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IEEE International Conference on communications (ICC)

作者： Zengmao Chen Cheng-Xiang Wang Xuemin Hong John Thompson Sergiy A. Vorobyov Dongfeng Yuan Joint Research Institute of Signal and Image Processing Heriot-Watt University Edinburgh UK Joint Research Institute of Signal and Image Processing University of Edinburgh Edinburgh UK Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Information Science and Engineering Shandong University Jinan Shandong China

In this paper, we investigate the interference mitigation from a cross-layer perspective for a cognitive radio (CR) multiple-input multiple-output (MIMO) network coexisting with a primary time-division-duplexing (TDD) system. The channel allocation in the media access control (MAC) layer and a subspace-based precoding scheme in the physical layer of the CR network are jointly considered to minimise the interference to the primary user and maximise the CR throughput. Two distributed cross-layer algorithms, namely, joint iterative channel allocation and precoding (JICAP) and non-iterative channel allocation and precoding (NICAP), are proposed for the cases with and without channel information among CR nodes, respectively. Moreover, a channel estimation scheme is also proposed to enable the NICAP. The effectiveness of the proposed algorithms over non-cross-layer counterpart is demonstrated via simulations.

关键词： Channel allocation Downlink Interference channels Channel estimation Covariance matrix Physical layer

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SAR Image Classification Combining Structural and Statistical Methods

SAR Image Classification Combining Structural and Statistica...

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International IEEE Conference on signal-Image Technologies and Internet-Based System

作者： Narcisse Talla Tankam Janvier Fotsing Albert Dipanda Emmanuel Tonye Computer Science Department University Institute of Technology-University of Dschang (IUTFV-UDs) Bandjoun Cameroon LE2i ESIREM University of Burgundy Dijon France Lab of Electronics and Signal Processing (LETS) National High School of Engineering (ENSP-UYI) Yaounde Cameroon

The main objective of this paper is to develop a new technique of SAR image classification. This technique combines structural parameters, including the Sill, the slope, the fractal dimension and the range, with statistical methods in a supervised image classification. Thanks to the range parameter, we define the suitable size of the image window used in the proposed approach of supervised image classification. This approach is based on a new way of characterising different classes identified on the image. The first step consists in determining relevant area of interest. The second step consists in characterising each area identified, by a matrix. The last step consists in automating the process for image classification.

关键词： Image classification Fractals Mathematical model Statistical analysis Structural engineering Educational institutions Equations

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A mining technique using n-grams and motion transcripts for body sensor network data repository 10

A mining technique using n-grams and motion transcripts for ...

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Wireless Health 2010

作者： Loseu, Vitali Ghasemzadeh, Hassan Khan, Latifur R. Jafari, Roozbeh Embedded Systems and Signal Processing Lab. Department of Electrical Engineering University of Texas at Dallas Richardson TX 75080-3021 United States Department of Computer Science University of Texas at Dallas Richardson TX 75080-3021 United States

ISBN: (纸本)9781605589893

Recent years have seen a large influx of applications in the field of Body Sensor Networks (BSN). BSN, and in general wearable computers with sensors, can give researchers, users or clinicians access to tremendously valuable information extracted from data that were collected in users' natural environment. With this information, one can monitor the progression of a disease, identify its early onset or simply assess user's wellness. One major obstacle is managing repositories that store large amounts of BSN data. To address this issue, we propose a data mining approach for large BSN data repositories. We represent sensor readings with motion transcripts that maintain structural properties of the signal. To further take advantage of the signal's structure, we define a data mining technique using n-grams. We reduce overwhelmingly large number of n-grams via information gain (IG) feature selection. We report the effectiveness of our approach in terms of the speed of mining while maintaining an acceptable accuracy in terms of precision and recall. We demonstrate that the system can achieve average 99% precision with an average 100% recall on our pilot data with the help of only one transition for each movement. © 2010 ACM.

