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Guest editorial: special issue on multi-robot and multi-agent systems

Nora Ayanian, Paolo Robuffo Giordano, Robert Fitch, Antonio Franchi, Lorenzo Sabattini
2020 Autonomous Robots  
Future robots will form a backbone of pervasive, efficient networks of taskable agents-but, crucially, these will be systems of communicating, cooperative devices.  ...  This special issue presents contributions that describe state-of-the-art results in distributed robotics and multi-agent systems research as applicable to autonomous robot systems.  ...  filter for distributed state estimation on hidden Markov models that is suited to unreliable networks.  ... 
doi:10.1007/s10514-020-09908-x fatcat:ty4zagmoa5bolojtkwg3gekenm

An HMM/MLP Architecture for Sequence Recognition

Sung-Bae Cho, Jin H. Kim
1995 Neural Computation  
Script recognition using hidden Markov models. Proc. ICASSP’86 2071-2074. Niles, L. T., and Silverman, H. F. 1990. Combining hidden Markov model and neural network classifiers. Proc.  ...  Maximum mu- tual information estimation of hidden Markov model parameters for speech recognition. Proc. ICASSP’86, 1, 49-52. Bengio, H., Mori, R. D., Flammia, G., and Kompe, R. 1991.  ... 
doi:10.1162/neco.1995.7.2.358 fatcat:vnrtwshor5a3ppo7c27xymdaja

Probabilistic QoS Analysis of Web Services [chapter]

Waseem Ahmed, Yong Wei Wu
2013 Lecture Notes in Computer Science  
In this paper, we propose a novel method for QoS metrification based on Hidden Markov Models.  ...  We demonstrate the feasibility and usefulness of our methodology by drawing experiments on real world data.  ...  I am extremely grateful to Cesar Roberto de Souza (Federal University of Sao Carlos) and for his support, encouragement & proofreading of the draft version of my paper.  ... 
doi:10.1007/978-3-642-40820-5_33 fatcat:rkqzlv6d55ewfe52yfyt6lcewq

Algorithms for Hidden Markov Models with Imprecisely Specified Parameters

Denis Deratani Maua, Cassio Polpo de Campos, Alessandro Antonucci
2014 2014 Brazilian Conference on Intelligent Systems  
Hidden Markov models (HMMs) are widely used models for sequential data.  ...  We present a generalized version of HMMs, whose quantification can be done by sets of, instead of single, probability distributions.  ...  Hidden Markov models (HMMs) are widely used generative probabilistic models of sequential data that assume observations to be produced from a chain of hidden (i.e., unobserved) states [1] .  ... 
doi:10.1109/bracis.2014.42 dblp:conf/bracis/MauaCA14 fatcat:uonsdi3vmrha5fltq676zqod3u

Layered Coding of Hidden Markov Sources [article]

Mehdi Salehifar, Tejaswi Nanjundaswamy, Kenneth Rose
2018 arXiv   pre-print
A new fundamental source coding approach for HMS is proposed, based on tracking an estimate of the state probability distribution, and is shown to be optimal.  ...  The paper studies optimal coding of hidden Markov sources (HMS), which represent a broad class of practical sources obtained through noisy acquisition processes, beside their explicit modeling use in speech  ...  ACKNOWLEDGMENT The authors would like to thank NSF funding under the code of NSF-CCF-1016861 and NSF-CCF-1320599.  ... 
arXiv:1802.02709v1 fatcat:ooog32l7p5eqxd5i7spbz3wdzm

Comparison of the Beta and the Hidden Markov Models of Trust in Dynamic Environments [chapter]

Marie E. G. Moe, Bjarne E. Helvik, Svein J. Knapskog
2009 IFIP Advances in Information and Communication Technology  
In this paper we present a comparison of our proposed hidden Markov trust model to the Beta reputation system.  ...  We show that the hidden Markov trust model performs better when it comes to the detection of changes in behavior of agents, due to its larger richness in model features.  ...  Hidden Markov Modeling A hidden Markov model (HMM) consists of a finite set of N hidden states S = {s 1 ,...,s N } with an associated probability distribution.  ... 
doi:10.1007/978-3-642-02056-8_18 fatcat:qgcsqcv27nduzl3dfjwcxfojvy

End-to-End Delay Distribution Analysis for Stochastic Admission Control in Multi-hop Wireless Networks

Wanguo Jiao, Min Sheng, King-Shan Lui, Yan Shi
2014 IEEE Transactions on Wireless Communications  
An efficient admission control algorithm requires an accurate estimation of the end-toend delay distribution of the network.  ...  In this paper, we propose a method to estimate the end-to-end delay distribution under the general traffic arrival process and Nakagami-m channel model.  ...  To the best of our knowledge, there is no existing model that can accurately estimate the end-toend delay distribution of multi-hop wireless networks with bursty traffic over time-varying and unreliable  ... 
doi:10.1109/twc.2013.013014.122055 fatcat:af2isab2kzdqhczek2szdzjmiq

Gaussian-Bernoulli restricted Boltzmann machines and automatic feature extraction for noise robust missing data mask estimation

Sami Keronen, KyungHyun Cho, Tapani Raiko, Alexander Ilin, Kalle Palomaki
2013 2013 IEEE International Conference on Acoustics, Speech and Signal Processing  
A missing data mask estimation method based on Gaussian-Bernoulli restricted Boltzmann machine (GRBM) trained on cross-correlation representation of the audio signal is presented in the study.  ...  The proposed system is shown to provide a performance improvement in the speech recognition accuracy over the previous multifeature approaches.  ...  models and GMMs as the basis of the state of the art ASR systems.  ... 
doi:10.1109/icassp.2013.6638964 dblp:conf/icassp/KeronenCRIP13 fatcat:bd6x3xppg5avbfpy6243w5anmu

