Operator for Describing Polarization States of a Photon

Based on the quantized electromagnetic field described by the Riemann-Silberstein complex vector $F$, we construct the eigenvector set of $F$, which makes up an orthonormal and complete representation. In terms of $$ we then introduce a new operator which can describe the relative ratio of the left-handed and right-handed polarization states of a polarized photon .In $F^{\prime}s$ eigenvector basis the operator manifestly exhibits a behaviour which is similar to a phase difference between two orientations of polarization of a light beam in classical optics.Comment: This version (5 pages) will be published in the European Physical Journal

Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics

Traditional direction-of-arrival (DOA) estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC) sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analog-to-digital converter (LC ADC) has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an $ell_1$ minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing norm-based constrained optimization compressive sensing (CS) algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal

Polyadic Constacyclic Codes

For any given positive integer $m$, a necessary and sufficient condition for the existence of Type I $m$-adic constacyclic codes is given. Further, for any given integer $s$, a necessary and sufficient condition for $s$ to be a multiplier of a Type I polyadic constacyclic code is given. As an application, some optimal codes from Type I polyadic constacyclic codes, including generalized Reed-Solomon codes and alternant MDS codes, are constructed.Comment: We provide complete solutions on two basic questions on polyadic constacyclic cdes, and construct some optimal codes from the polyadic constacyclic cde

Modified smoothed particle method and its application to transient heat conduction

Inspired by the idea of applying kernel approximation to Taylor series expansions proposed in the corrective smoothed particle method (CSPM), a modi¿cation is developed to improve the accuracy of the approximations especially for particles in the boundary region. The large global error of the function approximation in CSPM is reduced in the present method. The large local truncation error in the boundary region for the ¿rst derivative approximation and large local truncation error in the entire domain for the second derivative approximation are also resolved. The e¿ciency of the proposed method is demonstrated by solving one- and two-dimensional transient heat conduction problems

Integrated health monitoring for a steel beam : an experimental study

Civil infrastructures begin to deteriorate once they are built and used. Detecting damages in a structure to maintain its safety is a topic that has received considerable attention in the literature in recent years. Many methods are developed, including global vibration-based methods and local GW-based methods. The global vibration-based method uses changes in modal properties to detect damage. The advantage of this approach is that the vibration properties are straightforward to be measured. The disadvantage of this method is that it might not be sensitive to small damage. On the other hand, local method, such as the guided waves (GW) based method is sensitive to small damage, but its sensing range is small. In this paper, an integrated structural health monitoring test scheme is developed to detect damage in a steel beam. Different saw cuts of various depths are made to simulate crack damage. Vibration tests and guided wave tests are conducted after each cut. The vibration method is used to detect the overall condition change of the beam, whereas the GW method is used to locate and quantify the damage. Experimental results show that the integrated method is efficient to detect and quantify local crack damage in steel structures and its influence on the global structure conditions

EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342)

Robust Feature-Preserving Mesh Denoising Based on Consistent Sub-Neighborhoods

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