Rotation Angle Estimation Algorithms for Textures and Their Real-Time Implementation on the FU-SmartCam

2007 ◽  
Author(s):  
Cihan Ulas ◽  
Sumeyra Demir ◽  
Onur Toker ◽  
Kemal Fidanboylu
Keyword(s):  
Real Time ◽  
Rotation Angle ◽  
Angle Estimation ◽  
Estimation Algorithms

Investigation to Angular Resolution in Cube-Orientation-Based Interactions

2012 ◽  
Vol 569 ◽  
pp. 785-788
Author(s):  
Ji Bin Yin ◽  
Tao Liu
Keyword(s):  
Real Time ◽  
Angular Resolution ◽  
Rotation Angle ◽  
Fiducial Marker ◽  
Selection Task ◽  
Experimental Results ◽  
Cube Orientation ◽  
Angle Interval ◽  
The Relationship

This study empirically explores a cube-shaped tool, focusing on the properties of its angle input. By tracking the fiducial marker on each side of the cube in real-time, basic operations such as rotating and tilting can produce the angle input modalities. We conduct an experiment that investigates the use of the angle input to perform a set of experimental tasks. In the experiment we design an angle selection task, varying the angle position and interval. The experimental results show that for rotation angle even 1 degree of angle can still be accurately discriminated whereas that limit goes beyond 3 degrees in case of tilt. A logarithm model can describe the relationship between angle interval and selection time.

Doppler Angle Estimation of Pulsatile Flows Using AR Modeling

2002 ◽  
Vol 24 (2) ◽  
pp. 65-80 ◽  
Author(s):  
Chih-Kuang Yeh ◽  
Pai-Chi Li
Keyword(s):  
Real Time ◽  
Doppler Spectrum ◽  
Acquisition Time ◽  
Two Dimensional ◽  
Flow Measurements ◽  
Angle Estimation ◽  
Flow Angle ◽  
Doppler Angle ◽  
Wide Range ◽  
Pulsatile Flows

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.

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

2004 ◽  
Vol 35 (1) ◽  
pp. 330 ◽  
Author(s):  
Baoxin Li ◽  
Xiao-Fan Feng ◽  
Scott Daly ◽  
Ibrahim Sezan ◽  
Peter van Beek ◽  
...  
Keyword(s):  
Real Time ◽  
Color Correction ◽  
Viewing Angle ◽  
Angle Estimation

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

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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