关键词： Data mining

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Wideband DOA estimation using two sensors

Wideband DOA estimation using two sensors

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International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010

作者： Gu, Jian-Feng Li, Nan-Jun Wei, Ping Tai, Heng-Ming Department of Electronic Engineering University of Electronic Science and Technology of China Chengdu Sichuan 610054 China Centre for Signal Processing and Communications Dept. of Electrical and Computer Engineering Concordia University Montreal QC H3G 1M8 Canada Department of Electrical Engineering University of Tulsa Tulsa OK 74104 United States

ISBN: (纸本)9780769539621

This paper proposes an efficient direction-of-arrival (DOA) estimation algorithm for wideband signals from data collected using two sensors. Like the traditional wideband methods, this algorithm first decomposes wideband sources into narrowband bins. At each frequency bin, the cross-correlation between sensors is estimated to construct a Toeplitz matrix, which contains all angle information. Thus the physical size between two sensors is independent of the frequency bins and relies more on the inter-frequency bin difference. The proposed method is flexible in the design of the sensor array without inducing ambiguities. Simulation results confirm the ability of the proposed method that provides reliable estimates in multiple wideband signal environments. © 2010 IEEE.

关键词： Array processing

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Spectrum sensing in low SNR regime via stochastic resonance

Spectrum sensing in low SNR regime via stochastic resonance

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44th Annual Conference on Information sciences and Systems, CISS 2010

作者： Zheng, Kun Li, Husheng Djouadi, Seddik M. Wang, Jun Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States National Key Lab. of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781424474172

Spectrum sensing is essential in cognitive radio to enable dynamic spectrum access. In many scenarios, primary user signal must be detected reliably in low signal-to-noise ratio (SNR) regime under required sensing time. We propose to use stochastic resonance, a nonlinear filter having certain resonance frequency, to detect primary users when the SNR is very low. Both block and sequential detection schemes are studied. Simulation results show that, under the required false alarm rate, both detection probability and average detection delay can be substantially improved. A few implementation issues are also discussed. ©2010 IEEE.

关键词： Cognitive radio

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Inter-cell interference coordination with adaptive frequency-reuse for VoIP and data traffic in downlink of 3GPP-LTE

Inter-cell interference coordination with adaptive frequency...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Zahra Bakhti Shahriar Shirvani Moghaddam Department of Electrical Engineering Tehran South Branch Islamic Azad University Tehran Iran Digital Communications Signal Processing (DCSP) Research Lab Department of Electrical and Computer Engineering Shahid Rajaee Teacher Training University Tehran Iran

One of the most important challenges is how to manage inter-cell interference. Special care has to be taken regarding voice traffic which has more strict requirements on delay and packet loss. The goal of inter-cell interference coordination schemes is to decrease inter-cell interference and get higher cell throughput, so we evaluated the performance of inter-cell interference coordination schemes containing reuse-one, reuse-three, reuse-one with power-planning, Soft frequency reuse and Fractional frequency reuse schemes. With considering high traffic load the best option regarding to the cell throughput what can be seen is the Fractional reuse with b=0.7 (b, percentage of reduced power sub-band). In the second step for interference control, adaptive reuse is proposed. The frequency reuse of the system can be changed in function of the proportion of voice users. When two traffic types are present at the same time reuse-one and reuse-three are the best schemes for data and voice, respectively. However the best scheme in performance for one traffic type is the worst for the other in general. The proposed algorithm gives a performance between reuse-one and reuse-three.

关键词： Interference Throughput OFDM signal to noise ratio Adaptation model Bandwidth Resource management

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Spectral characteristics of a piecewise linear function in modeling power amplifier type nonlinearities

Spectral characteristics of a piecewise linear function in m...

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IEEE International Symposium on Personal, Indoor and Mobile Radio communications (PIMRC)

作者： Mei Yen Cheong Stefan Werner Juan E. Cousseau Risto Wichman Department of Signal Processing & Acoustics School of Science and Technology Aalto University Aalto Finland CONICET-Department of Electrical and Computer Engineering Universidad Nacional del Sur Bahia Blanca Argentina

Nonlinear effects of a power amplifier (PA) in communications systems include spectral spreading, which causes adjacent channel interference. For predistorter design, it is crucial to employ a model that can sufficiently model the intermodulation distortion (IMD) caused by the PA. The order of IMD generated by a polynomial is determined by its highest order basis. For a piecewise linear function, the order of nonlinearity cannot be directly deduced from its basis function. This paper investigates how IMD is introduced by the simplicial canonical piecewise linear (SCPWL) function. We examine the operation imposed by the SCPWL basis function on sinusoid and multi-tone signal in the time domain and frequency domain. Our analysis results in expressions that can be used to predict the output spectrum of the SCPWL basis. The SCPWL basis operation involves spreading of the input signal spectrum. The output spectrum of each SCPWL basis includes infinite IMD components. The IMD order of the SCPWL model is not determined by the highest order basis. Instead, the number of SCPWL parameters, i.e., the number of linear affine segments that defines the function, determines how accurately it can model the IMD components.

