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Automated Recovery of Compressedly Observed Sparse Signals From Smooth Background

IEEE SIGNAL PROCESSING LETTERS 2014年第8期21卷 1012-1016页

作者： Chen, Zhaofu Molina, Rafael Katsaggelos, Aggelos K. Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA Univ Granada Dept Ciencias Computac & IA E-18071 Granada Spain

We propose a bayesian based algorithm to recover sparse signals from compressed noisy measurements in the presence of a smooth background component. This problem is closely related to robust principal component analysis and compressive sensing, and is found in a number of practical areas. The proposed algorithm adopts a hierarchical bayesian framework for modeling, and employs approximate inference to estimate the unknowns. Numerical examples demonstrate the effectiveness of the proposed algorithm and its advantage over the current state-of-the-art solutions.

关键词： bayesian algorithm compressive sensing robust principal component analysis

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A Non Parametric Partial Histogram Bayes Learning algorithm for Classification Applications 4

A Non Parametric Partial Histogram Bayes Learning Algorithm ...

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4th IEEE International Conference on Control System Computing and Engineering (ICCSCE 2014)

作者： Lawend, Haider O. Muad, Anuar M. Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia

ISBN: (纸本)9781479956869

In many applications such as dealing with database, continuous environment and humanoid robots, the machine often deals with large amount of data every day of work. Dealing with large amount of data requires fast as well as accurate learning algorithms to do the classification. A new supervised non parametric Partial Histogram Bayes learning algorithm (PHBayes) is proposed and presented in this paper. The proposed algorithm was tested on image database and compared with other standard algorithms like Naive Bayes, Gaussian Mixture Model based Classifier, 1st Nearest Neighbor and Nearest Class Mean for classification purpose. The experimental results showed that the proposed algorithm is faster as well as more accurate compare with other algorithms, which makes it worthy to be considered in classification applications.

关键词： bayesian algorithm non parametric algorithm classification class histogram representation

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bayesian algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks

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SENSORS 2009年第2期9卷 731-755页

作者： Sarigiannis, Dimosthenis A. Karakitsios, Spyros P. Gotti, Alberto Papaloukas, Costas L. Kassomenos, Pavlos A. Pilidis, Georgios A. Commiss European Communities Joint Res Ctr Inst Hlth & Consumer Protect Phys & Chem Exposure Unit PCE I-21020 Ispra Va Italy Univ Ioannina Dept Biol Applicat & Technol Lab Bioinformat GR-45110 Ioannina Greece Univ Ioannina Dept Phys Lab Meteorol GR-45110 Ioannina Greece Univ Ioannina Dept Biol Appl & Technol Environm Chem Lab GR-45110 Ioannina Greece

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.

关键词： bayesian algorithm ANN PBPK benzene exposure

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Efficiency of Optimal Joint Detection and Estimation of Image Areas with Spatial Noise

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OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING 2013年第3期49卷 221-227页

作者： Trifonov, A. P. Zimovets, K. A. Korchagin, Yu. E. Voronezh State Univ Univ Pl 1 Voronezh 394006 Russia

bayesian and maximum likelihood algorithms are considered. A comparative analysis of their effectiveness is performed. The results are specified for an image in the shape of an ellipse with linearly varying intensity.

关键词： maximum likelihood algorithm bayesian algorithm characteristics of algorithms statistical modeling

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Compressive Sensing of Neural Action Potentials Using Nano Platinum Black Modified Microelectrode Array

Compressive Sensing of Neural Action Potentials Using Nano P...

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8th Annual IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

作者： Zhou, Shuai Zhang, Bingchen Xu, Shengwei Wang, Mixia Dai, Bowei Hong, Wen Wu, Yirong Cai, Xinxia Chinese Acad Sci Inst Elect State Key Lab Transducer Technol Beijing Peoples R China

ISBN: (纸本)9781467363525;9781467363518

Long-term wireless neural recording systems which are subject to stringent power consumption, are highly desired to reduce the rate of data transmission and computation complexity. In this paper, we propose using a combination of on-chip neural action potentials ('spikes') detection system and compressive sensing (CS) techniques to reduce the power required for data transmission and a random Bernoulli matrix to reduce the computation complexity consequently further reduce the power consumption. By analyzing the data detected by nano platinum black modified microelectrode array implanted in the hippocampus of the Sprague-Dawley (SD) rat, we prove that spikes are compressible in the wavelet domain. We use the bayesian CS algorithm to reconstruct them. Our results show that the mean compression ratio is 26: 1 achieved for 16-dB SNDR recovery using this mechanism.

