An investigation of new DSP algoithms for biomedical engineering and software defined radio; and processes for defect-free implementation on a variety of processor architectures

我们的研究目前包括两个不同领域的系统特征：电信和成像。这两个领域都需要(A)发现新算法的基础研究，(B)开发适当的软件工程过程，将这些新算法从容错研究环境转移到无缺陷的、基于嵌入式系统的产品上，以及(C)嵌入式产品的实际实现。在最近与我们的工业合作伙伴Analog Devices（加拿大和美国）的NSERC CRD拨款（2004 - 2007）期间，我们开发了面向嵌入式数字信号处理（DSP）平台开发的测试框架。我们建议扩展我们的框架，以支持将DSP密集型算法移动到基于fpga和低成本超媒体处理器的廉价、高速、客户端移动设备上。我们的另一个合作是与噪声存在下的边缘保留（检测）相关的图像处理，特别是在功能磁共振成像（fMRI）的背景下。我们从理论上证明了一种空间自适应小波噪声滤波算法的空间和尺度空间结合方法。我们打算研究如何通过结合我们小组成员最近申请专利的新的超高速s变换（UHS s变换）算法来增强这种多空间方法。我们建议在将算法通过我们的嵌入式系统测试框架移动到嵌入式系统平台之前，通过与现有的“金标准”各向异性滤波技术的比较来验证这些扩展。这项拨款还建议对这种新的S-transform实施进行初步但详细的研究。不言而喻，要使UHS s变换在以下条件下运行，需要做出不同的妥协：(i) DSP超媒体功能的实际限制，但低成本，手持消费产品（如fMRI和ECG分析）中使用的处理器；（ii）在专门设计用于多集群环境的VLIW处理器上实现该算法。

Our research currently encompasses the characterization of systems in two diverse areas: telecommunications and imaging. Both areas require (A) fundamental research to discover new algorithms, (B) the development of appropriate software engineering processes to move those new algorithms from a fault-tolerant research environment onto a defect free, embedded system based product, and (C) actual implementation of the embedded product. During a recent NSERC CRD grant (2004 - 2007) with our industrial partner Analog Devices (Canada and US), we developed testing frameworks oriented towards embedded digital signal processing (DSP) platform development. We propose to extend our framework to support the movement of DSP intensive algorithms onto inexpensive, high-speed, client-side, mobile devices based around FPGAs combined with low-cost hypermedia-capable processors. Another of our collaborations is associated with image processing related to edge retention (detection) in the presence of noise, particularly within the context of functional magnetic resonance imaging (fMRI). We have theoretically demonstrated a combined spatial and scale-space approach for a spatially adaptive wavelet-based noise filtering algorithm. We intend to investigate how this multi-space approach can be enhanced by incorporating a new ultra-high speed S-transform (UHS S-transform) algorithm recently patented by members of our group. We propose to validate these extensions through a comparison with existing 'gold-standard' anisotropic filtering techniques prior to moving the algorithms, through our embedded-system testing framework, onto embedded system platforms. This grant also proposes a preliminary, but detailed, study of this new S-transform implementation. It is self-evident that there will be different compromises necessary to get the UHS S-transform to operate within (i) the practical constraints of DSP hyper-media capable, but low-cost, processors used within hand-held consumer products e.g. fMRI and ECG analysis; and (ii) implementing the algorithm on VLIW processors specifically designed to work within a multi-cluster environment.