Accession number:20153101088991 Title:One methodology for spam review detection based on review coherence metrics Authors:Yang, Xinkai (1) Author affiliation:(1) College of Information, Mechanical and Electronical Engineering, Shanghai Normal University, Shanghai, China Corresponding author:Yang, Xinkai Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:99-102 Article number:7111547 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:In this paper, we propose an iterative computation framework to detect spam reviews based on coherent examination. We first define some reviews' coherent metrics to analyze review coherence in the granularity of sentence. Then the framework and its evaluation process are discussed in details. © 2015 IEEE. Number of references:18 Main heading:Internet Controlled terms:Intelligent computing - Iterative methods Uncontrolled terms:Coherence metric - Concurrence probabilities - Iterative computation - ITS evaluation - Transition probabilities Classification code:716 Telecommunication; Radar, Radio and Television - 717 Optical Communication - 718 Telephone Systems and Related Technologies; Line Communications - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 921.6 Numerical Methods DOI:10.1109/ICAIOT.2015.7111547 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088969 Title:Classification of power-quality disturbances using PSO-MP and parametric dictionaries Authors:Zhang, Jun (1); Zeng, Ping-Ping (2); Ma, Jian (3); Wu, Jian-Hua (1) Author affiliation:(1) Dept. of Electronic Information Eng., Nanchang University, Nanchang, China; (2) College of Science and Technology, Nanchang University, Nanchang, China; (3) Metering Center, State Grid Jiangxi Electric, Power Research Institute, Nanchang, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:21-25 Article number:7111529 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:This paper aims to develop a new scheme for the classification of power-quality disturbances (PQDs). We propose to employ two discriminative dictionaries, designed based on the structures of PQDs, to respectively decompose a disturbance signal. Matching pursuit optimized by particle swarm optimization (PSO-MP) is used as the decomposition method. Reconstruction errors after sparse coding are employed to coarsely classify the PQDs into two categories, corresponding to the two dictionaries. Next, the specific class can be identified by evaluating the value of parameters of atoms. One main advantage of the approach is that it does not require a training set as many other classification methods do. The PQDs considered in this paper include sag, swell, interruption, harmonic and oscillatory transient. Experimental results indicate that the proposed approach achieves a high classification accuracy and robustness against noise. © 2015 IEEE. Number of references:12 Main heading:Particle swarm optimization (PSO) Controlled terms:Classification (of information) - Coding errors - Intelligent computing - Power quality - Signal processing Uncontrolled terms:Atomic decomposition - Classification accuracy - Classification methods - Decomposition methods - Discriminative dictionaries - Oscillatory transients - Power quality disturbances - Robustness against noise Classification code:706.1.2 Electric Power Distribution - 716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications DOI:10.1109/ICAIOT.2015.7111529 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088971 Title:Layered perceptual representation for shadow vision: From detection to removal Authors:Li, Cheng (1); Gao, Jing (2); Shi, Yanbin (1); Han, Qingshun (1) Author affiliation:(1) Aviation University of Air Force, Changchun, China; (2) 95899 Unit of PLA, Beijing, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:59-63 Article number:7111538 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:On the research of shadow essence and visual scheme, we propose a single image shadow removal method based on certain layered perceptual representation models with the flowchart from shadow detection to shadow removal. Firstly, a modified intersecting cortical model, the typically useful image factorization technique, is applied to extract umbra and penumbra masks. Then, under the energy minimization framework, scale factors for umbra are computed. Furthermore, transparency-coupled atmospheric transfer function is introduced for penumbra compensation by pixel-by-pixel transparency estimation. For aerial images, experimental results illustrate that shadow regions are matted well, and the proposed method yields vivid shadow-free images with smooth boundaries. © 2015 IEEE. Number of references:19 Main heading:Transfer functions Controlled terms:Face recognition - Intelligent computing - Pixels - Transparency Uncontrolled terms:Energy minimization - Factorization techniques - Intersecting cortical models - Perceptual representations - Shadow detections - Shadow regions - Shadow removal - Smooth boundary Classification code:723.4 Artificial Intelligence - 723.5 Computer Applications - 741.1 Light/Optics DOI:10.1109/ICAIOT.2015.7111538 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088982 Title:Watershed image segmentation algorithm base on particle swarm and region growing Authors:Sun, Hui-Jie (1) Author affiliation:(1) College of Computer Science and Information Engineering, Harbin Normal University, Harbin, Heilongjiang, China Corresponding author:Sun, Hui-Jie Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:51-54 Article number:7111536 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:An improved watershed image segmentation algorithm is proposed to solve the problems of noise-sensitivity and over-segmentation. The new algorithm which combined region growing with classical watershed algorithm is established by constructing an objective function, the parameter of region growing is ensured based on Shannon entropy. The particle swarm optimization algorithm is employed to search global optimization of the objective function. Experimental results show that the new watershed image segmentation algorithm can solve effectively the problem of over-segmentation and turns out to be an efficient, accurate and applied image segmentation algorithm. © 2015 IEEE. Number of references:16 Main heading:Image segmentation Controlled terms:Algorithms - Global optimization - Intelligent computing - Mathematical morphology - Optimization - Particle swarm optimization (PSO) - Watersheds Uncontrolled terms:Image segmentation algorithm - Noise sensitivity - Objective functions - Over segmentation - Particle swarm optimization algorithm - Region growing - Water-shed algorithm - Watershed image segmentation Classification code:444.1 Surface Water - 723 Computer Software, Data Handling and Applications - 921 Mathematics - 921.5 Optimization Techniques DOI:10.1109/ICAIOT.2015.7111536 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088966 Title:Distributed CoMP transmission for cell range expansion with almost blank subframe in downlink heterogeneous networks Authors:Wang, Yi (1); Hu, Yanjun (1) Author affiliation:(1) Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:127-130 Article number:7111553 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:The almost blank subframe (ABS) has been studied in 3GPP as a way to mitigate downlink interference experienced by cell range expansion (CRE) user equipments (UEs). However, the throughput of CRE UEs highly depends on the ratios of ABS which are statically configured in many situations. The coordinated multi-point (CoMP) is one of the key solutions standardized in LTE-A which can be implemented in CRE with ABS to exploit the abundant spatial resources. And distributed cell selection could help guarantee the UEs' performance under different range expansion biases. In this paper, a distributed CoMP method for CRE with ABS is proposed in heterogeneous networks. Simulation results show that our proposed algorithm provides considerable performance gains in the spectrum efficiency with different bias and users settings. © 2015 IEEE. Number of references:10 Main heading:Heterogeneous networks Controlled terms:Intelligent computing - Mobile telecommunication systems Uncontrolled terms:Cell range expansions - CoMP - Coordinated multi point (CoMP) - Downlink interferences - Range expansion - Spatial resources - Spectrum