关键词： compressive sensing spikes wavelet bayesian algorithm

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An Integration of AHP Approach and Bayes Classification algorithm in Supplier Selection

An Integration of AHP Approach and Bayes Classification Algo...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Chan, F. T. S. Chung, S. H. Chow, J. C. L. Niu, B. Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479909865

Weight of criteria can only be changed through discussion or analysis of experts under traditional supplier selection method, which brings up two problems: (1) human effect problem and (2) incapability in timely decision making on updates of weights. In this paper, a new supplier selection method based on integration of Analytic Hierarchy Process, the bayesian Classifier algorithm and dynamic probabilities (AHP-BCA) is proposed. The method makes predictions with the probabilities of occurrences of criteria values based on historical records to avoid any human effects in decision making. It is also equipped with an instant self-update function to instantly update the probability values with new data, and be ready for next calculation. A simulation experiment is conducted to compare the performance of the proposed approach with a remarkable traditional approach in literature with historical data. Results show that the proposed approach can outperform

关键词： Analytic Hierarchy Process bayesian algorithm Supplier Selection

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Trust Evaluation based Content Filtering in Social Interactive Data

Trust Evaluation based Content Filtering in Social Interacti...

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International Conference on Cloud Computing and Big Data (CLOUDCOM-ASIA)

作者： Xu, Lingshan Zheng, Xianghan Rong, Chunming Fuzhou Univ Coll Math & Comp Sci Fuzhou 350002 Peoples R China Univ Stavanger Coll Comp & Informat Technol Stavanger Norway

ISBN: (纸本)9781479928293

A new Cloud-based Trust Awareness and Interaction Model (CTAIM) is proposed for trust evaluation based content filtering in social interactive data. The research is based on diversified information from social interactive data to analyse people's intention and evaluate people's trust. The proposed model is composed of bayesian content filtering algorithm and bayesian inference algorithm in Dirichlet distribution, and it's capable to provide 3rd party trustworthiness evaluation according to node behavior and interaction history with high-efficiency, security, and neutrality. Additionally, MapReduce-based computing and HBase storage framework are implemented for parallel computing among mass interactive data.

关键词： social interactive data cloud computing trustworthiness evaluation bayesian algorithm

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Application Research in Internet information retrieval of Data Mining Technology

Application Research in Internet information retrieval of Da...

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2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)

作者： Li Jie-yi Beijing Univ Posts & Telecommun Dept Econ & Management Beijing 100088 Peoples R China

ISBN: (纸本)9781479927166

According to the problem of the constant increase of internet data and the current Internet information retrieval system has been unable to meet the needs of data and information retrieval, this paper puts forward the data mining based Internet information retrieval system. The paper first uses data mining to excavate association rules, and evaluates the entire behavior simulation of users resources need according to a certain factor of user's retrieval resources, and then uses bayesian network algorithm to correlate the mining data. Experimental simulation results show that the application of bayesian network algorithm into Internet information retrieval has implemented intelligence and personalization of information retrieval to a certain extent, which has certain research value.

关键词： resource retrieval behavior pattern discrimination bayesian algorithm association rules mining

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Imaging heterochromatin in human embryonic stem cells with light-sheet bayesian microscopy

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Epigenetics Chromatin 2013年第SUPPL 1期6卷 P109-P109页

作者： Ying S Hu Quan Zhu James Fitzpatrick Inder M Verma Hu Cang Waitt Advanced Biophotonics Center Salk Institute for Biological Studies La Jolla CA 92037 USA Laboratory of Genetics Salk Institute for Biological Studies La Jolla CA 92037 USA

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Web-Based Biomedical Literature Mining

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Journal of Shanghai Jiaotong university(Science) 2012年第4期17卷 494-499页

作者：安建福薛惠平陈瑛吴建国章鲁 Department of Biomedical Engineering Basic Medical CollegeShanghai Jiaotong University School of Medicine Information and Resource Center Shanghai Jiaotong University School of Medicine Division of Gastroenterology and Hepatology Renji HospitalShanghai Jiaotong University School of Medicine Department of Nuclear Medicine Renji HospitalShanghai Jiaotong University School of Medicine

With an upsurge in biomedical literature,using data-mining method to search new knowledge from literature has drawing more attention of *** this study,taking the mining of non-coding gene literature from the network database of PubMed as an example,we first preprocessed the abstract data,next applied the term occurrence frequency(TF) and inverse document frequency(IDF)(TF-IDF) method to select features,and then established a biomedical literature data-mining model based on bayesian ***,we assessed the model through area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,precision rate and recall *** 1 000 features are selected,AUC,specificity,sensitivity,accuracy rate,precision rate and recall rate are 0.868 3,84.63%,89.02%,86.83%,89.02% and 98.14%,*** results indicate that our method can identify the targeted literature related to a particular topic effectively.

关键词： bayesian algorithm term occurrence frequency(TF) and inverse document frequency(IDF)(TFIDF) data-mining

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