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941.
由于电离层延迟会对卫星的定位精度产生很大的影响,传统的电离层延迟Klobuchar模型的预报精度不高,而且其对夜间电离层延迟以及对延迟峰值出现时刻的预报存在明显的不合理性。因此必须进行修正,根据实测电离层延迟数据的特点对传统的Klobuchar模型进行了改进,并利用非线性最小二乘迭代算法对两种模型进行参数拟合,采用拟合参数计算两种模型的拟合值。通过比较两种拟合值相对于实际测量值的精度,可以得出:改进模型比传统模型提高了电离层延迟预报的精度,并且改善了传统延迟模型预报中存在的不合理性。 相似文献
942.
在开发某型地空导弹半实物仿真训练模拟系统时,实现系统硬件结构面临的主要难点之一是如何设计、实现其评估子系统的硬件接口电路,它主要用于为评估子系统采集需要的定量数据;评估子系统通过分析采集到的数据,来判断操作动作的准确性;结合某型地空导弹半实物仿真模拟训练系统的开发,以数字I/O卡PCL722为核心器件设计了该子系统的接口电路;运行结果表明,基于PCL722卡的接口电路符合评估子系统的要求,是可行、有效的。 相似文献
943.
基于ATML标准的测试信息描述研究 总被引:4,自引:0,他引:4
当前测试领域缺乏一个广为接受的测试信息交换标准,这已成为自动测试系统向前发展的一个瓶颈,为了解决此问题,IEEE发布了ATML标准,该标准使用XML语言进行ATS的测试信息的标准化交换;文章首先介绍了ATML标准的目标与具体组成,然后以测试结果为例,实现了基于ATML标准的测试结果信息的标准化描述;为了方便测试软件的开发,本文开发了测试结果组件,测试软件中直接调用测试结果组件以生成符合ATML标准的测试结果文件。 相似文献
944.
为了克服舰船摇摆及风阻力矩干扰所造成舰载光学测量设备跟踪稳定性下降、目标丢失的影响,伺服系统通过接收主控微机提供的船摇信息,将船摇速度校正放大后引入速度回路,实现经纬仪的自稳定跟踪;通过对PXI总线技术进行探讨,分析PXI总线的规范和特点,结合伺服控制器的工作原理和组成,基于PXI总线的FPGA、DSP应用以及外围接口电路设计,大大减小了系统的体积,提高系统集成度、可靠性和稳定性。 相似文献
945.
Hua-Fu Li 《Multimedia Tools and Applications》2009,41(2):287-304
Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of
mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed.
First, we proposed an efficient online algorithm, FTP-stream (Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence
representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list
structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments
show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and
just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change
(changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody
structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst,
negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody
structures over continuous music query streams.
相似文献
Hua-Fu LiEmail: |
946.
The content–user gap is the difference between the limited range of content-relevant preferences that may be expressed using
the MPEG-7 user interaction tools and the much wider range of metadata that may be represented using the MPEG-7 content tools.
One approach for closing this gap is to make the user and content metadata isomorphic by using the existing MPEG-7 content
tools to represent user (as well as content) metadata (Agius and Angelides 2006, 2007). Subsequently, user preferences may
be specified for all content, without omission. Since there is a wealth of user preference and history metadata within the
MPEG-7 user interaction tools that can usefully complement these specific content preferences, in this paper we develop a
method by which all user and content metadata may be bridged.
相似文献
Marios C. AngelidesEmail: |
947.
948.
Sebastiano Battiato Giovanni Maria Farinella Giovanni Giuffrida Catarina Sismeiro Giuseppe Tribulato 《Multimedia Tools and Applications》2009,42(1):5-30
Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience.
Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect
to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing
process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in
particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate
that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes
a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features
to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS)
show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed
approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
Giuseppe TribulatoEmail: |
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
949.
In this paper, based on nonnegative matrix theory, the Halanay’s inequality and Lyapunov functional, some novel sufficient
conditions for global asymptotic robust stability and global exponential robust stability of neural networks with time-varying
delays are presented. It is shown that our results improve and generalize several previous results derived in the literatures.
From the obtained results, some linear matrix inequality criteria are derived. Finally, a simulation is given to show the
effectiveness of the results. 相似文献
950.
Broadcasting scheme with low client buffers and bandwidths for video-on-demand applications 总被引:1,自引:1,他引:0
Hsiang-Fu Yu Hung-Chang Yang Yao-Tien Wang Ping-Lin Fan Chu-Yi Chien 《Multimedia Tools and Applications》2009,42(3):295-316
Efficient data broadcasting is independent of request arrivals, and is thus highly promising when transmitting popular videos.
A conventionally adopted broadcasting method is periodic broadcasting, which divides a popular video into segments, which
are then simultaneously broadcast on different data channels. Once clients want to watch the video, they download the segments
from these channels. The skyscraper broadcasting (SkB) scheme supports clients with small bandwidths. An SkB client requires
only two-channel bandwidths to receive video segments. This work proposes a reverse SkB (RSkB) scheme, which extends SkB by
reducing buffering spaces. The RSkB is mathematically shown to achieve on-time video delivery and two-channel client bandwidths.
A formula for determining the maximum number of segments buffered by an RSkB client is presented. Finally, an analysis of
RSkB reveals that its client buffer requirements are usually 25–37% lower than SkB. Extensive simulations of RSkB further
demonstrate that RSkB yields lower client buffer demand than other proposed systems.
相似文献
Hsiang-Fu YuEmail: |