4 research outputs found
FoVR: Attention-based VR Streaming through Bandwidth-limited Wireless Networks
Consumer Virtual Reality (VR) has been widely used in various application
areas, such as entertainment and medicine. In spite of the superb immersion
experience, to enable high-quality VR on untethered mobile devices remains an
extremely challenging task. The high bandwidth demands of VR streaming
generally overburden a conventional wireless connection, which affects the user
experience and in turn limits the usability of VR in practice. In this paper,
we propose FoVR, attention-based hierarchical VR streaming through
bandwidth-limited wireless networks. The design of FoVR stems from the insight
that human's vision is hierarchical, so that different areas in the field of
view (FoV) can be served with VR content of different qualities. By exploiting
the gaze tracking capacity of the VR devices, FoVR is able to accurately
predict the user's attention so that the streaming of hierarchical VR can be
appropriately scheduled. In this way, FoVR significantly reduces the bandwidth
cost and computing cost while keeping high quality of user experience. We
implement FoVR on a commercial VR device and evaluate its performance in
various scenarios. The experiment results show that FoVR reduces the bandwidth
cost by 88.9% and 76.2%, respectively compared to the original VR streaming and
the state-of-the-art approach
Continuous Gaze Tracking With Implicit Saliency-Aware Calibration on Mobile Devices
Gaze tracking is a useful human-to-computer interface, which plays an
increasingly important role in a range of mobile applications. Gaze calibration
is an indispensable component of gaze tracking, which transforms the eye
coordinates to the screen coordinates. The existing approaches of gaze tracking
either have limited accuracy or require the user's cooperation in calibration
and in turn hurt the quality of experience. We in this paper propose vGaze,
continuous gaze tracking with implicit saliency-aware calibration on mobile
devices. The design of vGaze stems from our insight on the temporal and spatial
dependent relation between the visual saliency and the user's gaze. vGaze is
implemented as a light-weight software that identifies video frames with
"useful" saliency information, sensing the user's head movement, performs
opportunistic calibration using only those "useful" frames, and leverages
historical information for accelerating saliency detection. We implement vGaze
on a commercial mobile device and evaluate its performance in various
scenarios. The results show that vGaze can work at real time with video
playback applications. The average error of gaze tracking is 1.51 cm (2.884
degree) which decreases to 0.99 cm (1.891 degree) with historical information
and 0.57 cm (1.089 degree) with an indicator
Urban PM2.5 concentration prediction via attention-based CNN–LSTM.
Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN-LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coecient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance
Overall study of solar simulation optical system with large irradiated surface using free-form concentrator to improve uniformity
Summary: Large irradiation surface solar simulator often has the problem of low irradiation uniformity. Therefore, a method for designing a large irradiation surface solar simulator with high irradiation uniformity is proposed. According to the law of conservation of energy and the edge-ray principle of non-imaging optics, the free-form surface concentrator is designed and optimized by using the simulated annealing algorithm based on Bessel curve to improve the incident beam uniformity of the integrator. The optical integrator and projection system are also designed and optimized to eliminate aberrations, improve light efficiency, and enlarge the irradiation area. The design is verified using LightTools software and achieves an effective irradiation size of Φ1200 mm with an irradiance of a solar constant and an irradiation uniformity of less than 2.0%. This study provides accurate and reliable solar irradiation for laboratory calibration and performance testing of spacecraft payloads