Abstract A novel subspace projection approach was proposed to improve the robustness of adaptive beamforming and direction finding algorithms. The cost function of the signal subspace scaled multiple signal classification (SSMUSIC) is minimized in the uncertainty set of the signal steering vector, the optimal solution to the optimization problem is that the assumed steering vector can be modified as the weighed... A novel subspace projection approach was proposed to improve the robustness of adaptive beamforming and direction finding algorithms. The cost function of the signal subspace scaled multiple signal classification (SSMUSIC) is minimized in the uncertainty set of the signal steering vector, the optimal solution to the optimization problem is that the assumed steering vector can be modified as the weighed sum of the vectors orthogonally projected onto the signal subspace and the noise subspace. Using the estimated steering vector with small error to the true steering vector, the spectral peaks in the actual signal directions are guaranteed. Consequently, the problem of signal self-canceling encountered by adaptive beamforming due to steering vector mismatches is eliminated. Simulation and lake trial results show that the proposed method not only possesses high resolution performance, but also is robust to a few steering vector errors. Furthermore, the modified MUSIC algorithm outperforms the conventional MUSIC and SSMUSIC methods excellently. Read More Read Less

Abstract To reuse previous knowledge of objectoriented design and adapt them to solve new problems, the collaboration relationships and the responsibility distribution among software objects need to be thoroughly understood and precisely formulated. The paper proposes a knowledge-based approach that employs category theoretic models to formalize and mechanize objectoriented software design and synthesis by focusing concern on reasoning about the interdependency... To reuse previous knowledge of objectoriented design and adapt them to solve new problems, the collaboration relationships and the responsibility distribution among software objects need to be thoroughly understood and precisely formulated. The paper proposes a knowledge-based approach that employs category theoretic models to formalize and mechanize objectoriented software design and synthesis by focusing concern on reasoning about the interdependency relationships at different levels of abstraction and granularity. The major benefit of our approach is twofold: First, it provides an explicit semantics for formal object-oriented specifications, and therefore enables a high-level of reusability and dynamic adaptability. Second, it utilizes the ability of categorical computations to support automated software composition and refinement. A prototype tool that demonstrates the feasibility and effectiveness of our approach is also presented. Read More Read Less

Abstract Heuristic methods based on biological aspects have been successfully used in many computer science areas of research, including information retrieval (IR). This let us ask: why there is no biological environment for the problem of information retrieval. In this paper, we tried to introduce a new biological environment and model for information retrieval that broad the modeling concept from the... Heuristic methods based on biological aspects have been successfully used in many computer science areas of research, including information retrieval (IR). This let us ask: why there is no biological environment for the problem of information retrieval. In this paper, we tried to introduce a new biological environment and model for information retrieval that broad the modeling concept from the mathematical formula that simulates the basic elements of IR problem to their dual schemas in biology. It’s a new way of thinking of, and dealing with, the problem of information retrieval. Read More Read Less

Abstract Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself,... Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself, but also has relation with the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multi-source remote sensing images based on Mallat fusion and residual error in this paper. Firstly, the fusion of multi-source images can provide a fused image which is more preferable for classification. And then a featureselection scheme approach based on fused image is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feed-forward neural network is investigated. The results of computer experiments carried out on a multisource data set confirm the validity of the proposed approach. Read More Read Less

Abstract Recent developments in techniques for modeling, digitizing and visualizing three dimensional shapes have led to an explosion in the number of available three dimensional models on the Internet and in many domain-specific databases. However, how to describe three dimensional shapes is a difficult problem. A new three dimensional shape description method based on rotation is proposed in this paper. The... Recent developments in techniques for modeling, digitizing and visualizing three dimensional shapes have led to an explosion in the number of available three dimensional models on the Internet and in many domain-specific databases. However, how to describe three dimensional shapes is a difficult problem. A new three dimensional shape description method based on rotation is proposed in this paper. The first step is to sample points on the surface and compute normal vectors. Secondly, we do Gauss mapping and rotate model, at the same time, rotate the Gauss sphere. The third step is to analysis normal distributions on the sphere surface. Then the Euclidean distance is computed, and finally a statistic histogram is constructed. Experimental results show the effectiveness of this method. Read More Read Less

Abstract The problem considered in this paper is that of selecting, from a set of proposals or a subset of projects to be undertaken. Technology project assess- ment is influenced by a large number of factors, and many times it is hard to measure them in numbers or an objectively way. This paper addresses this by pre- senting a method for... The problem considered in this paper is that of selecting, from a set of proposals or a subset of projects to be undertaken. Technology project assess- ment is influenced by a large number of factors, and many times it is hard to measure them in numbers or an objectively way. This paper addresses this by pre- senting a method for identifying and assessing key project characteristics, which are crucial for a technol- ogy project’s assessment. Fistly, this paper builds a new set of indicators of technology project assessment after analysing the process of it. Secondly, this paper introduces two aggregation operators in the technol- ogy project to deal with two situations of technology project assessment respectively, including linguistic and subjective information and pair comparison of pref- erence relation. The methods consist of a number of well-defined steps, which are described in detail. Fi- nally, examples show that the proposed methods with the linguistic decision analysis are useful and provide an understanding way for the decision maker. Read More Read Less

Abstract This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System with a modification to the original YCS fitness function has been found to improve the identification of less-separated data sets. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop... This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System with a modification to the original YCS fitness function has been found to improve the identification of less-separated data sets. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of datasets. Read More Read Less

Abstract With the popularity of XML as data exchange over the Web, querying XML data has become an important issue to be addressed. Since the logical structure of XML is a tree, establishing a parent-child (P-C), ancestor-descendant (A-D) or sibling relationship between nodes is essential for structural query processing. Thus, we propose using a labeling scheme to encode each... With the popularity of XML as data exchange over the Web, querying XML data has become an important issue to be addressed. Since the logical structure of XML is a tree, establishing a parent-child (P-C), ancestor-descendant (A-D) or sibling relationship between nodes is essential for structural query processing. Thus, we propose using a labeling scheme to encode each element in the XML database by its positional information. Based on this labeling scheme, we further propose our TwigINLAB algorithm to optimize the query processing. Experimental results indicate that TwigINLAB can process both path queries and twig queries better than the TwigStack algorithm on an average of 27% and 14% respectively in terms of execution time using the XMARK benchmark dataset. Read More Read Less