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97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Zhu, Qian Ni, Rui Yang, Ganghua Lie, Qiang Cent Res Inst Wireless Technol Lab Shenzhen Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen Peoples R China

ISBN: (纸本)9798350311143

The phenomena of superdirectivity is explored for subwavelength spaced antenna subarrays. Then a larger multi-dimensional antenna array is composed to achieve higher directivity and better beamforming performance. Different methods for optimizing directivity, realized gain and total efficiency of arbitrary antenna array are investigated and applied in the design of such multi-dimensional array. All the results in this paper are verified by commercial full-wave simulator.

关键词： superdirective antenna multi-dimensional array three-dimensional array mutual coupling convex optimization

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Construction of multi-dimensional Periodic Complementary array Sets

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2010年第7期E93A卷 1392-1395页

作者： Zeng, Fanxin Zhang, Zhenyu Chongqing Commun Inst Dept Informat Engn Chongqing 400035 Peoples R China Chongqing Univ Dept Commun Engn Chongqing 400044 Peoples R China

multi-dimensional (MD) periodic complementary array sets (CASs) with impulse-like MD periodic autocorrelation function are naturally generalized to (one dimensional) periodic complementary sequence sets, and such array sets are widely applied to communication, radar, sonar, coded aperture imaging, and so forth. In this letter, based on multi-dimensional perfect arrays (MD PAs), a method for constructing MD periodic CASs is presented, which is carried out by sampling MD PAs. It is particularly worth mentioning that the numbers and sizes of sub-arrays in the proposed MD periodic CASs can be freely changed within the range of possibilities. In particular, for arbitrarily given positive integers M and L, two-dimensional periodic polyphase CASs with the number M-2 and size L x L of sub-arrays can be produced by the proposed method. And analogously, pseudo-random MD periodic CASs can be given when pseudo-random MD arrays are sampled. Finally, the proposed method's validity is made sure by a given example.

关键词： multi-dimensional array periodic complementary array set perfect array impulsive periodic autocorrelation function

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Regularized matrix regression

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2014年第2期76卷 463-483页

作者： Zhou, Hua Li, Lexin N Carolina State Univ Raleigh NC 27695 USA

Modern technologies are producing a wealth of data with complex structures. For instance, in two-dimensional digital imaging, flow cytometry and electroencephalography, matrix-type covariates frequently arise when measurements are obtained for each combination of two underlying variables. To address scientific questions arising from those data, new regression methods that take matrices as covariates are needed, and sparsity or other forms of regularization are crucial owing to the ultrahigh dimensionality and complex structure of the matrix data. The popular lasso and related regularization methods hinge on the sparsity of the true signal in terms of the number of its non-zero coefficients. However, for the matrix data, the true signal is often of, or can be well approximated by, a low rank structure. As such, the sparsity is frequently in the form of low rank of the matrix parameters, which may seriously violate the assumption of the classical lasso. We propose a class of regularized matrix regression methods based on spectral regularization. A highly efficient and scalable estimation algorithm is developed, and a degrees-of-freedom formula is derived to facilitate model selection along the regularization path. Superior performance of the method proposed is demonstrated on both synthetic and real examples.

关键词： Electroencephalography multi-dimensional array Nesterov method Nuclear norm Spectral regularization Tensor regression

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Covariate-Adjusted Tensor Classification in High Dimensions

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2019年第527期114卷 1305-1319页

作者： Pan, Yuqing Mai, Qing Zhang, Xin Florida State Univ Dept Stat Tallahassee FL 32306 USA

In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e., multi-dimensional array) and additional covariates. Motivated by applications in science and engineering, we propose a comprehensive and interpretable discriminant analysis model, called the CATCH model (short for covariate-adjusted tensor classification in high-dimensions). The CATCH model efficiently integrates the covariates and the tensor to predict the categorical outcome. It also jointly explains the complicated relationships among the covariates, the tensor predictor, and the categorical response. The tensor structure is used to achieve easy interpretation and accurate prediction. To tackle the new computational and statistical challenges arising from the intimidating tensor dimensions, we propose a penalized approach to select a subset of the tensor predictor entries that affect classification after adjustment for the covariates. An efficient algorithm is developed to take advantage of the tensor structure in the penalized estimation. Theoretical results confirm that the proposed method achieves variable selection and prediction consistency, even when the tensor dimension is much larger than the sample size. The superior performance of our method over existing methods is demonstrated in extensive simulated and real data examples.

关键词： Discriminant analysis multicategory classification multi-dimensional array Sparsity Tensor classification and regression

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Hyperdeterminantal point processes

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METRIKA 2009年第2-3期69卷 85-99页

作者： Evans, Steven N. Gottlieb, Alex Univ Calif Berkeley Dept Stat 3860 Berkeley CA 94720 USA Wolfgang Pauli Inst Fac Math A-1090 Vienna Austria

As well as arising naturally in the study of non-intersecting random paths, random spanning trees, and eigenvalues of random matrices, determinantal point processes (sometimes also called fermionic point processes) are relatively easy to simulate and provide a quite broad class of models that exhibit repulsion between points. The fundamental ingredient used to construct a determinantal point process is a kernel giving the pairwise interactions between points: the joint distribution of any number of points then has a simple expression in terms of determinants of certain matrices defined from this kernel. In this paper we initiate the study of an analogous class of point processes that are defined in terms of a kernel giving the interaction between 2M points for some integer M. The role of matrices is now played by 2M-dimensional "hypercubic" arrays, and the determinant is replaced by a suitable generalization of it to such arrays-Cayley's first hyperdeterminant. We show that some of the desirable features of determinantal point processes continue to be exhibited by this generalization.

