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25th European Signal Processing Conference (EUSIPCO)

作者： Halimi, Abdelghafour Batatia, Hadj Le Digabel, Jimmy Josse, Gwendal Tourneret, Jean-Yves Univ Toulouse ENSEEIHT IRIT 2 Rue CamichelBP 7122 F-31071 Toulouse 7 France Pierre Fabre Dermocosmet Ctr Rech Peau 2 Rue Viguerie F-31025 Toulouse 3 France

ISBN: (纸本)9780992862671

This paper studies a new bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification of human skin lentigo. The proposed bayesian approach takes advantage of the distribution of the multiplicative speckle noise affecting the true reflectivity of these images and of appropriate priors for the unknown model parameters. A Markov chain Monte Carlo (MCMC) algorithm is proposed to jointly estimate the model parameters and the image of true reflectivity while classifying images according to the distribution of their reflectivity. Precisely, a Metropolis-within-Gibbs sampler is investigated to sample the posterior distribution of the bayesian model associated with RCM images and to build estimators of its parameters, including labels indicating the class of each RCM image. The resulting algorithm is applied to synthetic data and to real images from a clinical study containing healthy and lentigo patients.

关键词： Reflectance confocal microscopy bayesian algorithm Classification Metropolis-within-Gibbs sampler

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bayesian Sparse Estimation of a Radar Scene with Weak and Strong Targets 3

Bayesian Sparse Estimation of a Radar Scene with Weak and St...

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3rd International Workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa)

作者： Lasserre, Marie Bidon, Stephanie Besson, Olivier Le Chevalier, Francois Univ Toulouse DEOS ISAE Toulouse France Delft Univ Technol MS3 Delft Netherlands

ISBN: (纸本)9781479974207

We consider the problem of estimating a finite number of atoms from a dictionary embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. In particular, the estimation of a radar scene consisting of targets with wide amplitude range can be challenging since the sidelobes of a strong target can disrupt the estimation of a weak one. In this paper, we present a bayesian algorithm able to estimate weak targets possibly hidden by strong ones. The main strength of this algorithm lies in a novel sparse-promoting prior distribution which decorrelates sparsity level and target power and makes the estimation process span the whole target power range. This algorithm is implemented through a Monte-Carlo Markov chain. It is successfully evaluated on synthetic and semiexperimental radar data.

关键词： Bayes methods Markov processes Monte Carlo methods compressed sensing estimation theory radar signal processing signal representation white noise bayesian algorithm bayesian sparse estimation Monte-Carlo Markov chain SSR estimation process radar applications radar data radar scene sparse signal representation sparse-promoting prior distribution sparsity level target power Compressed sensing Conferences Estimation Radar remote sensing white noise Bayes methods Compressed sensing signal representation Radar applications radar data Radar remote sensing Radar signal processing estimation theory Markov chain Monte Carlo technique semiconductor relays Targets

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bayesian Approach Based Comment Spam Defending Tool

Bayesian Approach Based Comment Spam Defending Tool

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3rd International Conference on Information Security and Assurance

作者： Dhinakaran, Beatrice Cynthia Nagamalai, Dhinaharan Lee, Jae-Kwang Hannam Univ Dept Comp Engn Daejeon South Korea Wireilla Net Solut Madras Tamil Nadu India

ISBN: (纸本)9783642026164

Spam messes up user's inbox, consumes network resources and spread worms and viruses. Spam is flooding of unsolicited,unwanted e mail. Spam in blogs is called blog spam or comment spam. It is done by posting comments or flooding spams to the services such as blogs, forums,news,email archives and guestbooks. Blog spams generally appears on guestbooks or comment pages where spammers fill a comment box with spam words. In addition to wasting user's time with unwanted comments, spam also consumes a lot of bandwith. In this paper, we propose a software. tool to prevent such blog spams by using bayesian algorithm based technique. It is derived from Bayes' Theorem. It gives an output which has a probability that any comment is spam, given that it has certain words in it. With using our past entries and a comment entry, this value is obtained and compared with a threshold value to find if it exceeds the threshold value or not. By using this cocept, we developed a software tool to block comment spam. The experimetal results shows that the bayesian based tool is working well. This paper has the major findings and their significance of blog spam filter.

关键词： bayesian algorithm spam comment spam blog spam

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Unknown Malicious Code Detection Based on bayesian

Unknown Malicious Code Detection Based on Bayesian

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International Conference on Advanced in Control Engineering and Information Science (CEIS)

作者： Lai, Yingxu Liu, Zhenghui Beijing Univ Technol Coll Comp Sci Beijing 100124 Peoples R China

Analyzed bayesian classifier with string, n-gram and API as features, we found that it is very difficult to improve bayesian classifier detection accuracy because selected features are not completely independent. In order to solve this problem, we propose a new improved choose features method which are most representative properties, and show that our method achieve high detection rates, even on completely new, previously unseen malicious executables. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

关键词： malicious codes detection bayesian algorithm

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bayesian and non-bayesian methods for iterative joint decoding and detection in the presence of phase noise

Bayesian and non-Bayesian methods for iterative joint decodi...

