Unidirectional wavelength filtering characteristics of the two-dimensional triangular-lattice photonic crystal structures with elliptical defects

The unidirectional wavelength filtering characters of the two-dimensional triangular-lattice photonic crystal structures consisting of two waveguides and an elliptic defect are theoretically studied by the finite-difference time-domain method. Through designing the coupling regions between the elliptical defect and the adjacent waveguides, the unidirectional wavelength filtering is achieved owing to the modes’ match and mismatch between the elliptical defect and waveguides, which converts the incident fundamental even-symmetric modes to the higher order odd modes. Based on the abundant defective modes with different symmetries supported by the elliptical defect, this kind of wavelength filters can allow the unidirectional light propagation with inverse forward directions, and the bidirectional propagation through the same structure at different wavelengths.     

Autonomous dynamic displacement estimation from data fusion of acceleration and intermittent displacement measurements

Addressing the importance of displacement measurement of structural responses in the field of structural health monitoring, this paper presents an autonomous algorithm for dynamic displacement estimation from acceleration integration fused with displacement data intermittently measured. The presented acceleration integration algorithm of multi-rate Kalman filtering distinguishes itself from the past study in the literature by explicitly considering acceleration measurement bias. Furthermore, the algorithm is formulated by unique state definition of integration errors and error dynamics system modeling. To showcase performance of the algorithm, a series of laboratory dynamic experiments for measuring structural responses of acceleration and displacement are conducted. Improved results are demonstrated through comparison between the proposed and past study.     

General equivalence between two kinds of noise-correlation filters

A recent comment (Chang, 2014) theoretically demonstrated that for the linear system with the correlated process and measurement noises, the GASF framework proposed in the paper Wang et al. (2012) was equivalent to the conventional de-coupling filtering framework. In this note, we would further show that such equivalence between the two frameworks can be justified in a more general way, even for the nonlinear system.     

Simultaneous 2D and 3D perception for stereoscopic displays based on polarized or active shutter glasses

Viewing stereoscopic 3D content is typically enabled either by using polarizing or active shutter glasses. In certain cases, some viewers may not wear viewing glasses and hence, it would be desirable to tune the stereoscopic 3D content so that it could be simultaneously watched with and without viewing glasses. In this paper we propose a video post-processing technique which enables good quality 3D and 2D perception of the same content. This is done through manipulation of one view by making it more similar to the other view to reduce the ghosting artifact perceived without viewing glasses while 3D perception is maintained. The proposed technique includes three steps: disparity selection, contrast adjustment, and low-pass filtering. The proposed approach was evaluated through an extensive series of subjective tests, which also revealed good adjustment parameters to suit viewing with and without viewing glasses with an acceptable 3D and 2D quality, respectively.     

Particle filtering with analytically guided sampling

Particle filtering (PF) is a popular nonlinear estimation technique and has been widely used in a variety of applications such as target tracking. Within the PF framework, one critical design choice is the selection of the proposal distribution from which particles are drawn. In this paper, we advocate using as proposal distribution a Gaussian-mixture-based approximation of the posterior probability density function (pdf) after taking into account the most recent measurement. The novelty of our approach is that the parameters of each Gaussian used in the mixture are determined analytically to match the modes of the underlying unknown posterior pdf. As a result, particles are sampled along the most probable regions of the state space, hence reducing the probability of particle depletion. Based on the analytically determined proposal distribution, we introduce a novel PF, termed analytically guided sampling-based PF, which is validated in range-only and bearing-only target tracking.     

A rapid hybrid algorithm for image restoration combining parametric Wiener filtering and wave atom transform

Image restoration refers to removal or minimization of known degradations in an image. This includes de-blurring images degraded by the limitations of sensors or source of captures in addition to noise filtering and correction of geometric distortion due to sensors. There are several classical image restoration methods such as Wiener filtering. To find an estimate of the original image, Wiener filter requires the prior knowledge of the degradation phenomenon, the blurred image and the statistical properties of the noise process. In this work, we propose a new rapid and blind algorithm for image restoration that does not require a priori knowledge of the noise distribution. The degraded image is first de-convoluted in Fourier space by parametric Wiener filtering, and then, it is smoothed by the wave atom transform after setting the threshold to its coefficients. Experiment results are significant and show the efficiency of our algorithm compared with other techniques in use.     

Recognizing the breathing resistances of wearing respirators from respiratory and sEMG signals with artificial neural networks

This study is devoted to recognizing the breathing resistances of wearing respirators from respiratory and surface electromyography (sEMG) signals. Ten subjects were required to sit for 5 min and walk for 5 min while wearing two different models of N95 filtering facepiece respirators (FFRs) and without a respirator. We recorded the sEMG signals from the respiratory muscles of the subjects, and the respiratory amplitude is also collected. Subsequently, fifteen features of the sEMG time domain and respiratory amplitude were extracted and used as input vectors to a recognition model based on artificial neural networks (ANNs). Finally, the experimental results show that these artificial neural networks are effective for recognizing different airway resistances of wearing respirators from sEMG and respiratory signals. The results also indicate that abdominal and scalene are the primary respiratory muscles affected by using N95 FFRs.Relevance to industryRespirator manufactures and administrations can readily employ this paper's findings for recognizing the breathing resistances of wearing respirators from respiratory and surface electromyography (sEMG) signals based on artificial neural networks automatically. Observations of the present study are in support of testing only the two primary muscles (abdominal and scalene) to simplify the evaluation of the effects of the breathing resistances of wearing respirators on respiratory muscles.     

Finite‐time H ∞ filtering for a class of discrete‐time Markovian jump systems with partly unknown transition probabilities

This paper is concerned with the problem of finite‐time H _∞ filtering for a class of Markovian jump systems subject to partial information on the transition probabilities. By introducing some slack matrix variables in terms of probability identity, a less conservative bounded real lemma is derived to ensure that filtering Markovian jump systems is finite‐time stable. Finally, the existence criterion of the desired filter is obtained such that the corresponding filtering error system is finite‐time bounded with a guaranteed H _∞ performance index. An example is given to illustrate the efficiency of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.     

Equivalence of quadratic performance criteria†

Two quadratic cost functionals are defined to be equivalent, if they generate the same optimal control law. Necessary and sufficient conditions are derived in terms of the system parameters and the quadratic weighting matrices for two cost functionals to be equivalent.