Design and Implementation of Digital Signal Processing Algorithms for Communication, Biomedical and other Applications

通信、生物医学和其他应用的数字信号处理算法的设计和实现

• 批准号: RGPIN-2016-06623
• 负责人: Ahmad, MOmair
• 金额: $ 2.62万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616419
• 关键词: Design; Implementation; Digital; Signal; Processing

Design and Implementation of Digital Signal Processing Algorithms for Communication, Biomedical and other Applications The field of digital signal processing (DSP) has experienced explosive growth during the past couple of decades. The DSP techniques have become integral parts of the products and services that we need or encounter in our daily lives. The research efforts of the applicant in this area during the past five years have led to some very concrete results that have been shared with the international scientific community, both from academia and industry, and have given rise to new ideas and directions that need to be further investigated. The overall objective of the proposed research program is to develop efficient algorithms and architectures and to lay sound mathematical foundations for reliable processing of image, video, biomedical and genomic signals, and cost-effective implementations for communication, biomedical and other applications. Discrete transforms are essential to signal processing tasks. To this end, new parameterized transforms capable of optimizing the performance of given applications will be investigated. Presence of noise makes the automatic interpolation of image data very difficult. Another concern is the security of the image data. This research will investigate the suitability of the various signal transforms and the statistical models of the transformed image data in finding effective solutions to the problems of denoising and watermarking. Object detection and tracking in images and videos have important applications in surveillance, traffic management and in cell biology. A study to develop a suitable collaborative model for detection based object tracking will be undertaken. Automatic cell cycle analysis is important for discovery of new drugs and in life science research. This research will aim at developing in a signal-processing framework the detection, tracking and identification of the different phases of cells in time-lapse microscopic images to automate cell cycle analysis while meeting challenges of clustering, deformation and uneven motion of cells. Proteins are the most important molecules in living organisms as they are involved in every function of the cells such as signal transmission, metabolic regulation, transportation of molecules and defense mechanism. Insertion and deletion of amino acids are common events that lead to the evolution and variations in protein sequences. This research will focus on developing signal processing techniques for alignment of multiple protein sequences by employing the information on insertion/deletion regions in the protein sequences. Use of a reliable personal identifier is a necessary to fight identity fraud. This research will investigate the score-level and feature-level fusions of multiple biometric traits in order to improve the recognition rate of multimodal biometric systems.

通信、生物医学和其他应用的数字信号处理算法的设计与实现 数字信号处理（DSP）领域在过去几十年中经历了爆炸式的增长。DSP技术已经成为我们日常生活中需要或遇到的产品和服务的组成部分。过去五年来，申请人在这一领域的研究工作取得了一些非常具体的成果，与国际学术界和工业界的科学界分享，并产生了新的想法和方向，需要进一步研究。拟议研究计划的总体目标是开发高效的算法和架构，并为图像，视频，生物医学和基因组信号的可靠处理奠定良好的数学基础，以及通信，生物医学和其他应用的成本效益实现。离散变换对于信号处理任务是必不可少的。为此，将研究能够优化给定应用程序的性能的新的参数化变换。噪声的存在使得图像数据的自动插值非常困难。另一个问题是图像数据的安全性。本研究将探讨各种信号变换的适用性和变换后的图像数据的统计模型，以找到有效的解决方案，去噪和水印的问题。图像和视频中的目标检测和跟踪在监控、交通管理和细胞生物学中具有重要的应用。将进行一项研究，以开发一个适当的协作模式，用于基于探测的物体跟踪。细胞周期自动分析对于新药开发和生命科学研究具有重要意义。这项研究的目的是在信号处理框架中开发对延时显微图像中细胞不同阶段的检测、跟踪和识别，以自动进行细胞周期分析，同时应对细胞聚类、变形和不均匀运动的挑战。蛋白质是生物体中最重要的分子，因为它们参与细胞的每个功能，如信号传递，代谢调节，分子运输和防御机制。氨基酸的插入和缺失是导致蛋白质序列进化和变异的常见事件。本研究将致力于开发利用蛋白质序列中插入/缺失区域的信息进行多蛋白质序列比对的信号处理技术。使用可靠的个人身份识别是打击身份欺诈的必要条件。本研究将探讨多个生物特征的分数级和特征级融合，以提高多模态生物特征系统的识别率。