@article{yin_ghovanloo_2009, title={Using Pulse Width Modulation for Wireless Transmission of Neural Signals in Multichannel Neural Recording Systems}, volume={17}, ISSN={["1558-0210"]}, DOI={10.1109/TNSRE.2009.2023302}, abstractNote={We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5-mum standard CMOS process and consumes 4.5 mW from plusmn1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of ~ 2.56 Mb/s.}, number={4}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Yin, Ming and Ghovanloo, Maysam}, year={2009}, month={Aug}, pages={354–363} }
@article{chen_lu_huo_yin_2001, title={Optimum percentile estimating equations for nonlinear random coefficient models}, volume={97}, ISSN={["0378-3758"]}, DOI={10.1016/S0378-3758(00)00219-6}, abstractNote={In nonlinear random coefficients models, the means or variances of response variables may not exist. In such cases, commonly used estimation procedures, e.g., (extended) least-squares (LS) and quasi-likelihood methods, are not applicable. This article solves this problem by proposing an estimate based on percentile estimating equations (PEE). This method does not require full distribution assumptions and leads to efficient estimates within the class of unbiased estimating equations. By minimizing the asymptotic variance of the PEE estimates, the optimum percentile estimating equations (OPEE) are derived. Several examples including Weibull regression show the flexibility of the PEE estimates. Under certain regularity conditions, the PEE estimates are shown to be strongly consistent and asymptotic normal, and the OPEE estimates have the minimal asymptotic variance. Compared with the parametric maximum likelihood estimates (MLE), the asymptotic efficiency of the OPEE estimates is more than 98%, while the LS-type of procedures can have infinite variances. When the observations have outliers or do not follow the distributions considered in model assumptions, the article shows that OPEE is more robust than the MLE, and the asymptotic efficiency in the model misspecification cases can be above 150%.}, number={2}, journal={JOURNAL OF STATISTICAL PLANNING AND INFERENCE}, author={Chen, D and Lu, JC and Huo, XM and Yin, M}, year={2001}, month={Sep}, pages={275–292} }
@article{yin_2000, title={Noninformative priors for multivariate linear calibration}, volume={73}, ISSN={["0047-259X"]}, DOI={10.1006/jmva.1999.1851}, abstractNote={This paper derives a class of first order probability matching priors and a complete catalog of the reference priors for the general multivariate linear calibration problem. In an important special case, a complete characterization of first order probability matching priors is given, and a fairly general class of second order probability matching priors is also provided. Orthogonal transformations (1987, D. R. Cox and N. Reid, J. Roy. Statist. Soc. Ser. B49, 1–18) are found to facilitate the derivations. It turns out that under orthogonal parameterization, reference priors (including Jeffreys' prior) are first order probability matching priors for unidimensional multivariate linear calibration. Also, in univariate linear calibration, the prior of W. G. Hunter and W. F. Lamboy (1981, Technometrics23, 323–350) is a second order probability matching prior.}, number={2}, journal={JOURNAL OF MULTIVARIATE ANALYSIS}, author={Yin, M}, year={2000}, month={May}, pages={221–240} }