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Advanced High-order Hidden Bivariate Markov Model Based Spectrum Prediction

Yangxiao Zhao, Zhiming Hong, Yu Luo, Guodong Wang, Lina Pu
2017 EAI Endorsed Transactions on Wireless Spectrum  
In this paper, we first develop a novel prediction approach termed high-order hidden bivariate Markov model (H 2 BMM) for a stationary CRN.  ...  The majority of existing spectrum prediction models in Cognitive Radio Networks (CRNs) don't fully explore the hidden correlation among adjacent observations.  ...  | e4 Advanced High-order Hidden Bivariate Markov Model Based Spectrum Prediction 4 Initial high-order HBMM parameters ߶ ൌ ሺɎǡ ǡ Ɋǡ ሻ Estimate new high-order HBMM parameters ߶ ൌ ሺߨ ොǡ ‫ܩ‬ ǡ ߤǡ ܴ ሻ  ... 
doi:10.4108/eai.12-12-2017.153466 fatcat:xaypdn3conhbfkntz2yjtdfrzi

Spectrum Sensing Using a Hidden Bivariate Markov Model

Thao Nguyen, Brian L. Mark, Yariv Ephraim
2013 IEEE Transactions on Wireless Communications  
A new statistical model, in the form of a hidden bivariate Markov chain observed through a Gaussian channel, is developed and applied to spectrum sensing for cognitive radio.  ...  The main advantage of the proposed model, compared to a standard hidden Markov model (HMM) is that it allows a phase-type dwell time distribution for the process in each state.  ...  Mark McHenry for making available the spectrum measurement data from [7] .  ... 
doi:10.1109/twc.2013.072513.121864 fatcat:owefcga3kzawffu2mqkcokp6gy

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Guoru Ding, Yutao Jiao, Jinlong Wang, Yulong Zou, Qihui Wu, Yu-Dong Yao, Lajos Hanzo
2018 IEEE Communications Surveys and Tutorials  
Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum  ...  In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference.  ...  State FPM-2D FPM in Two Dimension SLFNs Single Hidden Layer Neural Networks HBMM Hidden Bivariate Markov Model SNG Static Neighbor Graph HF High Frequency SVM Support Vector Machine LA Learning  ... 
doi:10.1109/comst.2017.2751058 fatcat:lv5z5bq4dneutpnctifj4v2tny

Joint detection scheme for cooperative spectrum sensing in cognitive radio network

Yijiang Nan, Chenglin Zhao, Bin Li
2016 EURASIP Journal on Wireless Communications and Networking  
The united mathematics model relies on a dynamic state-space model (DSM) and a Bernoulli filters (BF) algorithm. TVFF channels are modeled as finite-state Markov channel (FSMC).  ...  In order to reduce complexity of CSS, the particles are manipulated and reconstructed.  ...  The transitional function S (.) characterizes the stochastic evolution of the PU's state s n ∊S = {0,1} of the nth discrete time as the first-order Markov process.  ... 
doi:10.1186/s13638-016-0570-z fatcat:e3l4kpqshfcerkemwd2xxbagze

Probabilistic models and machine learning in structural bioinformatics

Thomas Hamelryck
2009 Statistical Methods in Medical Research  
Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable.  ...  These developments include generative models of protein structure, the estimation of the parameters of energy functions that are used in structure prediction, the superposition of macromolecules and structure  ...  It consists of a single first-order Markov chain of hidden nodes, each with three observed nodes attached.  ... 
doi:10.1177/0962280208099492 pmid:19153168 fatcat:odf5rfjtw5cp5mmzv6l7qxethu

Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models [article]

Hong Gao, Hua Tang, Carlos Bustamante
2016 bioRxiv   pre-print
The approach uses hidden Markov model and its derivate Markov hidden Markov model to estimate the posterior probabilities of a markers being in an associated state.  ...  With the rapid production of high dimensional genetic data, one major challenge in genome-wide association studies is to develop effective and efficient statistical tools to resolve the low power problem  ...  Several approaches have been proposed to use hidden Markov model (HMM) to predict the locations of such blocks across genomes.  ... 
doi:10.1101/039099 fatcat:gfwmslb7pzbsfjc6tkvwapseau

Automatic Music Transcription: From Monophonic to Polyphonic [chapter]

Fabrizio Argenti, Paolo Nesi, Gianni Pantaleo
2011 Springer Tracts in Advanced Robotics  
Several different approaches are based on techniques such as: pitch trajectory analysis, harmonic clustering, bispectral analysis, event tracking, nonnegative matrix factorization, hidden Markov model.  ...  The transcoding of polyphonic music is a one of the most complex and still open task to be solved in order to become a common tool for the above mentioned applications.  ...  Musical notes tracking is carried out by applying a high order hidden Markov model (HMM) having two states: attack and sustain.  ... 
doi:10.1007/978-3-642-22291-7_3 fatcat:zbgfzqhh2rfdvj6jzuo27xjr3u

