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检索条件"任意字段=3rd International Symposium on Information Processing in Sensor Networks"

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sensor networks for Drainage Monitoring: The STORM Architecture

Sensor Networks for Drainage Monitoring: The STORM Architect...

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Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), 2020 3rd international Conference on

作者： Heerish Bujun Kavi Khedo Faculty of Information Communication and Digital Technologies University of Mauritius Reduit Mauritius

ISBN: (数字)9781728157078

ISBN: (纸本)9781728157085

Localized heavy rainfall causes overflow of drains, and in more critical situation it leads to flood in cities with major infrastructural damages. Drastic climatic changes have increased the probability of high precipitation at arbitrary locations all around the world and have exposed the limitations of forecasting models based on historical data. Forecasts by the U.N. shows that cities that were considered safe in the previous years can now face high precipitation. To monitor the water level in urban drainage and mitigate the risk of overflow, the design of STORM is laid out; a 5-layered architecture comprising of a sensing layer, a communication layer, a local computing layer, a processing layer and a remote monitoring layer. STORM has a real-time monitoring algorithm to monitor the water level and water flow in the drainage. It also consists of a water overflow predictive algorithm to predict the water level and water flow at different sections of the drain. Predictive analysis for water streams is used to arbitrate the downstream parameters and obstacle detection algorithms to determine obstacles that are blocking the water flow. STORM uses a hydrological model that monitors the hydrological characteristics of water flowing in the drain. A critical evaluation of STORM is performed to depicts its capabilities and expose the areas of improvement.

关键词： Storms Urban areas Computer architecture Prediction algorithms Real-time systems Floods Monitoring

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Research on solar radiation error of sounding temperature sensor 3

Research on solar radiation error of sounding temperature se...

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3rd international symposium on Resource Exploration and Environmental Science, REES 2019

作者： Cao, Hongxia Liu, Minming Liu, Xinyu Wang, Zengxuan Tang, Yan Key Laboratory of Meteorological Disaster Ministry of Education Nanjing China Joint International Research Laboratory of Climate and Environment Change Nanjing China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster Nanjing China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China

The error of sounding temperature sensor with bead thermistor induced by solar radiation seriously affects the accuracy of temperature measurement. To solve this problem, this paper presents a novel numerical analysis method based on computational fluid dynamics to correct solar radiation error. The error thermal analysis model of sounding temperature sensor with bead thermistor is established from ground to 32km altitude with different air pressures, and computational fluid dynamics is employed for numerical simulation analysis of solar radiation error. The solar radiation errors are reported in different physical parameters such as the altitude, the direction of solar radiation and the surface coating reflectivity of sensor. The results show that the solar radiation errors present a nonlinear monotone increasing trend with the rise of altitude. Among the errors caused by solar radiation, the error is biggest in the direction perpendicular to the front of the sensor. The simulation results also indicate that the solar radiation error can be significantly reduced by improving the surface coating reflectivity of sensor. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Thermoanalysis

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Effective Representation gf Three-Dimension Nodules for False-Positive Reduction in Pulmonary Nodule Detection 3

Effective Representation gf Three-Dimension Nodules for Fals...

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3rd international symposium on Image Computing and Digital Medicine (ISICDM)

作者： Guo, Wei Pratama, AdevianFairuz Gong, Zhaoxuan Zhou, Wei Zhao, Dazhe Zhang, Guodong Shenyang Aerosp Univ Daoyi South St 37 Shenyang 110136 Liaoning Peoples R China Minist Educ Key Lab Intelligent Comp Med Image Culture RdHeping Dist 11 Shenyang 110819 Liaoning Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110014 Liaoning Peoples R China

