Dongchuan Lu

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Recent advances of experimental methods and neuroscience research have made neural signals constantly massive and analysis of these signals highly compute-intensive. This study explore the possibility proposes a massively parallel approach for analysis of neural signals using General-purpose computing on the graphics processing unit (GPGPU). We demonstrate(More)
The estimation of synchronization amongst multiple brain regions is a critical issue in understanding brain functions. There is a lack of an appropriate approach which is capable of 1) measuring the direction and strength of synchronization of activities of multiple brain regions, and 2) adapting to the quickly increasing sizes and scales of neural signals.(More)
Nonlinear interdependency (NLI) analysis is an effective method for measurement of synchronization among brain regions, which is an important feature of normal and abnormal brain functions. But its application in practice has long been largely hampered by the ultra-high complexity of the NLI algorithms. We developed a massively parallel approach to address(More)
We propose methods to suppress the interference noise (IN) of an all-fiber white-light interferometer (WLI), which is caused by the residual Fresnel reflective beam. The methods are proposed to ensure a wide dynamic range and enhance the accuracy of measurement. IN can cause misjudgment of the realistic optical characteristic parameters, such as the fault(More)
We propose an effective and promising method to extend the continuous range of the optical delay line in white light interferometry. The method adopts the combination of switchable fixed delays and a continuous-scanning stage laid in one arm of the interferometer. Moreover, a fiber ring constructed by a fiber coupler is employed in the other arm to achieve(More)
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