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检索条件"主题词=detection algorithms"

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Community detection in Scientific Co-Authorship Networks using Neo4j

Community Detection in Scientific Co-Authorship Networks usi...

引用

Computer Applications (ICCA), IEEE Conference on

作者： Thet Thet Aung Thi Thi Soe Nyunt Faculty of Computer Science University of Computer Studies Yangon Yangon Myanmar

ISBN: (数字)9781728159256

ISBN: (纸本)9781728159263

Community structure in scientific collaboration network has become an important research area. Co-author of a paper can be thought of as a collaborative document between more than one authors. Community detection in co-authorship network reveals characteristic patterns of scientific collaboration in computer science research and help to understand the identity-organization of the author community. Louvain algorithm is a simple, easy to implement and efficient to recognize community in huge networks. In this paper, it is used to examine the structure of community in Computer University's coauthor network in Myanmar. Neo4j is also used to visualize the co-authorship network analysis results. Modularity is used to measure the quality of the cluster structure found by community discovery algorithms. In experiment, Louvain algorithm gives more effective qualitative community structures than other algorithms in co-authorship network.

关键词： Clustering algorithms detection algorithms Collaboration Image edge detection Partitioning algorithms Libraries Software algorithms

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Low Complexity Illumination-Invariant Motion Vector detection Based on Logarithmic Edge detection and Edge Difference

Low Complexity Illumination-Invariant Motion Vector Detectio...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chuanqi Wei Jiangchao Wu Man-Kay Law Pui-In Mak Rui P. Martins State-Key Laboratory of Analog and Mixed-Signal VLSI IME and FST-ECE University of Macau Macau China Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper describes a low complexity illumination-invariant motion vector detection algorithm based on logarithmic edge detection and edge difference without periodic threshold adjustment. A logarithmic edge detector is employed to achieve accurate object movement over a wide illumination range. The threshold for edge detection is determined by one-time background logarithmic gradient extraction. Finally, the difference of the logarithmic edge of 3 consecutive frames is employed in a frame difference-based motion template model to obtain the motion vector. Experimental results show that the proposed algorithm achieves a motion vector detection accuracy of ∼90% over an illumination level change of 80%.

关键词： Image edge detection Lighting Motion detection Complexity theory detection algorithms Detectors Motion segmentation

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Efficient Algorithm for Initialization of Object Tracks Based on Changepoint detection Method

Efficient Algorithm for Initialization of Object Tracks Base...

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Engineering and Telecommunication (En&T), International Conference

作者： Valentin Spivak Alexander Tartakovsky Laboratory of Space Informatics Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9781728188300

In many problems of the initialization of objects' tracks, changepoint detection algorithms can be used. In the past when computational complexity was an issue, the K / N algorithm gained its popularity due to computational simplicity. Nowadays with the tremendous progress in computing technology, the problem of finding more efficient detection and track initiation algorithms is urgent. A substantially more efficient track initiation algorithm can be built based on the sequential change detection technique. In this paper, we consider the Finite Moving Average algorithm. We compare the performance of the K / N algorithm and the Finite Moving Average algorithm. The optimality criterion is to maximize the probability of correct detection in a certain time interval under the given false alarm risk measured as the local probability of a false alarm. For performance, we obtain a theoretical estimate and an estimate by Monte Carlo (MC) simulations. The results show that the Finite Moving Average algorithm performs significantly better than the K / N procedure.

关键词： Computers Monte Carlo methods Change detection algorithms Approximation algorithms Time measurement detection algorithms Computational complexity

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Social Network Analysis Of Legislative Candidates in Indonesia General Election 2019 Using Community detection

Social Network Analysis Of Legislative Candidates in Indones...

引用

International Conference of Computer and Informatics Engineering (IC2IE)

作者： Nur Aini Rakhmawati Karima Mufidah Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9781728182476

ISBN: (纸本)9781728182483

The 2019 general election in Indonesia aims to elect the President and Vice President, Legislative Assembly (DPR and DPRD), and Regional Representative Council (DPD). The candidates must fill out their personal data in the general election official website at the time of registration. We perform the Social Network Analysis (SNA) over legislative candidate data to determine the pattern of relationships between candidates based on these data. The community detection algorithms in SNA can map and illustrate the pattern of relationship that is owned by the candidates in the 2019 elections. The output of this analysis will be visualized in graphs to illustrate the pattern of relationships based on the results of the SNA algorithm calculation. Based on the calculation of community detection algorithms, 60 communities were consisting of two to four candidates.

