Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little...
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Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little effort has been focused on studying how spatial data redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial data redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.
To improve accuracy of localize suspicious lesion region in dermatoscopic images using Region Growing, the initial step based on fuzzy sets to enhance the lesion region of interest (ROI) is developed. The result of sk...
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To improve accuracy of localize suspicious lesion region in dermatoscopic images using Region Growing, the initial step based on fuzzy sets to enhance the lesion region of interest (ROI) is developed. The result of skin lesion detection is used to extract Asymmetry, Border Irregularity, Color Variation, Diameter (ABCD) features for melanoma skin cancer diagnosis. ABCD feature is the rule that is used dermatologist for obtain the important information of image dermatoscopic lesion. This feature is used to diagnose melanoma skin cancer based on Total Dermatoscopic Value (TDV). There are three diagnosis that is used on this research i.e. melanoma, suspicious, and benign skin lesion. The experiment uses 30 samples of image dermatoscopic lesion that is suspicious melanoma skin cancer. Based on the experiment, the accuracy using fuzzy region growing is 86.6% that there are 4 false diagnoses of 30 samples. But the accuracy using region growing is 76.6% that there are 7 false of 30 samples.
In this paper we study load balancing in a cluster based p2p system. The cluster based architecture is chosen after the study of various real systems. We then define the notion of load for that system and describe coe...
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In this paper we study load balancing in a cluster based p2p system. The cluster based architecture is chosen after the study of various real systems. We then define the notion of load for that system and describe coefficients that measure the different parameters of load. We use these coefficients to define load factors that can be used to measure each node's load. These factors are also used in two algorithms that balance the load fairly among all peers in a cluster. We improve the algorithms' balancing performance using replication of popular objects. A set of metrics is used to evaluate the cost of each algorithm and the effect on system's performance. Our simulation results show that it is possible to improve the fairness on load distribution without replication but with a small reduce on system's throughput. And under the cost of replicating a small amount of objects we improve fairness without reducing system's throughput.
In this paper a novel performance evaluation method for multistage interconnection networks (MINs) servicing unicast and multicast traffic is presented. The multicasting policy where a packet is copied and transmitted...
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In this paper a novel performance evaluation method for multistage interconnection networks (MINs) servicing unicast and multicast traffic is presented. The multicasting policy where a packet is copied and transmitted is not only applied when both next stage destination buffers are available but also when at least one destination buffer is able to accept packets. This mechanism, which is called "partial multicast", was employed by all SEs using the "Cell Replication While Routing" (CRWR) technique. The proposed analysis was applied to various network-size MINs constructed by symmetrical Switching Elements (SEs), supporting mixed traffic patterns such as unicast and multicast, under various offered loads in uniform traffic conditions. The performance of MINs with partial multicasting transmission, which is thoroughly analyzed by our analytical model, can also be useful in performance study and development of other alternative architectures of multistage networks that also support mixed traffic.
Localization is a fundamental task in mobile robotics and in indoor environments we can use various sensors to solve this problem. In the Intelligent Space environment we can use laser range finders or ultrasonic posi...
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Localization is a fundamental task in mobile robotics and in indoor environments we can use various sensors to solve this problem. In the Intelligent Space environment we can use laser range finders or ultrasonic positioning systems to localize and track mobile robots. Nevertheless, our final goal is to substitute these sensors and accomplish this task using just cameras. In this paper, we show the feasibility of determining the robot's location based on the images received from a single camera. In our experimental room we used a surveillance camera, which can be controlled to pan/tilt in order to change the point of view. The camera had been mounted on the ceiling and six preset positions were selected to cover the whole area. The object recognition is based on colour space filtering and contour detection. Finally, the contours of the detected objects are transformed from the image space to the world coordinate system and the polygons are reduced to simpler ones. The system is able to detect not only the position of the objects, but their orientations.
The voting rules proposed by Dodgson and Young are both designed to find the alternative closest to being a Condorcet winner, according to two different notions of proximity; the score of a given alternative is known ...
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The voting rules proposed by Dodgson and Young are both designed to find the alternative closest to being a Condorcet winner, according to two different notions of proximity; the score of a given alternative is known to be hard to compute under either rule. In this paper, we put forward two algorithms for approximating the Dodgson score: an LP-based randomized rounding algorithm and a deterministic greedy algorithm, both of which yield an O(logm) approximation ratio, where m is the number of alternatives; we observe that this result is asymptotically optimal, and further prove that our greedy algorithm is optimal up to a factor of 2, unless problems in NP have quasi-polynomial time algorithms. Although the greedy algorithm is computationally superior, we argue that the randomized rounding algorithm has an advantage from a social choice point of view. Further, we demonstrate that computing any reasonable approximation of the ranking produced by Dodgson's rule is NP-hard. This result provides a complexity-theoretic explanation of sharp discrepancies that have been observed in the Social Choice Theory literature when comparing Dodgson elections with simpler voting rules. Finally, we show that the problem of calculating the Young score is NP-hard to approximate by any factor. This leads to an inapproximability result for the Young ranking.
Applying high level parallel runtimes to data/compute intensive applications is becoming increasingly common. The simplicity of the MapReduce programming model and the availability of open source MapReduce runtimes su...
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We design and implement a multicost impairment- aware routing and wavelength assignment algorithm for online traffic. In transparent optical networks the quality of a transmission degrades due to physical layer impair...
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We design and implement a multicost impairment- aware routing and wavelength assignment algorithm for online traffic. In transparent optical networks the quality of a transmission degrades due to physical layer impairments. To serve a connection, the proposed algorithm finds a path and a free wavelength (a lightpath) that has acceptable signal quality performance by estimating a quality of transmission measure, called the Q factor. We take into account channel utilization in the network, which changes as new connections are established or released, in order to calculate the noise variances that correspond to physical impairments on the links. These, along with the time invariant eye impairment penalties of all candidate network paths, form the inputs to the algorithm. The multicost algorithm finds a set of so called non-dominated Q paths from the given source to the given destination. Various objective functions are then evaluated in order to choose the optimal lightpath to serve the connection. The proposed algorithm combines the strength of multicost optimization with low execution time, making it appropriate for serving online connections.
In future transparent optical networks, it is important to consider the impact of physical impairments in the routing and wavelengths assignment process, to achieve efficient connection provisioning. In this paper, we...
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In future transparent optical networks, it is important to consider the impact of physical impairments in the routing and wavelengths assignment process, to achieve efficient connection provisioning. In this paper, we use classical multi-objective optimization (MOO) strategies and particularly genetic algorithms to jointly solve the impairment aware RWA (IA-RWA) problem. Fiber impairments are indirectly considered through the insertion of the path length and the number of common hops in the optimization process. It is shown that blocking is greatly improved, while the obtained solutions truly converge towards the Pareto front that constitutes the set of global optimum solutions. We have evaluated our findings, using an Q estimator tool, that calculates the signal quality of each path analytically.
The first cluster‐based public computing for Monte Carlo simulation in Indonesia is introduced. The system has been developed to enable public to perform Monte Carlo simulation on a parallel computer through an integ...
The first cluster‐based public computing for Monte Carlo simulation in Indonesia is introduced. The system has been developed to enable public to perform Monte Carlo simulation on a parallel computer through an integrated and user friendly dynamic web interface. The beta version, so called publicMC@BATAN, has been released and implemented for internal users at the National Nuclear Energy Agency (BATAN). In this paper the concept and architecture of publicMC@BATAN are presented.
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