关键词： Polynomials Computational modeling Manganese Fourier transforms Shape Convolution Frequency domain analysis

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Classification of hyperspectral images by using morphological attribute filters and independent component analysis

Classification of hyperspectral images by using morphologica...

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Workshop on Hyperspectral Image and signal processing

作者： Mura, Mauro Dalla Villa, Alberto Benediktsson, Jon Atli Chanussot, Jocelyn Bruzzonea, Lorenzo Department of Information Engineering and Computer Science University of Trento Via Sommarive 14 I-38123 Povo Trento Italy Faculty of Electrical and Computer Engineering University of Iceland Hjardarhaga 2-6 101 Reykjavik Iceland GIPSA-lab Signal and Image Dept. Grenoble Institute of Technology - INP 961 rue de la Houille Blanche 38402 Grenoble Cedex France

ISBN: (纸本)9781424489077

In this paper, a technique based on Independent Component Analysis (ICA) and morphological attribute filters is presented for the classification of high geometrical resolution hyperspectral images. The ICA is computed instead of the conventional principal component analysis (PCA) in order to better model the information in the hyperspectral image. The spatial characteristics of the objects in the scene are modeled by different multi-level attribute filters. Moreover, a method for increasing the robustness of the analysis based on a decision fusion strategy is proposed. A hyperspectral high resolution image acquired over the city of Pavia (Italy) was considered in the experiments. ©2010 IEEE.

关键词： Independent component analysis

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Exact Expressions for the Condition Number Distribution of Complex Wishart Matrices

Exact Expressions for the Condition Number Distribution of C...

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IEEE International Conference on communications (ICC)

作者： M. Matthaiou M. R. McKay P. J. Smith J. A. Nossek Institute of Circuit Theory and Signal Processing Technische Universität München Munich Germany Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China Department of Electrical and Computer Engineering University of Canterbury Christchurch New Zealand

The standard condition number (SCN) is a fundamental metric in the context of multiple-input multiple-output communication systems, linear detection and classical linear algebra. Hence, in this paper we propose a novel generic framework for the SCN distribution of three different classes of Wishart matrices which leads to new results and insights. In particular, our analysis covers both central and non-central Wishart distributions of arbitrary dimension and therefore is applicable to uncorrelated/semi-correlated Rayleigh fading and Ricean fading scenarios. For the special case of dual semi-correlated central Wishart matrices, we derive exact and asymptotic polynomial expressions for the SCN distributions. All analytical results are validated via Monte-Carlo simulations with the attained accuracy being excellent in all cases. The impact of the model parameters on channel conditioning is also investigated in detail.

关键词： MIMO Covariance matrix Eigenvalues and eigenfunctions Polynomials Context Rayleigh channels Probability density function communications Society Circuit theory signal processing Society

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Initialisation and training procedures for wavelet networks applied to chaotic time series

Engineering Intelligent Systems

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engineering Intelligent Systems 2010年第1期18卷 15-23页

作者： Alarcon-Aquino, V. Starostenko, O. Ramirez-Cortes, J.M. Gomez-Gil, P. Garcia-Treviño, E.S. Communications and Signal Processing Research Group Department of Computing Electronics and Mechatronics Universidad de Las Americas Puebla Sta. Catarina Mártir. Cholula Puebla. 72820 Mexico Department of Electronics Engineering Mexico Departmentof Computer Science National Institute for Astrophysics Optics and Electronics Tonantzintla Puebla Mexico

Wavelet networks are a class of neural network that take advantage of good localization properties of multi-resolution analysis and combine them with the approximation abilities of neural networks. This kind of networks uses wavelets as activation functions in the hidden layer and a type of back-propagation algorithm is used for its learning. However, the training procedure used for wavelet networks is based on the idea of continuous differentiable wavelets and some of the most powerful and used wavelets do not satisfy this property. In this paper we report an algorithm for initialising and training wavelet networks applied to the approximation of chaotic time series. The proposed algorithm which has its foundations on correlation analysis of signals allows the use of different types of wavelets, namely, Daubechies, Coiflets, and Symmlets. To show this, comparisons are made for chaotic time series approximation between the proposed approach and the typical wavelet network. © 2010 CRL Publishing Ltd.

关键词： Multiresolution analysis

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