Correction of color information of a 3D model using a range intensity image

Most active optical range sensors record, simultaneously with the range image, the amount of light reflected at each measured surface location: this information forms what is called a range intensity image, also known as a reflectance image. This paper proposes a method that uses this type of image for the correction of the color information of a textured 3D model. This color information is usually obtained from color images acquired using a digital camera. The lighting condition for the color images are usually not controlled, thus this color information may not be accurate. On the other hand, the illumination condition for the range intensity image is known since it is obtained from a controlled lighting and observation configuration, as required for the purpose of active optical range measurement. The paper describes a method for combining the two sources of information, towards the goal of compensating for a reference range intensity image is first obtained by considering factors such as sensor properties, or distance and relative surface orientation of the measured surface. The color image of the corresponding surface portion is then corrected using this reference range intensity image. A B-spline interpolation technique is applied to reduce the noise of range intensity images. Finally, a method for the estimation of the illumination color is applied to compensate for the light source color. Experiments show the effectiveness of the correction method using range intensity images.     

Pharmacophore and docking-based combined in-silico study of KDR inhibitors

The growth and metastasis of solid tumors is dependent on angiogenesis. The vascular endothelial growth factor (VEGF) and its cell surface receptor in human KDR (kinase domain containing receptor or VEGFR-2) have particular interest because of their importance in angiogenesis. The development of novel inhibitors of VEGFR-2 would be helpful to check the growth of tumors. Quantitative structure activity relationship (QSAR) analyses used to understand the structural factors affecting inhibitory potency of thiazole-substituted pyrazolone derivatives. Several pharmacophore-based models indicated the importance of steric, hydrophobic and hydrogen bond acceptor groups to inhibitory activity. The comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analyses (CoMSIA) based 3D-QSAR models were derived using pharmacophore-based alignment. Both CoMFA (q² = 0.70, r² = 0.97 and ) and CoMSIA (q² = 0.54, r² = 0.82 and ) gave reasonable results. The molecular docking (receptor-guided technique) with a recently reported receptor structure (PDB = 1YWN) were performed. The docked alignment was subsequently used for 3D-QSAR (CoMFA; q² = 0.56, r² = 0.97, , CoMSIA; q² = 0.58 r² = 0.91, ). The overall both studies were indicated, steric, electrostatic and hydrogen bond acceptor effects contribute to the inhibitory activity. CoMFA and CoMSIA models suggested that a positive bulk with hydrophobic effect is desirable around position 4 and 5 and hydrogen bond acceptor groups around pyrazolones ring will be helpful.     

Automated analysis of images in documents for intelligent document search

Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure’s legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Auto-generation of IEC standard PLC code using t-MPSG

The objective of this paper is to reduce the development time of a PLC (Programmable Logic Controller) by automating the task of code generation. For this purpose, we applied t-MPSG (Timed-Message Based Part State Graph). The t-MPSG is an extended finite state automata used to model and generate an execution module for a real-time shop floor controller system. In our proposed method, t-MPSG is used to model the formal specification of the controller system that can be translated into textual structure. After the verification of the t-MPSG model, it can be used as an input to the plc-builder tool. The plc-builder tool is an extended version of a conventional MPSG simulator. It can be used to translate the textual structure of the t-MPSG into an IEC standard PLC code. Finally, the generated code can be downloaded to a PLC emulator or a PLC device for the purpose of simulation and execution. The similarity in the hierarchical structure of the t-MPSG and the IEC standard PLC program has made it convenient to transform from one form to another. Furthermore, an illustration of the methodology to auto-generate IEC standard PLC code using t-MPSG is explained with a suitable example. Recommended by Editorial Board member Young Soo Suh under the direction of Editor Jae Weon Choi. This work was partially supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD080042AD). Devinder Thapa is a Postdoc Research Fellow in the Department of Industrial & information systems at Ajou University, Korea. He completed his Ph.D. from Ajou University in Industrial and Information Systems Engineering. His area of research is related to manufacturing automation and intelligent decision support systems. Chang Mok Park is a Professor in the Department of Technology & Systems Management at Induk Institute of Technology. He completed his Ph.D. in 2002 from Ajou University in Industrial Engineering. His research interest is related to manufacturing optimization, discrete event system simulation and signal analysis. Sang C. Park is an Associate Professor in the Department of Industrial & Information Systems Engineering at Ajou University. He received his B.S., M.S., and Ph.D. degrees from KAIST in 1994, 1996, and 2000, respectively, all in Industrial Engineering. His research interests include geometric algorithms in CAD/CAM, process planning, engineering knowledge management, and discrete event system simulation. Gi-Nam Wang is the Head and a Professor in the Department of Industrial & Information Systems Engineering at Ajou University, Korea. He completed his Ph.D. in 1992 from Texas A&M University, in Industrial Engineering. He has worked as Visiting Professor at University of Texas at Austin during 2000–2001. His area of research is related to Intelligent Information & manufacturing systems, system integration & automation, e-Business solutions and image processing.     

基于ART2神经网络的车辆感应波形识别的方法

在智能交通系统中,无论是在交通监控领域还是在不停车收费系统方面,对车辆进行自动分类都十分重要;环形线圈车辆检测器以其良好的适应性、稳定性和高效性在车辆监测方面得到了广泛的应用,同时利用同类型或同种车辆经过环形线圈产生轮廓相似的电磁感应波形这一特点也可以进行车型识别;对波形进行预处理,以波形轮廓的抽样、量化值作为特征向量,将特征向量作为ART2神经网络的输入向量,经ART2神经网络的自动聚类最终实现车辆感应波形的聚类与识别。     

以学习理论探讨基于全球标准数字学习与测评的系统架构及有效学习模式的实证分析

本文通过研究学习理论探讨了ICT信息与网络通讯核心能力常用应用软件适性学习系统的架构。首先就IC3学习系统与精熟学习理论、适性学习理论、鹰架学习理论、示例学习理论及双代码学习理论基础进行了说明,其次利用学生学习数据进行IC3 M-B(Mentor-Benchmark)学习模式的实证性分析,最后就IC3常用应用软件适性学习系统对目前ICT信息与网络通讯技术能力的教学模式、策略以及未来的努力方向提出建议。