ISBN: (纸本)9781450372626

The convolutional neural networks (CNNs) can learn features representation from large amounts of training data, and it has achieved remarkable successes in image processing. However, due to expensive expert annotation and privacy issues, the lack of sufficient training data limits the application of CNNs, especially for 3D CNNs, in medical images. In this paper, we decomposed a 3D sample into a set of 2D images based on a series of sequential uniformly-distributed viewpoints and used some "effective" 2D images of them to train a 2D CNN for the false-positive reduction in pulmonary nodule detection with fewer model parameters and less computational burden. The 2D images from different representative viewpoints would provide more complete and independent information than simplifying a 3D nodule into several orthogonal planes. And the non-nodules appear clearly as non-circular linear structures in the "effective" 2D images, which enable us well to distinguish nodules from false positives. Our method was evaluated on 888 CT scans from the database of the LUNA16 challenge. Compared with other methods by using 2D CNNs, our proposed method achieves the highest competition performance metric (CPM) score in the false-positive reduction track. Compared to published work using 3D CNNs which needs significantly larger training data, our method achieves comparable performance by using only about 20% of training data. The "effective" 2D images from representative viewpoints augment the database for training a 2D CNN and provide more crucial information of 3D nodules and would improve the performance of false-positive reduction..

关键词： False-positive reduction Effective representation of a 3D nodule 2D convolutional neural networks

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Towards semantic interoperability for IoT: Combining social tagging data and wikipedia to generate a domain-specific ontology 3rd

Towards semantic interoperability for IoT: Combining social ...

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3rd international Conference of Reliable information and Communication Technology, IRICT 2018

作者： Alruqimi, Mohammed Aknin, Noura Al-Hadhrami, Tawfik James-Taylor, Anne Information Technology and Modeling Systems Research Unit Abdelmalek Essaadi University Tétouan Morocco School of Science and Technology Nottingham Trent University Nottingham United Kingdom

ISBN: (纸本)9783319990064

Handling large-scale heterogeneous data in the IoT and processing it in real time will be a key factor towards realizing IoT services in users’ daily lives. In this regard, semantic domain ontologies are increasingly seen as a solution for enabling interoperability across heterogeneous data and sensor-based applications. Several ontologies have been proposed with the aim of addressing interoperability issues and various aspects of IoT device observation. However, for most of these ontologies, they are either in the domain of sensor networks or the much broader domain of the IoT. Furthermore, these ontologies have shown slow improvement, as they have been developed by limited groups of domain experts. This paper proposes a model that exploits the collective intelligence which emerges from social tagging systems to generate up-to-date domain-specific ontologies. The evaluation of the proposed model, using a dataset extracted from BibSonomy, demonstrated that the model could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed model introduces a simple and effective method for the common problems related to tag ambiguity. © Springer Nature Switzerland AG 2019.

关键词： Ontology

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An Intelligent Human Fall Detection System Using a Vision-Based Strategy 14

An Intelligent Human Fall Detection System Using a Vision-Ba...

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14th IEEE international symposium on Autonomous Decentralized Systems (ISADS) / 3rd international Workshop on Service Assurance in System Wide information Management (SASWIM)

作者： Brieva, Jorge Ponce, Hiram Moya-Albor, Ernesto Martinez-Villasenor, Lourdes Univ Panamer Fac Ingn Augusto Rodin 498 Ciudad De Mexico 03920 Mexico

ISBN: (纸本)9781728116723

Elderly people is increasing dramatically during the current years, and it is expected that this population reaches 2.1 billion of individuals by 2050. In this regard, new care strategies are required. Assisted living technologies have proposed alternatives to support professional caregivers and families to take care of elderly people, such as in risk of falls. Currently, fall detection systems are able to alleviate the latter problem and reduce the time a person who suffered a fall receives assistance. Thus, this paper proposes a fall detection system based on image processing strategy to extract motion features through an optical flow method. For classification, we use these features as inputs to a convolutional neural network. We applied our approach in a dataset comprises video recordings of one subject performing different types of falls. In experimental results, our approach showed 92% accuracy on the dataset used.

关键词： assisted living convolutional neural networks optical flow human activity recognition fall detection

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Deep learning semantic statistical features' labeling of SAR image based on K-MEANS clustering and wavelet transform 3

Deep learning semantic statistical features' labeling of SAR...

