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检索条件"主题词=Expectation maximization algorithm"

共 648 条记录，以下是321-330 订阅

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AN IMPROVED COMPRESSIVE SENSING algorithm BASED ON SPARSE BAYESIAN LEARNING FOR RFPA RADAR

AN IMPROVED COMPRESSIVE SENSING ALGORITHM BASED ON SPARSE BA...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Ju Zhao, Yi Shan, Bingqi Zhong, Yi School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781839539954

The theory of Compressive Sensing (CS) has garnered significant attention in recent years due to its distinct advantages in mitigating the high sidelobe of Random Frequency and Pulse Repetition Interval Agile (RFPA) radar systems. Especially those CS algorithms based on Sparse Bayesian Learning (SBL), have played a pivotal role in enhancing signal recovery performance. Nevertheless, it is crucial to recognize that these algorithms possess certain limitations for RFPA radar, particularly in scenarios involving closely spaced targets, such as restricted dynamic range for target detection, slow convergence rates, and high computational complexity. To address these issues, this paper presents an improved SBL-based CS algorithm for RFPA radar systems. Specifically, the proposed algorithm enhances signal sparsity through the selection of a prior distribution, thereby improving its capability to detect between weak and strong targets. Additionally, the algorithm combines the expectation-maximization (EM) algorithm with a fixed-point update strategy, efficiently utilizing diagonal elements to expedite convergence while reducing computational complexity. Simulation results demonstrate that, in scenarios involving closely spaced targets, the proposed algorithm can effectively mitigate the masking of weak targets by strong target echoes, while exhibiting accelerated convergence with reduced computational overhead. © The Institution of Engineering & Technology 2023.

关键词： expectation maximization algorithm

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A direct method for the estimation of the sparse and latent variable components of a Gaussian graphical model 10

A direct method for the estimation of the sparse and latent ...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Ondrusi, Martin Cribben, Ivor Alberta Sch Business Edmonton AB Canada

ISBN: (纸本)9798350373981;9798350373974

Graphical models have been extensively used in, and provided novel insights across, a wide variety of research areas. Most current graph estimation methods assume a sparse structure. However, a sparse-only assumption is limiting and unrealistic in practice, hence sparse and low-rank forms have been suggested. In this work, we propose a new estimation method called sparse low-rank inverse covariance estimation (SLICE), which is a direct approach to jointly estimate the sparse and latent components of a Gaussian graphical model. SLICE utilizes a simple regularization term to ensure accurate estimates that are congruent with the covariance matrix. We derive an efficient and generalizable pseudo expectation maximization (EM) algorithm to solve for this estimator. Simulation and neuroimaging studies demonstrate the improvement of our SLICE approach over current state-of-the-art methods.

关键词： expectation maximization algorithm

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Data-Dependent Generalization Bounds via Variable-size Compressibility

Data-Dependent Generalization Bounds via Variable-size Compr...

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IEEE International Symposium on Information Theory (ISIT)

作者： Sefidgaran, Milad Zaidi, Abdellatif Huawei Technol France Paris Res Ctr Boulogne France Univ Gustave Eiffel Champs Sur Marne France

ISBN: (纸本)9798350382853;9798350382846

In this paper, we establish novel data-dependent upper bounds on the generalization error through the lens of a "variable-size compressibility" framework that we introduce newly here. In this framework, the generalization error of an algorithm is linked to a variable-size 'compression rate' of its input data. This is shown to yield bounds that depend on the value of the input training data sample at hand, rather than on its unknown distribution. Our new generalization bounds that we establish are tail bounds, tail bounds on the expectation, and in-expectations bounds. Moreover, it is shown that our framework also allows to derive general bounds on any function of the input data and output hypothesis random variables. In particular, these general bounds are shown to subsume and possibly improve over several existing PAC-Bayes and data-dependent intrinsic dimension-based bounds that are recovered as special cases, thus unveiling a unifying character of our approach.

关键词： expectation maximization algorithm

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CLASSIFICATION OF COMPLEX PEDESTRIAN ACTIVITIES FROM TRAJECTORIES

CLASSIFICATION OF COMPLEX PEDESTRIAN ACTIVITIES FROM TRAJECT...

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IEEE International Conference on Image Processing

作者： Nascimento, Jacinto C. Marques, Jorge S. Figueiredo, Mario A. T. Inst Super Tecn Inst Sistemas & Robot P-1049001 Lisbon Portugal Inst Super Tecn Inst Telecommun P-1049001 Lisbon Portugal

ISBN: (纸本)9781424479948

We propose a method to classify human trajectories, modeled by a set of motion vector fields, each tailored to describe a specific motion regime. Trajectories are modeled as being composed of segments corresponding to different motion regimes, each generated by one of the underlying motion fields. Switching among the motion fields follows a probabilistic mechanism, described by a field of stochastic matrices. This yields a space-dependent motion model which can be estimated using an expectation-maximization (EM) algorithm. To address the model selection question (how many fields to use?), we adopt a discriminative criterion based on classification accuracy on a held out set. Experiments with real data (human trajectories in a shopping mall) illustrate the ability of the proposed approach to classify complex trajectories into high level classes (client versus non-client).

