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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Yuan Fu Jin Guo-ping Zheng Lin-hua Ding Hong School of Electronic Science and Engineering National University of Defense Technology Beijing Global Information Centre of Application & Exploration

ISBN: (纸本)9781467321969

Resource allocation for relay assisted cognitive radio network was investigated in this paper. Interference temperature limits of corresponding primary users were considered. An optimal joint channel and power allocation for a centralized scenario was presented in this work. generalized expectation maximization (GEM) theory based optimization algorithm was presented to settle the non-convex power allocation problem. It broke the non-convex problem into two convex sub-problems and obtained the solutions iteratively. Numerical results are given to verify the availability of the power allocation algorithm. It shows that the end to end throughput can be enhanced by joint channel and power allocation. It also shows that channel allocation is more efficient than power allocation. An interesting phenomenon is found that the most efficient resource allocation scheme varies with interference temperature limits of primary users.

关键词： cognitive radio joint resource allocation relay generalized expectation maximization

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MULTICHANNEL NONNEGATIVE TENSOR FACTORIZATION WITH STRUCTURED CONSTRAINTS FOR USER-GUIDED AUDIO SOURCE SEPARATION

MULTICHANNEL NONNEGATIVE TENSOR FACTORIZATION WITH STRUCTURE...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ozerov, Alexey Fevotte, Cedric Blouet, Raphael Durrieu, Jean-Louis INRIA Campus Beaulieu F-35042 Rennes France

ISBN: (纸本)9781457705397

Separating multiple tracks from professionally produced music recordings (PPMRs) is still a challenging problem. We address this task with a user-guided approach in which the separation system is provided segmental information indicating the time activations of the particular instruments to separate. This information may typically be retrieved from manual annotation. We use a so-called multichannel nonnegative tensor factorization (NTF) model, in which the original sources are observed through a multichannel convolutive mixture and in which the source power spectrograms are jointly modeled by a 3-valence (time/frequency/source) tensor. Our user-guided separation method produced competitive results at the 2010 Signal Separation Evaluation Campaign, with sufficient quality for real-world music editing applications.

关键词： Audio source separation user-guided nonnegative tensor factorization generalized expectation maximization

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Asynchronous Hybrid Maximum Likelihood Classification of Linear Modulations

Asynchronous Hybrid Maximum Likelihood Classification of Lin...

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IEEE Global Communications Conference (GLOBECOM)

作者： Ozdemir, Onur Varshney, Pramod K. Su, Wei Boston Fus Corp Burlington MA USA Syracuse Univ Dept EECS Syracuse NY USA Army CERDEC Aberdeen Proving Ground MD USA ANDRO Computat Solut LLC Rome NY USA

ISBN: (纸本)9781479913534

In this paper, we consider the problem of linear modulation classification in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) approach based on a generalized expectation maximization (GEM) algorithm [1]. Our approach is applicable to all QAM and PSK modulations, and it does not require any assumptions on the received signal-to-noise ratio (SNR). The GEM algorithm provides a tractable procedure to obtain maximum likelihood (ML) estimates which are extremely hard to obtain otherwise. Moreover, our approach employs only a small number of samples (in the order of hundreds) to perform both time and phase synchronization, signal power estimation, followed by modulation classification. The proposed approach also enables maximum a posteriori (MAP) decoding of the unknown constellation symbol sequence as a by-product of the GEM algorithm. We provide simulation results that show that the proposed approach provides excellent classification performance.

关键词： Modulation classification time offset hybrid maximum likelihood generalized expectation maximization

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Noise-boosted recurrent backpropagation

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NEUROCOMPUTING 2023年 559卷

作者： Adigun, Olaoluwa Kosko, Bart Univ Southern Calif Signal & Image Proc Inst Dept Elect & Comp Engn Los Angeles CA 90089 USA

A statistical formulation of recurrent backpropagation (RBP) allows direct noise boosting for time-varying classification and regression. The noise boost reduces training iterations and improves accuracy. The injected noise is just that noise that makes the current signal more probable. This noise-boost result extends the two recent results that backpropagation is a special case of the generalized expectation maximization (EM) algorithm and that careful noise injection can always speed the average convergence of the EM algorithm to a local maximum of the log-likelihood surface. The noise-benefit conditions differ for additive and multiplicative noise in RBP. We tested noise-boosted RBP classifiers on 11 classes of sports video clips and tested RBP regressors on predicting the dollar-rupee exchange rate. Injecting noisy-EM (NEM) noise outperformed injecting blind noise or injecting no noise at all. Additive NEM noise usually outperformed multiplicative noise. The best case of NEM noise injection with RBP training of a recurrent neural classification model speeded up its training by 60% and improved its classification accuracy by 9.51% compared with noiseless RBP training and accuracy. The best performance of the NEM noise with the RBP training of a recurrent neural regression model yielded a 38% speedup in training and also reduced the squared error by 49.3%. The injection of the additive NEM noise in the output and hidden neurons performed best.

关键词： Recurrent backpropagation Recurrent neural network generalized expectation maximization Long short-term memory Gated recurrent unit NEM noise

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Cluster-weighted modeling with measurement error in covariates

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2024年第24期53卷 8916-8928页

作者： Zarei, Shaho Univ Kurdistan Fac Sci Dept Stat Sanandaj Iran

The cluster-weighted model (CWM) is a model-based clustering approach that utilizes a mixture of regression models to cluster data points based on both a response variable Y and covariates X, where the covariates are assumed to be random. The Gaussian CWM(GCWM) is the most commonly used member of the CWM family, where the Gaussian distribution is adopted for both the covariates and the response given the covariates. In mixture of regression, assignment of data points to the clusters is based on the conditional distribution of the response variable given covariates and is independent of the covariates' distribution. In CWM, to increase clustering performance, the covariates' distribution is also used to assign data points to the clusters. Existing researches on CWMs are limited to the directly observed covariates, which may not reflect real-world scenarios where measurement errors (MEs) occur. The measurement error can lead to inconsistent estimates, consequently, produce spurious or obscure clusters. In this article, we assume that random covariates X are latent, observed with an independent ME that has the Gaussian distribution. A new generalized expectationmaximization algorithm is defined for estimating model parameters. The performance of the proposal is illustrated and compared with the GCWM using both simulated and real data.