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2020 3rd international symposium on Big Data and Applied Statistics, ISBDAS 2020

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Song, Donghong Xue, Xiaoyong Wu, Aihua Department of Computer Science and Technology Xi'an JiaoTong University Xi'an Shaanxi710049 China Jinzhou Liaoning121001 China School of Computer and Information Engineering Anyang Normal University Anyang Henan455000 China Middle School of Luonan County Shaanxi726100 China Department of Ai and Software Engineering South China Institute of Software Engineering Guangzhou University Guangzhou Guangdong510990 China

[Purpose] Because Synthetic Aperture Radar (SAR) image contains much speckle, at present, it's hard to obtain good results for SAR image with the traditional image classification methods. However, SAR image has rich texture information which facilitates SAR image segmentation. There is a Deep Learning method of K-means data clustering that is used to calculate the optimal k value to avoid more-segmentation or less-segmentation, the advantages of fuzzy theory and neural network are used to improve the classification accuracy of image processing.[Methods] According to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis, an efficient image segmentation method based on Deep Learning Semantic analysis and wavelet transform is proposed to achieve precision of classification;Firstly, the texture features of SAR image are extracted by Deep Learning Semantic clustering;Secondly, the SAR image are characterized according to SAR semantics. Based on one of the Deep Learning Semantic k-means algorithms, the best k-value iterations of SSE(Sum of the Squared Errors)and SC(Silhouette Coefficient) are performed, and the suitable value k is selected;Finally, by Deep Learning segmentation based on the texture feature of the SAR image and the filtered gray component vector, and the SAR image is classified to facilitate the change detection of images.[Results] The experimental results show that a good k value detection is performed on the CPU/GPU platform. The SAR images is compared with two different results between the wavelet filtering and Deep Learning semantic method images which are single or multiple standard classified through detection-change SAR is better than the Empirical Approach, then the labels are classified for the accuracy and computational efficiency that are calculated. The classification results were accurately ameliorated. [Conclusions] The similarity calculation of k-value cluster is an important precondition, so it's

关键词： Semantics

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Network Size Estimation in Small-World networks under Byzantine Faults 33

Network Size Estimation in Small-World Networks under Byzant...

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33rd IEEE international Parallel and Distributed processing symposium (IPDPS)

作者： Chatterjee, Soumyottam Pandurangan, Gopal Robinson, Peter Univ Houston Dept Comp Sci Houston TX 77204 USA McMaster Univ Dept Comp & Software Hamilton ON Canada

ISBN: (纸本)9781728112466

We study the fundamental problem of counting the number of nodes in a sparse network (of unknown size) under the presence of a large number of Byzantine nodes. We assume the full information model where the Byzantine nodes have complete knowledge about the entire state of the network at every round (including random choices made by all the nodes), have unbounded computational power, and can deviate arbitrarily from the protocol. Essentially all known algorithms for fundamental Byzantine problems (e.g., agreement, leader election, sampling) studied in the literature assume the knowledge (or at least an estimate) of the size of the network. It is non-trivial to design algorithms for Byzantine problems that work without knowledge of the network size, especially in bounded-degree (expander) networks where the local views of all nodes are (essentially) the same and limited, and Byzantine nodes can quite easily fake the presence/absence of non-existing nodes. To design truly local algorithms that do not rely on any global knowledge (including network size), estimating the size of the network under Byzantine nodes is an important first step. Our main contribution is a randomized distributed algorithm that estimates the size of a network under the presence of a large number of Byzantine nodes. In particular, our algorithm estimates the size of a sparse, "small-world", expander network with up to O(n(1-delta)) Byzantine nodes, where n is the (unknown) network size and delta > 0 can be be any arbitrarily small (but fixed) constant. Our algorithm outputs a (fixed) constant factor estimate of log(n) with high probability;the correct estimate of the network size will be known to a large fraction ((1 - epsilon)-fraction, for any fixed positive constant) of the honest nodes. Our algorithm is fully distributed, lightweight, and simple to implement, runs in O (log(3) n) rounds, and requires nodes to send and receive messages of only small-sized messages per round;any node's local co

关键词： distributed algorithm counting byzantine behavior randomized algorithm small-world network expander

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Research Status of Image Fusion: A Review 4

Research Status of Image Fusion: A Review

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4th international Conference on Control, Robotics and Cybernetics (CRC)