efficiency - Sub-frame Classification code:716 Telecommunication; Radar, Radio and Television - 717 Optical Communication - 718 Telephone Systems and Related Technologies; Line Communications - 722 Computer Systems and Equipment - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence DOI:10.1109/ICAIOT.2015.7111553 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088975 Title:Color image encryption algorithm with realitypreserving fractional discrete cosine transform and spatiotemporal chaotic mapping Authors:Liang, Ya-Ru (1); Liu, Guo-Ping (1); Wu, Jian-Hua (2) Author affiliation:(1) School of Mechatronic Eng., Nanchang University, Nanchang, China; (2) Dept. of Electronic Information Eng., Nanchang University, Nanchang, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:94-98 Article number:7111546 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:A color image encryption scheme is proposed based on a reality-preserving fractional discrete cosine transform (RPFrDCT) and spatiotemporal chaotic mapping. The scrambling and diffusion operations are carried out after the RPFrDCT and make three components of image affect each other. The main advantages of this scheme are the real-valued output, the nonlinear manipulation, the high sensitiveness and high robustness to the cipher keys. Experimental results demonstrate that the proposed encryption scheme is feasible and effective. © 2015 IEEE. Number of references:15 Main heading:Cryptography Controlled terms:Algorithms - Discrete cosine transforms - Image processing - Intelligent computing - Mapping Uncontrolled terms:Chaotic mapping - Color image encryptions - Encryption schemes - High robustness - Image encryptions - Three component Classification code:716 Telecommunication; Radar, Radio and Television - 717 Optical Communication - 718 Telephone Systems and Related Technologies; Line Communications - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 741 Light, Optics and Optical Devices - 902.1 Engineering Graphics - 921 Mathematics - 921.3 Mathematical Transformations DOI:10.1109/ICAIOT.2015.7111546 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088986 Title:An intelligent method of detecting pork freshness based on digital image processing Authors:Chang, Huiyou (1); Li, Hao (2); Hu, Yongjun (2) Author affiliation:(1) School of Software, Sun Yat-sen University, Guangzhou, China; (2) School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:107-110 Article number:7111549 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:In this paper, an improved pork freshness detecting method based on digital image processing and BP neutral network was presented. The shape of the slaughtering fresh pork cell was oval and smooth and it will change obviously in the process of pork corruption. The edge of cell will rupture and merge together until being absolutely misshapen. Seven characteristic parameters about cell shape would be extracted to stand for the level of corruption. And designs then correspond to the pork freshness standard TVB-N and characteristic parameters finally trained by BP neural network. The experimental results showed that the characteristic parameter about cell shape could detect the freshness effectively. © 2015 IEEE. Number of references:10 Main heading:Image processing Controlled terms:Cells - Crime - Cytology - Intelligent computing - Meats - Neural networks Uncontrolled terms:BP neural networks - BP neutral network - Cell shapes - characteristic parameter - Detecting methods - Fresh pork - Intelligent method - Nueral networks Classification code:461.2 Biological Materials and Tissue Engineering - 723.4 Artificial Intelligence - 741 Light, Optics and Optical Devices - 822.3 Food Products - 971 Social Sciences DOI:10.1109/ICAIOT.2015.7111549 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088970 Title:Multi-resolution model consistency maintenance method based on ontology mapping Authors:Gou, Chen-Xi (1); Cai, Bai-Gen (1); Miao, Yang (2) Author affiliation:(1) School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China; (2) College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China Corresponding author:Miao, Yang Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:103-106 Article number:7111548 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Multi-resolution modeling (MRM) could describe a real system as a set of models of different resolutions. By doing this, real system could be analyzed at different abstraction levels for different simulation objectives. MRM is a useful method for complex and large-scale simulation. Since there are different models of the same object created for different purpose, consistency of the models should be considered for abnormal situation during model simulation. To maintain consistency of multi-resolution models, outputs of different models with same inputs could be mapped into a certain fields for comparison. Ontology mapping method was adopted for information process. For inconsistency situation, consistency maintenance procedure was proposed to ensure consistency of models during simulation. © 2015 IEEE. Number of references:8 Main heading:Mapping Controlled terms:Intelligent computing Uncontrolled terms:Consistency maintenance - Different resolutions - Large scale simulations - Model consistency - Multi-resolution Modeling - Multiresolution model - Ontology mapping - Ontology mapping methods Classification code:723.4 Artificial Intelligence - 902.1 Engineering Graphics DOI:10.1109/ICAIOT.2015.7111548 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088960 Title:Deep Belief Networks and deep learning Authors:Hua, Yuming (1); Guo, Junhai (1); Zhao, Hua (1) Author affiliation:(1) Beijing Institute of Tracking and Telecommunications Technology, Beijing, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:1-4 Article number:7111524 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Deep Belief Network is an algorithm among deep learning. It is an effective method of solving the problems from neural network with deep layers, such as low velocity and the overfitting phenomenon in learning. In this paper, we will introduce how to process a Deep Belief Network by using Restricted Boltzmann Machines. What is more, we will combine the Deep Belief Network together with softmax classifier, and use it in the recognition of handwritten numbers. © 2015 IEEE. Number of references:16 Main heading:Network layers Controlled terms:Algorithms - Bayesian networks - Character recognition - Intelligent computing Uncontrolled terms:classify Introduction - Deep belief networks - Deep layer - Deep learning - Low velocities - Overfitting - Restricted boltzmann machine Classification code:723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory DOI:10.1109/ICAIOT.2015.7111524 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088977 Title:Towards a hybrid approach of Primitive Cognitive Network Process and Self-Organizing Map for computer product recommendation Authors:Chen, Vincent Qi (1); Yuen, Kevin Kam Fung (1) Author affiliation:(1) Department of Computer Science and Software Engineering, Xi'An Jiaotong-Liverpool University, Suzhou, China Corresponding author:Yuen, Kevin Kam Fung Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:9-12 Article number:7111526 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Products have similarities which can be analyzed to recommend products to consumers with different preferences. This paper combines Primitive Cognitive Network Process (PCNP) and Self-Organizing Map (SOM) to cluster products into appropriate categories on the basis of consumer preferences and product similarities. PCNP is an ideal alternative of Analytic Hierarchy Process (AHP) to quantify the weights for the attributes used in SOM. To demonstrate the applicability of PCNP-SOM, an example of computer product recommendation is illustrated. © 2015 IEEE. Number of references:16 Main heading:Self organizing maps Controlled terms:Analytic hierarchy process - Conformal mapping - Intelligent computing - Recommender systems Uncontrolled terms:Analytic hierarchy process (ahp) - Cognitive network process - Computer products - Consumer preferences - Hybrid approach Classification code:723.4 Artificial Intelligence - 723.5 Computer Applications - 921 Mathematics DOI:10.1109/ICAIOT.2015.7111526 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088964 