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IEEE International Symposium on Information Theory

作者： Colavolpe, G Barbieri, A Caire, G Bonneau, N Univ Parma Dipartimento Ingn Informaz I-43100 Parma Italy

ISBN: (纸本)0780382803

This paper proposes new algorithms for joint iterative decoding and parameter estimation in bayesian and nonbayesian method. The low-density parity-check (LDPC) codes in the presence of phase noise is focused in this paper. The nonbayesian algorithm for an unknown phase channel is based on the application of the expectation-maximization (EM) algorithm. The proposed bayesian algorithms are obtained as an application of the sum-product algorithm to the factor graph. Comparing the performance of the considered algorithms in the case of LDPC code, the EM-KL (EM-Karhunen-Loeve) algorithm performs better than EM-SW (sliding window) algorithm.

关键词： iterative decoding phase noise parameter estimation parity check codes optimisation graph theory Karhunen-Loeve transforms bayesian algorithm nonbayesian algorithm joint iterative decoding low-density parity-check code LDPC phase channel expectation-maximization Karhunen-Loeve algorithm EM algorithm sum-product algorithm factor graph

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Nonlinear regression using smooth bayesian estimation 40

Nonlinear regression using smooth Bayesian estimation

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Halimi, Abderrahim Mailhes, Corinne Tourneret, Jean-Yves University of Toulouse IRIT/INP-ENSEEIHT/TéSA France

ISBN: (纸本)9781467369978

This paper proposes a new bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters/hyperparameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated parameters. © 2015 IEEE.

关键词： bayesian algorithm Hamiltonian Monte-Carlo MCMC Parameter estimation Radar altimetry

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Anthropogenic Carbon Monoxide Emissions During 2014–2020 in China Constrained by In Situ Ground Observations

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Journal of Geophysical Research: Atmospheres 2025年第10期130卷

作者： Mengwei Jia Fei Jiang Nikolaos Evangeliou Sabine Eckhardt Andreas Stohl Xin Huang Yang Shen Shuzhuang Feng Wei He Jun Wang Hengmao Wang Mousong Wu Weimin Ju Aijun Ding Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Nanjing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing China Frontiers Science Center for Critical Earth Material Cycling Nanjing University Nanjing China Department of Atmospheric and Climate Research NILU Kjeller Norway Department of Meteorology and Geophysics University of Vienna Vienna Austria Joint International Research Laboratory of Atmospheric and Earth System Sciences School of Atmospheric Sciences Nanjing University Nanjing China

China has been actively reducing anthropogenic air pollutant emissions over the past decade and is about to embark on the next phase of air quality management. Carbon monoxide (CO) is an ideal indicator of primary pollutants from combustion sources. A comprehensive assessment of the current situation of anthropogenic CO emissions can inform the implementation of future reduction policies. This work aims to determine the changes of anthropogenic CO emissions in mainland China from 2014 to 2020 at 0.2° × 0.2° spatial resolution. Hourly CO observations from over 1,600 national control sites were combined with Lagrangian dispersion modeling and multisectoral emission inventories in a bayesian inversion framework, to determine monthly CO emissions. From 2014 to 2020, the average annual anthropogenic CO emission in mainland China was 473.6 ± 117.2 Tg a −1 , which is 2.5 times higher than the prior emission. Northwest China stands out as the most underestimated region with a relative difference of an astonishing 6.3 times between prior and posterior emissions. The emissions generally decreased by 32.0% from 2014 to 2020, but with a clear rebound in 2017 and 2018, and Yunnan in the southwest and Xinjiang in the northwest are the most pronounced rebound provinces. Optimizing the management of direct emissions in the future requires not only focusing on key urban agglomerations but also strengthening controls in remote provinces. The anthropogenic CO emissions in China from 2014 to 2020 are constrained by in situ ground observations The average annual anthropogenic CO emission was 473.6 ± 117.2 Tg a −1 , which is 2.5 times higher than the emission inventory The emissions generally decreased by 32.0%, but with a clear rebound in 2017 and 2018 Over the past decade, China has implemented some of the strictest air pollution control measures in the world, leading to major changes in its industrial structure, energy production structure, energy intensity, and emissions intensity. A

关键词： carbon monoxide China anthropogenic emission Lagrangian model bayesian algorithm

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Segmentation-based approach for trend analysis and structural breaks in rainfall time series (1851-2006) over India

