Mesh Editing in ROI with Dual Laplacian

title: Cut-and-Paste Editing over 3D Meshes
abstract: A novel direct cut-and-paste editing method over 3D triangular meshes is proposed in this paper. The source feature mesh is transferred to the position where it is to be pasted through the corresponding planes. The planes are created using the least squares approximation. To decrease the distortion of pasting region, the local mesh representation is used, and the pasted region is reconstructed in the least squares sense. Then, the boundary vertices of the source feature mesh are connected with the target mesh. This method is applicable to source geometry features with non-zero genus. It can be performed at interactive rates and produces less distortion and natural results

title: Boundary Constrained Manifold Unfolding

title: An Energy-Minimizing Mesh Parameterization
abstract: In this paper, we propose a new energy-minimizing mesh parameterization method, which linearly combines two new energies EQ and EM. It not only avoids triangles overlap in the parameter domain, but also is invariant under rotation, translation and scale transformations. We first parameterize the original 3D mesh to the parameter plane by using the energy-minimizing parameterization, and get the optimal effect by optimizing the weights wij gradually. Experimental results indicate that this optimized energy-minimizing method has low distortion and good stability.

title: Classification by Association Rule Analysis
abstract: An interface between a dual mode mobile station and a piece of data terminal equipment supports both a logical digital interface and a logical analog interface, with the analog interface passing through a modem. At set-up of either a mobile originated or mobile terminated data/fax call, a supporting dual mode communications network determines whether a digital traffic channel is available on an air interface. If so, the channel is allocated to the mobile station, and data/fax communication with the data terminal equipment occurs using the logical digital interface. Otherwise, an analog voice channel is allocated, and the data/fax communication with the data terminal equipment occurs using the logical analog interface and modem. Within the supporting network, data/fax communications via the digital traffic channel are routed through an interworking functionality that terminates a radio link protocol utilized for communicating over the digital air interface. Analog voice channel data/fax communications are instead routed through a modem pool that terminates a selected modem protocol utilized by the modem for communicating over the analog air interface.

title: Analog circuit optimization system based on hybrid evolutionary algorithms
abstract: This paper investigates a hybrid evolutionary-based design system for automated sizing of analog integrated circuits (ICs). A new algorithm, called competitive co-evolutionary differential evolution (CODE), is proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through electrical simulation, to the optimization system in the MATLAB environment, once a circuit topology is selected. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met, even in highly-constrained situations. Comparisons with available methods like genetic algorithms and differential evolution, which use static penalty functions to handle design constraints, have also been carried out, showing that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

title: Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images
abstract: Region of interest (ROI) is a region used to extract features. In breast ultrasound (BUS) image, the ROI is a breast tumor region. Because of poor image quality (low SNR (signal/noise ratio), low contrast, blurry boundaries, etc.), it is difficult to segment the BUS image accurately and produce a ROI which precisely covers the tumor region. Due to the requirement of accurate ROI for feature extraction, fully automatic classification of BUS images becomes a difficult task. In this paper, a novel fully automatic classification method for BUS images is proposed which can be divided into two steps: ''ROI generation step'' and ''ROI classification step''. The ROI generation step focuses on finding a credible ROI instead of finding the precise tumor location. The ROI classification step employs a novel feature extraction and classification strategy. First, some points in the ROI are selected as the ''classification checkpoints'' which are evenly distributed in the ROI, and the local texture features around each classification checkpoint are extracted. For each ROI, all the classification checkpoints are classified. Finally, the class of the BUS image is determined by analyzing every classification checkpoint in the corresponding ROI. Both steps were implemented by utilizing a supervised texture classification approach. The experiments demonstrate that the proposed method is very robust to the segmentation of BUS images, and very effective and useful for classifying breast tumors.

title: Incremental Clustering Based on Swarm Intelligence
abstract: We propose methods for incrementally constructing a knowledge model for a dynamically changing database, using a swarm of special agents (ie an ant colony) and imitating their natural cluster-forming behavior. We use information-theoretic metrics to overcome some inherent problems of ant-based clustering, obtaining faster and more accurate results. Entropy governs the pick-up and drop behaviors, while movement is guided by pheromones. The primary benefits are fast clustering, and a reduced parameter set. We compared the method both with static clustering (repeatedly applied), and with the previous dynamic approaches of other authors. It generated clusters of similar quality to the static method, at significantly reduced computational cost, so that it can be used in dynamic situations where the static method is infeasible. It gave better results than previous dynamic approaches, with a much-reduced tuning parameter set. It is simple to use, and applicable to continuously- and batch-updated databases.

title: Targeting the right students using data mining
abstract: education domain offers a fertile ground for many interesting and challenging data mining applications. These applications can help both educators and students, and improve the quality of education. In this paper, we present a real-life application for the Gifted Education Programme (GEP) of the Ministry of Education (MOE) in Singapore. The application involves many data mining tasks. This paper focuses only on one task, namely, selecting students for remedial classes. Traditionally, a cut-off mark for each subject is used to select the weak students. That is, those students whose scores in a subject fall below the cut-off mark for the subject are advised to take further classes in the subject. In this paper, we show that this traditional method requires too many students to take part in the remedial classes. This not only increases the teaching load of the teachers, but also gives unnecessary burdens to students, which is particularly undesirable in our case because the GEP students are generally taking more subjects than non-GEP students, and the GEP students are encouraged to have more time to explore advanced topics. With the help of data mining, we are able to select the targeted students much more precisely.