Coexistence of Wi-Fi and ZigBee in 2.4 GHz ISM band is a long standing and challenging problem. Previous solutions either require modifications of current ZigBee protocols or Wi-Fi re-configurations, which is not feasible in large-scale wireless sensor networks. In this paper, we present WizBee, a coexistence system using single-antenna sink without changing current Wi-Fi and ZigBee design. WizBee is based on an observation that Wi-Fi signal is about 5 to 20 dB stronger than ZigBee signal in symmetric area, which leaves much room for applying interference cancelation technique to mitigate Wi-Fi interference, and extract ZigBee signals. However, we need to cancel the Wi-Fi interference perfectly for residual ZigBee signal decoding, which needs more accurate channel coefficient across data transmissions in spite of cross technology interference. For robust and accurate Wi-Fi decoding, we use soft Viterbi decoding with weighted confidence value over interfered subcarriers. Consequently, our solution uses decoded data for channel coefficient estimation instead of conventional training symbol based methods. The key insight is that, the signal recovery opportunity for cross technology coexistence, lies in multi-domain information, such as power, frequency and coding discrepancies. Using these information properly will improve the coexistence network throughput effectively. We implemented WizBee in USRP/GNURadio software radio platform, and studied the decoding performance of interference cancelation technique. Our extensive evaluations under real wireless conditions show that WizBee improves ZigBee throughput up to 1.9x, with median throughput gain of 1.2x.

RDF ontology can represent semantic information in semantic Web. Ontologies are often too large to be used in a single application. Extracting sub-ontology from large-scale ontology can solve this problem. RDF(S) ontologies are abstracted as graph models. According to the RDFS inference rules, the closure of RDFS ontology graph model can be constructed. To decrease the time of generating closure, a parallel closure algorithm is presented. We also present an algorithm based on the graph theory to extract a sub-graph with the concepts in the domain term lexicon as nodes. This sub-graph is regarded as the required domain ontology. The method is applicable to RDF(S) domain ontology extraction. Applying the method, domain ontology can be built fast and effectively.

Multimedia content description interface (MPEG-7) includes a number of image feature descriptors to represent low-level image features such as colors, textures and shapes effectively. But, the contribution of each descriptor may not be the same for a domain specific image database when computing the similarity measure. Machine learning techniques for the optimization of feature descriptor weights are desirable to enhance the accuracy of image annotation systems. In our system, we use a real coded chromosome genetic algorithm and support vector machine (SVM) classification accuracy as fitness function to optimize the weights of MPEG-7 image feature descriptors. The experimental results over 2000 classified Corel images show that with the real coded genetic algorithm, the accuracies of image annotation system are improved comparing to the method without machine learning techniques.

Typical description logics are limited to dealing with crisp concepts and crisp roles. However, Web applications based on description logics should allow the treatment of the inherent imprecision. Therefore, it is necessary to add fuzzy features to description logics. A family of extended fuzzy description logics is proposed to enable representation and reasoning for complex fuzzy information. The extended fuzzy description logics introduce the cut sets of fuzzy concepts and fuzzy roles as atomic concepts and atomic roles, and inherit the concept and role constructors from description logics. The definitions of syntax, semantics, reasoning tasks, and reasoning properties are given for the extended fuzzy description logic. The extended fuzzy description logics adopt a special fuzzify-method with more expressive power than the previous fuzzy description logics.

Ontology-based information retrieval is precise and effective but suffers from the problem of ontology heterogeneity. This paper focuses on the approximate information retrieval approach to solve the heterogeneity problem of both common ontologies as well as fuzzy ontologies on the semantic Web. Approximate information retrieval needs to find approximations of concepts. However, current methods cannot find the best approximations of concepts for common ontologies, and there is still no published work for fuzzy ontologies. This paper firstly proposes a method of approximate information retrieval between common ontologies. It defines multielement least upper bounds and multielement greatest lower bounds, and then simplifies the multielement bounds to remove redundancy. It provides effective algorithms to find the simplified multielement bounds, and get the best approximations of concepts from the bounds. Then for the fuzzy ontologies, the paper defines cut concepts to transform fuzzy concepts into common concepts, and then applies the proposed method for approximate information retrieval between fuzzy ontologies. The improved algorithms for fuzzy ontologies are given to make the method more feasible and effective.