作者： Zhang, Xinman Zhang, Jiayu Kou, Jie Xu, Xuebin Xi An Jiao Tong Univ MOE Key Lab Intelligent Networks & Network Secur Sch Elect & Informat Engn 28 Xianning West Rd Xian Peoples R China Xi An Jiao Tong Univ Guangdong Xian Jiaotong Univ Acad 3 Daliangshuxiang East Rd Foshan Guangdong Peoples R China

ISBN: (纸本)9781728146201

With the development of computer technology, sensor technology and information processing technology, image fusion, as a powerful tool of information fusion, has been widely used in automatic target recognition, military and medical image fields. This technology utilizes the complementarity of information advantages contained in each image to obtain a clear, comprehensive and accurate image description of the same scene through a certain algorithm. In this paper, we firstly summarized the research status of image fusion methods comprehensively. Secondly, the existing fusion methods of homogeneity and heterogeneity are summarized. Thirdly, several evaluation indexes are introduced. Finally, this paper points out the current problems of image fusion. In a word, the research of image fusion will still have a great development in the next few years.

关键词： image fusion heterogeneous image multiscale decomposition medical imaging

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Epipolar Geometry on Drones Cameras for Swarm Robotics Applications

Epipolar Geometry on Drones Cameras for Swarm Robotics Appli...

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IEEE international symposium on Circuits and Systems (ISCAS)

作者： Andres Erazo Eduardo Tayupanta Seok-Bum Ko Division of Biomedical Engineering University of Saskatchewan Member IEEE Saskatoon Canada Department of Electrical and Electronic Engineering Universidad de las Fuerzas Armadas − ESPE Quito Ecuador Department of Electrical and Computer Engineering and Division of Biomedical Engineering University of Saskatchewan Senior Member IEEE Saskatoon Canada

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Nowadays, Convolutional Neural networks are commonly used in classification of images due to their accuracy and performance. In robotics, cameras are used as the main sensor to be able to gather information of the environment; however, image processing could require great amounts of resources. An approach for object detection for 3D environment navigation was implemented, using onboard 2D cameras of mini-drones. A TensorFlow network was fine-tuned for specific object classification and epipolar geometry used to obtain measures of distance between three drones and the detected items. An average measurement accuracy of 0.6094 was obtained, with an average projection error of the cameras of 0.08779. The processing time for object prediction was approximately 0.02 seconds, which correspond to the 37% of the total time needed for a Node's iteration.

关键词： Cameras Drones Robots Optical distortion Three-dimensional displays Image reconstruction IP networks

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3rd international Conference on information and Communication Technology for Intelligent Systems, ICTIS 2018

3rd International Conference on Information and Communicatio...

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3rd international Conference on information and Communication Technology for Intelligent Systems, ICTIS 2018

ISBN: (纸本)9789811317460

The proceedings contain 147 papers. The special focus in this conference is on information and Communication Technology for Intelligent Systems. The topics include: Review of EEG signal classification;How to reverse engineer ICS protocols using pair-HMM;classification of plants using invariant features and a neural network;CSTeller: Client or server is a question;geographical zone-based cluster head for routing in urban vehicular network;Congestion management by Static Var Compensator (SVC) using power world simulator;combining user-based and item-based collaborative filtering using machine learning;Predictive monitoring system using K-NN, QDC classifiers of physiological data;Recommender system based on OSN data analytics;automation of process evaluation of saccharification of wheat starch followed by fermentation of glucose to prepare bioethanol using digital image processing;Early detection of ransomware by indicator analysis and WinAPI call sequence pattern;a novel integrated framework based on modular optimization for efficient analytics on twitter big data;study of controller techniques for precision positioning;an effective content-based strategy analysis for large-scale deduplication using a multi-level pattern-matching algorithm;tamil sign language translator—An assistive system for hearing- and speech-impaired people;aspect-based sentiment analysis of students’ feedback to improve teaching–learning process;fair mechanisms for combinatorial reverse auction-based cloud market;timbre-vibrato model for singer identification;a range-based adaptive and collaborative localization for wireless sensor networks;Image edge detection techniques using sobel, T1FLS, and IT2FLS;An optimal cryptographic approach for addressing security breaches to build resilient WSN;detection of mimicry attacks on speaker verification system for cartoon characters’ dataset;smart helmet for safety driving.

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