关键词： Fermionic point process Determinant Permanent multi-dimensional array Hypercubic array Tensor Hyperdeterminant Symmetric group Factorial moment

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Spar: a set of extensions to Java for scientific computation

Spar: a set of extensions to Java for scientific computation

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ACM 2001 Java Grande/International Symposium on Computing in Object-Oriented Parallel Environments

作者： van Reeuwijk, C Kuijlman, F Sips, HJ Delft Univ Technol NL-2628 CD Delft Netherlands

ISBN: (纸本)1581133596

We present a set of language extensions that improve the expressiveness and performance of Java for scientific computation. The language extensions allow the manipulation of multi-dimensional arrays to be expressed more naturally, and to be implemented more efficiently. Furthermore, data-parallel programming is supported, allowing efficient parallelization of a large class of operations on arrays. We also provide language extensions to construct specialized array representations, such as symmetric, block, and sparse matrices. These extensions are: tuples, parameterized types, array subscript overloading, and the inline modifier. These extensions are not only useful in the construction of special array representations, but are also useful in their own right. Finally, we add complex numbers as a primitive type to the language. We evaluate our language extensions using performance results. We also compare relevant code fragments of our extended language with standard Java implementations and language extensions proposed by others. Copyright (C) 2003 John Wiley Sons, Ltd.

关键词： Java multi-dimensional array tuple parameterized type

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TOWARDS A FRAMEWORK FOR OFFERING REMOTE SENSING DATA IN AN ANALYSIS-READY FORMAT 38

TOWARDS A FRAMEWORK FOR OFFERING REMOTE SENSING DATA IN AN A...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhao, Jianghua Wang, Xuezhi Zhou, Yuanchun Qin, Qiming Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Peking Univ Beijing 100871 Peoples R China

ISBN: (纸本)9781538671504

Diverse storage formats, archive dispersal, and inconsistent naming make it difficult for researchers and the general public to find and access remote sensing data. To facilitate the use of remote sensing data, this paper provides an integrated framework for direct reading remote sensing data in a widely compatible and analysis-ready format, NumPy ndarray. The framework is composed of two main components. One is the raster data processing and storage model. All the operational gridded remote sensing data are split into tiles, and reorganized in n-dimensional array. Then the N-dimensional data array is serialized into netCDF and stored into distributed file system. The other is the spatiotemporal filter to achieve parallel query, and it has been encapsulated into Internet-accessible application programming interfaces (APIs). The scenario of calculating NDVI of a specified spatiotemporal range given at last illustrate the efficiency and convenience of our platform provided for remote sensing data analysis.

关键词： remote sensing data spatiotemporal retrieval analysis-ready format multi-dimensional array

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Incremental Window Aggregates over array Database 2

Incremental Window Aggregates over Array Database

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IEEE International Conference on Big Data

作者： Jiang, Li Kawashima, Hideyuki Tatebe, Osamu Univ Tsukuba Grad Sch Syst & Informat Engn Tsukuba Ibaraki Japan Univ Tsukuba Fac Informat Syst & Engn Tsukuba Ibaraki Japan

ISBN: (纸本)9781479956661

We propose an efficient window aggregation method over multi-dimensional array data based on incremental computation. We improve several aggregations with different data structures exploited to achieve efficient computation: list for sum and avg, heap for max and min, and balanced binary search tree for percentile. We present time complexity analysis for the methods, and then evaluate performance with experiments in SciDB array database system with both synthetic and JRA55 meteorological dataset. Our analysis shows that performance improvement is proportional to the window size in the last dimension in theory, and the result of experiment is consistent with the analysis. In certain cases, it shows an acceleration factor more than 13 by the proposed method with percentile, while a factor over 28 with maximum.

关键词： Window Aggregates multi-dimensional array Incremental Computation array Database

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Spar: a set of extensions to Java for scientific computation

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2003年第3-5期15卷 277-297页

作者： van Reeuwijk, C Kuijlman, F Sips, HJ Delft Univ Technol NL-2628 CD Delft Netherlands

关键词： Java multi-dimensional array tuple parameterized type

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TOWARDS A FRAMEWORK FOR OFFERING REMOTE SENSING DATA IN AN ANALYSIS-READY FORMAT

TOWARDS A FRAMEWORK FOR OFFERING REMOTE SENSING DATA IN AN A...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Jianghua Zhao Xuezhi Wang Yuanchun Zhou Qiming, Qin University of Chinese Academy of Sciences Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China Peking University Beijing China

关键词： Remote sensing data Spatiotemporal retrieval Analysis-ready format multi-dimensional array

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