The first complete running time analysis of a stochastic divide and conquer algorithm was given for Quicksort, a sorting algorithm invented 1961 by Hoare. We analyse here the variant Random Median Quicksort. The analy...
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The first complete running time analysis of a stochastic divide and conquer algorithm was given for Quicksort, a sorting algorithm invented 1961 by Hoare. We analyse here the variant Random Median Quicksort. The analysis includes the expectation, the asymptotic distribution, the moments and exponential moments. The asymptotic distribution is characterized by a stochastic fixed point equation. The basic technic will be generating functions and the contraction method. (C) 2006 Elsevier B.V. All rights reserved.
Due to the circuit complexity and the number of parameters involved, test relationship of a Test Program (TP) might not be fully discovered. Traditionally, TP setup are defined based on the domain expertise and gather...
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ISBN:
(纸本)9780769530451
Due to the circuit complexity and the number of parameters involved, test relationship of a Test Program (TP) might not be fully discovered. Traditionally, TP setup are defined based on the domain expertise and gathered experience of an engineer. Such judgment is time consuming and could be inefficient especially when new products and technologies are rapidly developed for the competing market. If the complexity of a TP increases, the undetected interrelationship among tests in a TP will also increase. In this paper, inferences are performed to a huge and complex TP using different classification algorithms, with the primary goal to discover potential test relationships in a fast and efficient way. The mining output can be used as a reference and basis for test engineers to improve TP setup or to reprogram test machine to replace current exhaustive test policy.
As the rapid development of logistic technology in our country, the sorting efficiency of the distribution center becomes the bottle-neck in the whole process. At present, various sorting device emerge as the times re...
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ISBN:
(纸本)9781424415304;1424415306
As the rapid development of logistic technology in our country, the sorting efficiency of the distribution center becomes the bottle-neck in the whole process. At present, various sorting device emerge as the times require and the improvement of the sorting efficiency becomes the difficult problem of the sorting devices. This article begins from the optimize tactic of the sorting system, builds optimizing modal of the rapid sorting system, the result of simulation analyze and the practical run shows that this modal can improve the efficiency of the sorting system greatly. This modal based on the sorting device of the tobacco industry and can be applied to other industries.
This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as hyper...
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This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) and Landsat Thematic Mapper (TM) data acquired in 1998 were analysed for a test area in southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, was used to assess the results obtained for the considered test areas. Results from preliminary analysis showed that the use of unmixing techniques allows an increase in accuracy of around 7% compared with the accuracy level obtained by applying a widely used classification algorithm.
One of the least well understood regions of the human brain is rostral prefrontal cortex, approximating Brodmann's area, 10. Here, we investigate the possibility that there are functional subdivisions within this ...
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One of the least well understood regions of the human brain is rostral prefrontal cortex, approximating Brodmann's area, 10. Here, we investigate the possibility that there are functional subdivisions within this region by conducting a meta-analysis of 104 functional neuroimaging studies (using positron emission tomography/functional magnetic resonance imaging). Studies involving working memory and episodic memory retrieval were disproportionately associated with lateral activations, whereas studies involving mentalizing (i.e., attending to one's own emotions and mental states or those of other agents) were disproportionately associated with medial activations. Functional variation was also observed along a rostral-caudal axis, with studies involving mentalizing yielding relatively caudal activations and studies involving multiple-task coordination yielding relatively rostral activations. A classification algorithm was trained to predict the task, given the coordinates of each activation peak. Performance was well above chance levels (74% for the three most common tasks;45% across all eight tasks investigated) and generalized to data not included in the training set. These results point to considerable functional segregation within rostral prefrontal cortex.
Feature extraction is the first and most critical step in various vision applications. The detected features must be classified into different feature types before they can be efficiently and effectively applied on fu...
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Feature extraction is the first and most critical step in various vision applications. The detected features must be classified into different feature types before they can be efficiently and effectively applied on further vision tasks. In this paper, we propose a feature classification algorithm that classifies the detected regions into four types including blobs, edges and lines, textures, and texture boundaries, by using the correlations with the neighbouring regions. The effectiveness of the feature classification is evaluated on image retrieval.
A method of classification accuracy evaluation for a cloud and precipitation classifier applied to geostationary meteorological satellite data is presented. The method has been developed to evaluate the accuracy of a ...
