Based $$\ell _p$$ ℓ p -Norm Minimization Approach for Nonlinear System Identification in GGD Noise

Nonlinear system identification is an important and fundamental problem in many practical applications. It becomes more challenging when the noise is non-Gaussian. Inspired by the cognitive dynamic system concept, we propose a perception-based \(\ell _p\)-norm minimization approach for nonlinear system identification in generalized Gaussian distribution noise environments. Volterra model is utilized to describe the nonlinear system. The proposed cognitive algorithm incorporates a closed feedback loop between perceptions and actions to the environments. Computer simulations have been carried out to illustrate the effectiveness of the proposed method.

Cluster merging based on a decision threshold

Data clustering is an important unsupervised learning technique and has wide application in various fields including pattern recognition, data mining, image analysis and bioinformatics. A vast amount of clustering algorithms have been proposed in the past decades. However, existing algorithms still face many problems in practical applications. One typical problem is the parameter dependence, which means that user-specified parameters are required as input and the clustering results are influenced by these parameters. Another problem is that many algorithms are not able to generate clusters of non-spherical shapes. In this paper, a cluster merging method is proposed to solve the above-mentioned problems based on a decision threshold and the dominant sets algorithm. Firstly, the influence of similarity parameter on dominant sets clustering results is studied, and it is found that the obtained clusters become larger with the increase in similarity parameter. We analyze the reason behind this behavior and propose to generate small initial clusters in the first step and then merge the initial clusters to improve the clustering results. Specifically, we select a similarity parameter which generates small but not too small clusters. Then, we calculate pairwise merging decisions among the initial clusters and obtain a merging decision threshold. Based on this threshold, we merge the small clusters and obtain the final clustering results. Experiments on several datasets are used to validate the effectiveness of the proposed algorithm.

A fast and accurate frequency estimation algorithm for sinusoidal signal with harmonic components.

Frequency estimation is a fundamental problem in many applications, such as traditional vibration measurement, power system supervision, and microelectromechanical system sensors control. In this paper, a fast and accurate frequency estimation algorithm is proposed to deal with low efficiency problem in traditional methods. The proposed algorithm consists of coarse and fine frequency estimation steps, and we demonstrate that it is more efficient than conventional searching methods to achieve coarse frequency estimation (location peak of FFT amplitude) by applying modified zero-crossing technique. Thus, the proposed estimation algorithm requires less hardware and software sources and can achieve even higher efficiency when the experimental data increase. Experimental results with modulated magnetic signal show that the root mean square error of frequency estimation is below 0.032 Hz with the proposed algorithm, which has lower computational complexity and better global performance than conventional frequency estimation methods.

Both Directional Organic Light-Emitting Diodes Using Thin Film Electrodes

Recently, transparent organic light-emitting diodes (TOLEDs) have attracted much interest, because they can open new applications for bi-directional displays, transparent displays, wearable displays, smart glass, and window lighting. Typically, the device structure of TOLEDs needs to consist of an ITO bottom anode, emitting layer, and a thin metal cathode. To protect underlying organic layer from sputtering damage, a thin metal-based semi-transparent cathode was necessarily used by thermal evaporation. In this system, there were many problems in application of ITO electrodes to high efficient TOLEDs. First, these TOLEDs usually have a preferential one-sided emission due to differences in reflection between ITO and Ag electrodes. In particular, the ITO-side emission ratio has a strong intensity which is more than 70% of the total emission ratio because of the relatively low reflection of ITO compared with Ag film. Second, the majority of the light generated in the organic layers is confined in the ITO anode and glass substrates due to the large difference in the refractive index between their layers. This results in out-coupling efficiencies of around only 20% due to the total internal reflection at the ITO / glass and glass / air interfaces. These problems can be solved by employing a thin metal (Ag) electrode as an ITO alternative. However, it is very difficult to form smooth and very thin Ag layers for transparent anode. Ag layers exhibit three-dimensional island growth as a result of the poor wettability of Ag on the substrate. A discrete Ag film consisting of an island structure exhibits strong absorption and scattering of incident light, because of localized surface plasmon resonance. Thus, thin Ag film was not considered as a transparent electrode for TOLED. Here, we demonstrate an innovative method to make transparent and high performance OLEDs on substrates using only ultra-thin Ag electrodes as both the top and bottom transparent electrodes. We show that merely a minute-plasma treatment on the glass before the deposition of Ag layer leads to significantly improved growth homogeneity of the Ag layer. The ultra-thin Ag film is formed after oxygen plasma treatment, leading to improved visible range transmittance and sheet resistance. By designing the weak micro-cavity structure with the ultra-thin Ag electrodes, waveguide modes could be suppressed and optical transmittance of TOLEDs could be improved. According to FDTD simulation, waveguide modes in ITO and glass substrate can be remarkably extracted because of weak micro-cavity effect. Consequently, the luminance of TOLED could be improved by 47 %. In addition, the transmittance of TOLED with the ultra-thin Ag electrode showed a high transmittance (73.84 %), which was similar than that of ITO (73.19%). The optimized weak micro-cavity structured shows the high optical transmittance, identical emission rate and reduced wave-guided mode were achieved, enhancing the luminance of devices.