Community detection consists in searching cohesive subgroups in complex networks. It has recently become one of the domain pivotal questions for scientists in many different fields where networks are used as modeling ...
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Community detection consists in searching cohesive subgroups in complex networks. It has recently become one of the domain pivotal questions for scientists in many different fields where networks are used as modeling tools. algorithms performing community detection are usually tested on real, but also on artificial networks, the former being costly and difficult to obtain. In this context, being able to generate networks with realistic properties is crucial for the reliability of the tests. Recently, Lancichinetti et al. designed a method to produce realistic networks, with a community structure and power law distributed degrees and community sizes. However, other realistic properties such as degree correlation and transitivity are missing. In this work, we propose a modification of their approach, based on the preferential attachment model, in order to remedy this limitation. We analyze the properties of the generated networks and compare them to the original approach. We then apply different community detection algorithms and observe significant changes in their performances when compared to results on networks generated with the original approach.
Scene change detection algorithms become the key issues especially for video information in many digital video applications such as digital libraries and video servers. The accuracy and execution speed of the scene ch...
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ISBN:
(纸本)7563504028
Scene change detection algorithms become the key issues especially for video information in many digital video applications such as digital libraries and video servers. The accuracy and execution speed of the scene change detection algorithm is critical if large amounts of video data are to be processed. In this paper, we discuss the performance of the three previous proposed methods and present a new algorithm to use the histogram difference of DC images incorporating the HVS (human visual system) for fast and accurate detection. The simulation results are also presented for two test video sequences, commercials and news, to show that the proposed algorithm works better than the previous ones.
IP auto-configuration in mobile ad hoc networks has attracted much attention. Efficient DAD (duplicate address detection) techniques should be devised to provide each node with its unique address in the network. Gener...
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IP auto-configuration in mobile ad hoc networks has attracted much attention. Efficient DAD (duplicate address detection) techniques should be devised to provide each node with its unique address in the network. Generally, DAD schemes can be categorized into two classes: (a) active DAD and (b) passive DAD. In this paper, we focus on passive DAD schemes over on-demand ad-hoc routing protocols such as AODV and DYMO. In order to improve the accuracy of detecting address conflicts, we propose several schemes using additional information including sequence, location, or neighbor information
Shadow detection is critical for robust and reliable vision-based systems for traffic flow analysis. In this paper we discuss various shadow detection approaches and compare two critically. The goal of these algorithm...
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Shadow detection is critical for robust and reliable vision-based systems for traffic flow analysis. In this paper we discuss various shadow detection approaches and compare two critically. The goal of these algorithms is to prevent moving shadows being misclassified as moving objects (or parts of them), thus avoiding the merging of two or more objects into one and improving the accuracy of object localization. The environment considered is an outdoor highway scene with multiple lanes observed by a single fixed camera. The important features of shadow detection algorithms and the parameter set-up are analyzed and discussed. A critical evaluation of the results both in terms of accuracy and in terms of computational complexity are outlined. Finally, possible integration of the two approaches into a robust shadow detector is presented as future direction of our research.
In this paper we propose two alternative event-driven double threshold detection algorithms to be used in decentralized wireless sensor networks. The proposed approach assumes that a sensor may decide about the presen...
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In this paper we propose two alternative event-driven double threshold detection algorithms to be used in decentralized wireless sensor networks. The proposed approach assumes that a sensor may decide about the presence of an event of interest either directly or asking for additional data from nearby nodes. The proposed methods aim at minimizing the network energy consumption associated to the detection process. The problem is formulated associating a cost proportional to the (average) number of nodes involved in the decision. After a first activation phase, initiated by a single node, we examine two alternative approaches: a fixed sample size and a sequential detector. We show that there is a need of including an activation threshold when there is a stringent constraint on the power consumption or when the SNR on each sensor is quite low. We compare the performance of the proposed approaches showing that, also in this double threshold setup, sequential detection algorithms involve smaller average number of sensors to guarantee the same performance metrics.
Detecting rocks in images is a valuable capability for autonomous planetary science. Rock detection facilitates selective data collection and return. It also assists with image analysis on Earth. This work reviews sev...
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Detecting rocks in images is a valuable capability for autonomous planetary science. Rock detection facilitates selective data collection and return. It also assists with image analysis on Earth. This work reviews seven rock detection algorithms from the autonomous science literature. We evaluate each algorithm with respect to several autonomous geology applications. Tests show the algorithms' performance on Mars Exploration Rover imagery, terrestrial images from analog environments, and synthetic images from a Mars terrain simulator. This provides insight into the detectors' performance under different imaging conditions.
We describe a new resource for developers of QRS detection algorithms that provides a standardized assessment of algorithm performance that is blinded, objective, independent, and reproducible. This overcomes the prob...
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We describe a new resource for developers of QRS detection algorithms that provides a standardized assessment of algorithm performance that is blinded, objective, independent, and reproducible. This overcomes the problems of using a single data set for both development and assessment that leads to favorably biased estimates of performance.
We present a framework for improving conflict detection algorithms using a hybrid control paradigm. This allows us to separate the problem into two parts: state/mode estimation and threat prediction. Since the dynamic...
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We present a framework for improving conflict detection algorithms using a hybrid control paradigm. This allows us to separate the problem into two parts: state/mode estimation and threat prediction. Since the dynamic equations for a conflict can change discretely depending on the situation, we propose the use of multiple model (MM) estimators to predict the situation and ultimately improve threat assessment. We provide an example using two different MM estimators for a rear-end collision warning system. The estimators can be used to determine the scenario mode as well as improve the state estimates
Complex networks became a very important tool in machine learning field, helping researchers to investigate and mine data. They can model real dynamic networks, aiding to unveil information's about the systems the...
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Complex networks became a very important tool in machine learning field, helping researchers to investigate and mine data. They can model real dynamic networks, aiding to unveil information's about the systems they model. Communities are notable groups that may exist in a complex network and the community detection problem is the focus of attention of many researchers. The igraph library implements a good set of community detection algorithms, allowing researchers to easily apply them to data mining tasks. But each algorithm uses a different approach, leading to different performances. In this paper, the community detection algorithms implemented in the igraph library are investigated and ranked according to their performances in a set of different scenarios. Results show walktrap and multi-level got the highest scores while leading eigenvector and spinglass got the lowest ones. These findings are an important contribution for aiding researchers to select or discard algorithms in their own experiments using igraph library.
Vehicle-based pedestrian detection system receives more and more attentions in road safety applications of the modern intelligent transportation system. However, the existed detection algorithms are too computing exte...
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Vehicle-based pedestrian detection system receives more and more attentions in road safety applications of the modern intelligent transportation system. However, the existed detection algorithms are too computing extensive for single core vehicle-based processors. As the promising multi-core architecture provides both energy efficient and powerful computing solutions, it is relevant to evaluate the up-to-date pedestrian detection algorithm on such novel platforms. This paper implemented a popular template based pedestrian detection algorithm on such platforms and gave out computing bottlenecks by profiling. Furthermore, we proposed an application aware multithread accelerating technique. Experimental results showed that our design can achieve nearly 2 - 4x speedups on multi-core processors for critical function blocks, which reduces total execution time ranging from 26 to 41%.
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