A modified advancing layers mesh generation for thin three-dimensional objects with variable thickness

A method of generating modified advancing layers mesh is proposed. In this paper the mesh generation process of semi-unstructured prismatic/tetrahedral mesh is presented for relatively thin three-dimensional geometries with variable thickness, as in the case of injection molding analysis. Prismatic meshes are generated by offsetting initial surface triangular meshes. During the mesh generation process, mesh quality is efficiently improved with the use of a new node relocation method. Finally, tetrahedral meshes are automatically generated in the rest of the domain. The mesh generating capability of the proposed algorithm is demonstrated with the several practical test cases.     

Impact of bandwidth request schemes for Best-Effort traffic in IEEE 802.16 networks

In IEEE 802.16 networks, bandwidth request–grant schemes are employed for reducing data collision and supporting various QoS requirements. In this paper, we investigate the impact of such schemes on Best-Effort (BE) traffic. We examine three candidate schemes. In the first scheme, each Subscribe Station (SS) attempts to request bandwidth in every frame to reduce delay. In the second scheme, the number of bandwidth request is limited to avoid collisions in bandwidth request. In the third scheme, a base station allocates bandwidth to each SS based on the measured sending rate without explicit bandwidth request. We quantitatively analyze the performance of these schemes in terms of the collision rate and buffer length. We also present a simulation study to validate our analysis and to observe the impact of these schemes on BE traffic. This paper shows that IEEE 802.16 networks can be effectively managed through appropriate bandwidth request schemes. It is also shown that bandwidth allocation without request can be an alternative for increasing utilization.     

Tensor-Based AAM with Continuous Variation Estimation: Application to Variation-Robust Face Recognition

The active appearance model (AAM) is a well-known model that can represent a non-rigid object effectively. However, the fitting result is often unsatisfactory when an input image deviates from the training images due to its fixed shape and appearance model. To obtain more robust AAM fitting, we propose a tensor-based AAM that can handle a variety of subjects, poses, expressions, and illuminations in the tensor algebra framework, which consists of an image tensor and a model tensor. The image tensor estimates image variations such as pose, expression, and illumination of the input image using two different variation estimation techniques: discrete and continuous variation estimation. The model tensor generates variation-specific AAM basis vectors from the estimated image variations, which leads to more accurate fitting results. To validate the usefulness of the tensor-based AAM, we performed variation-robust face recognition using the tensor-based AAM fitting results. To do, we propose indirect AAM feature transformation. Experimental results show that tensor-based AAM with continuous variation estimation outperforms that with discrete variation estimation and conventional AAM in terms of the average fitting error and the face recognition rate.     

FDF: Frequency detection-based filtering of scanning worms

In this paper, we propose a simple algorithm for detecting scanning worms with high detection rate and low false positive rate. The novelty of our algorithm is inspecting the frequency characteristic of scanning worms instead of counting the number of suspicious connections or packets from a monitored network. Its low complexity allows it to be used on any network-based intrusion detection system as a real-time detection module for high-speed networks.Our algorithm need not be adjusted to network status because its parameters depend on application types, which are generally and widely used in any networks such as web and P2P services. By using real traces, we evaluate the performance of our algorithm and compare it with that of SNORT. The results confirm that our algorithm outperforms SNORT with respect to detection rate and false positive rate.     

Enabledness and termination in refinement algebra

Refinement algebras are abstract algebras for reasoning about programs in a total correctness framework. We extend a reduct of von Wright’s demonic refinement algebra with two operators for modelling enabledness and termination of programs. We show how the operators can be used for expressing relations between programs and apply the algebra to reasoning about action systems.     

Linkless Octree Using Multi‐Level Perfect Hashing

The standard C/C++ implementation of a spatial partitioning data structure, such as octree and quadtree, is often inefficient in terms of storage requirements particularly when the memory overhead for maintaining parent‐to‐child pointers is significant with respect to the amount of actual data in each tree node. In this work, we present a novel data structure that implements uniform spatial partitioning without storing explicit parent‐to‐child pointer links. Our linkless tree encodes the storage locations of subdivided nodes using perfect hashing while retaining important properties of uniform spatial partitioning trees, such as coarse‐to‐fine hierarchical representation, efficient storage usage, and efficient random accessibility. We demonstrate the performance of our linkless trees using image compression and path planning examples.     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.     

Tilt detection and servo control method for the holographic data storage system

Recently photopolymer with disk type substrate is selected as a media of the holographic data storage system. Because shape of the media is a disk, a disk tilt must be occur when the media rotates. When disk tilt occurs the angle between the reference beam and the media is changed by the tilt, thus the data can not be recorded with a right angle or another data page is retrieved. The holographic data storage system is very weak to the tilt disturbance by the Bragg effect, therefore it is necessary to detect the disk tilt angle and compensate it. In this study, we measure the amount of the tilt error using external photo detector and compensate it by rotating the angle of galvano mirror with our own algorithm.     

Design of moving magnet type pickup actuator using inserted coil

Recently, the demand of the information storage devices with large storage capacity such as Blu-ray Disc and high-definition television is increased. In keeping with this trend, the optical storage devices are also required to have high data transfer rate and large storage capacity. To satisfy these requirements, the actuator for optical disc drive should have a high servo bandwidth to compensate the vibration of optical disc. The servo bandwidth is limited by some flexible modes of the actuator, thus it is essential to make these frequencies of flexible modes to high frequency region. The frequency of flexible mode depends on materials and shape. Stiff materials and simple shape is useful to increase the frequency of flexible mode. In this paper, we suggested a moving magnet type actuator having flexible modes which are happened at high frequency region. Generally, the moving magnet type actuator has an advantage to increase the frequency of flexible mode because the moving magnet type actuator has simple structure and the Young’s modulus of magnet is high. However, large moving mass and inefficiency of Electromagnetic (EM) circuit cut down driving sensitivities of actuator. To improve driving sensitivities, we designed the model with the closed EM circuit for tracking actuation. The design of experiments (DOE) procedure is applied to get proper design parameters and the variable metric method (VMM) which is a technique of optimization is used to improve driving sensitivity. The lens holder is also improved based on the optimization result of EM circuit. And to make up for the low efficiency of EM circuit, the thermal stability is checked on condition that the input current is very high. At last, the final design of moving magnet type actuator is suggested and it is verified that the driving performance and the structural stiffness of the final design is sufficient.