Knowledge about traffic conditions on the road play an important role in route planning and avoiding traffic jams. With recent developments in technology, it is possible for vehicles to be equipped with communication ...
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Knowledge about traffic conditions on the road play an important role in route planning and avoiding traffic jams. With recent developments in technology, it is possible for vehicles to be equipped with communication and GPS systems. Equipped vehicles on the road can act as nodes to form a vehicular ad hoc network. These nodes can collect information regarding traffic conditions such as position, speed, and direction from other participating nodes. Depending on the number of participating nodes in the ad hoc network, this collected information can provide useful information on driving conditions to the node collecting this information. With proper analysis this information can be used in detecting and/or predicting traffic jam conditions on freeways. In this article the traffic information gathered by a node in an ad hoc network is viewed as a snapshot in time of the current traffic conditions on the road segment. This snapshot is considered as a pattern in time of the current traffic conditions. The pattern. is analyzed using pattern recognition techniques. A weight-of-evidence-based classification algorithm is presented to identify different road traffic conditions. The algorithm is tested using data generated by microscopic modeling of traffic flow for simulation of vehicle or node mobility in ad hoc networks. Test results are presented depicting different percentage levels of vehicles equipped with communication capability.
Complex-network theory is a new approach in studying different types of large systems in both the physical and the abstract worlds. In this paper, we have studied two kinds of network from software engineering: the co...
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Complex-network theory is a new approach in studying different types of large systems in both the physical and the abstract worlds. In this paper, we have studied two kinds of network from software engineering: the component dependence network and the sorting comparison network (SCN). It is found that they both show the same scale-free property under certain conditions as complex networks in other fields. These results suggest that complex-network theory can be a useful approach to the study of software systems. The special properties of SCNs provide a more repeatable and deterministic way to study the evolution and optimization of complex networks. They also suggest that the closer a sorting algorithm is to the theoretical optimal limit, the more its SCN is like a scale-free network. This may also indicate that, to store and retrieve information efficiently, a concept network might need to be scale-free.
Classical problems of sorting and searching assume an underlying linear ordering of the objects being compared. In this paper, we study these problems in the context of partially ordered sets, in which some pairs of o...
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Classical problems of sorting and searching assume an underlying linear ordering of the objects being compared. In this paper, we study these problems in the context of partially ordered sets, in which some pairs of objects are incomparable. This generalization is interesting from a combinatorial perspective, and it has immediate applications in ranking scenarios where there is no underlying linear ordering, e.g., conference submissions. It also has applications in reconstructing certain types of networks, including biological networks. Our results represent significant progress over previous results from two decades ago by Faigle and Turan. In particular, we present the first algorithm that sorts a width-w poset of size n with optimal query complexity O(n(w + logn)). We also describe a variant of Merge-sort with query complexity O(wn log (n/w)) and total complexity O(w~2n log (n/w)); an algorithm with the same query complexity was given by Faigle and Turan, but no efficient implementation of that algorithm is known. Both our sorting algorithms can be applied with negligible overhead to the more general problem of reconstructing transitive relations. We also consider two related problems: finding the minimal elements, and its generalization to finding the bottom k "levels", called the k-selection problem. We give efficient deterministic and randomized algorithms for finding the minimal elements with O(wn) query and total complexity. We provide matching lower bounds for the query complexity up to a factor of 2 and generalize the results to the k-selection problem. Finally, we present efficient algorithms for computing a linear extension of a poset and computing the heights of all elements.
This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namelythe prediction of protein functions. This problem is cast as a h...
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This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namelythe prediction of protein functions. This problem is cast as a hierarchical classificationproblem. The protein functions to be predicted correspond to classes that are ar-ranged in a hierarchical structure (this takes the form of a class tree). The main con-tribution of this paper is to propose a new Artificial Immune System that creates anew representation for proteins, in order to maximize the predictive accuracy of ahierarchical classification algorithm applied to the corresponding protein functionprediction problem.
This paper presents an automatic system for morphological screening of the bladder cells. This system is intended to increase efficiency of the subsequent fluorescence in situ hybridization examination by limiting the...
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ISBN:
(纸本)9781424456536;9781424456543
This paper presents an automatic system for morphological screening of the bladder cells. This system is intended to increase efficiency of the subsequent fluorescence in situ hybridization examination by limiting the number of suspicious cells. The system works in two major phases. The first phase is slide scanning. The second stage includes cells detection and morphological analysis. Both stages refine their results using supervised classification algorithm. The developed method was tested on nine microscopical slides, containing more than 12000 manually labeled cells. The results provided by the system were compared to the ground truth labeled by a human expert.
