The point set is a flexible surface representation suitable for both geometry processing and real-time rendering. In most applications, the control of the point cloud density is crucial and being able to refine a set ...
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The point set is a flexible surface representation suitable for both geometry processing and real-time rendering. In most applications, the control of the point cloud density is crucial and being able to refine a set of points appears to be essential. In this paper we present a new interpolatory refinement framework for point-based geometry. First we carefully select an appropriate one-ring neighborhood around the central interpolated point. Then new points are locally inserted where the density is too low using a root 3-like refinement procedure and they are displaced on the corresponding curved Point Normal triangle. Thus, a smooth surface is reconstructed by combining the smoothing property produced by the rotational effect of root 3-like refinements with the points/normal interpolation of PN triangles. In addition we show how to handle sharp features and how our algorithm naturally fills large holes in the geometry. Finally, we illustrate the robustness of our approach, its real-time capabilities and the smoothness of the reconstructed surface on a large set of input models, including irregular and sparse point clouds.
An anchoring theory of lightness perception comprehensively explains many characteristics of human visual system such as lightness constancy and its spectacular failures which are important in the perception of images...
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An anchoring theory of lightness perception comprehensively explains many characteristics of human visual system such as lightness constancy and its spectacular failures which are important in the perception of images. We present a novel approach to tone mapping of high dynamic range (HDR) images which is inspired by the anchoring theory. The key concept of this method is the decomposition of an HDR image into areas (frameworks) of consistent luminance and the local calculation of the lightness values. The net lightness of an image is calculated via the merging of the frameworks proportionally to their strength. We stress out the importance of relating the luminance to a known brightness value (anchoring) and investigate the advantages of anchoring to the luminance value perceived as white. We validate the accuracy of the lightness reproduction in the presented algorithm by simulating a well known perception experiment. Our approach does not affect the local contrast and preserves the natural colors of an HDR image due to the linear handling of luminance.
The authors consider transmitter power control of wireless communication systems. They propose an adaptive second-order distributed power control algorithm in which the relaxation factors are adaptively adjusted to im...
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The authors consider transmitter power control of wireless communication systems. They propose an adaptive second-order distributed power control algorithm in which the relaxation factors are adaptively adjusted to improve the rate of convergence. The algorithm updates power using a weighted combination of the distributed power control algorithm and the second-order power control (SOPC) algorithm. Simulation results show performance improvement over distributed constrained power control and constrained SOPC.
We introduce a parallel chaos-based encryption algorithm for taking advantage of multicore processors. The chaotic cryptosystem is generated by the piecewise linear chaotic map (PWLCM). The parallel algorithm is desig...
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We introduce a parallel chaos-based encryption algorithm for taking advantage of multicore processors. The chaotic cryptosystem is generated by the piecewise linear chaotic map (PWLCM). The parallel algorithm is designed with a master/slave communication model with the Message Passing Interface (MPI). The algorithm is suitable not only for multicore processors but also for the single-processor architecture. The experimental results show that the chaos-based cryptosystem possesses good statistical properties. The parallel algorithm provides much better performance than the serial ones and would be useful to apply in encryption/decryption file with large size or multimedia.
In the process of sudden natural disasters (such as earthquake or typhoon), the active mass damper (AMD) system can reduce the structural vibration response optimally, which serves as a frequently applied but less mat...
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In the process of sudden natural disasters (such as earthquake or typhoon), the active mass damper (AMD) system can reduce the structural vibration response optimally, which serves as a frequently applied but less mature vibration-reducing technology in wind and earthquake resistance of high-rise buildings. As the core of this technology, the selection of control algorithm is extremely challenging due to the uncertainty of structural parameters and the randomness of external loads. It is not necessary for the Model Reference Adaptive Control (MRAC) based on the Minimal Controller Synthesis (MCS) algorithm to know in advance the structural parameters, which produces special advantages in conditions of real-time change of system parameters, uncertain external disturbance, and the nonlinear dynamic system. This paper studies the application of the MRAC into the AMD active control system. The principle of MRAC algorithm is recommended and the dynamic model and the motion differential equation of AMD system based on MRAC is established under seismic excitation. The simulation analysis for linear and nonlinear structures when the structural stiffness is degenerated is performed under AMD system controlled by MRAC algorithm. To verify the validity of the MRAC over the AMD system, experimental tests are carried out on a linear structure and a structure with variable stiffness with the AMD system under seismic excitation on the shake table, and the experimental results are compared with those of the traditional pole assignment control algorithm. (C) 2014 Elsevier Ltd. All rights reserved.
A mathematical model must be established to study the motions of ships in order to control them effectively. An assessment of the model depends on the accuracy of hydrodynamic parameters. An algorithm for the paramete...
