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As part of video transmission, video quality assessment plays an important role in improving the user’s visual quality and reducing transmission bitrate. However, the existing assessment schemes for the tile-based transmission mechanism cannot accurately predict the video quality. In this paper, we propose a tile-based panoramic video quality assessment method to optimize the transmission strategy for tile-based streaming. Considering the distortion of panoramic video projection, the inverse of gnomonic projection and bilinear interpolation are utilized to convert the planar tiles to sphere. Video Multimethod Assessment Fusion (VMAF) is applied to obtain the tile quality score. Besides, the user’s head movement and eye movement are combined to generate a tile weight map, and generate the final score of the panoramic video through weighted averaging. In addition, considering the impact of the quality fluctuation of the tiles within field of view, the tile quality loss model is established from multiple dimensions including the encoding parameter, viewport position and video content. Experimental results show that there is a high correlation between the predicted results and actual subjective scores, under two video quality assessment datasets. Furthermore, we integrate the method into the panoramic video transmission system. Compared with the tile-based adaptive rate algorithm, the proposed method degrades the video quality by only 3%, while reducing the bandwidth consumption by 16.99%. [ABSTRACT FROM AUTHOR]

We put forward a focusing formula to describe the guided waves periodical focusing phenomenon, which, apart from being a fundamental problem in the guided waves’ propagation in the pipes, has essential applications in the field of nondestructive evaluation. Due to the partial circumferential loads or nonaxisymmetric defects, the guided waves are not only in the zeroth-order axisymmetric forms but also in the higher order nonaxisymmetric forms. When multiple orders of the same mode exist simultaneously, the angular profile is adopted to describe the circumferential energy distribution of the superposed wave field. However, the angular profile varies in the propagation process. In our finding, this variation is periodic, meaning that the circumferential energy will repeat the process of dispersing and focusing. Thus, we put forward a focusing formula to describe the phenomenon. The proposed formula indicates that the angular profile varies periodically with the ratio of propagation distance to wavenumber, and this period is only related to the pipe radius. Thus, three factors, including propagation distance, excitation frequency, and pipe radius, will affect the angular profile. Moreover, we established an experimental system to verify this phenomenon, based on which we have designed three groups of experiments to investigate these three factors. The experimental results are in good agreement with the theoretical predictions. [ABSTRACT FROM AUTHOR]

Recently, a novel machine learning technique, federated learning, attracts ever-increasing interests from academia to industry. The main idea of federated learning is to collaboratively train a global optimal machine learning model among all the participants. During the process of parameter updating, the communication cost of the system or network can be extremely huge with a large number of iterations and participants. Although the edge computing paradigm can decrease the latency to a certain extent, how to obtain further delay reduction is still a challenge. Therefore, to address the problem, we firstly model the corresponding problem into a finite-sum optimization problem. Then, we propose a federated stochastic variance reduced gradient based method to decrease the number of iterations between the participants and server from the system perspective, and guarantee the accuracy at the same time. Meanwhile, the corresponding convergence analysis is provided. Finally, we test our proposed method on the linear regression problem and the logistic regression problem. The simulation results show that our proposed method can reduce the communication cost significantly compared with general stochastic gradient descent based federated learning. [ABSTRACT FROM AUTHOR]

This paper investigates adaptive streaming of one or multiple tiled 360 videos from a multi-antenna base station (BS) to one or multiple single-antenna users, respectively, in a multi-carrier wireless system. We aim to maximize the video quality while keeping rebuffering time small via encoding rate adaptation at each group of pictures (GOP) and transmission adaptation at each (transmission) slot. To capture the impact of field-of-view (FoV) prediction, we consider three cases of FoV viewing probability distributions, i.e., perfect, imperfect, and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility, and worst total utility as the respective performance metrics. In the single-user scenario, we optimize the encoding rates of the tiles, encoding rates of the FoVs, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. In the multi-user scenario, we adopt rate splitting with successive decoding and optimize the encoding rates of the tiles, encoding rates of the FoVs, rates of the common and private messages, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. Then, we separate the challenging optimization problem into multiple tractable problems in each scenario. In the single-user scenario, we obtain a globally optimal solution of each problem using transformation techniques and the Karush-Kuhn-Tucker (KKT) conditions. In the multi-user scenario, we obtain a KKT point of each problem using the concave-convex procedure (CCCP). Finally, numerical results demonstrate that the proposed solutions achieve notable gains in quality, quality variation, and rebuffering time over existing schemes in all three cases. To the best of our knowledge, this is the first work revealing the impact of FoV prediction on the performance of adaptive streaming of tiled 360 videos. [ABSTRACT FROM AUTHOR]

Remote photoplethysmography (rPPG) is a kind of noncontact technique to measure heart rate (HR) from facial videos. As the demand for long-term health monitoring grows, rPPG attracts much attention from researchers. However, the performance of conventional rPPG methods is easily degenerated due to noise interference. Recently, some deep learning-based rPPG methods have been introduced and they revealed good performance against noise. In this article, we propose a new rPPG method with convolutional neural networks (CNNs) to build a mapping between a spatiotemporal HR feature image to its corresponding HR value. The feature map is constructed in a time-delayed way with noise-contaminated pulse signals extracted from existing rPPG methods. The CNN model is trained using transfer learning where images built from synthetic rPPG signals are taken to train the model first in order to generate initials for the practical one. The synthetic rPPG signals are interpolated from blood volume pulses or electrocardiograms through a modified Akima cubic Hermite interpolation. The proposed method is tested in both within-database and cross-database configurations on public databases. The results demonstrate that our method achieves overall the best performance compared to some other typical rPPG methods. The mean absolute error reaches 5.98 beats per minute and the mean error rate percentage is 7.97% in the cross-database testing on MAHNOB-HCI data set. Besides, some key factors that affect the performance of our method are also discussed which indicates potential ways for further improvements. [ABSTRACT FROM AUTHOR]