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Distribution and long-term trends in various fog types over South Korea

THEORETICAL AND APPLIED CLIMATOLOGY 2015年第3-4期122卷 699-710页

作者： Belorid, Miloslav Lee, Chong Bum Kim, Jea-Chul Cheon, Tae-Hun Kangwon Natl Univ Dept Environm Sci Coll Nat Sci Chunchon 200701 South Korea

This study analyzed the spatial and temporal distributions of various fog types over South Korea. Six types of fogs were identified using a classification algorithm based on simple conceptual models of fog formation. The algorithm was applied to a 25-year record of meteorological observations. The most common fog types were radiation fog, prevailing at inland stations, and precipitation fog at coastal and island stations. Declining temporal trends in the frequency of fog events ranging between 2.1 and 10.9 fog events per decade were found at eight inland and two coastal stations. Long-term trends for each fog type show that the decrease in the frequency of fog events is mainly due to a decrease in the frequency of radiation fogs ranging between 1.1 and 8.5 fog events per decade. To identify the potential factors related to the decrease in radiation fog events, the temporal trends in annual mean nocturnal maximal cooling rates and annual mean nocturnal specific humidity during nights with clear sky and clam winds were examined. The results show that the decrease in the frequency of radiation fog events is associated mainly with the pattern of urbanization occurring during the past two decades.

关键词： South Korea fog secular trend NEBULISED sorting algorithm

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A Robust Sclera Segmentation algorithm 7

A Robust Sclera Segmentation Algorithm

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IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)

作者： Radu, Petru Ferryman, James Wild, Peter Univ Reading Sch Syst Engn Computat Vis Grp Reading RG6 2AH Berks England

ISBN: (纸本)9781479987771

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm's robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities' space generated by the classifiers at the stage 1. The proposed method was ranked 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.

关键词： Classification algorithms Image color analysis Image segmentation Iris recognition Object segmentation Robustness Training image colour analysis Object segmentation iris recognition sorting algorithm Sclera image segmentation Classifiers Robustness Functional training segmentation Segmentation color image

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Induced sorting Suffixes in External Memory

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2015年第3期33卷 12-12页

作者： Nong, Ge Chan, Wai Hong Hu, Sheng Qing Wu, Yi Sun Yat Sen Univ Dept Comp Sci SYSU CMU Shunde Int Joint Res Inst Guangzhou 510275 Guangdong Peoples R China Hong Kong Inst Educ Dept Math & Informat Technol Kowloon Hong Kong Peoples R China Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China

We present in this article an external memory algorithm, called disk SA-IS (DSA-IS), to exactly emulate the induced sorting algorithm SA-IS previously proposed for sorting suffixes in RAM. DSA-IS is a new disk-friendly method for sequentially retrieving the preceding character of a sorted suffix to induce the order of the preceding suffix. For a size-n string of a constant or integer alphabet, given the RAM capacity Omega((nW)(0.5)), where W is the size of each I/O buffer that is large enough to amortize the overhead of each access to disk, both the CPU time and peak disk use of DSA-IS are O(n). Our experimental study shows that on average, DSA-IS achieves the best time and space results of all of the existing external memory algorithms based on the induced sorting principle.

关键词： algorithms Performance Suffix array sorting algorithm external memory

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An Open-source Toolbox For Standardized Use Of PhysioNet Sleep EDF Expanded Database 37

An Open-source Toolbox For Standardized Use Of PhysioNet Sle...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Imtiaz, Syed Anas Rodriguez-Villegas, Esther Univ London Imperial Coll Sci Technol & Med Elect & Elect Engn Dept Circuits & Syst Grp London SW7 2AZ England

ISBN: (纸本)9781424492701

PhysioNet Sleep EDF database has been the most popular source of data used for developing and testing many automatic sleep staging algorithms. However, the recordings from this database has been used in an inconsistent fashion. For example, arbitrary selection of start and end times from long term recordings, data-hypnogram mismatches, different performance metrics and hypnogram conversion from R&K to AASM. All these differences result in different data sections and performance metrics being used by researchers thereby making any direct comparison between algorithms very difficult. Recently, a superset of this database has been made available on PhysioNet, known as the Sleep EDF Expanded Database which includes 61 recordings. This provides an opportunity to standardize the way in which signals from this database should be used. With this goal in mind, we present in this paper a toolbox for automatically downloading and extracting recordings from the Sleep EDF Expanded database and converting them to a suitable format for use in MATLAB. This toolbox contains functions for selecting appropriate data for sleep analysis (based on our previous recommendations for sleep staging), hypnogram conversion and computation of performance metrics. Its use makes it simpler to start using the new sleep database and also provides a foundation for much-needed standarization in this research field.

