P-27: Adaptive Display Color Correction Based on Real-time Viewing Angle Estimation

In quantitative ultrasonic flow measurements, the beam-to-flow angle (i.e., Doppler angle) is an important parameter. An autoregressive (AR) spectral analysis technique in combination with the Doppler spectrum broadening effect was previously proposed to estimate the Doppler angle. Since only a limited number of flow samples are used, real-time two-dimensional Doppler angle estimation is possible. The method was validated for laminar flows with constant velocities. In clinical applications, the flow pulsation needs to be considered. For pulsatile flows, the flow velocity is time-varying and the accuracy of Doppler angle estimation may be affected. In this paper, the AR method using only a limited number of flow samples was applied to Doppler angle estimation of pulsatile flows. The flow samples were properly selected to derive the AR coefficients and then more samples were extrapolated based on the AR model. The proposed method was verified by both simulations and in vitro experiments. A wide range of Doppler angles (from 30° to 78°) and different flow rates were considered. The experimental data for the Doppler angle showed that the AR method using eight flow samples had an average estimation error of 3.50° compared to an average error of 7.08° for the Fast Fourier Transform (FFT) method using 64 flow samples. Results indicated that the AR method not only provided accurate Doppler angle estimates, but also outperformed the conventional FFT method in pulsatile flows. This is because the short data acquisition time is less affected by the temporal velocity changes. It is concluded that real-time two-dimensional estimation of the Doppler angle is possible using the AR method in the presence of pulsatile flows. In addition, Doppler angle estimation with turbulent flows is also discussed. Results show that both the AR and FFT methods are not adequate due to the spectral broadening effects from the turbulence.

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Viewing angle enhancement of a real-time integral imaging system using multi-directional projections and GPU parallel processing

10.1117/12.2251360 ◽

2017 ◽

Author(s):

Md. Ashraful Alam ◽

Md. Sifatul Islam ◽

Mohd. Zishan Tareque ◽

Mahfuze Subhani ◽

M. Rashidur Rahman Rafi ◽

...

Keyword(s):

Parallel Processing ◽

Real Time ◽

Imaging System ◽

Viewing Angle ◽

Integral Imaging ◽

Time Integral

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Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with viewing zenith angle correction

10.5194/amt-2016-79 ◽

2016 ◽

Cited By ~ 1

Author(s):

Benjamin R. Scarino ◽

Patrick Minnis ◽

Thad Chee ◽

Kristopher M. Bedka ◽

Christopher R. Yost ◽

...

Keyword(s):

Skin Temperature ◽

Real Time ◽

Single Channel ◽

Weather Prediction ◽

Surface Radiation ◽

Earth Orbit ◽

Viewing Angle ◽

Clear Sky ◽

Geo Satellite ◽

Surface Skin Temperature

Abstract. Surface skin temperature (Ts) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared- (TIR-) method to retrieve Ts over clear-sky land and ocean surfaces from data taken by geostationary-Earth orbit (GEO) satellite and low-Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of Ts over the diurnal cycle in non-polar regions, while polar Ts retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR) can complement the GEO measurements. The combined global coverage of remotely sensed Ts, along with accompanying cloud and surface radiation parameters, produced in near-real time and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-real-time hourly Ts observations can be assimilated in high-temporal resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived Ts, data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing angle. Therefore, Ts validation with established references is essential, as is proper evaluation of Ts sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based Ts product, derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirical means of correcting for the viewing-angle dependency of satellite land surface temperature (LST) is explained and validated. Application of a daytime nadir-normalization model yields improved accuracy and precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program/NOAA ESRL Surface Radiation network ground stations. These corrections serve as a basis for a means to improve satellite-based LST accuracy, thereby leading to better monitoring and utilization of the data. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

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Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

10.20944/preprints201804.0255.v1 ◽

2018 ◽

Author(s):

Javier Garcia-Guzman ◽

Lisardo Prieto González ◽

Jonatan Pajares Redondo ◽

Mat Max Montalvo Martinez ◽

María Jesús López Boada

Keyword(s):

Neural Networks ◽

Real Time ◽

Low Cost ◽

Roll Angle ◽

Measurement Unit ◽

Raspberry Pi ◽

State Variables ◽

Angle Estimation ◽

Experimental Environment ◽

Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

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