Title:Neural cognition and intelligent computing on the emotional symbols of cyber language Authors:Huang, Shuang (1); Dai, Weihui (2); Zhou, Xuan (3); Ivanovi, Mirjana (4) Author affiliation:(1) Overseas Training Center, Shanghai Foreign Language Studies, Shanghai, China; (2) School of Management, Fudan University, Shanghai, China; (3) School of Humanities and Social Science, Sichuan Conservatory of Music, Chengdu, China; (4) Department of Mathematics and Informatics, University of Novi Sad, Novi Sad, Serbia Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:164-167 Article number:7111561 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:This paper presented a mechanism model to describe the neural cognition of emotional symbols in cyber language, and discussed the subjective assessment of their emotional parameters in the PAD space. On this basis, an intelligent method was put forward for computing the dynamic emotional characteristics of the multi and mixed symbols in a cyber language message. © 2015 IEEE. Number of references:13 Main heading:Intelligent computing Controlled terms:Computational linguistics Uncontrolled terms:Affective Computing - cyber language - emotionla symbol - Intelligent method - Mechanism model - Neural mechanisms - Subjective assessments Classification code:721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory - 723.4 Artificial Intelligence DOI:10.1109/ICAIOT.2015.7111561 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088963 Title:Hand recognition based on finger-contour and PSO Authors:Liu, Fu (1); Liu, Huiying (1); Gao, Lei (1) Author affiliation:(1) College of Communication Engineering, Jilin University, Changchun, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:35-39 Article number:7111532 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Hand shape recognition method based on geometric features uses individual information limitedly and inadequately. To solve this problem, this paper proposes a hand shape recognition method based on contour features of fingers. Firstly, we separate the four fingers and use curve fitting method to position the axis of finger. Then the matched fingers are normalized by translation and rotational alignment, so we can conduct the matching of contour features. Finally, in order to further improve the recognition rate, particle swarm optimization (PSO for short) is used to optimize the cut-off coefficient and the weight values of different fingers. Experimental results show that the proposed method can locate hand more accurately and make full use of hand information. It can also avoid the influence of inaccurate feature points locating and unstable contour around finger valleys. The recognition rate can reach 94.78%. © 2015 IEEE. Number of references:12 Main heading:Palmprint recognition Controlled terms:Curve fitting - Feature extraction - Intelligent computing - Particle swarm optimization (PSO) Uncontrolled terms:Contour features - Contour matching - Curve fitting methods - Geometric feature - Hand shape recognition - PSO - Rotational alignment - Weight values Classification code:723 Computer Software, Data Handling and Applications - 921.6 Numerical Methods DOI:10.1109/ICAIOT.2015.7111532 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088962 Title:Chinese accent detection research based on RASTA - PLP algorithm Authors:Zhang, Long (1); Zhao, Yunxue (1); Zhang, Peng (2); Yan, Ke (3); Zhang, Wei (4) Author affiliation:(1) College of Computer Science and Information Engineering, Harbin Normal University, Harbin, China; (2) Department of Orthopaedics, Rehabilitation Hospital of Heilongjiang Province, Harbin, China; (3) Institute of Computer Application, China Academy of Engineering Physics, Mianyang, China; (4) School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Corresponding author:Zhao, Yunxue Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:31-34 Article number:7111531 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Accent is a critical important component of spoken communication, which plays a very important role in spoken communication. In this paper, we conduct accent by using RASTA - PLP algorithm to extract short-time spectrum features of each speech segment based on sub-segment splicing information. We build short-time spectrum feature sets based on RASTA - PLP algorithm. And we choose NaiveBayes classifier to model the feature sets. NaiveBayes is to choose the class with maximum posteriori probability as the object's class. This classification method makes full use of the related phonetic features of speech segment. Based on short-time spectrum of RASTA - PLP feature sets respectively achieve 80.8% accent detection accuracy on ASCCD and on ASCCD (NOISEX92-white). The experimental results indicate that based on sub-segment splicing feature structured method of RASTA - PLP can be used in Chinese accent detection study. RASTA-PLP algorithm is robust on ASSCD and on ASSCD (NOISEX92-white). © 2015 IEEE. Number of references:16 Main heading:Feature extraction Controlled terms:Algorithms - Intelligent computing - Spectrum analysis - Speech analysis Uncontrolled terms:accent - Accent detection - Classification methods - Naive-Bayes classifiers - Posteriori probability - Rasta-plp - Structured method - Time spectrum Classification code:716 Telecommunication; Radar, Radio and Television - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 751.5 Speech - 921 Mathematics - 941 Acoustical and Optical Measuring Instruments - 942 Electric and Electronic Measuring Instruments - 943 Mechanical and Miscellaneous Measuring Instruments - 944 Moisture, Pressure and Temperature, and Radiation Measuring Instruments DOI:10.1109/ICAIOT.2015.7111531 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088984 Title:A new cooperative spectrum sensing with radio environment map in cognitive radio networks Authors:Chen, Yi (1); Zhang, Hang (1); Hu, Hang (1); Wang, Qian (1) Author affiliation:(1) Collage of Communication Engineering, PLA University of Science and Technology, Nanjing, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:40-43 Article number:7111533 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Spectrum sensing is regarded as an important part of the CR network. Its detection accuracy is the key aspect which affects the performance of CR network. In this paper, a novel cooperative spectrum sensing based on radio environment map (REM) is proposed. This new algorithm utilizes the REM information of the primary users (PUs) and the secondary users (SUs) to raise the detective performance of the spectrum sensing. Simulations show that the detection accuracy is improved and this algorithm is more suitable for practice wireless channel because it needs no priori information of SNR. © 2015 IEEE. Number of references:11 Main heading:Cognitive radio Controlled terms:Balloons - Intelligent computing - Signal to noise ratio Uncontrolled terms:Co-operative spectrum sensing - Cognitive radio network - Detection accuracy - Priori information - Radio environment - Secondary users - Spectrum sensing - Wireless channel Classification code:652.5 Balloons and Gliders - 716.1 Information Theory and Signal Processing - 716.3 Radio Systems and Equipment - 723.4 Artificial Intelligence DOI:10.1109/ICAIOT.2015.7111533 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088972 Title:A segmentation method for remote sensing image region on Riemannian manifolds Authors:Zhu, Hailong (1); Zhao, Song (1); Duan, Xiping (1) Author affiliation:(1) School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China Corresponding author:Zhao, Song Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:26-30 Article number:7111530 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Focus on the issue of rotation and scale in-variance for remote sensing image(RSI) segmentation, a feature extraction and classification method is proposed based on differential space. A RSI is divided into many regions with different size, and all the covariance matrices of each region are calculated. Those covariance matrices construct a connected Riemannian manifold. The map relation between the Riemannian manifold and a Tangent space is built that contains an Exponent and a Logarithmic matrices computation. Furthermore, the distance measure is established on the