A novel method for camera external parameters online calibration using dotted road line

On board camera is the most important information source for driver assistant application which is based on computer vision. One problem for on board camera is that the external parameters are easy to be changed when moving on the road. Traditional method either calibrates external parameters offline or calibrates external parameters semi-online, which both need human intervention. By observation, it is found that corner connection of dotted road lane can form two groups of parallel lines. Using this geometric characteristics and through theoretical derivation, a novel online camera external parameter calibration method is proposed which focus on the situation when vehicle is moving. The pro of this method is that it maintains relatively high calculation accuracy and more important, it does not require any human intervention in whole calibration process. Experimental and comparison results show that this method is simple and have accurate results which fully meet the requirements of practical application.     

Teeth segmentation of dental periapical radiographs based on local singularity analysis

Teeth segmentation for periapical raidographs is one of the most critical tasks for effective periapical lesion or periodontitis detection, as both types of anomalies usually occur around tooth boundaries and dental radiographs are often subject to noise, low contrast, and uneven illumination. In this paper, we propose an effective scheme to segment each tooth in periapical radiographs. The method consists of four stages: image enhancement using adaptive power law transformation, local singularity analysis using Hölder exponent, tooth recognition using Otsu's thresholding and connected component analysis, and tooth delineation using snake boundary tracking and morphological operations. Experimental results of 28 periapical radiographs containing 106 teeth in total and 75 useful for dental examination demonstrate that 105 teeth are successfully isolated and segmented, and the overall mean segmentation accuracy of all 75 useful teeth in terms of (TP, FP) is (0.8959, 0.0093) with standard deviation (0.0737, 0.0096), respectively.     

基于CY7C68013A的USB2.0高速接口设计

为了充分利用USB2.0的带宽,解决数据传输时存在的速度瓶颈问题,提出了一种基于CY7C68013A的USB2.0高速接口设计方法。采用CY7C68013A的SLAVE FIFO工作模式,芯片内部CPU不参与数据传输,FPGA设计的外部控制电路直接读写芯片内部FIFO,有效避免了内部CPU参与数据传输时带来的时间开销,从而提高了传输速度。     

JackHare: a framework for SQL to NoSQL translation using MapReduce

As data exploration has increased rapidly in recent years, the datastore and data processing are getting more and more attention in extracting important information. To find a scalable solution to process the large-scale data is a critical issue in either the relational database system or the emerging NoSQL database. With the inherent scalability and fault tolerance of Hadoop, MapReduce is attractive to process the massive data in parallel. Most of previous researches focus on developing the SQL or SQL-like queries translator with the Hadoop distributed file system. However, it could be difficult to update data frequently in such file system. Therefore, we need a flexible datastore as HBase not only to place the data over a scale-out storage system, but also to manipulate the changeable data in a transparent way. However, the HBase interface is not friendly enough for most users. A GUI composed of SQL client application and database connection to HBase will ease the learning curve. In this paper, we propose the JackHare framework with SQL query compiler, JDBC driver and a systematical method using MapReduce framework for processing the unstructured data in HBase. After importing the JDBC driver to a SQL client GUI, we can exploit the HBase as the underlying datastore to execute the ANSI-SQL queries. Experimental results show that our approaches can perform well with efficiency and scalability.     

纳米微胶囊技术与纳米化妆品研究进展

本文着重阐述了有关纳米胶囊的制备方法及最新研究进展,并展望了纳米技术及纳米微胶囊在化妆品领域的应用前景.     

D-HOCS: an algorithm for discovering the hierarchical overlapping community structure of a social network

Social networks often demonstrate a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities, i.e. communities in social networks may be overlapping. In this paper, we define a hierarchical overlapping community structure to present overlapping communities of a social network at different levels of granularity. Discovering the hierarchical overlapping community structure of a social network can provide us a deeper understanding of the complex nature of social networks. We propose an algorithm, called D-HOCS, to derive the hierarchical overlapping community structure of social networks. Firstly, D-HOCS generates a probability transition matrix by applying random walk to a social network, and then trains a Gaussian Mixture Model using the matrix. Further D-HOCS derives overlapping communities by analyzing mean vectors of the Gaussian mixture model. Varying the number of components, D-HOCS repeatedly trains the Gaussian mixture model, detecting the overlapping communities at different levels of granularity. Organizing the overlapping communities into a hierarchy, D-HOCS can finally obtain the hierarchical overlapping community structure of the social network. The experiments conducted on synthetic and real dataset demonstrate the feasibility and applicability of the proposed algorithm. We further employ D-HOCS to explore Enron e-mail corpus, and obtain several interesting insights. For example, we find out a coordinator who coordinated many sections of the Enron Corporation to complete an important task during first half of 2001. We also identify a community that corresponds to a real organization in Enron Corporation.     

