Adaptive Time Window Linear Regression for Outage Prediction in Q/V Band Satellite Systems

2018 ◽  
Vol 7 (5) ◽  
pp. 808-811 ◽  
Author(s):  
Tomaso De Cola ◽  
Maurizio Mongelli
Keyword(s):  
Linear Regression ◽  
Time Window ◽  
Satellite Systems ◽  
V Band ◽  
Adaptive Time

scholarly journals High-contrast photoacoustic imaging through scattering media using correlation detection of adaptive time window

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Liqi Yu ◽  
Jialin Sun ◽  
Xinjing Lv ◽  
Qi Feng ◽  
Huimei He ◽  
...  
Keyword(s):  
Light Scattering ◽  
Time Window ◽  
Photoacoustic Imaging ◽  
Signal To Noise Ratio ◽  
Photoacoustic Signal ◽  
High Contrast ◽  
Scattering Media ◽  
Window Method ◽  
Adaptive Time ◽  
Imaging Depth

AbstractPhotoacoustic imaging has the advantages of high contrast and deep imaging depth. However, with the increasing of imaging depth, the signal-to-noise ratio (SNR) of the detected signal decreases, due to the light scattering that seriously affects the recovery image quality. In this paper, we experimentally demonstrated that higher contrast photoacoustic imaging was achieved using photoacoustic wavefront shaping technology in the presence of light scattering and low SNR signals. The imaging contrast is improved from 1.51 to 5.30. More importantly, we propose a dynamic time window method for the photoacoustic signal extraction algorithm, named correlation detection of adaptive time window, which further improves the contrast of photoacoustic imaging to 9.57. Our method effectively improves the contrast of photoacoustic imaging through scattering media.

Validation of ground technologies for future Q/V band satellite systems: The QV — LIFT project

2018 ◽  
Author(s):  
Giuseppe Codispoti ◽  
Giorgia Parca ◽  
Mauro De Sanctis ◽  
Marina Ruggieri ◽  
Tommaso Rossi ◽  
...  
Keyword(s):  
Satellite Systems ◽  
V Band

A Precise Weighting Approach with Application to Combined L1/B1 GPS/BeiDou Positioning

2014 ◽  
Vol 67 (5) ◽  
pp. 911-925 ◽  
Author(s):  
Changsheng Cai ◽  
Lin Pan ◽  
Yang Gao
Keyword(s):  
Time Window ◽  
A Priori ◽  
Estimation Method ◽  
Satellite System ◽  
Positioning Accuracy ◽  
Satellite Systems ◽  
Beidou System ◽  
Component Estimation ◽  
Navigation Service ◽  
Global Navigation Satellite

The BeiDou system has been providing a regional navigation service since 27 December 2012. The Global Navigation Satellite System (GNSS) user community will benefit from combined Global Positioning System (GPS)/BeiDou positioning due to improved positioning accuracy, reliability and availability. But to achieve the best positioning solutions, precise weights of the GPS and BeiDou observations are important since this involves the processing of measurements from two different satellite systems with different quality. Currently, a priori variances are typically used to determine the weights of different types of observations. However, such an approach may not be precise since many un-modelled errors are not accounted for. The Helmert variance component estimation method is more appropriate in this case to determine the weights of GPS and BeiDou observations. This requires high redundant observations in order to obtain reliable solutions, which will be a concern in the case of insufficient numbers of visible satellites. To address this issue, a weighting approach is proposed by a combination of the Helmert method and a moving-window average filter. In this approach, the filter is applied to combine all epoch-by-epoch weight estimates within a time window. As a result, more precise and reliable weights for GPS and BeiDou observations can be obtained at every epoch. Both static and kinematic tests in open sky and under tree environments are conducted to assess the performance of the new weighting approach. The results indicate significantly improved positioning accuracy.

Adaptive time-window length based on online performance measurement in SSVEP-based BCIs

2015 ◽  
Vol 149 ◽  
pp. 93-99 ◽  
Author(s):  
Janir Nuno da Cruz ◽  
Feng Wan ◽  
Chi Man Wong ◽  
Teng Cao
Keyword(s):  
Performance Measurement ◽  
Time Window ◽  
Window Length ◽  
Adaptive Time

scholarly journals Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based Models

2020 ◽  
Author(s):  
Ajit Kumar Pasayat ◽  
Satya Narayan Pati ◽  
Aashirbad Maharana
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Growth Model ◽  
Linear Regression Model ◽  
Exponential Growth ◽  
Time Window ◽  
Epidemic Curve ◽  
First Case ◽  
Exponential Growth Model

In this study, we analyze the number of infected positive cases of COVID-19 outbreak with concern to lockdown in India in the time window of February 11th 2020 to Jun 30th 2020. The first case in India was reported in Kerala on January 30th 2020. To break the chain of spreading, Government announced a nationwide lockdown on March 24th 2020, which is increased two times. The Ongoing lockdown 3.0 is over on May 18th, 2020. We derived how the lockdown relaxation is going to impact on containment of the outbreak. Here the Exponential Growth Model has been used to derive the epidemic curve based on the data collected from February 11th 2020, to May 11th 2020, and the Machine Learning based Linear Regression model that gives the epidemic curve to predict the cases with the continuous flow of the lockdown. We estimate that if the lockdown is continuing with more relaxation, then the estimated infected cases reach up to 1.16 crores by June 30th 2020, and the lockdown would persist with current restriction, then the expected predicted infected cases are 5.69 lacs. The Exponential Growth Model and the Linear Regression Model are advantageous to predict the number of affected cases of COVID-19. These models can be used for forecasting in long term intervals. It shows from our result that lockdown with certain restriction has a vital role in preventing the spreading of this epidemic in this current situation.

A Comparison Method for Mean Frequency Estimators for Doppler Ultrasound

1992 ◽  
Vol 14 (4) ◽  
pp. 367-386 ◽  
Author(s):  
Peter J. Brands ◽  
Arnold P.G. Hoeks
Keyword(s):  
Linear Regression ◽  
Doppler Ultrasound ◽  
Time Window ◽  
Comparison Method ◽  
Regression Estimator ◽  
Mean Frequency ◽  
Parameter Combination ◽  
Wide Range ◽  
Complex Linear ◽  
Linear Regression Estimator

Mean frequency estimators as used in pulsed Doppler ultrasound equipment should provide an accurate (quality) and consistent (robustness) estimate over a wide range of signal conditions. In a simplified signal model, the main parameters to consider are the noise level, mean frequency, bandwidth and power of both the Doppler signal and the stationary component over a given time window. It may be expected that one estimator for a given parameter combination exhibits a good performance while another estimator for the same parameter combination behaves poorly. To allow direct comparison between different types of frequency estimators, a method is introduced to evaluate the quality and robustness of estimators for a common signal space covering a wide range of realistic parameter combinations. The method is illustrated using three different mean frequency estimators: (1) a first order autoregressive estimator in combination with a stationary echo filter; (2) a second order autoregressive estimator; and (3) a complex linear regression estimator in combination with a stationary echo filter. It is concluded that, for the parameter combinations considered, the complex linear regression estimator exhibits the best quality (low variance and bias of the estimate) and robustness (consistent quality for all parameter combinations).

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