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HYDROLOGICAL SCIENCES JOURNAL 2020年第9期65卷 1583-1595页

作者： Priyadarshi, Niraj Bandyopadhyay, Soumya Chowdary, V. M. Chandrasekar, K. Chockalingam, Jeganathan Raj, Uday Jha, Chandra Shekhar NRSC ISRO Reg Remote Sensing Ctr East Kolkata India NRSC ISRO Reg Remote Sensing Ctr North New Delhi India Indian Space Res Org ISRO Natl Remote Sensing Ctr NRSC Hyderabad India Birla Inst Technol Dept Remote Sensing Mesra Bit Mesra Ranchi India

This study analysed long-term rainfall data (1851-2006) over seven climatic zones of India at seasonal and annual scales based on three techniques: (i) linear regression, (ii) multifractal detrended fluctuation analysis (MFDFA) and (iii) bayesian algorithm. The linear regression technique was used for trend analysis of short-term (30 years) and long-term (156 years) rainfall data. The MFDFA revealed small- and large-scale fluctuations, whereas the bayesian algorithm helped in quantifying the uncertainty in break-point detection from the rainfall time series. Major break points years identified through bayesian algorithm were 1888, 1904 and 1976. The MFDFA technique identified that high fluctuation years were between 1871-1890, 1891-1910 and 1951-1970. Linear regression-based analysis revealed 1881-1910 and 1971-2006 as break-point periods in the North Mountainous Indian region. A similar analysis was carried out for India as a whole, as well as its seven climatic zones.

关键词： time series rainfall linear regression detrended fluctuation analysis bayesian algorithm

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DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach

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NEW GENERATION COMPUTING 2022年第4期40卷 1053-1075页

作者： Demir, Fatih Demir, Kursat Sengur, Abdulkadir Firat Univ Vocat Sch Tech Sci Biomed Dept Elazig Turkey Firat Univ Technol Fac Mechatron Engn Dept Elazig Turkey Firat Univ Technol Fac Elect Elect Engn Dept Elazig Turkey

The new type of coronavirus disease, which has spread from Wuhan, China since the beginning of 2020 called COVID-19, has caused many deaths and cases in most countries and has reached a global pandemic scale. In addition to test kits, imaging techniques with X-rays used in lung patients have been frequently used in the detection of COVID-19 cases. In the proposed method, a novel approach based on a deep learning model named DeepCovNet was utilized to classify chest X-ray images containing COVID-19, normal (healthy), and pneumonia classes. The convolutional-autoencoder model, which had convolutional layers in encoder and decoder blocks, was trained by using the processed chest X-ray images from scratch for deep feature extraction. The distinctive features were selected with a novel and robust algorithm named SDAR from the deep feature set. In the classification stage, an SVM classifier with various kernel functions was used to evaluate the classification performance of the proposed method. Also, hyperparameters of the SVM classifier were optimized with the bayesian algorithm for increasing classification accuracy. Specificity, sensitivity, precision, and F-score, were also used as performance metrics in addition to accuracy which was used as the main criterion. The proposed method with an accuracy of 99.75 outperformed the other approaches based on deep learning.

关键词： COVID-19 Convolutional-autoencoder model Feature selection bayesian algorithm

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Particle Source Localization With a Low-Cost Robotic Sensor System: algorithmic Design and Performance Evaluation

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IEEE SENSORS JOURNAL 2020年第21期20卷 13074-13085页

作者： Chen, Xinxing Marjovi, Ali Huang, Jian Martinoli, Alcherio Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Ecole Polytech Fed Lausanne EPFL Sch Architecture Civil & Environm Engn Distributed Intelligent Syst & Algorithms Lab DIS CH-1015 Lausanne Switzerland

In this paper we tackle the problem of finding the source of particulate matter with a mobile robot equipped with a low-cost multi-channel optical particle counting sensor. The proposed method is based on the Infotaxis odor source localization algorithm and makes multiple modifications to adapt it to particle plumes. In particular, we propose three simple but efficient ways to fuse multiple probability maps associated with various particle sizes and use the resulted integrated map to guide the robot's movements. A refined measurement data collection is conducted in a wind tunnel to fit the particle plume model. The method with three proposed integration strategies is evaluated in simulation and in the wind tunnel emulating realistic environmental conditions in a repeatable fashion. In particular, we have investigated the impact of two environmental parameters - the wind speed and source release rate on the algorithm performance. The proposed algorithm with the weighted multi-modality map integration strategy outperforms the original Infotaxis and the other two variants. In high wind speed, the proposed algorithm is able on average to estimate the source location with less than 1 meter error in the 80 m(2) wind tunnel arena.

关键词： particle source localization low-cost optical particle counter mobile robotics bayesian algorithm wind tunnel experiments

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