A Bayesian Approach to Image Recognition Based on Separable Lattice Hidden Markov Models

2016 IEICE transactions on information and systems  
, separable lattice hidden Markov models, Bayesian approach, deterministic annealing  ...  Additionally, comparative experiment results showed that the proposed method was more robust to geometric variations than convolutional neural networks. key words: image recognition, hidden Markov models  ...  The details of E-and M-step are described in Appendix A.1. Bayesian Approach for SL-HMMs The ML method can efficiently estimate model parameters.  ... 
doi:10.1587/transinf.2016edp7112 fatcat:reesdcevcrcdxkxhy5rm2k6q5m

HMM-based characterization of channel behavior for networked control systems

Jian Chang, Krishna K. Venkatasubramanian, Chinwendu Enyioha, Shreyas Sundaram, George J. Pappas, Insup Lee
2012 Proceedings of the 1st international conference on High Confidence Networked Systems - HiCoNS '12  
We propose a behavior characterization mechanism based on a hidden Markov model (HMM).  ...  model complexity (number of states in the model).  ...  Hidden Markov Model To formally represent the class of behavior patterns discussed in Section 2, we adopt a hidden Markov model (HMM) framework [18] .  ... 
doi:10.1145/2185505.2185508 dblp:conf/hicons/ChangVESPL12 fatcat:eoe3vvvuczdkvfw3nefvpumowi

Building fast Bayesian computing machines out of intentionally stochastic, digital parts [article]

Vikash Mansinghka, Eric Jonas
2014 arXiv   pre-print
Here we show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude.  ...  But Bayesian inference, which underpins many computational models of perception and cognition, appears computationally challenging even given modern transistor speeds and energy budgets.  ...  Each X i,j node stores the hidden quantity to be estimated, e.g. the disparity of a pixel.  ... 
arXiv:1402.4914v1 fatcat:mnjmxywzyrgo5avrttcvsxosri

The discrete logic of the Brain - Explicit modelling of Brain State durations in EEG and MEG [article]

Nelson J Trujillo-Barreto, David Araya, Wael El-Deredy
2019 bioRxiv   pre-print
We propose using Hidden Semi Markov Models (HSMM), a generalisation of HMM that models the brain state duration distribution explicitly.  ...  The Hidden Markov Model (HMM) has emerged as a useful model-based approach for uncovering the hidden dynamics of brain state transitions based on observed data.  ...  Hidden Semi Markov Model for dynamic Brain State allocation HSMM and HMM are successful models of HDS. Dynamic Bayesian Network (DBN) representations of these models are shown in Figure 1 .  ... 
doi:10.1101/635300 fatcat:bvq74t3pvfcldj7soicl5lyuga

An efficient approximation of the forward-backward algorithm to deal with packet loss, with applications to remote speech recognition

Bengt J. Borgstrom, Abeer Alwan
2008 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing  
The paper discusses the role of Hidden Markov Models (HMMs) in the estimation process, and presents an approximation to the FB method by developing HMMs based on lower resolution quantizers, which are  ...  This paper proposes an efficient approximation of the forwardbackward (FB) algorithm, for the purpose of estimating missing features, based on downsampling statistical models.  ...  Two types of RSR systems exist: Distributed Speech Recognition (DSR) and Network Speech Recognition (NSR). Table 3 . 3 Root-Mean-Square Distortion Measures of Estimated Features.  ... 
doi:10.1109/icassp.2008.4518637 dblp:conf/icassp/BorgstromA08 fatcat:kqofq3smb5gdha3sixmquvcjza

2020 Index IEEE Transactions on Cybernetics Vol. 50

2020 IEEE Transactions on Cybernetics  
., Reference Trajectory Reshaping Optimi-zation and Control of Robotic Exoskeletons for Human-Robot Co-Manipulation; TCYB Aug. 2020 3740-3751 Wu, X., Jiang, B., Yu, K., Miao, c., and Chen, H  ...  ., +, TCYB May 2020 1887-1899 Hidden Markov Model-Based Nonfragile State Estimation of Switched Neu- ral Network With Probabilistic Quantized Outputs.  ...  ., +, TCYB Sept. 2020 4098-4109 Hidden Markov Model-Based Nonfragile State Estimation of Switched Neu- ral Network With Probabilistic Quantized Outputs.  ... 
doi:10.1109/tcyb.2020.3047216 fatcat:5giw32c2u5h23fu4drupnh644a

Towards a Framework for Self-Adaptive Reliable Network Services in Highly-Uncertain Environments

Andrea Ceccarelli, Jesper Grønbæk, Leonardo Montecchi, Hans-Peter Schwefel, Andrea Bondavalli
2010 2010 13th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops  
In future inhomogeneous, pervasive and highly dynamic networks, end-nodes may often only rely on unreliable and uncertain observations to diagnose hidden network states and decide upon possible remediation  ...  , network diagnosis, decision actions, and finally execution (and monitoring) of remediation actions.  ...  As many states (and particularly fault states) of the network are not directly observable (i.e., hidden), the network state estimation must rely on available observations and on a system model to provide  ... 
doi:10.1109/isorcw.2010.21 dblp:conf/isorc/CeccarelliGMSB10 fatcat:fk72k4dza5c3jh6y6jcoqn2glu
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