Improving the Parameterization of Approximate Subdivision Surfaces

We provide a method for improving the parameterization of patching schemes that approximate Catmull‐Clark subdivision surfaces, such that the new parameterization conforms better to that of the original subdivision surface. We create this reparameterization in real‐time using a method that only depends on the topology of the surface and is independent of the surface's geometry. Our method can handle patches with more than one extraordinary vertex and avoids the combinatorial increase in both complexity and storage associated with multiple extraordinary vertices. Moreover, the reparameterization function is easy to implement and fast.     

Deletion, replacement and mean-shift for diagnostics in linear mixed models

Deletion, replacement and mean-shift model are three approaches frequently used to detect influential observations and outliers. For general linear model with known covariance matrix, it is known that these three approaches lead to the same update formulae for the estimates of the regression coefficients. However if the covariance matrix is indexed by some unknown parameters which also need to be estimated, the situation is unclear. In this paper, we show under a common subclass of linear mixed models that the three approaches are no longer equivalent. For maximum likelihood estimation, replacement is equivalent to mean-shift model but both are not equivalent to case deletion. For restricted maximum likelihood estimation, mean-shift model is equivalent to case deletion but both are not equivalent to replacement. We also demonstrate with real data that misuse of replacement and mean-shift model in place of case deletion can lead to incorrect results.     

Histology image analysis for carcinoma detection and grading

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.     

3D navigation and monitoring for spinal milling operation based on registration between multiplanar fluoroscopy and CT images

Milling operations in spinal surgery demand much experience and skill for the surgeon to perform the procedure safely. A 3D navigation method is introduced aiming at providing a monitoring system with enhanced safety and minimal intraoperative interaction. An automatic registration method is presented to establish the 3D-3D transformation between the preoperative CT images and a common reference system in the surgical space, and an intensity-based similarity metric adapted for the multi-planar configuration is introduced in the registration procedure. A critical region is defined for real-time monitoring in order to prevent penetration of the lamina and avoid violation of nerve structures. The contour of the spinal canal is reconstructed as the critical region, and different levels of warning limits are defined. During the milling procedure, the position of the surgical instrument relative to the critical region is provided with augmented display and audio warnings. Timely alarm is provided for surgeons to prevent surgical failure when the mill approaches the critical region. Our validation experiment shows that real-time 3D navigation and monitoring is advantageous for improving the safety of the milling operation.     

Power-efficient video encoding on resource-limited systems: A game-theoretic approach

Most mobile video applications are often deployed on battery-operated devices. With limited power supply, it is a challenging issue to support video applications with high resolution due to the complex functionality and high resource requirements. Thus, power-efficient design is important in computation intensive applications especially for mobile video terminals. Previous works on power-efficient control in video encoding systems focus on the low complexity design while typically ignoring the impact of scalable design by considering various power consumption involved in the encoding process. This paper is dedicated to developing a power-scalable video encoding (PSVE) strategy for energy-limited mobile terminals. PSVE can help the video encoding terminal to adjust its power consumption budget efficiently so as to enhance the power-scalable capability in mobile video terminals. This paper first establishes game theoretical analysis to model the power consumption problem as a bargaining problem. Then, the tradeoff between encoding effect and power consumption achieved by the use of game theory. The scalable and low power video encoding system based on Nash equilibrium solution is derived through the analysis on the power consumption and encoding effect. Experimental results demonstrate the efficiency of the proposed approach.     

Pinning a stochastic neural network to the synchronous state

In this paper, the asymptotic stability of the pinning synchronous solution of stochastic neural networks with and without time-delays is analyzed. The delays are time-varying, and the uncertainties are norm-bounded that enter into all the parameters of network and control. The aim of this paper is not only to establish easily verifiable conditions under which the pinning synchronous solution of stochastic neural network is globally asymptotically stable but also to give a feasible way to offset the limitation of network itself in order to reach synchronization. In addition, a specific neurobiological network is also introduced, and some numerical examples are provided to illustrate the applicability of the proposed criteria.     

Hoeffding bound based evolutionary algorithm for symbolic regression

In symbolic regression area, it is difficult for evolutionary algorithms to construct a regression model when the number of sample points is very large. Much time will be spent in calculating the fitness of the individuals and in selecting the best individuals within the population. Hoeffding bound is a probability bound for sums of independent random variables. As a statistical result, it can be used to exactly decide how many samples are necessary for choosing i individuals from a population in evolutionary algorithms without calculating the fitness completely. This paper presents a Hoeffding bound based evolutionary algorithm (HEA) for regression or approximation problems when the number of the given learning samples is very large. In HEA, the original fitness function is used in every k generations to update the approximate fitness obtained by Hoeffding bound. The parameter 1?δ is the probability of correctly selecting i best individuals from population P, which can be tuned to avoid an unstable evolution process caused by a large discrepancy between the approximate model and the original fitness function. The major advantage of the proposed HEA algorithm is that it can guarantee that the solution discovered has performance matching what would be discovered with a traditional genetic programming (GP) selection operator with a determinate probability and the running time can be reduced largely. We examine the performance of the proposed algorithm with several regression problems and the results indicate that with the similar accuracy, the HEA algorithm can find the solution more efficiently than tradition EA. It is very useful for regression problems with large number of training samples.     

Tool path planning for 5-axis flank milling of ruled surfaces considering CNC linear interpolation

This paper investigates tool path planning for 5-axis flank milling of ruled surfaces in consideration of CNC linear interpolation. Simulation analyses for machining error show insights into the tool motion that generates a precision machined surface. Contradicting to previous thoughts, the resultant tool path does not necessarily produce minimal machining error when the cutter contacts the rulings of a developable surface. This effect becomes more significant as the distance between two cutter locations is increased. An optimizing approach that adjusts the tool position locally may not produce minimal error as far as the entire surface is concerned. The optimal tool path computed by a global search scheme based on dynamic programming supports this argument. A flank milling experiment and CMM measurement further validate the findings of this work.     

关于复正规矩阵的Rayleigh商

将Hermite矩阵的Rayleigh商推广到了复正规矩阵中,研究了复正规矩阵的Ray-leigh商的一些性质,其结果具有一定的理论价值和应用价值。