- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Survey of Object-Based Data Reduction Techniques in Observational Astronomy
摘要： Dealing with astronomical observations represents one of the most challenging areas of big data analytics. Besides huge variety of data types, dynamics related to continuous data flow from multiple sources, handling enormous volumes of data is essential. This paper provides an overview of methods aimed at reducing both the number of features/attributes as well as data instances. It concentrates on data mining approaches not related to instruments and observation tools instead working on processed object-based data. The main goal of this article is to describe existing datasets on which algorithms are frequently tested, to characterize and classify available data reduction algorithms and identify promising solutions capable of addressing present and future challenges in astronomy.
关键词： feature extraction,astronomy,dimensionality reduction,big data,data condensation
更新于2025-09-23 15:23:52
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A top-down approach for semantic segmentation of big remote sensing images
摘要： The increasing amount of remote sensing data has opened the door to new challenging research topics. Nowadays, significant efforts are devoted to pixel and object based classification in case of massive data. This paper addresses the problem of semantic segmentation of big remote sensing images. To do this, we proposed a top-down approach based on two main steps. The first step aims to compute features at the object-level. These features constitute the input of a multi-layer feed-forward network to generate a structure for classifying remote sensing objects. The goal of the second step is to use this structure to label every pixel in new images. Several experiments are conducted based on real datasets and results show good classification accuracy of the proposed approach. In addition, the comparison with existing classification techniques proves the effectiveness of the proposed approach especially for big remote sensing data.
关键词： Neural networks,Remote sensing images,Big data,Semantic segmentation
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Geospatial Big Data Processing in Hybrid Cloud Environments
摘要： The importance of big geospatial data hosted on cloud environments is constantly growing. Main reasons are the rapid increase in volume of remote sensing data, the trend to persistently store and share more in-situ data at higher sampling rates, and the reduced management overhead of data hosted on commercial cloud platforms compared to in-house solutions. At the same time, cloud computing has the advantage of high scalability (and often reliability) and the capability to match the increasing computational requirements entailed by Big Data processing. This paper discusses interoperability and portability issues of cloud computing architectures and introduces a standards-based architecture to facilitate geospatial big data processing in hybrid cloud environments by leveraging and extending standards released by the Open Geospatial Consortium, OGC.
关键词： Big Data,Standards,Geospatial,Cloud
更新于2025-09-23 15:21:21
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Wide band ridge gap waveguide (RGW) fan beam antenna with low side lobes based on parabolic reflector principle
摘要： Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering nontraditional, digital information to perform disease surveillance. We introduce a novel method of using multiple data sources for predicting the number of asthma-related emergency department (ED) visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. Our preliminary findings show that our model can predict the number of asthma ED visits based on near-real-time environmental and social media data with approximately 70% precision. The results can be helpful for public health surveillance, ED preparedness, and targeted patient interventions.
关键词： emergency department (ED) visits,Asthma,big data,environmental sensors,social media,predictive modeling
更新于2025-09-23 15:21:01
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Highly Reliable WLEDs With All-Inorganic Packaging Structure by Soldering Phosphor-in-Glass
摘要： Smart grids are electric networks that employ advanced monitoring, control, and communication technologies to deliver reliable and secure energy supply, enhance operation efficiency for generators and distributors, and provide flexible choices for prosumers. Smart grids are a combination of complex physical network systems and cyber systems that face many technological challenges. In this paper, we will first present an overview of these challenges in the context of cyber–physical systems. We will then outline potential contributions that cyber–physical systems can make to smart grids, as well as the challenges that smart grids present to cyber–physical systems. Finally, implications of current technological advances to smart grids are outlined.