关键词： complex pedestrian activity expectation maximization algorithm expectation-maximisation algorithm human trajectory classification image classification motion estimation motion vector field probabilistic mechanism space dependent motion model stochastic matrices video surveillance

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Embedded One-Class Classification on RF Generator using Mixture of Gaussians

Embedded One-Class Classification on RF Generator using Mixt...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Bowen, Ryan M. Sahin, Ferat Radomski, Aaron Sarosky, Dan Rochester Inst Technol Microsyst Engn Dept Rochester NY 14623 USA MKS ENI Prod Elect & Microelect Engn Dept Rochester NY USA MKS ENI Prod Adv Dev Grp Rochester NY USA

ISBN: (纸本)9781479938407

In this paper we apply a specific machine learning technique for classification of normal and not-normal operation of RF (Radio Frequency) power generators. Pre-processing techniques using FFT and bandpower convert time-series system signatures into single feature vectors. These feature vectors are modeled using k-component Mixture of Gaussians (MoG) where components and corresponding parameters are learned using the expectation maximization (EM) algorithm. Data is obtained from three different generator models operating under normal and multiple different not-normal conditions. Exploration into algorithmic parameter effects is conducted and empirical evidence used to select sub-optimum parameters. Robust testing is reported to achieve a 3 sigma classification accuracy of 95.91% for the targeted RF generator. Additionally, a custom C++ library is implemented to utilize the learned model for accurate classification of time-series data within an embedded environment such as a RF generator. The embedded implementation is reported to have a small storage footprint, reasonable memory consumption and overall fast execution time.

关键词： Gaussian processes electronic engineering computing embedded systems learning (artificial intelligence) mixture models pattern classification radiofrequency integrated circuits EM FFT MoG RF power generators bandpower convert time-series system signatures custom C++ library embedded environment embedded one-class classification expectation maximization algorithm machine learning technique mixture of Gaussians normal operation not-normal operation radio frequency single feature vectors time-series data Accuracy Data models Generators Radio frequency Robustness Training Vectors radiofrequency rf generator expectation-maximisation algorithm time series data electronic engineering computing Radio frequency integrated circuits Fast Fourier transform data models normal operation dynamos Gaussian processes Pattern recognition Gauss Embedded systems Functional training

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An EM algorithm-Based Channel Estimation for OFDM Amplify-and-Forward Relaying Systems

An EM Algorithm-Based Channel Estimation for OFDM Amplify-an...

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2010 IEEE International Conference on Communications

作者： Sheu, Jeng-Shin Sheen, Wern-Ho Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu Yunlin Taiwan Chaoyang Univ Technol Dept Informat & Commun Engn Taichung 41349 Taiwan

ISBN: (纸本)9781424464043

The issue of channel estimation is investigated for the orthogonal frequency-division multiplexing (OFDM) relaying system with amplify-and-forward (AF) operation mode. Channel estimation in the AF mode is unique in that both the source-to-relay and relay-to-destination channels are needed at the destination in order to perform optimum combining of the received signals from the direct (source-to-destination) and relay paths if a cooperative diversity transmission is employed. Until now, however, there have been only literatures to study the estimation of the composite channel of the relay path. In this work, an iterative expectation-maximization (EM) algorithm-based channel estimation is proposed for respective estimations of the source-to-relay and relay-to-destination links at the destination. Computer simulations show that the iterative channel estimator performs satisfactorily and converges rapidly.

关键词： Channel estimation amplify-and-forward OFDM relaying systems expectation maximization algorithm

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First-Person Indoor Navigation via Vision-Inertial Data Fusion

First-Person Indoor Navigation via Vision-Inertial Data Fusi...

