14899618
Robust and fast similarity search for moving object trajectories
An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imperfections. Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning techniques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increasing the pruning power significantly. The experimental results confirm the superior efficiency of the combined methods.
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.
262231
Monochromatic and bichromatic reverse skyline search over uncertain databases
Reverse skyline queries over uncertain databases have many important applications such as sensor data monitoring and business planning. Due to the existence of uncertainty in many real-world data, answering reverse skyline queries accurately and efficiently over uncertain data has become increasingly important. In this paper, we model the probabilistic reverse skyline query on uncertain data, in both monochromatic and bichromatic cases, and propose effective pruning methods to reduce the search space of query processing. Moreover, efficient query procedures have been presented seamlessly integrating the proposed pruning methods. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach with various experimental settings.
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.
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.
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.
42950226
Automated Design Approach for Analog Circuit Using Genetic Algorithm
The technology of electronic design automation (EDA) has improved the efficiency of design process, however, designer is still required much special knowledge of circuit. During the past decade, using genetic algorithm (GA) to design circuit had attracted many experts and scholars. However, too much more attention was focus on a circuit's function and many other factors had been neglected which caused the circuit had little applicability. This paper proposes an automated design approach for analog circuit based on a multi-objective adaptive GA. The multi-objective fitness evaluation method, which can dynamic adjust parameter, is selected. And a parallel evolution strategy which separates evolution of circuit structure and element value is adopted but also organically combined them by weight vectors. The experimental results indicate that this approach obviously be able to improve the evolution efficiency and could generate numbers of suitable circuits.
15477358
Cooperative Spectrum Sensing with Multi-Bits Local Sensing Decisions in Cognitive Radio Context
There are two important constraints for cooperative spectrum sensing in cognitive radio (CR) context. Firstly, as the CR system can only tolerate low transmitting overhead, the local sensing data must be compressed before transmitting. Secondly, many sophisticated data fusion techniques cannot be used in CR system because of the lack of the signal's prior knowledge. In this paper, we proposed a novel cooperative spectrum sensing scheme which adapts to different overhead tolerance and does not need any prior knowledge. This scheme consists of two main parts: the quantization schemes and the data fusion rule. Two different quantization schemes, according to whether or not the distribution functions of test statistic are known, are proposed. Correspondingly, the optimal data fusion rule of multi-bits decisions is derived. Furthermore, to make the optimal fusion rule more practical, an iterative scheme, which does not need any prior knowledge of the signal, is proposed to estimate the likelihood ratio of local decisions. Simulation results show that our scheme achieves better performance than the schemes with "OR" and "AND" combinations. Furthermore, it is also shown that the proposed scheme could achieve the theoretically optimal performance by only two or three bits quantization.
17212464
A unified SWSI-KAMs framework and performance evaluation on face recognition
The Kernel method is an effective and popular trick in machine learning. In this paper, by introducing it into conventional auto-associative memory models (AMs), we construct a unified framework of kernel auto-associative memory models (KAMs), which makes the existing exponential and polynomial AMs become its special cases. Further, in order to reduce KAM's connect complexity, inspired by ''small-world network'' recently described by Watts and Strogatz, we propose another unified framework of small-world structure (SWS) inspired kernel auto-associative memory models (SWSI-KAMs), which, in principle, makes KAMs simpler in structure. Simulation results on the FERET face database show that, the SWSI-KAMs adopting kernels such as Exponential and Hyperbolic tangent kernels have advantages of configuration simplicity while their recognition performance is almost as good as or even better than corresponding KAMs with full connectivity. In the end, the SWSI-KAM adopting Exponential kernel with different connectivities was emphatically investigated for robustness based on those face images which were added random noises and/or partially occluded in a mosaic way, and the experiments demonstrate that the SWSI-KAM with Exponential kernel is more robust in all cases of network connectivity of 20%, 40% and 60% than both PCA and recently proposed (PC)^2A algorithms for face recognition. cognition.
17397024
Multi-level secure video streaming over SRTP
In this paper, we propose a set of solutions to four different levels on data selection for encrypting Motion JPEG video frames over SRTP. These solutions are based on our intensive analysis of the JPEG image compression procedure. We select the most important data which contains most information of an image for encryption; in consequence provide a set of balances between security and computation for different hardware environments. Extensive experiments are conducted and performance data has been analyzed to evaluate the proposed solutions. Results show that these solutions are well suited in different scenarios with various computation power and security requirements.
23064856
Equivalent Relationship of Feedforward Neural Networks and Real-Time Face Detection System
In this paper, we mainly investigate a fast algorithm, Extreme Learning Machine (ELM), on its equivalent relationship, approximation capability and real-time face detection application. Firstly, an equivalent relationship is presented for neural networks without orthonormalization (ELM) and orthonormal neural networks. Secondly, based on the equivalent relationship and the universal approximation of orthonormal neural networks, we successfully prove that neural networks with ELM have the property of universal approximation, and adjustable parameters of hidden neurons and orthonormal transformation are not necessary. Finally, based on the fast learning characteristic of ELM, we successfully combine ELM with AdaBoost algorithm of Viola-Jones in face detection applications such that the whole system not only retains a real-time learning speed, but also possesses high face detection accuracy.