关键词： control,cyber–physical systems,modeling,smart grids,Big data,optimization,renewable energy,cloud computing,multiagent systems,intelligent systems,complex networks
更新于2025-09-23 15:19:57
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CMOS Backplane Pixel Circuit With Leakage and Voltage Drop Compensation for an Micro-LED Display Achieving 5000 PPI or Higher
摘要： Smart grids are electric networks that employ advanced monitoring, control, and communication technologies to deliver reliable and secure energy supply, enhance operation efficiency for generators and distributors, and provide flexible choices for prosumers. Smart grids are a combination of complex physical network systems and cyber systems that face many technological challenges. In this paper, we will first present an overview of these challenges in the context of cyber–physical systems. We will then outline potential contributions that cyber–physical systems can make to smart grids, as well as the challenges that smart grids present to cyber–physical systems. Finally, implications of current technological advances to smart grids are outlined.
关键词： control,cyber–physical systems,modeling,smart grids,Big data,optimization,renewable energy,cloud computing,multiagent systems,intelligent systems,complex networks
更新于2025-09-23 15:19:57
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - High Efficiency GaN HEMT Class F Power Amplifier for S-band Telemetry Application
摘要： In today’s highly intertwined network society, the demand for big data processing frameworks is continuously growing. The widely adopted model to process big data is parallel and distributed computing. This paper documents the significant progress achieved in the field of distributed computing frameworks, particularly Apache Hama, a top level project under the Apache Software Foundation, based on bulk synchronous parallel processing. The comparative studies and empirical evaluations performed in this paper reveal Hama’s potential and efficacy in big data applications. In particular, we present a benchmark evaluation of Hama’s graph package and Apache Giraph using PageRank algorithm. The results show that the performance of Hama is better than Giraph in terms of scalability and computational speed. However, despite great progress, a number of challenging issues continue to inhibit the full potential of Hama to be used at large scale. This paper also describes these challenges, analyzes solutions proposed to overcome them, and highlights research opportunities.
关键词： bulk synchronous parallel,Giraph,MapReduce,big data,Hadoop,BSP,distributed computing,Apache Hama,Spark
更新于2025-09-23 15:19:57
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[IEEE 2019 National Power Electronics Conference (NPEC) - Tiruchirappalli, India (2019.12.13-2019.12.15)] 2019 National Power Electronics Conference (NPEC) - Dual Frequency Series Resonant converter based LED driver
摘要： Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering nontraditional, digital information to perform disease surveillance. We introduce a novel method of using multiple data sources for predicting the number of asthma-related emergency department (ED) visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. Our preliminary findings show that our model can predict the number of asthma ED visits based on near-real-time environmental and social media data with approximately 70% precision. The results can be helpful for public health surveillance, ED preparedness, and targeted patient interventions.
关键词： emergency department (ED) visits,Asthma,big data,environmental sensors,social media,predictive modeling
更新于2025-09-23 15:19:57
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[IEEE 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) - Bangkok, Thailand (2018.12.16-2018.12.19)] 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) - Distributed-based Hierarchical Clustering System for Large-scale Semiconductor Wafers
摘要： In this paper, we propose a Distributed-based Hierarchical Clustering System for Large-Scale Semiconductor Wafers (DHCSSW). By applying the big-data clustering algorithm, the proposed system makes it feasible to cluster large-scale wafers with up to 320,000 wafers. To verify the performance of our approach, we used simulated wafer maps. The experimental results show that our system outperformed in processing large-scale wafers, suggesting that currently used hierarchical clustering is insufficient in analyzing large-scale wafer maps. In addition, some failure patterns, which the existing approach is not able to detect, can be found with the DHCSSW. We anticipate that the DHCSSW will contribute to identifying failure patterns in semiconductor wafers.
关键词： Distributed computing system,Big data analytics,Semiconductor wafer map,Hierarchical clustering
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT) - Nanjing, China (2019.8.28-2019.8.30)] 2019 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT) - Continuous Frequency-Sweep Covering Normal Direction Using Spoof Plasmonic Waveguide
摘要： One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy ?eld in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We ?rst present an overview of the battle ground by de?ning the roles and operations of privacy systems. Second, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Third, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourth, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of this paper, hoping to shed light on the exciting and almost uncharted land.
关键词： privacy,differential privacy,Big data,data clustering
更新于2025-09-19 17:13:59