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A method of classification accuracy evaluation for a cloud and precipitation classifier applied to geostationary meteorological satellite data is presented. The method has been developed to evaluate the accuracy of a rather precise classification algorithm. The algorithm produces nine classes, four of which involve precipitation. The classes are: (1) clear or insignificant cloud, (2) low thin cloud with no rain, (3) low or middle thin cloud with no rain, (4) low or middle thick cloud with no rain, (5) middle or high cloud with no rain, (6) middle or high cloud with the possibility of rain, (7) middle or high cloud with light-moderate precipitation, (8) middle-high cloud with moderate-heavy precipitation, (9) heavy thunderstorm. The evaluation classifier has been tested for its accuracy (ground truth) using comparison between actual meteorological weather reports and classification results derived from the algorithm applied. For the estimation of classification accuracy, the omission/commission method is applied between the observed and the classification-produced values. The classifier used has proved to be very reliable for classifying major cloud types and precipitation, tested during the synoptic situation of depression systems approaching the south Balkan Peninsula from the west. In that synoptic situation, different intensities of rainfall as well as heavy thunderstorm were present, and the results are very satisfactory. The method can be used to evaluate classification results produced by algorithms applied to meteorological satellite data, classifying precipitation areas as well as the heaviness of precipitation.
In this research, the potential of using a multi-spectral imaging system was investigated for detecting bruises on 'Golden Delicious' apples. For this purpose, a hyperspectral imaging system has been built in ...
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In this research, the potential of using a multi-spectral imaging system was investigated for detecting bruises on 'Golden Delicious' apples. For this purpose, a hyperspectral imaging system has been built in the spectral region between 400 and 1000nm. Based on the principal components analysis for the hyperspectral images, four wavebands were selected for a multi-spectral imaging test. The principal components analysis on the multi-spectral images gave very similar results as on the hyperspectral images. The second and third principal components scores images were found to be suitable for identifying the presence of bruises. An image processing and classification algorithm based on moments thresholding was developed. The classification results indicated that, for the sample population used in this experiment, about 93% of the non-bruised apples were recognised as sound. An accuracy of about 86% was achieved for detecting bruises, although all the bruised regions were found to remain in the images after moments thresholding. (C) 2004 Published by Elsevier Ltd. on behalf of Silsoc Research Institute.
Object and face representations in ventral temporal (VT) cortex were investigated by combining object confusability data from a computational model of object classification with neural response confusability data from...
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Object and face representations in ventral temporal (VT) cortex were investigated by combining object confusability data from a computational model of object classification with neural response confusability data from a functional neuroimaging experiment. A pattern-based classification algorithm learned to categorize individual brain maps according to the object category being viewed by the subject. An identical algorithm learned to classify an image-based, view-dependent representation of the stimuli. High correlations were found between the confusability of object categories and the confusability of brain activity maps. This occurred even with the inclusion of multiple views of objects, and when the object classification model was tested with high spatial frequency "line drawings" of the stimuli. Consistent with a distributed representation of objects in VT cortex, the data indicate that object categories with shared image-based attributes have shared neural structure.
Wireless magnetic sensor networks offer an attractive, low-cost alternative to inductive loops for traffic measurement in freeways and at intersections. In addition to providing vehicle count, occupancy, and speed, th...
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Wireless magnetic sensor networks offer an attractive, low-cost alternative to inductive loops for traffic measurement in freeways and at intersections. In addition to providing vehicle count, occupancy, and speed, these sensors yield information (such as non-axle-based vehicle classification) that cannot be obtained from standard loop data. Because such networks can be deployed quickly, they can be used (and reused) for temporary traffic measurement. This paper reports the detection capabilities of magnetic sensors on the basis of two field experiments. The first experiment collected a 2-h trace of measurements on Hearst Avenue in Berkeley, California. The vehicle detection rate was better than 99% (100% for vehicles other than motorcycles), and estimates of average vehicle length and speed appear to have been better than 90%. The measurements also yield intervehicle spacing or headways, revealing interesting phenomena such as platoon formation downstream of a traffic signal. Results of the second experiment are preliminary. Sensor data from 37 passing vehicles at the same site are processed and classified into six types. Sixty percent of the vehicles are classified correctly when length is not used as a feature. The classification algorithm can be implemented in real time by the sensor node itself, in contrast to other methods based on high-scan-rate inductive loop signals, which require extensive off-line computation. It is believed that if length were used as a feature, 80% to 90% of vehicles would be correctly classified.
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