Riemannian manifold. It is employed to segment regions of a RSI. Experiment results show that the method is efficient and has robust rotation and scale invariance. © 2015 IEEE. Number of references:14 Main heading:Remote sensing Controlled terms:Covariance matrix - Extraction - Feature extraction - Geometry - Image processing - Image reconstruction - Image segmentation - Intelligent computing - Matrix algebra - Space optics Uncontrolled terms:Covariance matrices - Different sizes - Distance measure - Feature extraction and classification - Remote sensing images - Riemannian manifold - Scale invariance - Segmentation methods Classification code:716 Telecommunication; Radar, Radio and Television - 723.2 Data Processing and Image Processing - 723.4 Artificial Intelligence - 731.1 Control Systems - 741 Light, Optics and Optical Devices - 741.1 Light/Optics - 802.3 Chemical Operations - 921 Mathematics - 921.1 Algebra DOI:10.1109/ICAIOT.2015.7111530 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088989 Title:Probability distribution function based iris recognition boosted by the mean rule Authors:Pjatkin, Kert (1); Daneshmand, Morteza (1); Rasti, Pejman (1); Anbarjafari, Gholamreza (1) Author affiliation:(1) ICV Group, IMS Lab, Institute of Technology, University of Tartu, Nooruse 1, Tartu, Estonia Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:47-50 Article number:7111535 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:In this work, a new iris recognition algorithm based on tonal distribution of iris images is introduced. During the process of identification probability distribution functions of colored irises are generated in HSI and YCbCr color spaces. The discrimination between classes is obtained by using Kullback-Leibler divergence. In order to obtain the final decision on recognition, the multi decision on various color channels has been combined by employing mean rule. The decisions of H, S, Y, Cb and Cr color channels have been combined. The proposed technique overcome the conventional principle component analysis technique and achieved a recognition rate of 100% using the UPOL database. The major advantage is the fact that it is computationally less complex than the Daugman's algorithm and it is suitable for using visible light camera as opposed to the one proposed by Daugman where NIR cameras are used for obtaining the irises. © 2015 IEEE. Number of references:14 Main heading:Distribution functions Controlled terms:Algorithms - Biometrics - Cameras - Classification (of information) - Color - Intelligent computing - Principal component analysis - Probability - Probability distributions Uncontrolled terms:Daugman's algorithms - Identification probability - Iris recognition - Iris recognition algorithm - Kullback Leibler divergence - Mean rule - Principle component analysis - Ycbcr color spaces Classification code:461 Bioengineering and Biology - 716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 732 Control Devices - 741.1 Light/Optics - 742.2 Photographic Equipment - 921 Mathematics - 922.1 Probability Theory - 922.2 Mathematical Statistics DOI:10.1109/ICAIOT.2015.7111535 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088976 Title:Density induced p-norm support vector machine for binary classification Authors:Ma, Ruikun (1); Li, Zhi (1); Tan, Junyan (1) Author affiliation:(1) College of Science, China Agricultural University, Beijing, China Corresponding author:Tan, Junyan Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:5-8 Article number:7111525 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:This paper presents a new version of support vector machine (SVM) named density induced p-norm SVM (0 < p < 1), DPSVM for shot. Our DPSVM introduces the density degrees into the standard p-norm SVM. It extracts the relative density degrees for the training examples and takes these degrees as relative margins for corresponding training examples. Our DPSVM not only inherits good performance of p-norm SVM which can realize feature selection and classification simultaneously, but also improves the performance of p-norm SVM. The numerical experiments results show that our DPSVM is more effective than some usual methods in feature selection and classification. © 2015 IEEE. Number of references:13 Main heading:Support vector machines Controlled terms:Classification (of information) - Density (specific gravity) - Feature extraction - Intelligent computing - Numerical methods Uncontrolled terms:Binary classification - Feature selection and classification - Numerical experiments - Relative density degrees - Training example Classification code:716 Telecommunication; Radar, Radio and Television - 716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 921.6 Numerical Methods - 931.2 Physical Properties of Gases, Liquids and Solids DOI:10.1109/ICAIOT.2015.7111525 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088988 Title:A flame detection algorithm based on Bag-of-Features in the YUV color space Authors:Liu, Zhao-Guang (1); Zhang, Xing-Yu (1); Yang-Yang (1); Wu, Ceng-Ceng (2) Author affiliation:(1) School of Information Science and Engineering, Shandong University, Jinan, China; (2) School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:64-67 Article number:7111539 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm, which is based on a Bag-of-Features technique in the YUV color space. Inspired by that the color of flame in image and video will fall in certain regions in the color space, models of flame pixels and non-flame pixels are established based on code book in the training phase in our proposal. In the testing phase, the input image is split into some N×N blocks and each block is classified respectively. In each N×N block, the pixels values in the YUV color space are extracted as features, just as in the training phase. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while it also ensures the accurate classification of positive samples. The classification performance of our proposed method is better than that of alternative algorithms. © 2015 IEEE. Number of references:19 Main heading:Color Controlled terms:Algorithms - Color image processing - Computer vision - Feature extraction - Intelligent computing - Pixels - Signal detection Uncontrolled terms:Alternative algorithms - Bag of features - Classification performance - Flame detection - Flame detection algorithm - Number of false alarms - Smoke detection - YUV color space Classification code:716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications - 741.1 Light/Optics - 741.2 Vision - 921 Mathematics DOI:10.1109/ICAIOT.2015.7111539 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088987 Title:Industrie 4.0: Enabling technologies Authors:Wan, Jiafu (1); Cai, Hu (2); Zhou, Keliang (2) Author affiliation:(1) School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China; (2) School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China Corresponding author:Cai, Hu Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:135-140 Article number:7111555 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:With the development of industries, we have realized the third industrial revolution. Following the development of Cyber-Physical Systems (CPS), industrial wireless network and some other enabling technologies, the fourth industrial revolution is being gradually rolled out. This paper presents an overview of the background, concept, basic methods, major technologies and application scenarios for industrie 4.0. In our view, industrie 4.0 as an abstract concept can closely integrate the physical world with virtual world. This strategy of industrie 4.0 will lead to more and more people coming to participate in the manufacturing process and further popularize our products through CPS technology. The typical approach for industrie 4.0 is the social manufacturing. In fact, the social manufacturing can directly link our customers' need and our industries, but it must be based on the enabling technologies, such as embedded systems, wireless sensor network, industrial robots, 3D printing, cloud computing, and big data. Therefore, this paper in detail explains these concepts, advantages and the relations to industries. We can foresee that our life will be changed to be more efficient, fast, safe and convenient due to the development of industrie 4.0 