5638647
Processing and Analysis of Ground Penetrating Radar Landmine Detection
There are mainly two types of landmine, anti-tank mine (ATM) and anti-personnel mine (APM), which kill or maim people around the world. Much research effort has been devoted in trying to detect and remove APM. APM is smaller than ATM and in addition, most of them are made of plastic making them more difficult to be detected. Some of the common mine detection techniques include using sensors to get raw data, signal processing and image processing. Plastic APM has dielectric properties, this makes it similar to that of soil and the reflection from the mine is usually weak and masked by background. It is therefore very difficult to detect this type of landmine with conventional Ground Penetrating Radar (GPR), though it is a good method to detect metallic landmine. Many new techniques have appeared in recent years in an effort to solve this so called “inverse scattering problem”. In this paper, we review and discuss the GPR data acquisition, signal processing, image processing methods.
2813744
Landmine Feature Extraction and Classification of GPR Data Based on SVM Method
In this paper, the problem of detecting buried landmine is tackled in the feature extraction and classification. Determining the likelihood set of an unknown pattern (feature vector), extracted from ground penetrating radar data by using SVM method. The advantage of SVM method in feature extraction and classification of image processing is: A classifier works well both on the training samples and on previously unseen samples; In addition, the SVM provides, enable a classification performance improvement based on from high feature dimensions to two or three feature dimensions. Finally, SVM method has a standard theory and a good implementation algorithm.
6285095
Building XML Data Warehouse Based on Frequent Patterns in User Queries
With the proliferation of XML-based data sources available across the Internet, it is increasingly important to provide users with a data warehouse of XML data sources to facilitate decision-making processes. Due to the extremely large amount of XML data available on web, unguided warehousing of XML data turns out to be highly costly and usually cannot well accommodate the users needs in XML data acquirement. In this paper, we propose an approach to materialize XML data warehouses based on frequent query patterns discovered from historical queries issued by users. The schemas of integrated XML documents in the warehouse are built using these frequent query patterns represented as Frequent Query Pattern Trees (FreqQPTs). Using hierarchical clustering technique, the integration approach in the data warehouse is flexible with respect to obtaining and maintaining XML documents. Experiments show that the overall processing of the same queries issued against the global schema become much efficient by using the XML data warehouse built than by directly searching the multiple data sources.
18482586
On the Integration Frame of Collaborative Innovation Support System for Telecom Operators
As the industry value-chain getting complicated, collaborative innovation becomes an important competitive factor for telecom operators. However, collaborative innovation means more participators and greater uncertainty in the innovation procedure. As a result, collaborative innovations usually cost more in time and money, which could make the innovations lose or bring serious problems even when new services have been launched into the market. In this paper, the integration frame of collaborate innovation support system (CISS) is put forward. Firstly, the participants and resource flows in the collaborative innovation in telecom industry are analyzed. Based on that, the requirements and functions of CISS are summarized. The system should help to connect the necessary resources for innovations and implement process management in an efficient way. Secondly, the framework of CISS is designed, defining functional construction and information flows. Then the key technologies supporting CISS are illustrated. Functionally, the CISS should be a part of BSS/OSS (Business/Operating Support System), and the relationship between CISS and BSS/OSS is explained. Finally, the organizational and managerial supports for the system are discussed.
18244826
A Simulating Algorithm for Protein Folding Process
It is a difficult problem of studying the protein folding process in the protein research field. In this paper, a new simulation algorithm-snake simulation algorithm is put forward to solve the problem. In order to prove the key function in snake simulation algorithm is correct and feasible, theory of molecule oscillation and molecule kinetics are used. In addition, bring forward a model that describes the process of protein folding, concludes that all the protein structure can be constructed by two type functions. Compared with other five protein folding algorithms, the snake simulation algorithm has a great advantage in the energy and similar of protein structure.
15782545
Rapid and Accurate Protein Side Chain Prediction with Local Backbone Information
High-accuracy protein structure modeling demands accurate and very fast side chain prediction since such a procedure must be repeatedly called at each step of structure refinement. Many known side chain prediction programs, such as SCWRL and TreePack, depend on the philosophy that global information and pairwise energy function must be used to achieve high accuracy. These programs are too slow to be used in the case when side chain packing has to be used thousands of times, such as protein structure refinement and protein design. 
 
We present an unexpected study that local backbone information can determine side chain conformations accurately. LocalPack, our side chain packing program which is based on only local information, achieves equal accuracy as SCWRL and TreePack, yet runs 4-14 times faster, hence providing a key missing piece in our efforts to high-accuracy protein structure modeling.
6635923
Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance
In this paper, we identify a new task for studying the outlying degree (OD) of high-dimensional data, i.e. finding the subspaces (subsets of features) in which the given points are outliers, which are called their outlying subspaces. Since the state-of-the-art outlier detection techniques fail to handle this new problem, we propose a novel detection algorithm, called High-Dimension Outlying subspace Detection (HighDOD), to detect the outlying subspaces of high-dimensional data efficiently. The intuitive idea of HighDOD is that we measure the OD of the point using the sum of distances between this point and itsknearest neighbors. Two heuristic pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been implemented. Experimental results show that HighDOD is efficient and outperforms other searching alternatives such as the naive top–down, bottom–up and random search methods, and the existing outlier detection methods cannot fulfill this new task effectively.