scholarly journals High-Frequency Acoustic Estimation of Time-Varying Underwater Sparse Channels Using Multiple Sources and Receivers Operated Simultaneously

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 10569-10580
Author(s):  
Samar Kaddouri ◽  
Pierre-Philippe J. Beaujean ◽  
Pierre-Jean Bouvet
Keyword(s):  
High Frequency ◽  
Time Varying ◽  
Multiple Sources ◽  
Sparse Channels ◽  
Estimation Of Time

scholarly journals Frequency variations of gravity waves interacting with a time-varying tide

2013 ◽  
Vol 31 (10) ◽  
pp. 1731-1743 ◽  
Author(s):  
C. M. Huang ◽  
S. D. Zhang ◽  
F. Yi ◽  
K. M. Huang ◽  
Y. H. Zhang ◽  
...  
Keyword(s):  
Gravity Waves ◽  
High Frequency ◽  
Lower Atmosphere ◽  
Horizontal Wind ◽  
Frequency Variation ◽  
Time Varying ◽  
Transient Nature ◽  
Critical Layers ◽  
Favorable Conditions ◽  
Frequency Variations

Abstract. Using a nonlinear, 2-D time-dependent numerical model, we simulate the propagation of gravity waves (GWs) in a time-varying tide. Our simulations show that when a GW packet propagates in a time-varying tidal-wind environment, not only its intrinsic frequency but also its ground-based frequency would change significantly. The tidal horizontal-wind acceleration dominates the GW frequency variation. Positive (negative) accelerations induce frequency increases (decreases) with time. More interestingly, tidal-wind acceleration near the critical layers always causes the GW frequency to increase, which may partially explain the observations that high-frequency GW components are more dominant in the middle and upper atmosphere than in the lower atmosphere. The combination of the increased ground-based frequency of propagating GWs in a time-varying tidal-wind field and the transient nature of the critical layer induced by a time-varying tidal zonal wind creates favorable conditions for GWs to penetrate their originally expected critical layers. Consequently, GWs have an impact on the background atmosphere at much higher altitudes than expected, which indicates that the dynamical effects of tidal–GW interactions are more complicated than usually taken into account by GW parameterizations in global models.

Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults

2006 ◽  
Vol 129 (3) ◽  
pp. 352-356 ◽  
Author(s):  
Wen Chen ◽  
Mehrdad Saif
Keyword(s):  
Fault Diagnosis ◽  
Estimation Error ◽  
Fault Estimation ◽  
Time Varying ◽  
Satellite Systems ◽  
Adaptive Observers ◽  
Error Dynamics ◽  
The Stability ◽  
Estimation Of Time ◽  
Identification Strategy

This paper presents a novel fault diagnosis approach in satellite systems for identifying time-varying thruster faults. To overcome the difficulty in identifying time-varying thruster faults by adaptive observers, an iterative learning observer (ILO) is designed to achieve estimation of time-varying faults. The proposed ILO-based fault-identification strategy uses a learning mechanism to perform fault estimation instead of using integrators that are commonly used in classical adaptive observers. The stability of estimation-error dynamics is established and proved. An illustrative example clearly shows that time-varying thruster faults can be accurately identified.

scholarly journals Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys

2021 ◽  
Vol 118 (31) ◽  
pp. e2103272118
Author(s):  
Nicholas J. Irons ◽  
Adrian E. Raftery
Keyword(s):  
Herd Immunity ◽  
The United States ◽  
Sir Model ◽  
Mitigation Strategies ◽  
Time Varying ◽  
Fatality Rate ◽  
Multiple Sources ◽  
Population Prevalence ◽  
Sample Testing ◽  
True Infection

There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available data sources. It is based on a discrete-time Susceptible–Infected–Removed (SIR) model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data on deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random-sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the United States. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress toward herd immunity.

scholarly journals Maximum Likelihood Estimation of Time-Varying Sparsity Level for Dynamic Sparse Signals

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 136687-136701
Author(s):  
Thiruppathirajan S. ◽  
Lakshmi Narayanan R. ◽  
Sreelal S. ◽  
Manoj B. S.
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Sparse Signals ◽  
Time Varying ◽  
Sparsity Level ◽  
Estimation Of Time
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