... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing最新文献

LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION. 基于物理定律的大规模随机学习及其在全波形反演中的应用。

... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing Pub Date : 2018-11-01 Epub Date: 2019-02-21 DOI: 10.1109/GlobalSIP.2018.8646507

Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song

{"title":"LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION.","authors":"Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song","doi":"10.1109/GlobalSIP.2018.8646507","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646507","url":null,"abstract":"<p><p>The rapid convergence rate, high fidelity learning outcome and low computational cost are key targets in solving the learning problem of the complex physical system. Guided by physical laws of wave propagation, in full waveform inversion (FWI), we learn the subsurface images through optimizing the media velocity model in a large scale non-linear problem. In this paper, we combine randomized subsampling techniques with a second-order optimization algorithm to propose the Sub-Sampled Newton (SSN) method for learning velocity model of FWI. By incorporating the curvature information, SSN preserves comparable convergence rate to Newtons method and significantly reduces the iteration cost by approximating the Hessian matrix through a non-uniform subsampling scheme. The numerical experiments demonstrate that the proposed SSN method has a faster convergence rate, and achieves a more accurate velocity model in terms of mean squared error than commonly used methods.</p>","PeriodicalId":91429,"journal":{"name":"... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing","volume":"2018 ","pages":"66-70"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/GlobalSIP.2018.8646507","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37338519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

THE USE OF CO-OCCURRENCE PATTERNS IN SINGLE IMAGE BASED FOOD PORTION ESTIMATION. 在基于单个图像的食物分量估计中使用共现模式。

... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing Pub Date : 2017-11-01 Epub Date: 2018-03-08 DOI: 10.1109/GlobalSIP.2017.8308685

Shaobo Fang, Fengqing Zhu, Carol J Boushey, Edward J Delp

引用次数: 0

Perceptual long-term harmonic plus noise modeling for speech data compression 语音数据压缩的感知长时谐波加噪声建模

... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing Pub Date : 2015-01-01 DOI: 10.1109/GlobalSIP.2015.7418423

F. Ali, S. Larbi

引用次数: 0

The Impact of RNA-seq Alignment Pipeline on Detection of Differentially Expressed Genes. RNA-seq比对管道对差异表达基因检测的影响。

... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing Pub Date : 2014-12-01 Epub Date: 2015-02-09 DOI: 10.1109/GlobalSIP.2014.7032351

Cheng Yang, Po-Yen Wu, John H Phan, May D Wang

引用次数: 3

Narrowing the gap: Probabilistic interfaces for signal enhancement and pattern recognition 缩小差距:信号增强和模式识别的概率接口

... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032171

D. Kolossa

{"title":"Narrowing the gap: Probabilistic interfaces for signal enhancement and pattern recognition","authors":"D. Kolossa","doi":"10.1109/GlobalSIP.2014.7032171","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2014.7032171","url":null,"abstract":"Human beings are highly effective at integrating multiple sources of uncertain information, and mounting evidence points to this integration being practically optimal in a Bayesian sense. Yet, in speech processing systems, the two central tasks of speech signal enhancement and of speech or phonetic-state recognition are often performed almost in isolation, with only estimates of mean values being exchanged between them. This paper describes concepts for enhancing the interface of these two systems, considering a range of appropriate probabilistic representations. Examples will illustrate how such interfaces can improve the quality of both components: On the one hand, more reliable pattern recognition can be attained, while on the other hand, enhanced signal quality is achieved when feeding back information from a pattern recognition stage to the signal preprocessing. This latter idea will be described using the example of twin-HMMs, audiovisual speech models that help to recover lost acoustic information by exploiting video data. Overall, it will be shown how broader, probabilistic interfaces between signal processing and pattern recognition can help to achieve better performance in real-world conditions, and to more closely approximate the Bayesian ideal of using all sources of information in accordance with their respective degree of reliability.","PeriodicalId":91429,"journal":{"name":"... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing","volume":"41 1","pages":"517-521"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74815437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On waveform design for MIMO radar with matrix completion 基于矩阵补全的MIMO雷达波形设计

... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing Pub Date : 2014-01-01 DOI: 10.1109/GlobalSIP.2014.7032162

Shunqiao Sun, A. Petropulu

引用次数: 3

Dataflow programming of real-time radar signal processing on manycores 多核实时雷达信号处理的数据流编程

... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing Pub Date : 2014-01-01 DOI: 10.1109/GlobalSIP.2014.7032069

Zain-ul-Abdin, Mingkun Yang

引用次数: 0

Technical program overview 技术方案概述

... IEEE Global Conference on Signal and Information Processing. IEEE Global Conference on Signal and Information Processing Pub Date : 2013-12-01 DOI: 10.1109/GlobalSIP.2013.6736791

C. Bouman, R. Nowak, A. Scaglione

引用次数: 0