IA Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Mduduzi C. Hlophe, Bodhaswar T. Maharaj
2021 IEEE Access  
Decision Processes These are a combination of MDPs and hidden Markov models.  ...  This procedure can be performed based on either univariate or bivariate methods.  ... 
doi:10.1109/access.2021.3104099 fatcat:ucyvpx36drdj5dcl2npxipkhd4

Extratropical Sub-seasonal to Seasonal Oscillations and Multiple Regimes: The Dynamical Systems View [chapter]

Michael Ghil, Andreas Groth, Dmitri Kondrashov, Andrew W. Robertson
2019 Sub-Seasonal to Seasonal Prediction  
The chapter relies on the theoretical framework of dynamical systems and the practical tools this framework helps provide to low-order modeling and prediction of S2S variability.  ...  Empirical model reduction and the forecast skill of the models thus produced in real-time prediction are reviewed.  ...  Low-order data-driven modeling, dynamical analysis, and prediction Linear and nonlinear LOMs and the role of memory effects. Empirical model reduction (EMR) & EMR-based prediction.  ... 
doi:10.1016/b978-0-12-811714-9.00006-1 fatcat:objv56wz7vab3omh4izwv65xfa

A fast Bayesian model for latent radio signal prediction

B. Houlding, A. Bhattacharya, S.P. Wilson, T.K. Forde
2009 2009 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks  
of the hidden model parameters.  ...  These estimates are based on having received several noisy, but spatially correlated, observations of the true latent signal.  ...  Nevertheless, recent developments into the application of Bayesian statistical methodology are now offering real opportunity for high level approximations and accurate predictions to be determined in a  ... 
doi:10.1109/wiopt.2009.5291568 dblp:conf/wiopt/HouldingBWF09 fatcat:xro65u5nsre3tjfh72pqwecb2e

Causal discovery in machine learning: Theories and applications

Ana Rita Nogueira, João Gama, Carlos Abreu Ferreira
2021 Journal of Dynamics & Games  
Causality can be seen as a mean of predicting the future, based on information about past events, and with that, prevent or alter future outcomes.  ...  This algorithm was developed to deal with high dimensional data. The HITON-PC [3] is another constraint-based algorithm that is also based in the PC.  ...  In order to develop and understand these kinds of models, certain assumptions must be fulfilled (in general). These assumptions are [104] : Markov condition and Faithfulness.  ... 
doi:10.3934/jdg.2021008 fatcat:vh4dng5lsfcj3fydwbgy457ejm


A. Srivastava, A.B. Lee, E.P. Simoncelli, S.-C. Zhu
2012 Journal of Mathematical Imaging and Vision  
Statistical analysis of images reveals two interesting properties: (i) invariance of image statistics to scaling of images, and (ii) non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy  ...  In this paper we review some recent results in statistical modeling of natural images that attempt to explain these patterns.  ...  Bivariate Probability Models So far we have discussed only univariate models but the complexity of observed images points to statistical interactions of high orders.  ... 
doi:10.1023/a:1021889010444 fatcat:xiwo3b5j3bf65eoplzn2tqlpwm

Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images [article]

Guillermo Terrén-Serrano, Manel Martínez-Ramón
2021 arXiv   pre-print
We have found that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics.  ...  The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model.  ...  Authors would like to thank the UNM Center for Advanced Research Computing, supported in part by the National Science Foundation, for providing the high performance computing and large-scale storage resources  ... 
arXiv:2105.03535v3 fatcat:d72m7u7hnvhrvdnray6vqat4a4

A comprehensive review on deep learning approaches in wind forecasting applications

Zhou Wu, Gan Luo, Zhile Yang, Yuanjun Guo, Kang Li, Yusheng Xue
2022 CAAI Transactions on Intelligence Technology  
This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy.  ...  Featured approaches include timeseries-based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto-encoder-based approaches.  ...  In order to improve the accuracy of prediction and the robustness of the model, researchers have also developed many hybrid models based on the ARIMA model, such as WT-ARIMA [50] , RWT-ARIMA [43] , and  ... 
doi:10.1049/cit2.12076 fatcat:mr67mk2t3ndhroizkejcxon4ve

A deep learning mixed-data type approach for the classification of FHR signals

Edoardo Spairani, Beniamino Daniele, Maria Gabriella Signorini, Giovanni Magenes
2022 Frontiers in Bioengineering and Biotechnology  
This is achieved by setting a neural model with two connected branches, consisting respectively of a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN).  ...  of the correct development of the fetus, and thus are related to the fetal healthy status, are combined with features implicitly extracted from various representations of the FHR signal (images), in order  ...  On the opposite, predictive models inspired by AI, can be learned on high dimensional datasets including many features (Naylor, 2018) .  ... 
doi:10.3389/fbioe.2022.887549 pmid:36003538 pmcid:PMC9393210 fatcat:lyvxwfeue5hqviyox7ayp4trji
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