39739560
Optimal Motion Generation of Flexible Macro-micro Manipulator Systems Using Estimation of Distribution Algorithm
In this paper, a new approach for motion generation and optimization of the flexible macro-micro manipulator system is proposed based on Estimation of Distribution Algorithm (EDA). The macro-micro manipulator system is a redundant system, of which inverse kinematics remains challenging, with no generic solution to date. Here, the manipulator system configurations, or the optimal joint motions, are generated using the EDA algorithm base on Gaussian probability model. Compared with simple genetic algorithms (SGA), this approach uses fewer parameters and the time for motion optimization is remarkably reduced. The proposed approach shows excellent performance on motion generation and optimization of a flexible macro-micro manipulator system, as demonstrated by the simulation results.
44825167
Restoring warped document image through segmentation and full page interpolation
In camera-based optical character recognition (OCR) applications, warping is a primary problem. Warped document images should be restored before they are recognized by traditional OCR algorithm. This paper presents a novel restoration approach, which first makes an estimation of baseline and vertical direction estimation based on rough line and character segmentation, then selects several key points and determines their restoration mapping as a result of the estimation step, at last performs Thin-Plate Splines (TPS) interpolation on full page image using these key points mapping. The restored document image is expected to have straight baselines and erect character direction. This method can restore arbitrary local warping as well as keep the restoration result natural and smooth, consequently improves the performance of the OCR application. Experiments on several camera captured warped document images show effectiveness of this approach.
35510017
A Link-Based Rank of Postings in Newsgroup
Discussion systems such as Usenet, BBS, Forum are important resources for information sharing, view exchanging, problem solving and product feedback, etc. on Internet. The postings in newsgroups on Usenet represents the judgments and choices of participators. The structure of postings could provide helpful information for the users. In this paper, we present a method called PostRank to rank the postings based on the structure of newsgroup. Its results correspond to the eigenvectors of the transition probability matrix and the stationary vectors of the Markov chains. It could provide useful global information for the newsgroup and it can be used to help the users access information in it more effectively and efficiently. This method can be also applied on other discussion systems. Some experimental results and discussions on real data sets collected by us are also provided.
9190159
Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene Expression Data
Abstract Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectation maximization (SEM) to model gene relationship. It is well-suited for analyzing the time-series data and can deal with cyclical structures that can not be tackled by static Bayesian network. We applied the new method to learning the regulatory network and the metabolic pathway from Saccharomyces Cerevisiae cell cycle gene expression data. The results show that the proposed method is capable of handling missing values in expression data sets, and the inference accuracy can further be improved. Keyword: Microarrays; Gene regulatory networks; Dynamic Bayesian network; Structural expectation maximization 1. Introduction The establishment of gene regulatory network is critical to the understanding of the genetic regulation process. This problem has become an important challenge in recent years. The invention of microarray technology is viewed as a milestone, which helps scientist measure expression levels of thousands of genes simultaneously. Several methods have been presented so far to learn gene network from microarray data, such as Boolean networks [2, 3], differential equations [4, 5], and Bayesian networks [6-8]. Among all of them, Bayesian network based approach has received a lot of attention because of the probabilistic nature of this model. It can be used to learn causal relationship and particularly, combine it with prior knowledge readily. However, there exist a lot of shortcomings for the static version of Bayesian network. First, it is unable to capture the temporal information. Second, it is impossible to model cyclic network, which is often considered to be an accurate description of real gene regulation mechanism. In this paper, we employ dynamic Bayesian network (DBN) [1, 9,10], instead of its earlier static version, to model a gene network with cyclic regulation. In general, the DBN is well-suited for characterizing time-series gene expression data. Owing to the limitation of experimental condition, there are many missing values in the gene expression data sets, which usually have an impact on the inference accuracy. To address this problem, we propose a new DBN model embedded with
58833855
EDFCES: a new example-driven 3D face construction and editing system
This paper presents an automatic runtime system for generating varied, realistic face models by synthesizing a global face shape and local facial features according to intuitive, high-level control parameters. Our method takes as examples 3D face scans in order to exploit the parameter-to-geometry correlations present in real faces. In order to establish the correspondences among the scanned models, we use a three-step model fitting approach to conform a generic head mesh onto each scanned model. We transform the obtained data sets of global face shapes and local feature shapes into vector space representations by applying a principal component analysis (PCA). We compute a set of face anthropometric measurements to parameterize the exemplary shapes in the measurement spaces. Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions designed to generate the desired face shape by taking anthropometric parameters as input. At runtime, the interpolation functions are evaluated for the input parameter values to produce new face geometries at an interactive rate. The correspondence among all exemplary face textures is obtained by parameterizing the 3D generic mesh over a 2D image domain. The new feature texture with the desired attributes is synthesized by interpolating the example textures. The resulting system is intuitive to control and fine-grained. We demonstrate our method by applying different parameters to generate a wide range of face models.
