L-smoothers and M-smoothers are introduced as generalizations of the median filter for nonlinear smoothing of noisy data, and their properties are derived. In addition, a double-window smoothing algorithm which is shown to be a data-dependent modification of L- and M-smoothers is proposed for filtering noisy signals with sharp edges. Simulation results are given to demonstrate the performance characteristics of these smoothing algorithms.
In the development of crystal engineering and supramolecular chemistry, cocrystallization has been used as a way to develop novel explosives with tailored properties. We present a novel cocrystal s...
The existing particle image velocimetry (PIV) techniques do not consider the curvature effect of the nonstraight particle trajectory because it seems to be impossible to obtain the curvature information from a pair of particle images. In this work, the particle curved trajectory between two recordings is first explained with the streamline segment of a steady flow (diffeomorphic transformation) instead of a single vector, and this novel idea is termed diffeomorphic PIV. Specifically, a deformation field is introduced to describe the particle displacement along the streamline, i.e., we try to find the optimal velocity field, of which the corresponding deformation vector field agrees with the particle displacement. Because the variation of the deformation function can be approximated with the variation of the velocity function, the diffeomorphic PIV can be implemented as special iterative PIV. This says that the diffeomorphic PIV warps the images with deformation vector field instead of velocity field and keeps the rest procedures as the same as a conventional iterative PIV. Two diffeomorphic deformation schemes—forward diffeomorphic deformation interrogation (FDDI) and central diffeomorphic deformation interrogation (CDDI)—are proposed in this article. Tested on synthetic PIV images, the FDDI achieves significant accuracy improvement across different one-pass displacement estimators (cross correlation, optical flow (OF), and deep learning flow). Besides, the results on three real PIV image pairs demonstrate the nonnegligible curvature effect for central difference interrogation (CDI)-based measurement, and our FDDI provides larger velocity estimation—more accurate—in the fast curvy streamline areas. The significant accuracy improvement of the combination of FDDI and accurate dense estimator (e.g., OF) means that our diffeomorphic PIV paves a completely new way for complex flow field measurement.
We developed a high-speed (over 30 GHz) and high-responsivity (nearly 0.8 A/W) back-illuminated PD with a newly developed high reflective reflector, and successfully demonstrated an error free 25-Gbps 10-km SMF transmission.
In this study we treat TXO price as a dynamic system which changes over time and characterize it by differential equations. Our goal is to construct a model more suitable for TXO. We use ―Parabola Approximation‖ proposed by Li et al. (2011) to solve the differential equations and try to find the model which fits our data the most. Empirical study shows the model used all produce accurate estimates of TXO prices.
A prototype transceiver composed of a 1.3-μm-range lens-integrated laser diode and photodiode as well as a complementary metal-oxide-semiconductor (CMOS) laser diode driver and a CMOS transimpedance amplifier for high-speed optical interconnections was developed. It demonstrated 25-Gb/s error-free 100-m multimode fiber transmission, with power dissipation of only 9 mW/Gb/s, for the first time.
This work describes an adaptive spatial variable threshold outlier detection algorithm for raw gridded particle image velocimetry data using a locally estimated noise variance. This method is an iterative procedure, and each iteration is composed of a reference vector field reconstruction step and an outlier detection step. We construct the reference vector field using a weighted adaptive smoothing method (Garcia 2010 Comput. Stat. Data Anal. 54 1167–78), and the weights are determined in the outlier detection step using a modified outlier detector (Ma et al 2014 IEEE Trans. Image Process. 23 1706–21). A hard decision on the final weights of the iteration can produce outlier labels of the field. The technical contribution is that the spatial variable threshold motivation is embedded in the modified outlier detector with a locally estimated noise variance in an iterative framework for the first time. It turns out that a spatial variable threshold is preferable to a single spatial constant threshold in complicated flows such as vortex flows or turbulent flows. Synthetic cellular vortical flows with simulated scattered or clustered outliers are adopted to evaluate the performance of our proposed method in comparison with popular validation approaches. This method also turns out to be beneficial in a real PIV measurement of turbulent flow. The experimental results demonstrated that the proposed method yields the competitive performance in terms of outlier under-detection count and over-detection count. In addition, the outlier detection method is computational efficient and adaptive, requires no user-defined parameters, and corresponding implementations are also provided in supplementary materials.
We propose a novel global load balancing algorithm based on the Cross Stratum Optimization architecture to realize the joint optimization of application resource and network resource on Optical as a Service (OaaS) testbed.
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