2155976
Maximal Subspace Coregulated Gene Clustering
Clustering is a popular technique for analyzing microarray data sets, with n genes and m experimental conditions. As explored by biologists, there is a real need to identify coregulated gene clusters, which include both positive and negative regulated gene clusters. The existing pattern-based and tendency-based clustering approaches cannot directly be applied to find such coregulated gene clusters, because they are designed for finding positive regulated gene clusters. In this paper, in order to cluster coregulated genes, we propose a coding scheme that allows us to cluster two genes into the same cluster if they have the same code, where two genes that have the same code can be either positive or negative regulated. Based on the coding scheme, we propose a new algorithm for finding maximal subspace coregulated gene clusters with new pruning techniques. A maximal subspace coregulated gene cluster clusters a set of genes on a condition sequence such that the cluster is not included in any other subspace coregulated gene clusters. We conduct extensive experimental studies. Our approach can effectively and efficiently find maximal subspace coregulated gene clusters. In addition, our approach outperforms the existing approaches for finding positive regulated gene clusters.
8931794
Speed up semantic search in p2p networks
Peer-to-peer architectures become popular in modern massively distributed systems, which are often in very large scale and contain a huge volume of heterogeneous data. To facilitate the information retrieval process in P2P networks, we consider semantic search approach, where syntax-based queries are shipped to peers based on semantic correlations. Motivated by an interesting experience in Web information retrieval, we propose a novel ontology-based scheme to measure similarity of peer interests accurately and consistently in a decentralized way, and group peers under a scalable hierarchical overlay network. Given queries, our approach either floods them within local peer groups or guides them towards remote groups based on the similarity of interests. Our work overcomes the limitations of the existing P2P hybrid-search approaches by avoiding costly data popularity measurement. Performance evaluation and comparison against baseline algorithms show that our approach provides a better solution for information retrieval in large-scale P2P networks.
8704395
Summarization Graph Indexing: Beyond Frequent Structure-Based Approach
Graph is an important data structure to model complex structural data, such as chemical compounds, proteins, and XML documents. Among many graph data-based applications, sub-graph search is a key problem, which is defined as given a query Q, retrieving all graphs containing Q as a sub-graph in the graph database. Most existing sub-graph search methods try to filter out false positives (graphs that are not possible in the results) as many as possible by indexing some frequent sub-structures in graph database, such as [20, 22, 4, 23]. However, due to ignoring the relationships between sub-structures, these methods still admit a high percentage of false positives. In this paper, we propose a novel concept, Summarization Graph, which is a complete graph and captures most topology information of the original graph, such as sub-structures and their relationships. Based on Summarization Graphs, we convert the filtering problem into retrieving objects with set-valued attributes. Moreover, we build an efficient signature file-based index to improve the filtering process. We prove theoretically that the pruning power of our method is larger than existing structure-based approaches. Finally, we show by extensive experimental study on real and synthetic data sets that the size of candidate set generated by Summarization Graph-based approach is only about 50% of that left by existing graph indexing methods, and the total response time of our method is reduced 2-10 times.
12758296
A Concise Representation of Range Queries
With the advance of wireless communication technology, it is quite common for people to view maps or get related services from the handheld devices, such as mobile phones and PDAs. Range queries, as one of the most commonly used tools, are often posed by the users to retrieve needful information from a spatial database. However, due to the limits of communication bandwidth and hardware power of handheld devices, displaying all the results of a range query on a handheld device is neither communication efficient nor informative to the users. This is simply because that there are often too many results returned from a range query. In view of this problem, we present a novel idea that a concise representation of a specified size for the range query results, while incurring minimal information loss, shall be computed and returned to the user. Such a concise range query not only reduces communication costs, but also offers better usability to the users, providing an opportunity for interactive exploration. The usefulness of the concise range queries is confirmed by comparing it with other possible alternatives, such as sampling and clustering. Then we propose algorithms to find a good concise representation.
9576587
Non-Threshold based Event Detection for 3D Environment Monitoring in Sensor Networks
Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values, and thus are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds, but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a non-threshold based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatio-temporal data patterns. Finally, we conduct trace driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.
7517195
Spotting Significant Changing Subgraphs in Evolving Graphs
Graphs are popularly used to model structural relationships between objects. In many application domains such as social networks, sensor networks and telecommunication, graphs evolve over time. In this paper, we study a new problem of discovering the subgraphs that exhibit significant changes in evolving graphs. This problem is challenging since it is hard to define changing regions that are closely related to the actual changes (i.e., additions/deletions of edges/nodes) in graphs. We formalize the problem, and design an efficient algorithm that is able to identify the changing subgraphs incrementally. Our experimental results on real datasets show that our solution is very efficient and the resultant subgraphs are of high quality.
