The paper presents a new efficient method of introduction of plane wave source into finite difference time domain (FDTD) grids: the method of splitting the plane-wave FDTD (SP-FDTD). The new equations for the 1-D prop...
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In the paper, the method of splitting plane wave FDTD (SP-FDTD) algorithm, an efficient method of introducing plane wave source into finite difference time domain grids, is presented for solving oblique incident wave ...
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The finite difference time domain (FDTD) is a powerful method in electromagnetic simulation and also widely used in research of the metamaterials recently. In this paper a novel FDTD method based on split operator is ...
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The high-order finite-difference time-domain (HO-FDTD) technique is used in the simulation of ground-penetrating radar modeling in three dimensions (3-D), which can improve accuracy and reduce the error caused by nume...
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Tissue P systems are distributed parallel and non-deterministic computing models in the framework of membrane computing, which are inspired by intercellular communication and cooperation between neurons. Recently, cel...
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Tissue P systems are distributed parallel and non-deterministic computing models in the framework of membrane computing, which are inspired by intercellular communication and cooperation between neurons. Recently, cell separation is introduced into tissue P systems, which enables systems to generate an exponential workspace in a polynomial time. In this work, the computational power of tissue P systems with cell separation is investigated. Specifically, a uniform family of tissue P systems with cell separation is constructed for effciently solving a well-known NP-complete problem, the partition problem.
In the paper, the authors derive the method of splitting plane wave FDTD (SP-FDTD) method for initiation of plane wave sources in the total-field and scattered-field (TF/SF) formulation of 3D FDTD (2, 4) scheme. By sp...
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Complex networks are extensively studied in various areas such as social networks, biological networks, Internet and WWW. Those networks have many characters such as small-diameter, higher cluster and power-law degree...
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In this paper, the periodic structures are simulated by the split-field finite difference time domain (FDTD) method. By using this method, a set of auxiliary elements are introduced to represent the discretization of ...
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We present an unsupervised speaker identification system for personal annotations of conversations and meetings. The system dynamically learns new speakers and recognizes already known speakers using one audio channel...
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Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to ...
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Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to recover a low-rank higher-order representation of the given high dimensional data in the presence of outliers and missing entries, i.e., the so-called robust LRTF problem. The L1-norm LRTF is a popular strategy for robust LRTF due to its intrinsic robustness to heavy-tailed noises and outliers. However, few L1-norm LRTF algorithms have been developed due to its non-convexity and non-smoothness, as well as the high order structure of data. In this paper we propose a novel cyclic weighted median(CWM) method to solve the L1-norm LRTF problem. The main idea is to recursively optimize each coordinate involved in the L1-norm LRTF problem with all the others fixed. Each of these single-scalar-parameter sub-problems is convex and can be easily solved by weighted median filter, and thus an effective algorithm can be readily constructed to tackle the original complex problem. Our extensive experiments on synthetic data and real face data demonstrate that the proposed method performs more robust than previous methods in the presence of outliers and/or missing entries.
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