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Road map extraction from satellite imagery using connected component analysis and landscape metrics

Road map extraction from satellite imagery using connected c...

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IEEE International Conference on Big Data

作者： Kulsawasd Jitkajornwanich Peerapon Vateekul Teerapong Panboonyuen Siam Lawawirojwong Siwapon Srisonphan Dept. of Computer Science King Mongkut's Institute of Technology Bangkok Thailand Dept. of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand GISTDA Academy Geo-Informatics & Space Technology Development Agency (PO) Bangkok Thailand Dept. of Electrical Engineering Kasetsart University Bangkok Thailand

Road map extraction is considered an essential task in GIS as its results are the basis of location-based applications in various domains. Examples include GPS navigation on cell phone, delivery route optimization and planning, tourist attraction locator, and location-based marketing. Satellite imagery, one of the big spatial data sources, is used in this research - though other types of remotely-sensed images can also be applied, such as aerial photographs from aircrafts, UAVs or drones. Despite several methods and techniques proposed and accompanied with different performance criteria, the focus was mainly on the accuracy aspect rather than the completeness of the result sets. That is, the results were said to be satisfactory if it met a certain accuracy criterion associated with some benchmark data sets, regardless of whether all results were retrieved. In many cases; however, both accuracy and completeness are equally important. In this paper, we enhance the result accuracy by incorporating connected component analysis into the method as well as the completeness performance by utilizing an ecology concept, called Landscape Metrics, which describes spatial characteristics, patterns, and correlations of areas/patches through different indices. Two types of metrics are used: shape metrics and isolation metrics. The performance is evaluated based on four criteria: precision, recall, quality, and F1 scores. The results show that more than 90% of performance is achieved in all four criteria.

关键词： Roads Measurement Shape Indexes Satellites Filtering Feature extraction

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Understanding customers' behaviour of telecommunication companies increasing the efficiency of clustering techniques

Understanding customers' behaviour of telecommunication comp...

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Telecommunications Forum (TELFOR)

作者： I. K. Savvas C. Chaikalis F. Messina D. Tselios Dept. of Computer Science and Engineering TEI of Thessaly Larissa Greece Dept. of Mathematics and Informatics University of Catania Catania Italy Dept. of Business Administration TEI of Thessaly Larissa Greece

The customers' data of Telecommunication Companies represent a powerful tool to explore their behaviour and then to increase their satisfaction. The data produced is too large to extract on time useful information, which could be beneficial for both sides, companies and customers. One of the solutions to explore this data is representing it by the adoption of clustering techniques. In this work, two distributed / multi-core versions of clustering algorithms were used, namely DBSCAN and k-means, which both of them cluster data according to its characteristics. While DBSCAN is a density-based spatial clustering algorithm and groups data based on the minimum size of participating objects per cluster and the minimum required distance between them, k-means clusters the data objects according the pre-desired number of groups. Thus, since the two methods use different roads to group the data objects, they form different clusters but each one has its importance depending on the characteristics of the applied method. The experiment results of both proposed distributed / multi-core techniques proved their efficiency.

关键词： Clustering algorithms Distributed databases Data mining Companies Telecommunications Algorithm design and analysis Tools

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Interdependence between Power Grids and Communication Networks: A Resilience Perspective

Interdependence between Power Grids and Communication Networ...

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DRCN 2017 - Design of Reliable Communication Networks;13th International Conference

作者： Lucia Martins Rita Girao-Silva Luisa Jorge Alvaro Gomes Francesco Musumeci Jacek Rak Department of Electrical and Computer Engineering University of Coimbra Polytechnic Institute of Braganca Politecnico di Milano Dept. of Electronics Information and Bioengineering Department of Computer Communications Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology

ISBN: (纸本)9781509008940

Power network resilience is increasingly dependent on communication networks. Besides traditional generation, power networks need to accommodate increasingly high penetration levels of dispersed micro generation, mostly based on renewable sources, and increasing and challenging demand, such as electric vehicles. At the same time the deployment of enabling technologies throughout the power grid makes available new demand resources and storage capacity. These resources have to be jointly managed for efficient and resilient operation. Therefore a resilient communication network is mandatory for the smart control of these different resources. Communication networks in turn also rely on the power networks in spite of some power backup that they may have. This interdependence is especially critical in bad weather conditions where both networks can be severely affected, even more when they strongly depend on each other. This work describes ongoing research related with the interdependence between both networks regarding resilience, cascading failure effects, applicable models and simulation techniques.

关键词： Multicast communication Cognitive radio Routing protocols Delays Switches Channel allocation

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How image degradations affect deep CNN-based Face recognition? 15

How image degradations affect deep CNN-based Face recognitio...