in the near future. © 2015 IEEE. Number of references:37 Main heading:Distributed computer systems Controlled terms:3D printers - Big data - Cloud computing - Embedded systems - Industrial robots - Intelligent computing - Manufacture - Virtual reality - Wireless networks - Wireless sensor networks Uncontrolled terms:Cyber physical systems (CPSs) - Cyber-physical systems (CPS) - Industrial revolutions - Industrial wireless network - Industrie 4.0 - Manufacturing process - Technologies and applications - Third industrial revolutions Classification code:537.1 Heat Treatment Processes - 722.4 Digital Computers and Systems - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 745.1.1 Printing Equipment - 912.1 Industrial Engineering DOI:10.1109/ICAIOT.2015.7111555 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088967 Title:Image segmentation algorithm based on swarm intelligence technology Authors:Sun, Hui-Jie (1) Author affiliation:(1) College of Computer Science and Information Engineering, Harbin Normal University, Harbin, Heilongjiang, China Corresponding author:Sun, Hui-Jie Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:68-71 Article number:7111540 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Image segmentation is one of the key technologies in image processing, image segmentation quality relates to subsequent processing directly such as image measurement and image recognition, etc. This paper presents a new intelligent optimization algorithm: (artificial fish swarm algorithm, artificial bacterial swarm algorithm and artificial bee colony swarm algorithm), a new method of image segmentation, namely the wavelet transform for segmented images, combined with gray-scale morphology and rough sets theory to solve the problem of image noise, uses a new intelligent optimization algorithm to improve the effect of segmentation, the segmentation performance better, faster, and has important theoretical significance and practical value. © 2015 IEEE. Number of references:17 Main heading:Image segmentation Controlled terms:Algorithms - Artificial intelligence - Computation theory - Image processing - Image recognition - Intelligent computing - Optimization - Rough set theory - Wavelet transforms Uncontrolled terms:Artificial bee colonies - Artificial fish swarm algorithms - Grayscale morphology - Image segmentation algorithm - Intelligent optimization algorithm - Segmentation performance - Segmentation quality - Swarm Intelligence Classification code:716 Telecommunication; Radar, Radio and Television - 723 Computer Software, Data Handling and Applications - 741 Light, Optics and Optical Devices - 741.1 Light/Optics - 921 Mathematics DOI:10.1109/ICAIOT.2015.7111540 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088980 Title:A robust watermarking scheme for 3D models based on encrypted holographic algorithm Authors:Qin, Yang (1); Sun, Liujie (1); Wang, Wenju (1) Author affiliation:(1) College of Printing and Publishing, University of Shanghai for Science and Technology, Shanghai, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:85-89 Article number:7111544 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:We present a digital watermarking algorithm for 3D model which is based on encrypted holographic digital watermarking algorithm to protect the embedded watermark information (such as a specific identity of the copyright information, etc.) and to improve the security and robustness of the digital watermark information. Firstly, the watermark image is processed by the double random phase modulation to get the hologram watermark which makes a high security. Then in the embedding procedures, affine invariant preprocessing is used for 3D models. We calculate the distance of each vertex to center of gravity of the 3D model which is noted as r, and take it as the watermark embedding element to embed the watermark. The experimental results show that the algorithm had the good performance of the watermark robustness to attacks such as noise addition, model simplification, model cropping, and affine attacks. This algorithm can be widely used for digital copyright protection and other aspects of identity hidden. © 2015 IEEE. Number of references:14 Main heading:Three dimensional computer graphics Controlled terms:Algorithms - Copyrights - Cryptography - Digital watermarking - Holograms - Holography - Intelligent computing - Phase modulation Uncontrolled terms:3-d modeling - Affine invariant - Data confidentiality - Holographic algorithms - Information optics Classification code:716 Telecommunication; Radar, Radio and Television - 717 Optical Communication - 718 Telephone Systems and Related Technologies; Line Communications - 723 Computer Software, Data Handling and Applications - 743 Holography - 746 Imaging Techniques - 903 Information Science - 921 Mathematics DOI:10.1109/ICAIOT.2015.7111544 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088961 Title:Research on Virtual Endoscopy path planning Authors:Wang, Hongxia (1); Wang, Xiuzhen (1) Author affiliation:(1) College Computer Science and Information Engineering, Harbin Normal University, Harbin, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:77-80 Article number:7111542 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Some key techniques and realizations of Virtual Endoscopy are systematically investigated in this thesis. It consists of the reading of CT data, the pre-processing of medical images, the techniques of segment tissues, 3D Reconstruction and the techniques of path planning. Great stress is laid on 3D Reconstruction and the techniques of path planning in this thesis. The primary work of studies is as follows: First of all, Study the loading of the chest CT data and the pre-process of filtering and interpolation. Secondly, the semi-automatic thresholding algorithm based on thresholding segmentation is proposed. The CT images are segmented using the methods. In addition, Marching Cubes algorithms is improved in the process of VE rendering algorithm to improve the efficiency of rendering. Finally, based on the technique of distance transform method, this thesis puts forward a distance transform method on the basis of maximum-cost spanning tree, which is able to create central path quickly and to solve some branch questions. © 2015 IEEE. Number of references:10 Main heading:Motion planning Controlled terms:Algorithms - Computerized tomography - Endoscopy - Image reconstruction - Image segmentation - Intelligent computing - Medical image processing - Medical imaging - Rendering (computer graphics) - Tissue - Virtual reality Uncontrolled terms:3D reconstruction - Distance transforms - Marching Cubes algorithm - Pre-processing - Rendering algorithms - Semi-automatics - Thresholding segmentation - Virtual endoscopy Classification code:461.2 Biological Materials and Tissue Engineering - 461.6 Medicine and Pharmacology - 531 Metallurgy and Metallography - 723 Computer Software, Data Handling and Applications - 801 Chemistry DOI:10.1109/ICAIOT.2015.7111542 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088979 Title:Research on the capacity fading characteristics of a Li-ion battery based on an equivalent thermal model Authors:Liu, Xintian (1); Zeng, Guojian (1); He, Yao (1); Dong, Bo (1); Xu, Xingwu (2) Author affiliation:(1) New Energy Automobile Engineering Research Institute, Hefei University of Technology, Hefei, China; (2) Hefei GuoXuan High-Tech Power Energy Co., Ltd, Hefei, China Corresponding author:He, Yao Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:145-150 Article number:7111557 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Temperature has a direct impact on the capacity fading of a power Li-ion battery during the battery's lifecycle; however, the inner temperature of the battery cannot be measured directly because of the sealed structure. To address this problem, we estimate the inner temperature of a Li-ion battery by measuring the surface and ambient temperature using an equivalent thermal model to build the ETM-Arrhenius (equivalent thermal model-Arrhenius) model that models the dependence of the battery capacity fading on the inner temperature to enable the accurate prediction of the capacity fading characteristics of a Li-ion battery during its lifecycle. We conducted a lifecycle test on a Li-ion battery at different temperatures, and