Pointing error statistics

This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.

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Performance analysis of l ong‐reach 40/ 40 Gbps mode division multiplexing‐based hybrid time and wavelength division multiplexing passive optical network / free‐space optics using Gamma‐Gamma fading model with pointing error under different weather conditions

Transactions on Emerging Telecommunications Technologies ◽

10.1002/ett.4214 ◽

2021 ◽

Author(s):

Meet Kumari ◽

Reecha Sharma ◽

Anu Sheetal

Keyword(s):

Performance Analysis ◽

Wavelength Division Multiplexing ◽

Optical Network ◽

Weather Conditions ◽

Passive Optical Network ◽

Free Space Optics ◽

Pointing Error ◽

Wavelength Division ◽

Space Optics ◽

Mode Division Multiplexing

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Impact of Pointing Error on the BER Performance of an OFDM Optical Wireless Communication Link over Turbulent Condition

Journal of Optical Communications ◽

10.1515/joc-2020-0021 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Bobby Barua ◽

S. P. Majumder

Keyword(s):

Noise Analysis ◽

Data Rate ◽

Channel Noise ◽

Communication Link ◽

Optical Wireless ◽

Power Penalty ◽

Major Power ◽

Pointing Error ◽

Turbulent Condition ◽

Ber Performance

AbstractAn analytical approach is developed in this paper to evaluate the bit error rate (BER) performance of an optical wireless (OW) communication system with multiplexing of the RF orthogonal frequency division (OFDM) over turbulent condition taking into account the effect of pointing error. The received signal is detected through direct detection receiver followed by RF synchronous demodulation including the effect of OW channel and different form of noises such as receiver thermal noise, background channel noise and photo detector shot noise. Analysis is developed for an OFDM system over the OW channel, taking into account the effect of pointing error between the transmitter and the receiver in turbulent condition and the analysis reveals that the OFDM OW system is less affected by pointing error with deference to the major power penalty at BER performance. For instance, power penalty at BER 10−9 is found to be 3 dB for 256 OFDM subcarriers with 9 millidegree displacement angle at a data rate of 10 Gbps under turbulent condition. It is found that the system is more influenced by the atmospheric turbulence at a higher data rate.

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Optimization of Signal Processing Applications Using Parameterized Error Models for Approximate Adders

ACM Transactions on Embedded Computing Systems ◽

10.1145/3430509 ◽

2021 ◽

Vol 20 (2) ◽

pp. 1-25

Author(s):

Celia Dharmaraj ◽

Vinita Vasudevan ◽

Nitin Chandrachoodan

Keyword(s):

Signal Processing ◽

Circuit Design ◽

Computational Efficiency ◽

Noise Power ◽

Mean Square ◽

Error Statistics ◽

Error Models ◽

Optimization Framework ◽

Accuracy Constraint ◽

Multi Level

Approximate circuit design has gained significance in recent years targeting error-tolerant applications. In the literature, there have been several attempts at optimizing the number of approximate bits of each approximate adder in a system for a given accuracy constraint. For computational efficiency, the error models used in these routines are simple expressions obtained using regression or by assuming inputs or the error is uniformly distributed. In this article, we first demonstrate that for many approximate adders, these assumptions lead to an inaccurate prediction of error statistics for multi-level circuits. We show that mean error and mean square error can be computed accurately if static probabilities of adders at all stages are taken into account. Therefore, in a system with a certain type of approximate adder, any optimization framework needs to take into account not just the functionality of the adder but also its position in the circuit, functionality of its parents, and the number of approximate bits in the parent blocks. We propose a method to derive parameterized error models for various types of approximate adders. We incorporate these models within an optimization framework and demonstrate that the noise power is computed accurately.

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