Optimal compressive sensing systems for signal acquisition, reconstruction and processing

用于信号采集、重建和处理的最佳压缩传感系统

• 批准号: 4062-2011
• 负责人: Lu, WuSheng
• 金额: $ 2.33万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637497
• 关键词: Optimal; compressive; sensing; systems; signal

In a nutshell, a compressive sensing (CS) system acquires a signal of interest indirectly by correcting a small number of its "projections" rather than evenly sampling it at the so-called Nyquist rate which can be prohibitively high for broadband signals encountered in many applications. This new signal acquisition paradigm has revolutionized the way digital data are traditionally acquired. The objectives of the proposed research are threefold. First, we aim at developing optimal CS systems that require fewer-than-ever number of measurements that yet contain complete information of the data. This entails reduced number of sensing devices (or lower software complexity in the case of software implementation), hence improving processing efficiency and reducing cost. This goal will be achieved by exploring and maximizing a new measure for the incoherence between the measurement subsystem and an overcomplete dictionary for sparse representation of signals. Second, we investigate new methods to recover the data of interest from the limited number of measurements with better accuracy relative to existing algorithms. Our interest is in large-scale data, hence we must develop fast algorithms for them to be useful in real-time data processing. To this end, we shall develop a class of proximal-gradient algorithms for solving Lp type (with p < 1) mixed convex-nonconvex problems. Third, the algorithms developed are to be applied to problems in digital signal processing such as de- nosing, de-blurring and segmentation in medical imaging and communications such as wireless channel estimation and message recovery problems. These problems are current, significant and technically challenging in their respective fields either because of their involvement in large amount of data (medical imaging) or because of their real-time nature (wireless communication applications). The information processing communities will regard our research endeavors as significant when the proposed algorithms begin to play a crucial role in solving these and other related problems with satisfactory processing speed and performance.

简而申请。这种新的信号获取范式彻底改变了传统上获取数字数据的方式。拟议研究的目标是三倍。首先，我们旨在开发最佳的CS系统，这些系统需要较少的测量数量，但其中包含数据的完整信息。这需要减少的传感设备（或在软件实施的情况下降低软件复杂性），从而提高处理效率并降低成本。通过探索和最大化测量子系统与稀疏表示信号的疏忽字典之间的不一致的新措施来实现此目标。其次，我们研究了从有限数量的测量数据中恢复感兴趣数据的新方法，相对于现有算法，其精度更好。我们的兴趣是大规模数据，因此我们必须开发快速算法，以便它们在实时数据处理中有用。为此，我们将开发一类用于求解LP类型（具有P <1）混合凸 - 孔孔孔问题的近端梯度算法。第三，开发的算法应应用于数字信号处理中的问题，例如在医学成像和通信中的脱位，脱毛和细分，例如无线通道估计和消息恢复问题。这些问题在各自的领域都是当前的，重要的，而且在技术上具有挑战性，要么是因为它们参与了大量数据（医学成像）或实时性质（无线通信应用）。当提出的算法开始在解决这些和其他相关的问题以令人满意的处理速度和性能方面扮演至关重要的角色时，信息处理社区将把我们的研究努力视为重要的。