the results indicate that the ETM-Arrhenius model can predict the capacity fading characteristics of a Li-ion battery accurately during its lifecycle; in addition, when the model is used in EFK, UKF and other common state-of-charge (SOC) estimation algorithms, the accuracy of the SOC estimation can be improved significantly. © 2015 IEEE. Number of references:12 Main heading:Lithium-ion batteries Controlled terms:Battery management systems - Charging (batteries) - Electric batteries - Intelligent computing - Ions - Life cycle - Lithium - Secondary batteries - Thermography (temperature measurement) Uncontrolled terms:Accurate prediction - Arrhenius models - Capacity fading - Equivalent thermal model - Estimation algorithm - Li-ion batteries - Power li ion batteries - Sealed structure Classification code:549.1 Alkali Metals - 702.1 Electric Batteries - 702.1.2 Secondary Batteries - 723.4 Artificial Intelligence - 801 Chemistry - 913.1 Production Engineering - 944.6 Temperature Measurements DOI:10.1109/ICAIOT.2015.7111557 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088965 Title:A review on refactoring sequential program to parallel code in multicore era Authors:Zhao, Song (1); Bian, Yixin (1); Zhang, Sensen (1) Author affiliation:(1) College of Computer Science and Information Engineering, Harbin Normal University, Harbin, China Corresponding author:Bian, Yixin Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:151-154 Article number:7111558 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Nowdays it is inevitable to face the emergence of multicore processors and parallel platforms. In the multicore time, sequential programs need to be refactored for parallelism. Refactoring is a process of adjusting the code structure of a program keeping its internal function. However, refactoring sequential code to concurrent program is not trivial. In this paper, a survey of the current literature that frequently reported parallel refactoring are carried out. It is important for programmers or computer researchers to exploit multicore platform in order to get better understand and perform refactoring in parallel. This review can help the research community to know the different directions about development of parallel refactoring in order to improve the quality of software. © 2015 IEEE. Number of references:29 Main heading:Multicore programming Controlled terms:Codes (symbols) - Intelligent computing Uncontrolled terms:Concurrent program - Multi core - parallel - Refactorings - Sequential programs Classification code:723 Computer Software, Data Handling and Applications DOI:10.1109/ICAIOT.2015.7111558 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088974 Title:Implementation of elliptic curve Diffie-Hellman key agreement scheme on IRIS nodes Authors:Zhang, Xing (1); Ma, Shaohua (1); Han, Dong (1); Shi, Wei (1) Author affiliation:(1) School of Electronics and Information Engineering, Liaoning University of Technology, LNUT, Jinzhou City, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:160-163 Article number:7111560 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:To meet the need of key agreement and pairwise-key creation between sensors in wireless sensor network composed of IRIS nodes, this paper presents the implementation of ECDH (elliptic curve Diffie-Hellman) key agreement scheme on IRIS nodes. We choose 6 kinds of optimization algorithms for ECDH to test and compare these optimization algorithms. By turning these optimization techniques on or off, we compare the ROM/RAM consumptions, initialization time and key establishment time. © 2015 IEEE. Number of references:11 Main heading:Sensor nodes Controlled terms:Algorithms - Geometry - Intelligent computing - Optimization - Wireless sensor networks Uncontrolled terms:ECDH - implementation - IRIS - Key agreement - security - WSNs Classification code:722 Computer Systems and Equipment - 723.4 Artificial Intelligence - 732 Control Devices - 921 Mathematics - 921.5 Optimization Techniques DOI:10.1109/ICAIOT.2015.7111560 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088981 Title:Study of cognitive model for Ad hoc network based on high-order multi-type π calculus modeling Authors:Zhao, Guosheng (1); Zhang, Nan (1); Sheng, Linyang (1) Author affiliation:(1) College of Computer Science and Information Engineering, Harbin Normal University, Harbin, China Corresponding author:Sheng, Linyang Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:141-144 Article number:7111556 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:This paper proposed a cognitive model for Ad hoc network, and combined formal modeling of cognitive model with quantitative analysis of cognitive performance, thus at the same time of formal modeling through quantitative analysis obtained Ad hoc network cognitive performance parameters. © 2015 IEEE. Number of references:10 Main heading:Ad hoc networks Controlled terms:Calculations - Chemical analysis - Intelligent computing Uncontrolled terms:Cognitive model - Cognitive performance - Formal model - High-order - Pi calculus Classification code:721 Computer Circuits and Logic Elements - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 801 Chemistry - 804 Chemical Products Generally - 921 Mathematics DOI:10.1109/ICAIOT.2015.7111556 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088992 Title:Visual tracking via weighted sparse representation Authors:Duan, Xiping (1); Liu, Jiafeng (1); Tang, Xianglong (1) Author affiliation:(1) School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China; (2) College of Computer Science and Information Engineering, Harbin Normal University, Harbin, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:81-84 Article number:7111543 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Recently, sparse representation has been used in visual tracking, and related trackers have emerged. However, such sparse representation is not stable and has the potential to represent a candidate with dissimilar target templates. Therefore, a new tracker based weighted sparse representation (WSRT) is proposed. Specifically, to represent a candidate, each target template is weighted according to its similarity to the candidate. The bigger the similarity is, the bigger the probability of the target template to be chosen will be. The proposed tracker chooses the similar target templates to represent each candidate and reflects the locality structure between the candidate and target templates. Experimental results show that the proposed tracker has excellent performance. © 2015 IEEE. Number of references:14 Main heading:Tracking (position) Controlled terms:Computer vision - Intelligent computing Uncontrolled terms:Sparse representation - Visual Tracking Classification code:716.2 Radar Systems and Equipment - 723.4 Artificial Intelligence - 723.5 Computer Applications DOI:10.1109/ICAIOT.2015.7111543 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088993 Title:SAR image restoration and change detection based on game theory Authors:Bi, Chujian (1); Zhang, Qiushi (2); Bao, Rui (3); Wang, Haoxiang (4) Author affiliation:(1) Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis; MN, United States; (2) Northwestern Polytechnical University, Xi'an, China; (3) Dept. of Science and Applied Math, National University of Singapore, Singapore, Singapore; (4) Dept. of Electrical and Computer Engineering, Cornell University, New York; NY, United States Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:55-58 Article number:7111537 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:In this paper, a novel unsupervised change detection algorithm based on game theory is proposed for synthetic aperture radar(SAR) images. With the introduction of Nash-game theory, we find the balance of segmentation accuracy and overall restoration performance. Restoration of images plays a denoising role due to the complex movement while obtaining a SAR image. The Segmentation procedure transfers the difference map into change map. To make the algorithm less time-consuming, we analyze the state-of-the-art methods for generating the change map and finally select the minus map as the preferred one. The experiment based on the proposed methodology proves the accuracy and robustness of our algorithm compared with several well-known change detection techniques on both