关键词： Voting Clustering algorithms Social networking (online) Visualization Partitioning algorithms Databases detection algorithms

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A Robust Target detection Algorithm Using MEMS Inertial Sensors for Shipboard Video System

A Robust Target Detection Algorithm Using MEMS Inertial Sens...

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Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)

作者： Wen Liu Xuefeng Yang JianBao Zhang National Engineering Laboratory of Transport Safety and Emergency Informatics China Transport Telecommunications & Information Center Beijing China School of Shipping and Naval Architecture Chongqing Jiaotong University Chongqing China Research Center of Maritime Security Technology China Waterborne Transport Research Institute Beijing China

ISBN: (数字)9785919950684

ISBN: (纸本)9781728187983

Marine situational awareness is the core technology of intelligent ship, and the target detection is the important foundation of situational awareness. In this paper, a robust target detection algorithm using MEMS inertial sensors is proposed for sky-sea line detection for shipboard video system, the key idea is using the inertial data of the shipborne camera to estimate the position and angle of the sky-sea lines in the video frames, so as to achieve the accuracy of the detection area, improve the detection accuracy and reduce the interference of wave clutter. And it is proved by experiments that robust detection results have been achieved.

关键词： Cameras Transforms Image edge detection Marine vehicles Estimation detection algorithms

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A Hybrid Model for Container-code detection

A Hybrid Model for Container-code Detection

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Cai Sun Kuikun Liu Haoyuan Chi Mesoume Zareapoor School of Electrical Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9780738105451

ISBN: (纸本)9781665422994

In this paper, we propose a container code detection algorithm that combines PSENet and CRNN in a manner that ensures that the obtained images are greatly affected by the other containers as well as text information in the picture. The proposed algorithm divided into three parts: object detection module, text detection module and text recognition module. At the initial step, the object detection module is used to calculate the position of those areas in container code that needs to be predicted, we then use the text detection module based on pixel segmentation, and finally obtain the container code through the end-to-end text recognition module. This algorithm is able to detect different container code for a vertical and horizontal scaling. We show that, in a complex multi-container scenario, the detection performance is good, and highly stable when trained with multi-angle container, achieving relative improvement of up to 95%.

关键词： Containers Feature extraction Text recognition Object detection detection algorithms Proposals Image segmentation

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Abrupt Shot Change detection using Midhinge Local Binary Pattern

Abrupt Shot Change Detection using Midhinge Local Binary Pat...

引用

HYDCON

作者： H.M. Nandini H.K. Chethan B.S. Rashmi Maharaja Research Foundation Maharaja Institute of Technology Mysuru India Karnataka State Open University Mysuru India

ISBN: (数字)9781728149943

ISBN: (纸本)9781728149950

Video shot change detection is an extremely important task for video analysis and is considered as the preliminary step for effective video summarization, browsing and retrieval. In this work, a new texture based descriptor has been proposed using Midhinge Local Binary Pattern (MHLBP) concept for identification of abrupt transition in videos. The discrimination capability of MHLBP is better than basic LBP and its versions (mean LBP and midrange LBP). MHLBP histogram is constructed by applying midhinge statistics on each mask of video frame. The distance between histogram features of the adjacent frames are evaluated using Euclidean distance. The obtained distance values are subjected to adaptive thresh-old mechanism for identifying of abrupt shot changes in a video. The planned framework is tested on a subset of TRECVID 2001 dataset. The result shows that the proposed method outperforms with other existing shot change detection algorithms in terms of precision, recall and F-measures.

