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表征：电信和成像。这两个领域都需要（A）基础研究以发现新的算法，（B）开发适当的软件工程过程以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。 在最近的NSERC CRD资助（2004 - 2007）期间，我们与工业合作伙伴ADI公司（加拿大和美国）合作，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，以支持移动的DSP密集型算法到廉价的，高速的，客户端，移动的设备的FPGA结合低成本的超媒体处理器。 我们的另一个合作是与图像处理相关的边缘保留（检测）在噪声的存在下，特别是在功能性磁共振成像（fMRI）的背景下。我们从理论上证明了一个空间和尺度空间相结合的方法，空间自适应小波基噪声滤波算法。我们打算研究如何通过结合我们小组成员最近获得专利的新的超高速S变换（UHS S变换）算法来增强这种多空间方法。我们建议通过与现有的“黄金标准”的各向异性过滤技术的比较，移动算法之前，通过我们的嵌入式系统测试框架，到嵌入式系统平台上，验证这些扩展。这项资助还提出了一个初步的，但详细的，这种新的S变换实现的研究。不言而喻，要使UHS S变换在以下范围内运行，需要做出不同的妥协：（i）手持消费类产品（如fMRI和ECG分析）中使用的DSP超媒体功能但低成本处理器的实际限制;（ii）在专门设计用于多集群环境的VLIW处理器上实现算法。

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.