noise-free and noisy satellite images. Further optimization methods are discussed in the end. © 2015 IEEE. Number of references:20 Main heading:Radar imaging Controlled terms:Algorithms - Computation theory - Game theory - Image reconstruction - Intelligent computing - Optimization - Radar - Restoration - Signal detection - Synthetic aperture radar Uncontrolled terms:Change detection - Mathematical optimizations - Nash games - Segmentation accuracy - Segmentation procedure - State-of-the-art methods - Synthetic aperture radar (SAR) images - Unsupervised change detection Classification code:402 Buildings and Towers - 409 Civil Engineering, General - 716.1 Information Theory and Signal Processing - 716.2 Radar Systems and Equipment - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 741 Light, Optics and Optical Devices - 921 Mathematics - 921.5 Optimization Techniques - 922.1 Probability Theory DOI:10.1109/ICAIOT.2015.7111537 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088978 Title:Analysis and research of network measurement technologies Authors:Lv, Chengmin (1); Ma, Baohong (2); Du, Xin (5); Li, Bo (6); Liang, Tao (3) Author affiliation:(1) Navy University of Engineering, Wuhan, China; (2) Chongqing Key Laboratory of Emergency Communication, Chongqing, China; (3) Chongqing Communication Institute, Chongqing, China; (4) Unit 73680, Nanjing, China; (5) Unit 91550, Dalian, China; (6) System Engineering Institute of Engineering Equipment, Beijing, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:117-121 Article number:7111551 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:In this article, the chief research and analysis of the network performance measurement and its research trend in the world are introduced. The network measurement technologies are classified according to consultative layer and other method. Measurement technologies of network layer are emphasized. The advantages/disadvantages of the bandwidth measurement technologies and traffic measurement technologies are analyzed. The future research emphasis of the measurement technologies is summarized. © 2015 IEEE. Number of references:15 Main heading:Network layers Controlled terms:Bandwidth - Intelligent computing - Network performance Uncontrolled terms:Active measurement - Bandwidth measurements - End-to-end path - Measurement technologies - Network measurement - Network performance measurement - Research and analysis - Traffic measurements Classification code:716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence DOI:10.1109/ICAIOT.2015.7111551 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088994 Title:A rank sequence method for detecting black hole attack in ad hoc network Authors:Xiong, Kai (1); Yin, Mingyong (2); Li, Wenkang (2); Jiang, Hong (1) Author affiliation:(1) Southwest University of Science and Technology, Mianyang, Sichuan, China; (2) Institute of Computer Application, China Academy of Engineering Physics, Mianyang, Sichuan, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:155-159 Article number:7111559 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:This paper discusses one of the route security problems called the black hole attack. In the network, we can capture some AODV route tables to gain a rank sequences by using the FP-Growth, which is a data association rule mining. We choose the rank sequences for detecting the malicious node because the rank sequences are not sensitive to the noise interfered. A suspicious set consists of nodes which are selected by whether the rank of a node is changed in the sequence. Then, we use the DE-Cusum to distinguish the black hole route and normal one in the suspicious set. In this paper, the FP-Growth reflects an idea which is about reducing data dimensions. This algorithm excludes many normal nodes before the DE-Cusum detection because the normal node has a stable rank in a sequence. In the simulation, we use the NS2 to build a black hole attack scenario with 11 nodes. Simulation results show that the proposed algorithm can reduce much vain detection. © 2015 IEEE. Number of references:13 Main heading:Network security Controlled terms:Ad hoc networks - Gravitation - Intelligent computing - Stars Uncontrolled terms:AODV - Black hole attack - Data association - Data dimensions - DE-Cusum - FP growths - Malicious nodes - Security problems Classification code:657.2 Extraterrestrial Physics and Stellar Phenomena - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 931.5 Gravitation, Relativity and String Theory DOI:10.1109/ICAIOT.2015.7111559 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088990 Title:Performance of Advanced Metering Infrastructure using cellular communication based on uplink CDMA Authors:Arias, Diego (1); Rodriguez, Guillermo (1) Author affiliation:(1) Electrical Engineering Department, Universidad Politécnica Salesiana, Quito, Ecuador Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:111-116 Article number:7111550 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:This paper studies the Okumura- Hat propagation model on CDMA technology applied in AMI (Advanced Metering Infrastructure). The aim of this proposal is to get information from the intelligent meter such us: outage management system, status and ratings reading. Prepaid services are oriented to the specific characteristics of rural dwellers. Furthermore, it allows to the energy distribution companies to know the energy consumption in real time. The Okumura - Hata propagation model helps to evaluate a communication system due to its good practices and results obtained with this method using cellular technology. The use of CDMA technology in electrical measurement systems helps to reduce the costs of non-technical losses, resulting in: better quality in the electrical systems and obtaining actual data readings that improves communication between the consumer and the distributor. © 2015 IEEE. Number of references:36 Main heading:Code division multiple access Controlled terms:Advanced metering infrastructures - Balloons - Electric power transmission networks - Energy utilization - Information management - Intelligent computing - Long Term Evolution (LTE) - Mobile telecommunication systems - Wireless networks - Wireless telecommunication systems Uncontrolled terms:Diversification strategies - Mobile network operators - Okumura -HATA - Radio spectra - Smart grid Classification code:525.3 Energy Utilization - 652.5 Balloons and Gliders - 703.1 Electric Networks - 716 Telecommunication; Radar, Radio and Television - 716.3 Radio Systems and Equipment - 722 Computer Systems and Equipment - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 903.2 Information Dissemination DOI:10.1109/ICAIOT.2015.7111550 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088968 Title:Design and realization of double-mode communication platform based on CVIS Authors:Wei, Shangguan (1); Ge, Manqiang (2) Author affiliation:(1) State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China; (2) School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China Corresponding author:Wei, Shangguan Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:122-126 Article number:7111552 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:With the rapidly development of economy, traffic congestion has become a bottleneck restricting of the development of the urban economy. As the core of future development and direction of intelligent transportation, the CVIS (Cooperative Vehicle Infrastructure System) is the key to solve the urban traffic congestion problems. And the wireless communication technology of high quality is the key point of research in CVIS. The work of this paper is to build the wireless communication platform of CVIS and research problem of information interaction between DSRC (Dedicated Short Range Communications) and WIFI (Wireless Fidelity) under environment of CVIS. This paper analyzes the application between DSRC and WIFI in CVIS and builds a wireless communication platform based on PCM-9562. Through the study of car-following model with single lane, this paper achieves data exchange between application and communication platform by using GPS information. Combined with MapX, the communication platform completes the real-time display of position, velocity and driving strategy. The result of test shows that double-mode