关键词： database management systems medical administrative data processing public domain software AASM Matlab automatic sleep staging algorithms data source data-hypnogram mismatches hypnogram conversion open-source toolbox performance metrics physionet sleep EDF expanded database sleep analysis algorithm design and analysis Classification algorithms Databases Electroencephalography MATLAB Sleep Database Performance metrics MATLAB algorithm design and analysis open source software Data sources Sleep sorting algorithm database management systems Interleukin-5 European Development Fund Electroencephalography tool cases

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Mahalanobis Distance and Maximum Likelihood Based Classification for Identifying Tobacco in Pakistan 7

Mahalanobis Distance and Maximum Likelihood Based Classifica...

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7th International Conference on Recent Advances in Space Technologies (RAST)

作者： Ahmed, Aziz Muaz, Muhammad Ali, Manzoor Yasir, Muhammad Ullah, Sadiq Khan, Shahbaz Univ Engn & Technol Dept Telecommun Engn Mardan Campus Peshawar Pakistan

ISBN: (纸本)9781479976973

Classifying cash crops through satellite based remote sensing has proved to be effective for reliable ground based agricultural statistics. In this study, frequently used simple and fast classification algorithms i.e., Mahalanobis Distance and Maximum Likelihood Classification (MLC) are compared for classifying tobacco crops by the end of June in north-western Pakistan. High Geometric Resolution imagery of SPOT-5 (2.5m) is used as the base image for comparison over a large pilot region. Our results indicate that MLC is more accurate than its simple form Mahalanobis distance with overall accuracy of 93.91% and kappa coefficient of 0.9181. Though it is visually seen that MLC has over-estimated tobacco crops in the unclassified region but this effect is mitigated with the help of two additional classes namely 'interfering separation' and 'interfering settlements'. It is recommended to use and compare MLC for future detection of tobacco crops in north-western Pakistan.

关键词： microprocessor logic circuit MLC1 gene Pakistan maximum likelihood Tobacco Products crops agricultural statistics sorting algorithm cash crop based

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Intellectual Scaling in a Distributed Cloud Application Architecture: a Message Classification algorithm

Intellectual Scaling in a Distributed Cloud Application Arch...

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2015 International Conference "Stability and Control Processes" in Memory of V.I. Zubov (SCP)

作者： Iakushkin, Oleg St Petersburg State Univ 7-9 Univ Skaya Nab St Petersburg 199034 Russia

ISBN: (纸本)9781467376983

The service systems deployed in a cloud environment have a volatile business logic which cannot be fully described by messaging patterns now available. Firstly, the work of distributed service systems is heavily influenced by the type, format, topic, and content of messages. Secondly, the existing middleware solutions for message exchange have a very narrow functionality focused exclusively on message passing between the nodes of a distributed system. They pay no attention to the features of the messages sent. However, scaling in hybrid and geo-distributed cloud solutions requires a more comprehensive approach. So does the analysis of the state of system modules. This paper offers an algorithm allowing to classify messages which are sent via the same protocol but have various content and do not correspond to a pre-set format. We provide an example of using such algorithm in middleware architecture. The major advantages and drawbacks of the algorithm are described with regard to a distributed cloud system.

关键词： Messages CLOUDS Clouds program architecture information exchange sorting algorithm water service reliability Distributed Systems message passing Scale formation Middleware intellectuals

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To Be Or Not To Be IID: Can Zipf's Law Help?

To Be Or Not To Be IID: Can Zipf's Law Help?

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IEEE International Symposium on Technologies for Homeland Security (HST)

作者： Behe, Leo Wheeler, Zachary Nelson, Christie Knopp, Brian Pottenger, William M. Lehigh Univ Bethlehem PA 18015 USA Grove City Coll Grove City PA USA Rutgers State Univ Piscataway NJ USA Montana Tech Univ Butte MT USA

