Statistical evaluation of the performance of free-space-optical link affected by optical turbulence with gamma-gamma distribution through time series generated by simulation

2021 ◽  
Vol 67 (1 Jan-Feb) ◽  
pp. 146
Author(s):  
J. A. Lopez-Leyva ◽  
A. Arvizu-Mondragon ◽  
J. Santos-Aguilar ◽  
F. J. Mendieta-Jimenez
Keyword(s):  
Time Series ◽  
Gamma Distribution ◽  
Statistical Evaluation ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Practical Implementation ◽  
Free Space Optical ◽  
Optical Turbulence ◽  
The Mean ◽  
The Impact

In this article, the statistical evaluation of the performance of FSO links subject to dynamic fluctuations of atmospheric optical turbulence that affect the instantaneous value of the received optical power is presented. We reproduce this temporal domain effect with time series generated by simulation considering the optical turbulence as a stochastic process with Gamma-Gamma probability distribution. Also, a phase screen was used in order to observe the impact that optical turbulence has over the optical information field's spatial phase. With our simulations, it is possible to get the two most essential performance parameters required for the practical implementation of FSO links. We obtained the mean signal-to-noise ratio (SNR) and the mean bit error rate (BER) of FSO links affected by optical turbulence with Gamma-Gamma distribution.  The methodology presented in this paper may be readily used to design and implement real-world FSO links.

scholarly journals Adaptive Noise Cancellation of an Analog Fiber Optic Link

2021 ◽  
Author(s):  
Mohammad Nazrul Islam
Keyword(s):  
Optical Fiber ◽  
Fiber Optic ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Noise Cancellation ◽  
Adaptive Noise Cancellation ◽  
Fiber Link ◽  
The Mean ◽  
Simulation Results ◽  
Adaptive Noise

There are three dominant noise mechanisms in an analog optical fiber link. These are shot noise that is proportional to the mean optical power, relative intensity noise (RIN) that is proportional to the square of the instanteaneous optical power. This report describes an adaptive noise cancellation of these dominant noise processes that persist an analog optical fiber link. The performance of an analog optical fiber link is analyzed by taking the effects of these noise processes. Analytical and simulation results show that some improvement in signal to noise ratio (SNR) and this filter is effective to remove noise adaptively from the optical fiber link.

scholarly journals Initial impact of COVID-19’s stay-at-home order on motor vehicle traffic and crash patterns in Connecticut: an interrupted time series analysis

2020 ◽  
pp. injuryprev-2020-043945
Author(s):  
Mitchell L Doucette ◽  
Andrew Tucker ◽  
Marisa E Auguste ◽  
Amy Watkins ◽  
Christa Green ◽  
...  
Keyword(s):  
Time Series ◽  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Data Repository ◽  
Interrupted Time Series Analysis ◽  
Crash Rates ◽  
Single Vehicle ◽  
The Mean ◽  
The Impact ◽  
At Home

IntroductionUnderstanding how the COVID-19 pandemic has impacted our health and safety is imperative. This study sought to examine the impact of COVID-19’s stay-at-home order on daily vehicle miles travelled (VMT) and MVCs in Connecticut.MethodsUsing an interrupted time series design, we analysed daily VMT and MVCs stratified by crash severity and number of vehicles involved from 1 January to 30 April 2017, 2018, 2019 and 2020. MVC data were collected from the Connecticut Crash Data Repository; daily VMT estimates were obtained from StreetLight Insight’s database. We used segmented Poisson regression models, controlling for daily temperature and daily precipitation.ResultsThe mean daily VMT significantly decreased 43% in the post stay-at-home period in 2020. While the mean daily counts of crashes decreased in 2020 after the stay-at-home order was enacted, several types of crash rates increased after accounting for the VMT reductions. Single vehicle crash rates significantly increased 2.29 times, and specifically single vehicle fatal crash rates significantly increased 4.10 times when comparing the pre-stay-at-home and post-stay-at-home periods.DiscussionDespite a decrease in the number of MVCs and VMT, the crash rate of single vehicles increased post stay-at-home order enactment in Connecticut after accounting for reductions in VMT.

scholarly journals Time-Series Laplacian Semi-Supervised Learning for Indoor Localization †

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3867 ◽  
Author(s):  
Jaehyun Yoo
Keyword(s):  
Time Series ◽  
Supervised Learning ◽  
Indoor Localization ◽  
Learning Algorithm ◽  
Unlabeled Data ◽  
Training Data ◽  
Practical Implementation ◽  
Additional Information ◽  
Localization Scheme ◽  
The Impact

Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localization methods to reduce use of labeled training data. To boost the efficiency and the accuracy of indoor localization, this paper proposes a new time-series semi-supervised learning algorithm. The key aspect of the developed method, which distinguishes it from conventional semi-supervised algorithms, is the use of unlabeled data. The learning algorithm finds spatio-temporal relationships in the unlabeled data, and pseudolabels are generated to compensate for the lack of labeled training data. In the next step, another balancing-optimization learning algorithm learns a positioning model. The proposed method is evaluated for estimating the location of a smartphone user by using a Wi-Fi received signal strength indicator (RSSI) measurement. The experimental results show that the developed learning algorithm outperforms some existing semi-supervised algorithms according to the variation of the number of training data and access points. Also, the proposed method is discussed in terms of why it gives better performance, by the analysis of the impact of the learning parameters. Moreover, the extended localization scheme in conjunction with a particle filter is executed to include additional information, such as a floor plan.

scholarly journals Utilization of MIMO Concept for Optical Communication System under Fog Condition

2019 ◽  
Vol 17 (2) ◽  
pp. 130-135
Author(s):  
Farouk Shakir ◽  
Mazin Ali A. Ali ◽  
Firas Ameer
Keyword(s):  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Cost Effective ◽  
Free Space Optical ◽  
Single Input Single Output ◽  
High Data ◽  
Single Output ◽  
Received Power ◽  
Single Input

Free-space optical (FSO) communication consider license free, high data rate, wide bandwidth and cost-effective. Multi-input Multi-output (MIMO) systems can be employed to reduce the attenuation by heavy fog and improve FSO channel capacity. In this paper a single-input single-output and multi–input multi-output examined to investigate the performance of these systems under heavy fog. A comparison is made in terms of received optical power, signal to noise ratio, and bit error rate (BER) using OptiSystem version 7.0. The signal reaches to link up to 1.7km, 1.55km, 1.5km, and 1.4km for 4Tx/4Rx, 3Tx/3Rx, 2Tx/2Rx, 1Tx/1Rxrespectively. The results showed that the quality of received power is enhancement by using up to four beams.

Study and Simulation on Turbulent Atmosphere Modeled by Gamma-Gamma Distributions

2013 ◽  
Vol 760-762 ◽  
pp. 204-208
Author(s):  
Jun Ao ◽  
Yun Zhi Xia ◽  
Long Che ◽  
Tao Zhang ◽  
Chun Bo Ma
Keyword(s):  
Gamma Distribution ◽  
Communication Systems ◽  
Structure Constant ◽  
Distribution Model ◽  
Turbulence Structure ◽  
Free Space Optical ◽  
Gamma Distributions ◽  
Optical Communication Systems ◽  
Space Optical Communication ◽  
The Impact

Gamma-Gamma distribution model is widely used in studying the impact of the atmospheric turbulence on the Free Space Optical communication systems. This study introduces the Gamma-Gamma distribution model, simulates and studies the spot changes over distance, wavelength, turbulence structure constant and transmit aperture, respectively. Finally, the simulation results show that the Gamma-Gamma distribution is more suitable for middle-strong turbulence than weak turbulence.

Changes in antibiotic usage and susceptibility in nosocomial Enterobacteriaceae and Pseudomonas isolates following the introduction of ertapenem to hospital formulary

2011 ◽  
Vol 140 (1) ◽  
pp. 115-125 ◽  
Author(s):  
C. J. GRABER ◽  
C. HUTCHINGS ◽  
F. DONG ◽  
W. LEE ◽  
J. K. CHUNG ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Interrupted Time Series ◽  
Antibiotic Usage ◽  
Cross Resistance ◽  
Hospital Formulary ◽  
Time Series Analyses ◽  
The Mean ◽  
The Impact ◽  
Change In Mean

SUMMARYThere is concern that widespread usage of ertapenem may promote cross-resistance to other carbapenems. To analyse the impact that adding ertapenem to our hospital formulary had on usage of other broad-spectrum agents and on susceptibilities of nosocomial Enterobacteriaceae and Pseudomonas isolates, we performed interrupted time-series analyses to determine the change in linear trend in antibiotic usage and change in mean proportion and linear trend of susceptibility pre- (March 2004–June 2005) and post- (July 2005–December 2008) ertapenem introduction. Usage of piperacillin-tazobactam (P=0·0013) and ampicillin-sulbactam (P=0·035) declined post-ertapenem introduction. For Enterobacteriaceae, the mean proportion susceptible to ciprofloxacin (P=0·016) and piperacillin-tazobactam (P=0·038) increased, while the linear trend in susceptibility significantly increased for cefepime (P=0·012) but declined for ceftriaxone (P=0·0032). For Pseudomonas, the mean proportion susceptible to cefepime (P=0·011) and piperacillin-tazobactam (P=0·028) increased, as did the linear trend in susceptibility to ciprofloxacin (P=0·028). Notably, no significant changes in carbapenem susceptibility were observed.

