CISE Research Instrumentation: Multiprocessor Workstation for Advanced Digital Signal Processing Research

CDA-9529520巴雷托，阿曼多B。 Yen，Kang 佛罗里达国际大学 CISE研究仪器：用于高级数字信号处理研究的多处理器工作站 新兴的数字信号处理技术正被应用于解决信号检测和系统建模的挑战性问题：动态神经网络被用作时空分类器，用于检测可以自愿引发的脑电图（EEG）模式，作为运动准备过程的一部分。 这些模式的可靠检测将提供一个新的形式的计算机接口，将访问甚至被剥夺了运动执行功能的个人的发展的基础。同样，混沌动力学分析正在应用于海洋声波三维传播的详细研究。指数，如李雅普诺夫指数的数值估计使用真实的声音传播数据收集在北大西洋的混沌特性的现象。虽然这些新兴的DSP技术有很大的希望，以提高我们的能力，从信号中获得有用的信息，并制定准确的物理现象模型，他们是复杂的和计算密集型。多处理器工作站的可用性对于缩短算法开发和分析周期是必要的（即，大规模动态神经网络训练、数值李雅普诺夫指数估计等）到实际水平。此外，这些系统将使我们的教师和学生熟悉并行算法开发技术

CDA-9529520 Barreto, Armando B. Yen, Kang Florida International University CISE Research Instrumentation: Multiprocessor Workstation for Advanced Digital Signal Processing Research Emerging Digital Signal Processing techniques are being applied to the solution of challenging problems of signal detection and system modeling: Dynamic neural networks are used as spatio-temporal classifiers for the detection of electroencephalographic (EEG) patterns that can be elicited voluntarily, as part of the process of motion preparation. Reliable detection of these patterns will provide the basis for the development of a new form of computer interface that will be accessible even to individuals deprived of motor execution functions. Similarly, chaotic dynamics analysis is being applied for the detailed study of the three-dimensional propagation of ocean acoustic waves. Indices such as the Lyapunov exponent are numerically estimated using real sound propagation data collected in the North Atlantic to characterize the chaotic properties of the phenomenon. While these emerging DSP techniques hold great promise to increase our capability to obtain useful information from signals and develop accurate models of physical phenomena, they are complex and computationally intensive. The availability of multiprocessor workstations is necessary to shorten the algorithm development and analysis cycles (i.e., large-scale dynamic neural network training, numerical Lyapunov exponent estimation, etc.) to practical levels. Additionally, these systems will familiarize our faculty and students with parallel algorithm development techniques