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15th International Conference of the Biometrics Special Interest Group, BIOSIG 2016

作者： Karahan, Şamil Yildirim, Merve Kilinč Kirtaç, Kadir Rende, Ferhat Şükrü Bütün, Gültekin Ekenel, Hazim Kemal Tubitak Bilgem Gebze Kocaeli41470 Turkey Faculty of Computer and Informatics Dept. of Computer Engineering Maslak Istanbul34469 Turkey

ISBN: (纸本)9783885796541

Face recognition approaches that are based on deep convolutional neural networks (CNN) have been dominating the field. The performance improvements they have provided in the so called in-the-wild datasets are significant, however, their performance under image quality degradations have not been assessed, yet. This is particularly important, since in realworld face recognition applications, images may contain various kinds of degradations due to motion blur, noise, compression artifacts, color distortions, and occlusion. In this work, we have addressed this problem and analyzed the influence of these image degradations on the performance of deep CNN-based face recognition approaches using the standard LFW closed-set identification protocol. We have evaluated three popular deep CNN models, namely, the AlexNet, VGG-Face, and GoogLeNet. Results have indicated that blur, noise, and occlusion cause a significant decrease in performance, while deep CNN models are found to be robust to distortions, such as color distortions and change in color balance. © 2016 Gesellschaft für Informatik e.V., Bonn, Germany.

关键词： Face recognition

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Rate-distortion trade-offs in acquisition of signal parameters

Rate-distortion trade-offs in acquisition of signal paramete...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Miguel R. D. Rodrigues Nikos Deligiannis Lifeng Lai Yonina C. Eldar Dept. Electronic and Electrical Engineering University College London United Kingdom Dept. Electronics and Informatics Vrije Universiteit Brussel Belgium Dept. Electrical and Computer Engineering University of California Davis USA Dept. Electrical Engineering Technion - Israel Institute of Technology Israel

ISBN: (纸本)9781509041183

We consider problems where one wishes to represent a parameter associated with a signal source - subject to a certain rate and distortion - based on the observation of a number of realizations of the source signal. By reducing these indirect vector quantization problems to a standard vector quantization one, we provide a bound to the fundamental interplay between the rate and distortion in the large-rate setting. We specialize this characterization to two particular quantization scenarios: i) the representation of the mean of a multivariate Gaussian source;and ii) the representation of the eigen-spectrum of a multivariate Gaussian source. Numerical results compare our quantization approach to an approach where one recovers the parameters from the representation of the source signals itself: in addition to revealing that the characterization is sharp in the large-rate setting, the results also show that our approach offers considerable gains.

关键词： Signal Acquisition Signal Parameters Acquisition Rate-Distortion Vector Quantization Rate distortion Vector quantization signal acquisition signal parameter signal sources distortion

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Toward opportunistic services for the industrial Internet of Things

Toward opportunistic services for the industrial Internet of...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Giancarlo Fortino Claudio Savaglio Mengchu Zhou Dept. of Informatics Modelling Electronics and Systems (DIMES) University of Calabria Rende CS Italy Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Services are expected to represent the real drivers for all Internet of Things (IoT) application scenarios, including Industrial IoT (IIoT). Indeed, rather than focusing only on products, enterprises are now investing on product-service hybrids to improve operational efficiency, boost productivity and, most importantly, build new markets, hence diversifying their revenues. In this direction, IoT service modeling is a crucial activity requiring a multi-facet in-depth examination. In this paper, we first discuss the importance of IoT services by eliciting related benefits and challenges. Then, we tackle service modeling from both a developer-oriented (comprising highlevel descriptive metamodels and ontologies) and an enterprise-oriented (comprising operational models subject to formal verification and execution, such as extensions of the conventional Business Process Models, Petri Nets and WorkFlows) perspective, providing a conceptual mapping between these two complementary approaches. The proposed new paradigm of “Opportunistic IoT Service” extends the available models by explicitly considering essential features for service provision, i.e., dynamicity, context-awareness, co-location and transience.

关键词： Business Computational modeling Context modeling Analytical models Biological system modeling Ontologies Sensors

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Formal Ontology Generation by deep machine learning 16

Formal Ontology Generation by deep machine learning

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16th IEEE International Conference on Cognitive informatics and Cognitive Computing, ICCI*CC 2017

作者： Wang, Yingxu Valipour, Mehrdad Zatarain, Omar D. Gavrilova, Marina L. Hussain, Amir Howard, Newton Patel, Shushma International Institute of Cognitive Informatics and Cognitive Computing Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive CalgaryALT2N1N4 Canada Dept. of Computer Science University of Stirlling United Kingdom Oxford Neurocomputation Lab University of Oxford United Kingdom School of Engineering South Bank University London United Kingdom

ISBN: (纸本)9781538607701

An ontology is a taxonomic hierarchy of lexical terms and their syntactic and semantic relations for representing a framework of structured knowledge. Ontology used to be problem-specific and manually built due to its extreme complexity. Based on the latest advances in cognitive knowledge learning and formal semantic analyses, an Algorithm of Formal Ontology Generation (AFOG) is developed. The methodology of AFOG enables autonomous generation of quantitative ontologies in knowledge engineering and semantic comprehension via deep machine learning. A set of experiments demonstrates applications of AFOG in cognitive computing, semantic computing, machine learning and computational intelligence. © 2017 IEEE.