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IEEE/ION Position, Location and Navigation Symposium (PLANS)

作者： Farnoosh, Amirreza Nabian, Mohsen Closas, Pau Ostadabbas, Sarah Northeastern Univ Elect & Comp Engn Dept Boston MA 02115 USA

ISBN: (纸本)9781538616475

In this paper, we aim to enhance the first-person indoor navigation and scene understanding experience by fusing inertial data collected from a smartphone carried by the user with the vision information obtained through the phone's camera. We employed the concept of vanishing directions together with the orthogonality constraints of the man-made environments in an expectation maximization framework to estimate person's orientation with respect to the known indoor coordinates from video frames. This framework allows to include prior information about camera rotation axis for better estimations as well as to select candidate edge-lines for estimation of hallways' depth and width from monocular video frames, and 31) modeling of the scene. Our proposed algorithm concurrently combines the vision-based estimated orientation with the inertial data using a Kalman filter in order to reline estimations and remove substantial measurement drift from inertial sensors. We evaluated the performance of our vision-inertial data fusion method on an IMU-augmented video recorded from a rotary hallway in which a participant completed a full lap. We demonstrated that this fusion provides virtually drift-free instantaneous information about the person's relative orientation. We were able to estimate hallways' depth and width, and generate a closed-path map from the rotary hallway over a roughly 60-meter lap.

关键词： Computer vision Data fusion expectation maximization algorithm In-door navigation Simultaneous localization and mapping (SLAM)

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Robust Air Quality Prediction Based on Regression and XGBoost 6

Robust Air Quality Prediction Based on Regression and XGBoos...

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6th International Conference on Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)

作者： Varghese, Angel Ann Krishnadas, J. Anly, Antony M. Sahrdaya Coll Engn & Technol Dept Comp Sci & Engn Trichur India

ISBN: (纸本)9798350398441

Despite air quality prediction algorithms based on a single model are frequently overfit, they are nonetheless a crucial tool for meteorological forecast and air management. A prediction model based on regression and the XGBoost (eXtreme Gradient Boosting) model is developed in an effort to address the complexity of air quality prediction. The air quality dataset is obtained from Kaggle during the model's first round of data collecting. In the forecasting process, each element of air quality is seen as a time series of data. The air quality impacting elements, such as the data from nearby stations and meteorological data, are taken into account in the model. These include lead (Pb), ammonia, sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O-3), sulphur dioxide (SO2), nitrogen dioxide (NO2, carbon monoxide (CO), and particulate matter (PM) 10 and 2.5. (NH3). Then, a novel method called the expectation- maximization algorithm is used to manage the missing values. The performance of the air quality indices is predicted and assessed using certain regression techniques. The most cutting-edge technology, XGBoost, is used to anticipate the severity of air quality based on the expected values. Then the performance is assessed using five performance metrices like accuracy,Precision, Recall, F1 Score and Matthews correlation coefficient (MCC). The accuracy of the prediction data has significantly increased in this model when compared with previous models.

关键词： Time series model expectation maximization algorithm Regression models Performance metrics XGBoost

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EM based semi-blind beamforming algorithm in MIMO-OFDM systems

EM based semi-blind beamforming algorithm in MIMO-OFDM syste...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Zamiri-Jafarian, H. Bokharaiee-Najafee, S. Pasupathy, S. Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781424411894

A new EM based semi-blind beamforming algorithm is proposed in this paper for multiple input multiple output (MIMO) systems in which orthogonal frequency division multiplexing (OFDM) technique is used. Employing singular value decomposition (SVD) technique, the semi-blind beamforming algorithm eliminates space interference in an iterative manner by shaping antenna patterns in transmitter and receiver sides in frequency domain. A 16-QAM MIMO-OFDM system is considered for computer simulations in order to evaluate the performance of the new algorithm under different scenarios. The results show that the proposed algorithm outperforms subspace based semi-blind beamforming method.

关键词： beamforming expectation maximization algorithm MIMO OFDM semi-blind SVD

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POINT-SPREAD FUNCTION RETRIEVAL FOR FLUORESCENCE MICROSCOPY

POINT-SPREAD FUNCTION RETRIEVAL FOR FLUORESCENCE MICROSCOPY

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IEEE International Symposium on Biomedical Imaging - From Nano to Macro

作者： Pankajakshan, Praveen Blanc-Feraud, Laure Kam, Zvi Zerubia, Josiane INRIA I3S ARIANA Project Team 2004 Route LuciolesBP 93 F-06902 Sophia Antipolis France Weizmann Inst Sci Dept Mol Cell Biol IL-76100 Rehovot Israel

ISBN: (纸本)9781424439317

In this paper we propose a method for retrieving the Point Spread Function (PSF) of an imaging system given the observed image sections of a fluorescent microsphere. Theoretically calculated PSFs often lack the experimental or microscope specific signatures while empirically obtained data are either over sized or (and) too noisy. The effect of noise and the influence of the microsphere size can be mitigated from the experimental data by using a Maximum Likelihood expectation maximization (MLEM) algorithm. The true experimental parameters can then be estimated by fitting the result to a model based on the scalar diffraction theory with lower order Spherical Aberration (SA). The algorithm was tested on some simulated data and the results obtained validate the usefulness of the approach for retrieving the PSF from measured data.

关键词： fluorescence microscopy point-spread function expectation maximization algorithm deconvolution spherical aberration

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