Application rates input errors

Although TCSPC is used successfully for BIFL experiments, little has been published about applications for FID A. The reason is probably that the dead time of TCSPC is considered a drawback. However, a simple consideration shows that the detectable burst rate is not substantially reduced by the dead time of the TCSPC device. The commonly used detectors lose 50% of the photons at an input rate of about 1610 s" [408], fast TCSPC modules at 10-10 s" (see Fig. 5.94, page 162). This means the dead time of the TCSPC module is only slightly longer than the dead time of the detector. The use of TCSPC for PCH experiments therefore does not result in a considerable increase of dead-time-related errors. Moreover, BIFL applications have shown that the burst count rates are well within the counting capability of TCSPC (see below). 

This chapter presents our recent efforts to develop and calibrate a sand transport model that is suited for practical applications but contains the basic mechanics of sand suspension and bedload movement on beaches. The hydrodynamic input required for the sand transport model is limited to the variables of irregular waves and currents which can be predicted efficiently and fairly accurately using a combined wave and current model based on time-averaged continuity, momentum, and energy equations. More advanced but computationally-demanding wave and current models may not improve the accuracy of the sand transport model with errors of a factor of about 2. Moreover, practical coastal sediment problems require the prediction of sediment transport rates for a duration of days to years. The computational efficiency is hence essential for practical applications. 

Abstract— Estimation of muscie force is needed for monitoring or control purposes in many studies and applications that include direct human invoivement such as control of prosthetic arms and human-robot interaction. A new model is introduced to estimate the force of muscie from the EMG signals. Estimation is based on Hammerstein-Wiener Model which consists of three biocks. These biocks are used to describe the nonlinearity of input and output and iinear behavior of the model. The nonlinear network is designed base on the sigmoid network. The introduced modei is trained by some data sets which are recorded from different peopie and tested by some other data sets. The simuiation resuits show iow error rate between measured force and estimated force. 

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