A family of extended fuzzy description logics, which includes a framework of syntax, semantics, knowledge base form and reasoning tasks, is proposed to enable representation and reasoning for complex fuzzy information. This paper discusses the reasoning technique for reasoning tasks of extended fuzzy description logics, which adopts classical description logics to discretely simulate extended fuzzy description logic in polynomial time and reuses the existing reasoning result to prove the complexity of reasoning tasks of extended fuzzy description logics

Multiontologies environment is common such as the semantic annotation applications based on multiontologies. Based on analyzing and classifying the relationships between multiontologies, we propose the idea of bridge ontology, and present its detailed structure too. To deal with the semantic annotation problem based on multiontologies, a new approach is proposed which employing bridge ontology to express the complex relationships between multiontologies. The bridge ontology is a peculiar ontology, which can be created and maintained conveniently, and is effective in the applications based on multiontologies. The approach using bridge ontology has the advantages of low-cost, scalable, robust in the Web circumstance, avoiding the unnecessary ontology extending and integration, and promoting ontology reuse.

Multiontologies environments are common in semantic Web and knowledge representation applications. The key problem of these applications is how to represent the complex relationships between ontologies. For the purpose of solving the problem, This work analyzed and classified the relationships between multiontologies, then proposed the theory of bridge ontology. The bridge ontology is a peculiar ontology, has four-layer structures, and is able to express the twelve kinds of relations between multiontologies. Subsequently, on the premise of keeping the semantic consistency, the detailed discussions focused on the semantic checking and refining methods for the multiontologies with bridge ontology. Bridge ontology can be created and maintained conveniently, and is effective in the applications based on multiontologies. It has the advantages of low-cost, scalable, robust in the Web circumstance, avoiding the unnecessary ontology extending and integration, and promoting ontology reuse.

The principle and steps of the algorithm for mining fuzzy association rules is studied, and the parallel algorithm for mining fuzzy association rules is presented. In this parallel mining algorithm, quantitative attributes are partitioned into several fuzzy sets by the parallel fuzzy c-means algorithm, and fuzzy sets are applied to soften the partition boundary of the attributes. Then, the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Last, the fuzzy association rules with at least fuzzy confidence are generated on all processors. The parallel mining algorithm is implemented on the distributed linked PC/workstation. The experiment results show that the parallel mining algorithm has fine scaleup, sizeup and speedup.

Some problems of mining association rules with linguistic terms are discussed. First, an incremental updating algorithm of association rules with linguistic terms is presented. The collection of frequent linguistic attribute sets and its negative border along with their support count are maintained, which makes scan the entire database once at most in the process of updating association rules. The experiment shows that the updating algorithm can not only update association rules effectively but also avoid the repeated cost. Secondly, the parallel algorithm for mining association rules with linguistic terms is presented. The Boolean parallel mining algorithm is improved to discover frequent linguistic attribute sets, and the association rules with at least confidence are generated on all processors. This parallel mining algorithm has fine scale-up, size-up and speed-up.

Web-based logs contain potentially useful data with which designers can assess the usability and effectiveness of their choices. Clustering techniques have been used to automatically discover typical user profiles from Web access logs recently. But it is a challenging problem to design effective similarity measure between the session vectors, which are usually high dimensional and sparse. Nonnegative matrix factorisation approaches are applied to dimensionality reduction of the session-URL matrix, and the spherical k-means algorithm is used to partition the projecting vectors of the user session vectors into several clusters. Two methods for discovering typical user session profiles from the clusters are presented last. The results of experiment show that our algorithms can mine interesting user profiles effectively.