ISBN: (纸本)9781479917372

Classification is a popular problem within machine learning, and increasing the effectiveness of classification algorithms has many significant applications within industry and academia. In particular, focus will be given to Higher-Order Naive Bayes (HONB), a relational variant of the famed Naive Bayes (NB) statistical classification algorithm that has been shown to outperform Naive Bayes in many cases [1,10]. Specifically, HONB has outperformed NB on character n-gram based feature spaces when the available training data is small [2]. In this paper, a correlation is hypothesized between the performance of HONB on character n-gram feature spaces and how closely the feature space distribution follows Zipf's Law. This hypothesis stems from the overarching goal of ultimately understanding HONB and knowing when it will outperform NB. Textual datasets ranging from several thousand instances to nearly 20,000 instances, some containing microtext, were used to generate character n-gram feature spaces. HONB and NB were both used to model these datasets, using varying character n-gram sizes (2-7) and dictionary sizes up to 5000 features. The performances of HONB and NB were then compared, and the results show potential support for our hypothesis: namely, the results support the hypothesized correlation for the Accuracy and Precision metrics. Additionally, a solution is provided for an open problem which was presented in [1], giving an explicit formula for the number of SDRs from k given sets, which has connections to counting higher-order paths of arbitrary length, which are important in the learning stage of HONB.

关键词： Eigenvector sorting algorithm Bayes CHARACTER Character

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A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks 13

A hypothesize-and-verify framework for Text Recognition usin...

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13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Ray, Anupama Rajeswar, Sai Chaudhury, Santanu Indian Inst Technol Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9781479918058

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors.

关键词： image matching image segmentation image sequences recurrent neural nets text detection tree searching abstraction best first search bidirectional long short term memory deep BLSTM deep recurrent neural network hybrid text recognizer hypothesize-and-verify framework language model line sequences multihypotheses tree architecture recognition errors segmentation algorithms sub-string match unicode sequences Classification algorithms Training Transforms modelling languages recurrent neural nets Printed text recognition Tree searching sorting algorithm Abstraction image matching image sequences Transform best-first search image segmentation segmentation Functional training

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A Spatio-temporal Network Model to Represent and Analyze LBSNs 13

A Spatio-temporal Network Model to Represent and Analyze LBS...

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IEEE International Conference on Pervasive Computing and Communication Workshops PerCom Workshops

作者： Moreno, B. N. Times, V. C. Matwin, S. Univ Fed Pernambuco Ctr Informat Recife PE Brazil Dalhousie Univ Fac Comp Sci Halifax NS B3H 3J5 Canada Inst Comp Sci Warsaw Poland

ISBN: (纸本)9781479984251

With the increasing popularity of Location-based Social Networks (LBSNs), users have shared information about places they have visited, creating a link between the real world (their movements on the globe) and the virtual world (what they express about these movements on the LBSNs). In this article, we propose the SiST model, which contains information captured from different dimensions (Social, Spatial and Temporal). The proposed model is a graph that links two users, as long as both of them are friends and have published that they were at the same place within a predefined time interval. In addition to movement patterns that can be extracted using SiST, this model may be used to predict if two users will meet in a short time span by executing a classification algorithm. Performance tests were conducted with SiST networks that were built based on three real LBSN datasets. Results indicated that it is possible to forecast with high accuracy (ranging from 80.50% to 96.32%) whether two people will meet or not using two days of historical data.

关键词： network model patterning of movement historical data Consumers Friends Globe Time intervals Movements Information Sharing lamb meat sorting algorithm

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Visual Appearance based Document Classification Methods: Performance Evaluation and Benchmarking 13

Visual Appearance based Document Classification Methods: Per...

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13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Bukhari, Syed Saqib Dengel, Andreas German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany

ISBN: (纸本)9781479918058

Most of the traditional document image classification techniques concentrate on document segmentation and OCR analysis, in spite of so many complexities and limitations involved. Recently, many of the document image classification problems are easily solved just by adapting standard computer vision approaches for natural image retrieval and classification, that are referred as visual appearance based document classification techniques. These approaches have reported better results as compared to the traditional approaches on proprietary datasets. However, so far these approaches are not compared with each other and, despite having potential, they are not evaluated on distorted camera-captured documents, which is one of the challenging requirements in our present commercial document analysis projects. In this paper, we present simple and effective descriptions of different visual appearance based document image classification techniques. We compare their performance on various standard and publicly available datasets, that are differ in degree of image degradations and content variations. We also demonstrate their advantages and limitations. Additionally, we make the implemented versions of these method publicly available to research community for usage and further testing on other domains.

关键词： document image processing image classification image segmentation optical character recognition OCR analysis content variations document segmentation image degradations natural image classification natural image retrieval performance evaluation publicly available datasets standard computer vision visual appearance based document classification method algorithm design and analysis Chlorine Classification algorithms Distortion Image segmentation Optical character recognition software Programming image degradation optical character recognition softwares image segmentation algorithm design and analysis document image processing optical character recognition sorting algorithm Performance Evaluation Image classification abnormal shapes Chlorine Programming distortion document image BENCHMARKS

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