The conventional Bayesian framework of filtering is based on the assumption that the measurements are available at each time-step without any delay. But in real-life problems, measurements may be randomly delayed in t...
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ISBN:
(纸本)9781509017966
The conventional Bayesian framework of filtering is based on the assumption that the measurements are available at each time-step without any delay. But in real-life problems, measurements may be randomly delayed in time. In this paper, we modified the unscented Kalman filter (UKF) for arbitrary time delayed measurements. With the help of simulation results, it has been shown that the proposed filter provides more accurate estimation compared to the ordinary UKF in presence of randomly delayed measurements.
In this paper, we propose a cross-layer power allocation scheme over wireless relay networks for quality-of-service(QoS) guarantees. We formulate our original throughput maximization problem into effective capacity ma...
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In this paper, we propose a cross-layer power allocation scheme over wireless relay networks for quality-of-service(QoS) guarantees. We formulate our original throughput maximization problem into effective capacity maximization problem by applying information theory and the concept of the effective capacity. In our scheme, we focus on full duplex mode and amplify-and-forward(AF) protocol. In particular, our proposed scheme derives closed-form expressions and analyzes the impacts of the SNR of the interference channel on the performance of full duplex relaying system. For comparison purpose, we also give the analysis of half duplex relaying system. Simulation results show that our proposed power allocation scheme can support diverse QoS guarantees and achieve better effective capacity than equal power allocation scheme and direct transmission scheme. Our analysis also indicate that the perfect full duplex mode can achieve twice optimal effective capacity of the half duplex mode.
Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorith...
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Stereoscopic 3D (S3D) image color correction is a major issue in the field of image processing. However, existing color correction algorithms have limitations. Global color correction algorithms cannot handle local co...
Stereoscopic 3D (S3D) image color correction is a major issue in the field of image processing. However, existing color correction algorithms have limitations. Global color correction algorithms cannot handle local color discrepancies, and local color correction algorithms are sensitive to matching quality between reference and target images. In this study, we propose an S3D image color correction algorithm that combines global and local color information to correct color discrepancies between S3D images. Sparse feature matching usually generates only a few matching features, producing error correction results in some local regions. Our algorithm uses dense stereo matching and global color correction algorithms to initialize color values, and improves the local color smoothness and global color consistency of the resulting image, while maintaining the initial color in that image as much as possible. Experimental results show that our algorithm performs better than do five state-of-the-art color correction algorithms.
This paper surveys research on the Resource Space Model RSM. RSM is a classification-based, multi-dimensional and content-based space model for efficiently and effectively managing various resources. As a non-relation...
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This paper surveys research on the Resource Space Model RSM. RSM is a classification-based, multi-dimensional and content-based space model for efficiently and effectively managing various resources. As a non-relational data model, it has a rather complete theoretical basis and has significant applications in faceted search and the future cyber-physical society. Applications in picture resources and email resources are introduced.
The pilot contamination is caused by non-orthogonal pilot sequences reuse in uplink, which affects the performance of Massive MIMO systems seriously, so it is necessary to mitigate pilot contamination. In this paper, ...
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The pilot contamination is caused by non-orthogonal pilot sequences reuse in uplink, which affects the performance of Massive MIMO systems seriously, so it is necessary to mitigate pilot contamination. In this paper, we propose a pilot contamination precoding scheme to mitigate multi-cell pilot contamination. In the uplink, we propose a cell-defined training scheme, where the same pilot sequence is used in the same cell, and different cells use orthogonal pilot sequences, which eliminates inter-cell interference and introduces intra-cell interference artificially. In the downlink, we adopt Truncated Polynomial Expansion (TPE) precoding to reduce intra-cell interference, since the truncated polynomial of TPE precoding can replace the matrix inversion of Regularized Zero-Forcing (RZF) precoding, which reduces the complexity of RZF precoding and approximates the performance of RZF precoding by suitable truncation orders. Simulation results show the effectiveness of the proposed scheme.
Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected p...
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Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is non-trivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.
In this paper spectral Galerkin approximation of optimal control problem governed by fractional elliptic equation is *** deal with the nonlocality of fractional Laplacian operator the Caffarelli-Silvestre extension is...
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In this paper spectral Galerkin approximation of optimal control problem governed by fractional elliptic equation is *** deal with the nonlocality of fractional Laplacian operator the Caffarelli-Silvestre extension is *** first order optimality condition of the extended optimal control problem is derived.A spectral Galerkin discrete scheme for the extended problem based on weighted Laguerre polynomials is developed.A priori error estimates for the spectral Galerkin discrete scheme is *** experiments are presented to show the effectiveness of our methods and to verify the theoretical findings.
A novel compact broadband circularly polarized (CP) antenna is proposed for use in the Global Navigation Satellite System (GNSS). Circular polarization for the presented antenna is achieved by introducing two L-shaped...
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ISBN:
(纸本)9781538616093
A novel compact broadband circularly polarized (CP) antenna is proposed for use in the Global Navigation Satellite System (GNSS). Circular polarization for the presented antenna is achieved by introducing two L-shaped branches in the ground plane and inverted L-shaped microstrip fed. The antenna exhibits a wide impedance bandwidth of 44.9% (1.14-1.8GHz) for reflection coefficient<-10dB and an axial ratio (AR) bandwidth of 36.9% (1.15-1.67GHz) for AR<3dB. The CP bandwidth (reflection coefficient<-10dB and AR<3dB) can cover all GNSS frequency bands. Although simple in structure, the antenna can meet the requirements for handheld wireless terminals.
It is well known that a triangle can be divided by mid-point refinement into four sub-triangles with the same shape. Similarly, a tetrahedron can be parted into eight subtetrahedra, which are generally not uniform in ...
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It is well known that a triangle can be divided by mid-point refinement into four sub-triangles with the same shape. Similarly, a tetrahedron can be parted into eight subtetrahedra, which are generally not uniform in shape. This paper proves that there exist a set of tetrahedra, which is called isometrically subdivisible tetrahedra(IST) and can be divided into eight isometric subtetrahedra, including identical and reflection ones. And a new classification of tetrahedra is put forward, based on which all tetrahedra can be categorized into 26 classes according to both the number of maximum equal edges and topological relations. The IST belongs only to three of the classes. That result provides a new viewpoint of spatial structure and may be used to tile or subdivide space uniformly or isometrically.
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