scholarly journals Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules

2020 ◽  
Vol 10 (23) ◽  
pp. 8380
Author(s):  
Laialy Darwesh ◽  
Natan Kopeika
Keyword(s):  
Deep Learning ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Optical Power ◽  
Detection Methods ◽  
Free Space Optical ◽  
Strong Turbulence ◽  
Energy Efficiency Improvement ◽  
Mmse Estimator

Free space optical communication (FSO) is widely deployed to transmit high data rates for rapid communication traffic increase. Asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation is a very efficient FSO communication technique in terms of transmitted optical power. However, its performance is limited by atmospheric turbulence. When the channel includes strong turbulence or is non-deterministic, the bit error rate (BER) increases. To reach optimal performance, the ACO-OFDM decoder needs to know accurate channel state information (CSI). We propose novel detection using different deep learning (DL) algorithms. Our DL models are compared with minimum mean square error (MMSE) detection methods in different turbulent channels and improve performance especially for non-stationary and non-deterministic channels. Our models yield performance very close to that of the MMSE estimator when the channel is characterized by weak or strong turbulence and is stationary. However, when the channel is non-stationary and variable, our DL model succeeds in improving the performance of the system and decreasing the signal to noise ratio (SNR) by more than 8 dB compared to that of the MMSE estimator, and it succeeds in recovering the received data without needing to know accurate CSI. Our DL decoders also show notable speed and energy efficiency improvement.

Aperture shapes and the effectiveness of ground-based large and extremely large telescopes

2020 ◽  
Vol 496 (4) ◽  
pp. 5414-5422
Author(s):  
Armen V Hakobyan
Keyword(s):  
Adaptive Optics ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Optical Transmittance ◽  
Circular Aperture ◽  
Point Spread Functions ◽  
Light Gathering ◽  
Observational Mode ◽  
Large Telescopes ◽  
The Impact

ABSTRACT Aperture shapes in modern large and forthcoming extremely large telescopes (ELTs), with effective light-gathering sizes more than D ∼ 10 m, differ significantly from the desirable circular one. They deliver specific point spread functions, which may also differ notably from that of the fine structure of the classical Airy pattern. The optical power of such a telescope can be changed notably compared with a circular aperture with the same area. The presence of atmospheric optical turbulence complicates the effect additionally and makes it seeing- and wavelength-dependent. So, what is the impact of a non-circular pupil on telescope exploitation? It concerns the efficiency, which is an important point, especially for instruments of such a class. In this research an attempt is made to assess the values of these changes in the context of the Keck, HDRT, GMT, TMT and ELT telescopes. Relative performance characteristics (integral contrast and signal-to-noise ratio, S/N) of the telescopes, working in the seeing-limited regime, under a range of plausible turbulence conditions, for a wide (from UV to mid-IR) spectral region are obtained. The partial role of central obscuration is assessed. The effect of adaptive optics implementation in this context is also analysed. It is shown that, for instance, maximal S/N degradation due to the non-circularity of the pupil shape can be as much as $\sim 6~{{\ \rm per\ cent}}$ (TMT) to $30~{{\ \rm per\ cent}}$ (HDRT), depending on the telescope and observational mode. The numbers are comparable with or may even substantially exceed the losses that could be caused by the other parameters (e.g. residual wave-front error, optical transmittance) relevant to the quality of the optical system.

scholarly journals Forecasting time series with multivariate copulas

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Clarence Simard ◽  
Bruno Rémillard
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Financial Data ◽  
Practical Implementation ◽  
Estimation Errors ◽  
Marginal Distributions ◽  
Test Of Independence ◽  
Multivariate Method ◽  
Forecasting Method ◽  
The Impact

AbstractIn this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies, as well as the structure of the dependence. We also look at the impact of the marginal distributions. The impact of estimation errors on the performance of the predictions is also considered. In all the experiments, we compare predictions from our multivariate method with predictions from the univariate version which has been introduced in the literature recently. To simplify implementation, a test of independence between univariate Markovian time series is proposed. Finally, we illustrate the methodology by a practical implementation with financial data.

scholarly journals RITS: A Toolbox for Assessing Complex Interventions via Interrupted Time Series Models

2021 ◽  
Author(s):  
Maricela Francis Cruz ◽  
Marco A. Pinto-Orellana ◽  
Daniel L. Gillen ◽  
Hernando Ombao
Keyword(s):  
Time Series ◽  
Change Point ◽  
Complex Interventions ◽  
Interrupted Time Series ◽  
Correlation Structure ◽  
Experimental Methodology ◽  
Post Intervention ◽  
Correlation Structures ◽  
The Mean ◽  
The Impact

Abstract Background: Various interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure. Results: This article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures. Conclusions: The RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.

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