communication platform based on CVIS can realize the real-time transmission of data effectively and assist driver to get the information of position and velocity from itself and other cars and finally provide effective reference information to the driver in the pattern of car-following. © 2015 IEEE. Number of references:10 Main heading:Dedicated short range communications Controlled terms:Amphibious vehicles - Electronic data interchange - Global positioning system - Intelligent computing - Motor transportation - Traffic congestion - Urban transportation - Wi-Fi - Wireless telecommunication systems Uncontrolled terms:Car following models - CVIS - DSRC - Information interaction - Intelligent transportation - Urban traffic congestion - Wi-Fi (wireless fidelity) - Wireless communication technology Classification code:432 Highway Transportation - 432.4 Highway Traffic Control - 674.1 Small Marine Craft - 716 Telecommunication; Radar, Radio and Television - 716.3 Radio Systems and Equipment - 717 Optical Communication - 723 Computer Software, Data Handling and Applications DOI:10.1109/ICAIOT.2015.7111552 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088983 Title:An Internet of things approach for motion detection using Raspberry Pi Authors:Ansari, Aamir Nizam (1); Sedky, Mohamed (1); Sharma, Neelam (2); Tyagi, Anurag (1) Author affiliation:(1) Faculty of Computing, Engineering and Sciences, Staffordshire University, Stoke-on-Trent, United Kingdom; (2) Electrical and Instrumentation Engineering, Thapar University, Patiala, India Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:131-134 Article number:7111554 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:Internet of things is the communication of anything with any other thing, the communication mainly transferring of use able data, for example a sensor in a room to monitor and control the temperature. It is estimated that by 2020 there will be about 50 billion internet-enabled devices. This paper aims to describe a security alarm system using low processing power chips using Internet of things which helps to monitor and get alarms when motion is detected and sends photos and videos to a cloud server. Moreover, Internet of things based application can be used remotely to view the activity and get notifications when motion is detected. The photos and videos are sent directly to a cloud server, when the cloud is not available then the data is stored locally on the Raspberry Pi and sent when the connection resumes. Therefore, advantages like these make this application ideal for monitoring homes in absence. © 2015 IEEE. Number of references:17 Main heading:Internet of things Controlled terms:Alarm systems - Intelligent computing - Internet - Motion analysis - Video signal processing Uncontrolled terms:Cloud servers - Monitor and control - Motion detection - Processing power - Security alarm system Classification code:716 Telecommunication; Radar, Radio and Television - 716.4 Television Systems and Equipment - 717 Optical Communication - 718 Telephone Systems and Related Technologies; Line Communications - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 914.2 Fires and Fire Protection DOI:10.1109/ICAIOT.2015.7111554 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088973 Title:A condition monitoring algorithm based on image geometric analysis for substation switch Authors:Lu, Jicang (1); Lin, Hui (2); Zhang, Weizheng (2); Shi, Xiaowei (1) Author affiliation:(1) Zhengzhou Information Science and Technology Institute, Henan, Zhengzhou, China; (2) State Grid Henan Electric Power Company, Zhengzhou Power Supply Company, Henan, Zhengzhou, China Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:72-76 Article number:7111541 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:For the problem of real-time monitoring and condition decision of high-voltage substation switches, a real-time monitoring algorithm is proposed for the substation folded switch based on geometric analysis of monitoring images. Firstly, capture images containing the folded switch using the properly installed camera, and transfer them to the data processing terminal. Then, search for straight line segments representing two arms of switch using the method of image contour description, and calculate the angles between the segments using the cosine theorem. At last, determine the condition of the switch that whether it is closed well. The experimental results show that the proposed method could accurately calculate the real-time angle of the switch, and then achieve unmanned and real-time monitoring for condition of the switch. © 2015 IEEE. Number of references:10 Main heading:Condition monitoring Controlled terms:Algorithms - Computation theory - Data handling - Geometry - Image analysis - Intelligent computing - Monitoring - Time switches Uncontrolled terms:Angle measuring - Capture images - Geometric analysis - High voltage substations - Method of images - Real time monitoring - Straight-line segments - substation Classification code:603 Machine Tools - 706 Electric Transmission and Distribution - 715 Electronic Equipment, General Purpose and Industrial - 723.2 Data Processing and Image Processing - 723.4 Artificial Intelligence - 921 Mathematics - 941 Acoustical and Optical Measuring Instruments - 942 Electric and Electronic Measuring Instruments - 943 Mechanical and Miscellaneous Measuring Instruments - 944 Moisture, Pressure and Temperature, and Radiation Measuring Instruments DOI:10.1109/ICAIOT.2015.7111541 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES Accession number:20153101088985 Title:A secondary framework for small targets segmentation in Remote Sensing Images Authors:Zhu, Hailong (1); Sun, Hongzhi (2) Author affiliation:(1) School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China; (2) Fourth Affiliated Hospital, Harbin Medical University, Harbin, China Corresponding author:Sun, Hongzhi Source title:Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Abbreviated source title:Proc. Int. Conf. Intell. Comput. Internet Things, ICIT Part number:1 of 1 Issue date:May 21, 2015 Publication year:2015 Pages:168-171 Article number:7111562 Language:English ISBN-13:9781479975334 Document type:Conference article (CA) Conference name:2015 International Conference on Intelligent Computing and Internet of Things, ICIT 2015 Conference date:January 17, 2015 - January 18, 2015 Conference location:Harbin, China Conference code:112481 Publisher:Institute of Electrical and Electronics Engineers Inc. Abstract:The automatic interpreting of small object using computer in Remote Sensing Image(RSI) is sharply limited by low resolution and the uncertainty of imaging season, leading to the results of low recognition rate and poor generalization ability. In this paper, the Erlongshan Reservoir region of Heilongjiang province is selected as research area, and a secondary segmentation framework is proposed for small objects recognition based on salience detection and Hough Transform. Firstly, the salience of particular small objects is calculated to find candidates of small objects. Next, the Hough Transform is performed on an enhanced RSI constrained by the size of small size to identify small objects from others, such as highway fragment, river fragment, house and farmland and so on. The experiments results regarding small reservoir segmentation show that the method has high robustness and generalization ability, and the idea of classification can be used to the automatic interpreting process of other kind of small objects of RSI. © 2015 IEEE. Number of references:11 Main heading:Remote sensing Controlled terms:Feature extraction - Hough transforms - Image reconstruction - Image segmentation - Intelligent computing - Object detection - Object recognition Uncontrolled terms:Generalization ability - High robustness - Low resolution - Remote sensing images - Saliency detection - Small objects - Small reservoirs - Small targets Classification code:716 Telecommunication; Radar, Radio and Television - 723.2 Data Processing and Image Processing - 723.4 Artificial Intelligence - 731.1 Control Systems - 741 Light, Optics and Optical Devices - 741.1 Light/Optics - 921.3 Mathematical Transformations DOI:10.1109/ICAIOT.2015.7111562 Database:Compendex Compilation and indexing terms, Copyright 2015 Elsevier Inc.Compendex references:YES