A robust music genre classification framework is proposed that combines the rich, psycho-physiologically grounded properties of slow temporal modulations of music recordings and the power of sparse representation-base...
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ISBN:
(纸本)9781617388767
A robust music genre classification framework is proposed that combines the rich, psycho-physiologically grounded properties of slow temporal modulations of music recordings and the power of sparse representation-based classifiers. Linear subspace dimensionality reduction techniques are shown to play a crucial role within the framework under study. The proposed method yields a music genre classification accuracy of 91% and 93.56% on the GTZAN and the ISMIR2004 Genre dataset, respectively. Both accuracies outperform any reported accuracy ever obtained by state of the art music genre classification algorithms in the aforementioned datasets.
The article addresses a simulation-based optimization approach for allocation of ADMs in WDM optical networks with stochastic dynamic traffic. Since ADMs are expensive, it is desirable that if each node in WDM optical...
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The article addresses a simulation-based optimization approach for allocation of ADMs in WDM optical networks with stochastic dynamic traffic. Since ADMs are expensive, it is desirable that if each node in WDM optical networks can use a minimum number of ADMs to achieve a near-ideal performance. In this article, first, the utilization statistics of ADMs are gathered by simulation. Then, ADMs are allocated based on the utilization statistics. In this respect, a simple sorting mechanism is used. The distinguished feature of the proposed approach is that it shows the way to allocate ADMs at the nodes of WDM optical networks with stochastic dynamic traffic. The experimental results ensure that the proposed approach can solve the problem of allocating ADMs in practical WDM optical networks considering stochastic dynamic traffic.
In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requir...
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In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised accordingly and timely. To detect concept change, a common method is to observe the online classification accuracy. If accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this methodology is that any drop in accuracy can be interpreted as a symptom of concept change. Unfortunately however, this assumption is often violated in the real world where data streams carry noise and missing values that can also introduce a significant reduction in classification accuracy. To compound this problem, traditional noise cleansing methods are not applicable to data streams. These methods normally need to scan data multiple times whereas learning in data streams can only afford one-pass scan because of data's high speed and huge volume. To solve these problems, this paper proposes a novel classification algorithm, Class Specific Fuzzy Decision Trees (CSFDT), which utilizes fuzzy logic to classify data streams. The base classifier of CSFDT is a binary fuzzy decision tree. Whenever the problem of concern contains q classes (q > 2), CSFDT learns one binary classifier for each class to distinguish instances of this class from instances of the remaining (q - 1) classes. The CSFDT's advantages are three folds. First, it offers an adaptive structure to effectively and efficiently handle concept change. Second, it is robust to noise. Third, it deals with missing values in an elegant way. As a result, accuracy drop can be safely attributed to concept change. Extensive evaluations are conducted to compare CSFDT with representative existing data stream classification algorithms on a large variety of data. Experimental results suggest that CSFDT provides a significant benefit
Based on a CCD camera, we have developed an in-house sky imager system for the purpose of cloud cover estimation and characterization. The system captures a multispectral image every 5 min, and the analysis is done wi...
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Based on a CCD camera, we have developed an in-house sky imager system for the purpose of cloud cover estimation and characterization. The system captures a multispectral image every 5 min, and the analysis is done with a method based on an optimized neural network classification procedure and a genetic algorithm. The method discriminates between clear sky and two cloud classes: opaque and thin clouds. It also divides the image into sectors and finds the percentage of clouds in those different regions. We have validated the classification algorithm on two levels: image level, using the cloud observations included in the METAR register performed at the closest meteorological station, and pixel level, determining whether the final classification is correct. (c) 2007 Optical Society of America.
Target classification is an important enabling technology for the monitoring task in transportation sensing networks. In the paper the magnetic signal and seismic acceleration signal are collected, analyzed and transm...
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ISBN:
(纸本)9780769533421
Target classification is an important enabling technology for the monitoring task in transportation sensing networks. In the paper the magnetic signal and seismic acceleration signal are collected, analyzed and transmitted for a mobile road target. A classification algorithm based on the sensor network is proposed, which adopts a peak and valley pattern of hybrid detection signals from different sensors. The hardware equipment system of sensor node is devised. The terminal nodes are small with low power consumption so that the transportation sensing network is easy to deploy. The advantage of the algorithm lies on its sorting exactness of several kinds of targets. The results from many field tests have been shown that it is capable of identifying mobile targets on some roads in intelligent transportation system.
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