40423470
Proactive Communication in Observation-Based Agent Team Corporation
46276460
ACO Based QoS Routing Algorithm for Wireless Sensor Networks
In this paper, we proposed an approach for Quality of Service (QoS) routing algorithm of Wireless Sensor Networks (WSNs) based on Ant Colony Optimization (ACO). The special characteristics of WSNs need to reduce the computational complexity and energy consumption of the QoS routing algorithm especially. We note that ACO algorithm using collective intelligence of artificial ants as intelligent agents is very appropriate to solve the combinatorial optimization problems in a fully distributed way, so in this paper we use modified ACO approach to solve Delay Constraint Maximum Energy Residual Ratio (DCMERR) QoS routing problem of WSNs. The QoS routing solution proposed in this manuscript, which is named as ACO based QoS routing algorithm (ACO-QoSR), searches for the best paths, which are satisfied with the QoS requirements with intelligent artificial ants. To overcome the problem of limited energy in WSNs, there are some modifications to enhance ACO’s convergence rate. ACO-QoSR algorithm is the tradeoff between a certain guaranteed QoS requirements and acceptable computational complexity. The simulation results verify that ACO-QoSR algorithm can reduce the selected paths’ delay and improve the selected paths’ normalized energy residual ratio at the similar levels of routing overhead.
17443935
Facilitating an Active Transmit-only RFID System Through Receiver-based Processing
Many asset tracking applications demand long-lived, low-cost, and continuous monitoring of a large number of items, which has posed a significant challenge to today's RFID design. In order to satisfy these requirements, we propose to adopt transmit-only tags without a receiver, which can offer both low power and low cost. In spite of their great potential, such a platform faces many challenges since it cannot sense the channel, causing the collisions among tag transmissions to be high. It is thus crucial to employ effective multi-user detection schemes at the tag reader to extract valid information from collided signals. Traditional detection schemes, such as successive cancellation, cannot be directly applied to the targeted system. Firstly, due to the simplicity of receiver-less transmit-only tags, there is no mechanism for feedback to the tags that is traditionally needed for accurate multi-user detection. More importantly, these schemes impose serious processing and memory requirements on the underlying system, which makes real-time tracking impossible. In this study, we address these challenges by performing a statistical estimation of the signal amplitude, and by dividing the received signal sequence (from all the tags) and assigning each block to one reader. We also adopt an online learning mechanism so that readers can anticipate the tags that belong to them. We show that the proposed detection algorithm can achieve low detection error under realistic system conditions.
18825124
A Trajectory Clustering Algorithm Based on Symmetric Neighborhood
Trajectory clustering is attractive for the task of class identification in spatial database. Existing trajectory clustering algorithm TRCLUS uses global parameters to discover common trajectories. However, it can not discover small and dense clusters and be sensitive to two input parameters. Based on the partition-and-group framework, we propose a simple but effective trajectory clustering algorithm based on symmetric neighborhood named BSNTC, which needs only one input parameter which eases the sensitivity of parameters in a certain extent. The proposed measure considers both neighbors and reverse neighbors of trajectories when estimating its density distribution. Also, we use an accumulator without calculating influence outlier of each trajectory to reduce the computation cost and corresponding storage cost. A comprehensive performance evaluation and analysis shows that our method is not only efficient in the computation but also effective in arbitrary shape and different densities trajectory databases.