Ad-hoc mobile array based audio segmentation using latent variable stochastic model

2019 ◽  
Author(s):  
Srikanth Raj Chetupalli ◽  
Anirban Bhowmick ◽  
Thippur V. Sreenivas
Keyword(s):  
Stochastic Model ◽  
Latent Variable ◽  
Ad Hoc ◽  
Audio Segmentation

scholarly journals Delay-Constrained Routing Based on Stochastic Model for Flying Ad Hoc Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shaojie Wen ◽  
Chuanhe Huang
Keyword(s):  
Ad Hoc Networks ◽  
Stochastic Model ◽  
Ad Hoc ◽  
Transition Probability ◽  
Relay Node ◽  
Link Quality ◽  
Mathematical Form ◽  
End To End Delay ◽  
End To End ◽  
Hoc Networks

This paper aims at solving the end-to-end delay-constrained routing problem in a local way for flying ad hoc networks (FANETs). Due to the high mobility, it is difficult for each node in FANETs to obtain the global information. To solve this issue, we propose an adaptive delay-constrained routing with the aid of a stochastic model, which allows the senders to deliver the packets with only local information. We represent the problem in a mathematical form, where the effective transmission rate is viewed as the optimization objective and the link quality and end-to-end delay as the constraints. And, some mathematical tools are used to obtain the approximate solutions for the optimization problem. Before designing the routing scheme, the senders calculate the transition probability for its relay node by jointly considering local delay estimation and expected one-hop delay. Then, the sender transmits the packets to their relay node with transition probability. Finally, we prove the convergence of the proposed routing algorithm and analyse its performances. The simulation results show that the proposed routing policy can improve the network performance effectively in terms of throughput, loss rate, and end-to-end delay.

scholarly journals Stochastic model for COVID-19 in slums: interaction between biology and public policies

2021 ◽  
Author(s):  
H.G. Solari ◽  
M.A. Natiello
Keyword(s):  
Stochastic Model ◽  
Ad Hoc ◽  
Detection Efficiency ◽  
Public Policies ◽  
Model Structure ◽  
Slum Area ◽  
Rt Pcr ◽  
The Public ◽  
Intervention Method ◽  
Free Running

AbstractWe present a mathematical model for the simulation of the development of an outbreak of COVID-19 in a slum area under different interventions. Instead of representing interventions as modulations of the parameters of a free running epidemic we introduce a model structure that accounts for the actions but does not assume the results. The disease is modelled in terms of the progression of viremia reported in scientific works. The emergence of symptoms in the model reflects the statistics of a nation-wide highly detailed database consisting of more than 62000 cases (about a half of the confirmed by RT-PCR tests) with recorded symptoms in Argentina. The stochastic model displays several of the characteristics of COVID-19 such as a high variability in the evolution of the outbreaks, including long periods in which they run undetected, spontaneous extinction followed by a late outbreak and unimodal as well as bimodal progressions of daily counts of cases (second waves without ad-hoc hypothesis). We show how the relation between undetected cases (including the “asymptomatic” cases) and detected cases changes as a function of the public policies, the efficiency of the implementation and the timing with respect to the development of the outbreak. We show also that the relation between detected cases and total cases strongly depends on the implemented policies and that detected cases cannot be regarded as a measure of the outbreak, being the dependency between total cases and detected cases in general not monotonic as a function of the efficiency in the intervention method. According to the model, it is possible to control an outbreak with interventions based on the detection of symptoms only in the case when the presence of just one symptom prompts isolation and the detection efficiency reaches about 80% of the cases. Requesting two symptoms to trigger intervention can be enough to fail in the goals.

scholarly journals Improving latent variable descriptiveness by modelling rather than ad-hoc factors

2019 ◽  
Vol 108 (8-9) ◽  
pp. 1601-1611
Author(s):  
Alex Mansbridge ◽  
Roberto Fierimonte ◽  
Ilya Feige ◽  
David Barber
Keyword(s):  
Latent Variable ◽  
Ad Hoc

A stochastic model to study the impact of the transmission frequency of hello messages on the connectivity of ad hoc networks

2013 ◽  
Vol 57 (2) ◽  
pp. 197-207 ◽  
Author(s):  
Karima Adel-Aissanou ◽  
Djamil Aïssani ◽  
Nathalia Djellab ◽  
Noufissa Mikou
Keyword(s):  
Ad Hoc Networks ◽  
Stochastic Model ◽  
Ad Hoc ◽  
Transmission Frequency ◽  
Hoc Networks ◽  
The Impact ◽  
Hello Messages

scholarly journals Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 311 ◽  
Author(s):  
Nyothiri Aung ◽  
Weidong Zhang ◽  
Sahraoui Dhelim ◽  
Yibo Ai
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Risk Prediction ◽  
Latent Variable ◽  
Ad Hoc ◽  
Hidden Markov ◽  
Urban Environments ◽  
Prediction System ◽  
Accident Prediction ◽  
Driving Risk

With the emergence of autonomous vehicles and internet of vehicles (IoV), future roads of smart cities will have a combination of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of the road commuters in such a network, it is imperative to enhance the performance of Advanced Driver Assistance Systems (ADAS). Real-time driving risk prediction is a fundamental part of an ADAS. Many driving risk prediction systems have been proposed. However, most of them are based only on vehicle’s velocity. But in most of the accident scenarios, other factors are also involved, such as weather conditions or driver fatigue. In this paper, we proposed an accident prediction system for Vehicular ad hoc networks (VANETs) in urban environments, in which we considered the crash risk as a latent variable that can be observed using multi-observation such as velocity, weather condition, risk location, nearby vehicles density and driver fatigue. A Hidden Markov Model (HMM) was used to model the correlation between these observations and the latent variable. Simulation results showed that the proposed system has a better performance in terms of sensitivity and precision compared to state of the art single factor schemes.

Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2007 ◽  
Vol 19 (5) ◽  
pp. 1362-1399 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Stochastic Model ◽  
Ad Hoc ◽  
Detailed Examination ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Experimental Results ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Natural Way ◽  
Exact Timing

Recently we presented a stochastic, ensemble-based model of spike-timing-dependent plasticity. In this model, single synapses do not exhibit plasticity depending on the exact timing of pre- and postsynaptic spikes, but spike-timing-dependent plasticity emerges only at the temporal or synaptic ensemble level. We showed that such a model reproduces a variety of experimental results in a natural way, without the introduction of various, ad hoc nonlinearities characteristic of some alternative models. Our previous study was restricted to an examination, analytically, of two-spike interactions, while higher-order, multispike interactions were only briefly examined numerically. Here we derive exact, analytical results for the general n-spike interaction functions in our model. Our results form the basis for a detailed examination, performed elsewhere, of the significant differences between these functions and the implications these differences have for the presence, or otherwise, of stable, competitive dynamics in our model.

Mixtures of Probabilistic Principal Component Analyzers

1999 ◽  
Vol 11 (2) ◽  
pp. 443-482 ◽  
Author(s):  
Michael E. Tipping ◽  
Christopher M. Bishop
Keyword(s):  
Expectation Maximization ◽  
Latent Variable ◽  
Ad Hoc ◽  
Expectation Maximization Algorithm ◽  
Principal Component ◽  
Latent Variable Model ◽  
Variable Model ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Density Modeling

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing, and visualizing data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Therefore, previous attempts to formulate mixture models for PCA have been ad hoc to some extent. In this article, PCA is formulated within a maximum likelihood framework, based on a specific form of gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analyzers, whose parameters can be determined using an expectation-maximization algorithm. We discuss the advantages of this model in the context of clustering, density modeling, and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition.

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