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A mathematical model must be established to study the motions of ships in order to control them effectively. An assessment of the model depends on the accuracy of hydrodynamic parameters. An algorithm for the parameter identification of the coupled pitch and heave motions in ships is, thus, put forward in this paper. The algorithm proposed is based on particle swarm optimization (PSO) and the opposition-based learning theory known as opposition-based particle swarm optimization (OPSO). A definition of the opposition-based learning algorithm is given first of all, with ideas on how to improve this algorithm and its process being presented next. Secondly, the design of the parameter identification algorithm is put forward, modeling the disturbing force and disturbing moment of the identification system and the output parameters of the identification system. Then, the problem involving the hydrodynamic parameters of motions is identified and the coupled pitch and heave motions of a ship described as an optimization problem with constraints. Finally, the numerical simulations of different sea conditions with unknown parameters are carried out using the PSO and OPSO algorithms. The simulation results show that the OPSO algorithm is relatively stable in terms of the hydrodynamic parameters identification of the coupled pitch and heave motions.
This paper proposes a novel particle filter based gradient iterative algorithm for the identification of dual-rate nonlinear systems. The novel particle filter is applied to estimate the missing outputs, and the measu...
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This paper proposes a novel particle filter based gradient iterative algorithm for the identification of dual-rate nonlinear systems. The novel particle filter is applied to estimate the missing outputs, and the measurable outputs are utilized to adjust the weights of particles during each interval of the slow sampled rate. Then the missing outputs and the unknown parameters can be estimated iteratively by the novel particle filter based gradient iterative algorithm. The simulation results indicate that the proposed method is more effective than the classical auxiliary model method. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
An algorithm for machine recognition of human gender by face images based on machine learning methods is described. The synthesized algorithm consists of two stages, viz. extraction of adaptive features and support ve...
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An algorithm for machine recognition of human gender by face images based on machine learning methods is described. The synthesized algorithm consists of two stages, viz. extraction of adaptive features and support vector machine classification. Comparative analysis of operation of the proposed algorithm is performed, and the training and testing technique is given.
作者：
Hsu, Chia-YuYuan Ze Univ
Dept Informat Management Taoyuan 32003 Taiwan Yuan Ze Univ
Innovat Ctr Big Data & Digital Convergence Taoyuan 32003 Taiwan
Wafer bin map (WBM) represents specific defect pattern that provides information for diagnosing root causes of low yield in semiconductor manufacturing. In practice, most semiconductor engineers use subjective and tim...
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Wafer bin map (WBM) represents specific defect pattern that provides information for diagnosing root causes of low yield in semiconductor manufacturing. In practice, most semiconductor engineers use subjective and time-consuming eyeball analysis to assess WBM patterns. Given shrinking feature sizes and increasing wafer sizes, various types of WBMs occur;thus, relying on human vision to judge defect patterns is complex, inconsistent, and unreliable. In this study, a clustering ensemble approach is proposed to bridge the gap, facilitating WBM pattern extraction and assisting engineer to recognize systematic defect patterns efficiently. The clustering ensemble approach not only generates diverse clusters in data space, but also integrates them in label space. First, the mountain function is used to transform data by using pattern density. Subsequently, k-means and particle swarm optimization (PSO) clustering algorithms are used to generate diversity partitions and various label results. Finally, the adaptive response theory (ART) neural network is used to attain consensus partitions and integration. An experiment was conducted to evaluate the effectiveness of proposed WBMs clustering ensemble approach. Several criterions in terms of sum of squared error, precision, recall, and F-measure were used for evaluating clustering results. The numerical results showed that the proposed approach outperforms the other individual clustering algorithm.
A cross-layer scheduling algorithm that combines packet scheduling and an efficient bandwidth allocation for orthogonal frequency division multiple access (OFDMA) systems with heterogeneous user's delay requiremen...
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A cross-layer scheduling algorithm that combines packet scheduling and an efficient bandwidth allocation for orthogonal frequency division multiple access (OFDMA) systems with heterogeneous user's delay requirements are proposed. The proposed work employs channel fluctuation and queue states in their decision rules for packet scheduling, which determines the optimum subcarrier allocation policy to maximise the system throughput subject to individual user's delay constraints. The delay sensitive subcarrier allocation strategy has linear complexity with respect to the number of users and number of subcarriers. The cross-layer scheduling algorithm also use an adaptive modulation and coding (AMC) scheme, which adapt according to the channel condition and queue states in terms of signal-to-noise ratio (SNR) constraints to improve the bandwidth usage in an efficient manner. The simulation results show that substantial throughput gain and spectral efficiency improvement are obtained while satisfying the delay constraint when the delay sensitive jointly optimum rate and subcarrier allocation policy with AMC is adopted.
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