关键词： Cluster-weighted model measurement error generalized expectation maximization mixture of regression

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Three-dimensional FLASH laser radar range estimation via blind deconvolution

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JOURNAL OF APPLIED REMOTE SENSING 2010年第1期4卷 043517-043517页

作者： McMahon, Jason R. Martin, Richard K. Cain, Stephen C. USAF Inst Technol Grad Sch Engn & Management Wright Patterson AFB OH 45433 USA

Three dimensional (3D) FLASH Laser Radar (LADAR) sensors are unique due to the ability to rapidly acquire a series of two dimensional remote scene data (i.e. range images). Principal causes of 3D FLASH LADAR range estimation error include spatial blur, detector blurring, noise, timing jitter, and inter-sample targets. Unlike previous research, this paper accounts for pixel coupling by defining the range image mathematical model as a 2D convolution between the system spatial impulse response and the object (target or remote scene) at a particular point in time. Using this model, improved range estimation is possible by object restoration from the data observations. Object estimation is performed by deriving a blind deconvolution generalized expectation maximization (GEM) algorithm with the range determined from the estimated object by a normalized correlation method. Theoretical derivations and simulation results are verified with experimental data of a bar target taken from a 3D FLASH LADAR system in a laboratory environment. Simulation examples show that the GEM improves range estimation over the unprocessed data and a Wiener filter method by 75% and 26% respectively. In the laboratory experiment, the GEM improves range estimation by 34% and 18% over the unprocessed data and Wiener filter method respectively.

关键词： 3D FLASH LADAR laser radar range estimation blind deconvolution object restoration waveform processing generalized expectation maximization

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Accurate vision-based displacement and vibration analysis of bridge structures by means of an image-assisted total station

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ADVANCES IN MECHANICAL ENGINEERING 2018年第6期10卷

作者： Omidalizarandi, Mohammad Kargoll, Boris Paffenholz, Jens-Andre Neumann, Ingo Leibniz Univ Hannover Geodet Inst Nienburger Str 1 D-30167 Hannover Germany

Today, short- and long-term structural health monitoring of bridge infrastructures and their safe, reliable and cost-effective maintenance have received considerable attention. For this purpose, image-assisted total station (here, Leica Nova MS50 MultiStation) as a modern geodetic measurement system can be utilized for accurate displacement and vibration analysis. The Leica MS50 measurements comprise horizontal angles, vertical angles and distance measurements in addition to the captured images or video streams with practical sampling frequency of 10Hz using an embedded on-axis telescope camera. Experiments were performed for two case studies under (1) a controlled laboratory environment and (2) a real-world situation observing a footbridge structure using a telescope camera of the Leica MS50. Furthermore, two highly accurate reference measurement systems, namely, a laser tracker Leica AT960-LR and a portable shaker vibration calibrator 9210D in addition to the known natural frequencies of the footbridge structure calculated from the finite element model analysis are used for validation. The feasibility of an optimal passive target pattern and its accurate as well as reliable detection at different epochs of time were investigated as a preliminary step. Subsequently, the vertical angular conversion factor of the telescope camera of the Leica MS50 was calibrated, which allows for an accurate conversion of the derived displacements from the pixel unit to the metric unit. A linear regression model in terms of a sum of sinusoids and an autoregressive model of the coloured measurement noise were employed and solved by means of a generalized expectation maximization algorithm to estimate amplitudes and frequencies with high accuracy. The results show the feasibility of the Leica MS50 for the accurate displacement and vibration analysis of the bridge structure for frequencies less than 5Hz.

关键词： Image-assisted total stations calibration autoregressive model generalized expectation maximization displacement and vibration analysis footbridge monitoring

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Asynchronous Hybrid Maximum Likelihood Classification of Linear Modulations

Asynchronous Hybrid Maximum Likelihood Classification of Lin...

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IEEE Global Communications Conference

作者： Onur Ozdemir Pramod K. Varshney Wei Su Boston Fusion Corp. Department of EECS Syracuse University Army CERDEC Aberdeen Proving Ground

ISBN: (纸本)9781479913510

In this paper, we consider the problem of linear modulation classification in the presence of unknown time off-set, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) approach based on a generalized expectation maximization (GEM) algorithm [1]. Our approach is applicable to all QAM and PSK modulations, and it does not require any assumptions on the received signal-to-noise ratio (SNR). The GEM algorithm provides a tractable procedure to obtain maximum likelihood (ML) estimates which are extremely hard to obtain otherwise. Moreover, our approach employs only a small number of samples (in the order of hundreds) to perform both time and phase synchronization, signal power estimation, followed by modulation classification. The proposed approach also enables maximum a posteriori (MAP) decoding of the unknown constellation symbol sequence as a by-product of the GEM algorithm. We provide simulation results that show that the proposed approach provides excellent classification performance.

关键词： Modulation classification Time offset Hybrid maximum likelihood generalized expectation maximization generalized EM algorithm linear modulation maximum likelihood signal to noise ratios Taxonomy Quadrature amplitude modulation

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