关键词： Knowledge representation

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Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining

Ocean surface current prediction based on HF radar observati...

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IEEE International Conference on Big Data

作者： Kulsawasd Jitkajornwanich Peerapon Vateekul Upa Gupta Teeranai Kormongkolkul Arnon Jirakittayakorn Siam Lawawirojwong Siwapon Srisonphan Dept. of Computer Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Dept. of Computer Engineering Chulalongkorn University Bangkok Thailand Chulalongkorn University Big Data Analytics & IoT Center (CUBIC) Dept. of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand GISTDA Academy Geo-Informatics & Space Technology Development Agency (PO) Bangkok Thailand Dept. of Electrical Engineering Kasetsart University Bangkok Thailand

HF (high frequency) coastal radar system is used to capture the surface current behavior - in terms of velocity and direction - in the ocean near the coast. 18 HF coastal radar stations were implemented along the Gulf of Thailand in order to monitor for disasters (e.g., Tsunami) as well as relevant risks. The HF systems are also to serve other life-critical applications, such as water quality control and monitoring, chemical spill backtracking, and marine navigation. However, not all the applications can benefit from this near-real-time HF data; some applications in different domains require forecast values. The examples include search-and-rescue system and hazardous materials spill trajectory prediction. Therefore, in this paper, we propose a predictive model for future current data based on historical HF coastal radar data sets, utilizing association rule mining combined with an object dispersion concept. So, the full potential of HF radar systems can be exploited. The spatial and temporal dimensions are taken into account when designing our predictive system, which consists of two phases: ocean surface current track formulation and spatio-temporal association rule mining. The experiments are performed on a two-year HF radar dataset (2014-2015) using Google Cloud Platform. The resulting forecast current values: velocity and direction are then compared with testing datasets (using 10-fold cross validation) of the actual recorded values and evaluated based on percentage accuracy and RMSE, respectively.

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A Gaze-Centered Multimodal Approach to Human-Human Social Interaction

A Gaze-Centered Multimodal Approach to Human-Human Social In...

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International Conference on Cybernetics

作者： ülkü Arslan Aydin Sinan Kalkan Cengiz Acartürk Cognitive Science Dept. Informatics Institute Ankara Turkey Computer Engineering Dept. Middle East Technical University Ankara Turkey Orta Dogu Teknik Universitesi Ankara Ankara TR

This study aims at investigating gaze aversion behavior in human-human dyads during the course of a conversation. Our goal is to identify the parametric infrastructure, which will underlie the development of gaze behavior in Human Robot Interaction. We employed a job interview setting, where pairs (an interviewer and an interviewee) conducted mock job interviews. Three pairs of native speakers took part in the experiment. Two eye-tracking glasses recorded the scene video, the audio and the eye gaze positions of the participants. The analyses involved synchronization of multimodal data, including video recording data for face tracking, gaze data from the eye trackers, and the audio data for speech segmentation. We investigated frequency, duration, timing and spatial positions of gaze aversions relative to interlocutor's face. The results revealed that the interviewees perform more frequent gaze aversion compared to the interviewers. Moreover, gaze aversion takes longer when accompanied by speech. Also, specific speech instances, such as pause and speechend signals have significant impact on gaze aversion behavior.

关键词： Gaze Aversion Mobile Eye Tracking Job Interview task

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Recursive Structure Similarity: A Novel Algorithm for Graph Clustering

Recursive Structure Similarity: A Novel Algorithm for Graph ...

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IEEE International Conference on Tools with Artificial Intelligence

作者： Han Hu Yixin Fang Rouming Jin Wei Xiong Xiaoning Qian Dejing Dou Hai Phan Dept. of Mathematical Sciences New Jersey Inst. of Technology Newark New Jersey USA Dept. of Computer Science Kent State University Kent Ohio USA Dept. of Electrical & Computer Engineering Texas A&M University College Station Texas USA Dept. of Computer and Information Science University of Oregon Eugene Oregon USA Dept. of Informatics New Jersey Inst. of Technology Newark New Jersey USA

A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focus on optimizing specified quality measures at the global network level, without carefully considering the destruction of local structures which could be informative and significant in practice. In this paper, we propose a novel clustering algorithm for undirected graphs based on a new structure similarity measure which is computed in a recursive procedure. Our method can provide robust and high-quality clustering results, while preserving informative local structures in the original graph. Rigorous experiments conducted on a variety of benchmark and protein datasets show that our algorithm consistently outperforms existing algorithms.

关键词： Graph clustering Social network

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