Enhancing decisions with life cycle analysis for risk management

Although corporate financial distress is an infrequent occurrence, it has an extremely debilitating effect on the stability of a firm when it does occur. For this reason, an accurate risk assessment mechanism is needed in numerous industry sectors, particularly in financial institutions and banking. Based on corporation life cycle theory and risk management, this study develops a risk pre-warning model, namely the RSVMDT model, to eliminate serious financial punching and to examine the effectiveness of transparency and the full disclosure index (TFDI) during each life cycle stage. The RSVMDT model includes three techniques: random forest (RF), support vector machines (SVMs), and decision trees (DTs). The RF is used to determine the essential attributes of firms and therefore decrease the computational complexity of financial analysis and improve the classification accuracy. The SVM is employed as a classifier to identify corporations in financial distress. Finally, the DT is utilized as a rule generator that allows decision makers to adjust the financial structures of firms at specific life cycle stages. Together, these three techniques can increase the probability of corporate survival in a highly competitive environment. Additionally, the study further evaluates the importance of the TFDI during a turbulent economy. The public sectors can benefit from this evaluation by formulating future policies based on the rules derived from the developed RSVMDT model.     

Elastomeric properties of ethylene/propylene copolymers prepared by zirconocene/methylaluminoxane catalysts

Ethylene/propylene copolymers (EPM) have been prepared by a liquid propylene suspension process, using homogeneous catalysts based on nonbridged zirconocenes and methylaluminoxane (MAO). When bis(η⁵-cyclopentadienyl)zirconium dichloride (Cp₂ZrCl₂), bis(η⁵-cyclopentadienyl) dimethyl zirconium (Cp₂ZrMe₂) and bis(η⁵-cyclopentadienyl), dibenzyl zirconium (Cp₂ZrBz₂) were used as catalyst components, EPM having low average molecular weight (MW) were in general obtained in low yield. On the contrary, a very large improvement of productivity and MW was observed when bis(η⁵-indenyl)zirconium dichloride (Ind₂ZrCl₂) and bis(η⁵-indenyl) dimethyl zirconium (Ind₂ZrMe₂) as well as bis(η⁵-tetrahydroindenyl)zirconium dichloride [(IndH₄)₂ZrCI₂] were employed in combination with MAO. In particular, this last catalytic system allowed one to obtain EPM with a narrower distribution of MW in addition to the above characteristics. Better performances, in terms of rheological and elastomeric properties, were also found for the crude and vulcanized samples prepared by (IndH₄)₂ZrCl₂/MAO catalyst.     

Propylene epoxidation over Ti/MCM-41 catalysts prepared by chemical vapor deposition

Ti-containing mesoporous catalysts were prepared by chemical vapor deposition (CVD) of TiCl₄ on silica MCM-41 in the 700–900 °C temperature range. These samples were characterized (with XRD, ICP, nitrogen adsorption, FT-IR, ESCA, and TEM) and evaluated for the epoxidation of propylene with two alkyl hydroperoxides. The increase of CVD temperature resulted in the decrease of titanium content, catalyst hydroxyl population, crystallinity, and surface area. Catalyst selectivity to the desired product – propylene oxide – was highly sensitive to the deposition temperature. The best Ti/MCM-41 catalyst was prepared at the temperature of 800 °C, which had the maximum propylene oxide yield of 94.3%.     

正丁醇水溶液通氨气法制备TiO2粉体

以TiCl4为原料,正丁醇和水的混合液为溶剂,通入氨气促使TiCl4水解,制备出不同粒径的TiO2粉体.由X射线衍射、场发射扫描电镜和激光粒度仪对样品进行了表征.研究了温度、pH值对粉体的晶型、形貌和粒径的影响.结果表明:在80 ℃通氨气,3≤pH≤8时,所得TiO2粉体均为锐钛矿和金红石的混相,且pH=3时制得的粉体为60 nm左右的球形颗粒,分散性很好;而pH=6,8时所得的粉体团聚严重;室温(20～30 ℃)下通氨至pH=6也可得到分散性较好的无定型态TiO2粉体,但粉体粒径增大,约170nm左右.