1592504
Analog Error-Control Coding Based on Dimension-Expanding Shannon Mapping for Robust Image Communication
Analog transmission has the advantage of allowing the quality of recovered data to vary gracefully as the channel conditions vary. For the purpose of robust image communication, two analog error-control coding schemes based on dimension-expanding Shannon mapping are discussed in this paper, including the encoding processes, decoding processes and joint source channel optimizing design methods. In addition, a method of getting the upper and lower bounds of the theoretically optimal coder is presented. In comparison with the optimal coder, these two analog error-control coders are easy to implement and simulations show that they both perform well and are robust to channel and source mismatch. It can be concluded that they are both good candidates for image subband coding to protect important source messages and suitable for robust image communication where channel conditions vary.
32539682
A combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater
Abstract This paper deals with modeling a power plant component with mild nonlinear characteristics using a modified neural network structure. The hidden layer of the proposed neural network has a combination of neurons with linear and nonlinear activation functions. This approach is particularly suitable for nonlinear system with a low grade of nonlinearity, which can not be modeled satisfactorily by neural networks with purely nonlinear hidden layers or by the method of least square of errors (the ideal modeling method of linear systems). In this approach, two channels are installed in a hidden layer of the neural network to cover both linear and nonlinear behavior of systems. If the nonlinear characteristics of the system (i.e. de-superheater) are not negligible, then the nonlinear channel of the neural network is activated; that is, after training, the connections in nonlinear channel get considerable weights. The approach was applied to a de-superheater of a 325 MW power generating plant. The actual plant response, obtained from field experiments, is compared with the response of the proposed model and the responses of linear and neuro-fuzzy models as well as a neural network with purely nonlinear hidden layer. A better accuracy is observed using the proposed approach.
14979419
QoS-aware routing based on bandwidth estimation for mobile ad hoc networks
Routing protocols for mobile ad hoc networks (MANETs) have been explored extensively in recent years. Much of this work is targeted at finding a feasible route from a source to a destination without considering current network traffic or application requirements. Therefore, the network may easily become overloaded with too much traffic and the application has no way to improve its performance under a given network traffic condition. While this may be acceptable for data transfer, many real-time applications require quality-of-service (QoS) support from the network. We believe that such QoS support can be achieved by either finding a route to satisfy the application requirements or offering network feedback to the application when the requirements cannot be met. We propose a QoS-aware routing protocol that incorporates an admission control scheme and a feedback scheme to meet the QoS requirements of real-time applications. The novel part of this QoS-aware routing protocol is the use of the approximate bandwidth estimation to react to network traffic. Our approach implements these schemes by using two bandwidth estimation methods to find the residual bandwidth available at each node to support new streams. We simulate our QoS-aware routing protocol for nodes running the IEEE 802.11 medium access control. Results of our experiments show that the packet delivery ratio increases greatly, and packet delay and energy dissipation decrease significantly, while the overall end-to-end throughput is not impacted, compared with routing protocols that do not provide QoS support.
39027040
Fabric Handle Clusters Based on Fuzzy Clustering Algorithm
Fabric handle depends on physical and mechanical properties, which are measured by the two well-know systems, i.e., Kawabata's Evaluation System for Fabrics (KES-F) by Japan and Fabric Assurance by Simple Testing (FAST) by Austria. However, the two systems are too expensive and time-costing. A new comprehensive handle evaluation system for fabrics and yarns (CHES-FY) developed based on the single-test multiple indicator measuring principle can reflect the weight, bending, friction and tension basic mechanical behavior as well as handle through only one pulling-out test. The present paper is to select characteristic indexes from the pulling-out force and distance curve acquired by the CHES-FY system, and the characteristics make a vector which expresses the handle of the fabric based on Karhunen-Loeve (K-L) transformation; the other is to make clustering on handle of measured fabrics based on K-means fuzzy clustering. The experiments of thirty fabrics and comparisons between experimental and theoretical results were conducted, which shows that K-means fuzzy clustering algorithm is effective and accurate in sorting fabric handle based on the features selected from the pulling-out force and distance curve of the CHES-FY system.
18449881
Tracking Articulated Hand Underlying Graphical Model with Depth Cue
Visual tracking of articulated objects in real 3D space is challenging with applications in advanced human–computer interfaces and gesture semantic understanding. In this paper, graphical model, constructing articulated human hand, and NBP algorithm embedded with CAMSHIFT, inferencing hand configuration in 3D space, are applied for visual hand tracking. We also introduce image depth cue captured by two calibrated cameras together with color and edge as observation model. Depth cue allows our method to track human hand incomparatively accurate distance from cameras, especially in cluttered scenes with objects of similar color or edge. All those image cues are converted necessarily to probability distribution applied for the graphical model frame. The proposed method promotes the tracking efficiency and robustness proved by experiments and theory analysis.