Applications of frames to speech recognition, distributed processing, bio-medical engineering and more

框架在语音识别、分布式处理、生物医学工程等方面的应用

• 批准号: 0704216
• 负责人: Peter Casazza
• 金额: --
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0704216&HistoricalAwards=false
• 关键词: Applications; frames; speech; recognition; distributed

CasazzaDMS-0704216 The investigator and his colleagues work on designingHilbert space frames for applications. This involves workingdirectly with the groups needing such frames such as SiemensCorporate Research, the Bio-Medical Engineering Center atCarnegie Mellon University, and the Digital Signal ProcessingCenter in the Electrical Engineering department at RiceUniversity. One project involves designing frames and algorithmsfor doing signal reconstruction without phase. This project isdesigned to remove certain background noises from a signal sothat the voice signals are clearer. Another project involvesanswering the question: What is the role and what can imaging dofor systems biology? The goal is to have distributed yetintegrated large bioimage databases that would allow researchersto upload their data, have it processed, share the data, downloaddata as well as having platform-optimized code, all in a commonformat. A third project involves doing signal reconstruction witherasures. The investigator has designed a system that accountsfor erasures without any extra coefficient calculations, which isboth fast and accurate. This method needs to be custom designedfor the myriad of applications where there are transmissionlosses or erasures. Another project involves designing "fusionframes" for problems in distributed processing. This requiresdesigning localized frames that globally fuse data both quicklyand accurately -- especially in the face of losses. These areused for a variety of problems including sensor networks. Another project is geared to finding equal-norm equi-angularParseval frames and mutually unbiased bases. These frames areused in quantum state tomography, quantum cryptography andfoundational issues in quantum mechanics. The signal reconstruction project is designed to clean upcertain types of audio signals that have noise embedded in theirphase to present a clearer signal. Another project involvessensors that are used to observe the world around us. The amountof sensed data that is currently being collected is more than canbe collected in one place for analysis. The investigator and hiscolleagues are designing intelligent ways to reduce the amount ofraw data that is analyzed, and this necessitates understandingthe nature and role of the redundancy in the measurements. Making mathematical contributions to our fundamentalunderstanding of redundant information could advance theengineering solutions to problems in environmental monitoring(including agricultural technology), military surveillance,understanding the operation of sensory neural systems, andimproving general data collection methods that efficiently turnthe analog events in the natural world into digital signals readyfor analysis. Bioimaging has a serious need for accurateclassification, some of which involves life and death decisionsand are currently very sensitive to misclassification (such asfalse negative decisions in detecting cancer). The goal here isto design systems that are able to classify with close-to-perfectaccuracy.

CasazzaDMS-0704216这位研究人员和他的同事们致力于为应用设计希尔伯特空间框架。这涉及到与需要此类框架的团队直接合作，例如西门子公司研究中心、卡内基梅隆大学的生物医学工程中心和里奇大学电机系的数字信号处理中心。其中一个项目涉及设计无相位信号重建的框架和算法。该项目旨在消除信号中的某些背景噪声，使语音信号更清晰。另一个项目是回答这个问题：成像对系统生物学有什么作用和作用？其目标是建立分布式的、尚未集成的大型生物图像数据库，使研究人员能够上传数据、进行处理、共享数据、下载数据以及拥有平台优化的代码，所有这些都是以通用格式进行的。第三个项目涉及信号重建和恢复。这位研究人员设计了一种无需计算任何额外系数就能计算擦除的系统，既快又准确。这种方法需要为大量存在传输损失或擦除的应用程序定制设计。另一个项目涉及为分布式处理中的问题设计“融合框架”。这就要求设计本地化框架，既快速又准确地在全球范围内融合数据--特别是在面临损失的情况下。它们用于包括传感器网络在内的各种问题。另一个项目是寻找等范数等角Parseval框架和相互无偏基。这些标架被用于量子态层析、量子密码学和量子力学的基础问题。信号重建项目旨在清理某些类型的音频信号，这些音频信号的相位中嵌入了噪声，以呈现更清晰的信号。另一个项目涉及用来观察我们周围世界的船只。目前正在收集的大量传感数据不只是可以在一个地方收集用于分析的数据。这位研究人员和他的同事正在设计智能方法来减少分析的原始数据量，这就需要了解测量中冗余的性质和作用。为我们对冗余信息的基本理解做出数学贡献，可以推动环境监测(包括农业技术)、军事监视、理解感觉神经系统的运行以及改进常规数据收集方法等问题的工程解决方案，这些方法可以有效地将自然世界中的模拟事件转换为准备分析的数字信号。生物成像迫切需要准确的分类，其中一些涉及生死决定，目前对错误分类非常敏感(例如检测癌症时的假阴性决定)。这里的目标是设计能够以近乎完美的准确性进行分类的系统。