关键词： Histograms Euclidean distance Feature extraction Task analysis detection algorithms

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Transient Pattern detection from Streaming Nature Data

Transient Pattern Detection from Streaming Nature Data

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Thanapol Phungtua-eng Yoshitaka Yamamoto Shigeyuki Sako Faculty of Business Administration and Information Technology Rajamangala University of Technology Tawan-Ok Chakrabongse Bangkok Thailand Graduate School of Integrated Science and Technology Shizuoka University Shizuoka Japan Institute of Astronomy Graduate School of Science University of Tokyo Tokyo Japan

ISBN: (数字)9781728199191

ISBN: (纸本)9781728189314

A novel challenging for scientific discovery is finding an un-expected phenomenon from nature data. For such data-intensive discovery, we study online algorithms for detecting transient patterns in streaming nature data. There are two issues to be addressed. First, due to the limitation of memory resources, we need to process every incoming data by on-the-fly manner. Secondly, we need to avoid the false alert as far as possible, which adversely impacts on decisions. For tackling the first issue, we propose a fast detection algorithm based on the Chebyshev inequality. The Chebyshev inequality is a classic but general tail- bound inequality satisfied over any observed data. Together with adaptive windowing technique, the proposed algorithm can quickly decide if the prior data is in a normal or transient zone. We then introduce a two-phase alert mechanism into the baseline, which is effective for reducing the false alert rate. Consequently, we demonstrate the performance of our proposed method obtained by using both the artificial data and real data.

关键词： Conferences Chebyshev approximation Transient analysis detection algorithms

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An Energy-Efficient Cell Outage detection Algorithm for Self-Organizing Networks

An Energy-Efficient Cell Outage Detection Algorithm for Self...

引用

International Symposium on Telecommunications, IST

作者： Layla Majzoobi Mohammad Akbari Elaheh Vaezpour Communication Technology Faculty ICT Research Institute (ITRC) Tehran Iran

ISBN: (数字)9781728180120

ISBN: (纸本)9781728180137

Faults and failures are inevitable in dense heterogeneous wireless networks consisting of a massive number of elements. Automatic management of faulty situations is critical in such complex networks. Among the common network failures, cell outage calls for an especial attention. Automatic and efficient management of cell outage improves users' quality of experience and leads to a significant reduction in operational expenditure (OPEX). Timely and rapid detection of cell outage is the first and somehow the most important step in the cell outage management. In this work, we propose a low complexity and energy efficient cell outage detection algorithm. The proposed algorithm utilizes minimization of drive test (MDT) mechanism to gather users' measurements. Applying outlier detection algorithm followed by a novel localization method, occurrence of cell outage and its location can be determined precisely. Simulation results demonstrate the efficiency of the proposed method in both detection and localization of the cell outage.

关键词： Location awareness Simulation Self-organizing networks Minimization Energy efficiency Quality of experience detection algorithms

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Research and Application of Improved Community detection Algorithm Based on Wi-Fi Location Data Association Measurement Model

Research and Application of Improved Community Detection Alg...

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International Conference on Computer and Communications (ICCC)

作者： Bo She Qiang Wang Zhe Zhang Jingru Cui Weijie Rao Xiaoge Zhong The Network Information Center Xi'an Jiaotong University Xi'an China State Key Laboratory of Astronautic Dynamics Xi'an Satellite Control Center Xi'an China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

In order to make full use of the Wi-Fi Location data on university campus, we established a Structural model of neural network, and use Data Mining on students' offline social behavior through a Community detection algorithm. Starting from the theory and technology of the Community detection algorithm, the paper introduces the structure of neural network, the definition of community, clustering algorithm DBSCAN*-Martingale, and evaluation criteria of Community detection algorithm; For proposing an association measurement model based on Wi-Fi Location Data, the paper also introduces the definition of similarity. And in order to obtain the similarity measurement of the edges between nodes, we used feature engineering to extract individual, global, and temporal features combined them in some ways. This paper corrects the problem of preempting nodes in the DBSCAN*-Martingale algorithm, modify the propagation characteristics of the algorithm, acquires the overlapping Community detection algorithm DBSCAN*-Martingale pro. At the end of the paper, the algorithm was verified on artificial synthetic networks, real public data sets and Wi-Fi data sets. The improved DBSCAN*-Martingale pro algorithm performed better than the unimproved algorithm on artificial synthetic networks and real data sets and it has increased the overlap modularity from 0.613 to 0.695 compared to the unimproved algorithm.

关键词： Social networking (online) Neural networks Clustering algorithms Feature extraction Data models detection algorithms Wireless fidelity

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