Stereoscopic video quality assessment based on 3D convolutional neural networks

The research of stereoscopic video quality assessment (SVQA) plays an important role for promoting the development of stereoscopic video system. Existing SVQA metrics rely on hand-crafted features, which is inaccurate and time-consuming because of the diversity and complexity of stereoscopic video distortion. This paper introduces a 3D convolutional neural networks (CNN) based SVQA framework that can model not only local spatio-temporal information but also global temporal information with cubic difference video patches as input. First, instead of using hand-crafted features, we design a 3D CNN architecture to automatically and effectively capture local spatio-temporal features. Then we employ a quality score fusion strategy considering global temporal clues to obtain final video-level predicted score. Extensive experiments conducted on two public stereoscopic video quality datasets show that the proposed method correlates highly with human perception and outperforms state-of-the-art methods by a large margin. We also show that our 3D CNN features have more desirable property for SVQA than hand-crafted features in previous methods, and our 3D CNN features together with support vector regression (SVR) can further boost the performance. In addition, with no complex preprocessing and GPU acceleration, our proposed method is demonstrated computationally efficient and easy to use

Deep learning-based edge caching for multi-cluster heterogeneous networks

© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability

Cu-MOF-Derived C‑Doped CuO/Cu₂O Hollow Nano-Octahedrons for Room-Temperature NO₂ Sensing at the ppb Level

P-type semiconductors [copper oxide (CuO) and cuprous oxide (Cu2O)] have been widely researched as good gas sensing materials due to their unique oxygen adsorption properties. In this work, C-doped CuO/Cu2O hollow nano-octahedrons have been prepared by thermal decomposition of the Cu-based metal–organic framework ([Cu3(BTC)2]n). The CuO and Cu2O nanoparticles form nano-octahedron heterojunctions by a close packing mode. A large number of active heterojunction sites were exposed, which significantly increased the adsorbed oxygen (O2–) content. Meanwhile, doping C in the lattice of the heterojunction effectively reduces the band gap of CuO/Cu2O and promotes the chemical reaction of the target gas on the surface of the material. Density functional theory calculations indicate that the CuO/Cu2O heterojunction has strong adsorption ability for NO2. Electrochemical impedance spectroscopy further reveals that C-doped CuO/Cu2O hollow nano-octahedrons-40 min heterojunction has the best electron transport performance. The sensors have good gas sensing response (22.88–50 ppm) and selectivity to nitrogen dioxide (NO2) at room temperature (28 °C). In addition, the sensor has extremely low detection, which can detect parts per billion level NO2 (20.50% to 100 ppb). It is expected to provide a reference for the development of a room-temperature NO2 sensor

Joint optimization in cached-enabled heterogeneous network for efficient industrial IoT

In the era of industrial 4.0, industrial Internet of Things (IIoT) has brought essential changes to human society. For IIoT, communication in network can be defined as the basic condition for further development and integrated information exchange. In this way, cached-enabled heterogeneous industrial network is necessary to be optimized. In this paper, we consider the optimal geographical placement of contents in cache-enabled heterogeneous networks to minimize the total missing probability. And the probability represents that typical user cannot find requested file in the nearby base stations (BSs). In contract to existing works which only concern content placement, we jointly optimize content placement at BSs and activation densities of BSs of different tiers subject to the cache size limits and the constraint on the BSs energy consumption cost. In addition, the user distribution in this work is modeled by a homogeneous Poisson Point Process. We prove that the original optimization problem can be transformed to a convex problem. The convexity of the optimization problem allows us to apply the KKT conditions to derive useful analytical results of the optimal solution. Based on this, we propose a low-complexity near-optimal algorithm to find the approximated content placement probabilities. We further extend the optimization to heterogeneous networks with the user distribution modeled by the modified Cluster Process. Extensive simulation results show the superior performance of joint optimization of content placement and BSs activation densities compared to only optimizing content placement

Cache-enabled in cooperative cognitive radio networks for transmission performance

The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user's effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching

A comprehensive study on the longissius dorsi muscle of Ashdan yaks under different feeding regimes based on transcriptomic and metabolomic analyses

Yak is an important dominant livestock species at high altitude, and the growth performance of yak has obvious differences under different feeding methods. This experiment was conducted to compare the effects of different feeding practices on growth performance and meat quality of yaks through combined transcriptomic and metabolomic analyses. In terms of yak growth performance, compared with traditional grazing, in-house feeding can significantly improve the average daily weight gain, carcass weight and net meat weight of yaks; in terms of yak meat quality, in-house feeding can effectively improve the quality of yak meat. A combined transcriptomic and metabolomic analysis revealed 31 co-enriched pathways, among which arginine metabolism, proline metabolism and glycerophospholipid metabolism may be involved in the development of the longissimus dorsi muscle of yak and the regulation of meat quality-related traits. The experimental results increased our understanding of yak meat quality and provided data materials for subsequent deep excavation of the mechanism of yak meat quality.</p

Iron-Based Catalysts with Oxygen Vacancies Obtained by Facile Pyrolysis for Selective Hydrogenation of Nitrobenzene

The development of preparation strategies for iron-based catalysts with prominent catalytic activity, stability, and cost effectiveness is greatly significant for the field of catalytic hydrogenation but still remains challenging. Herein, a method for the preparation of iron-based catalysts by the simple pyrolysis of organometallic coordination polymers is described. The catalyst Fe@C-2 with sufficient oxygen vacancies obtained in specific coordination environment exhibited superior nitro hydrogenation performance, acid resistance, and reaction stability. Through solvent effect experiments, toxicity experiments, TPSR, and DFT calculations, it was determined that the superior activity of the catalyst was derived from the contribution of sufficient oxygen vacancies to hydrogen activation and the good adsorption ability of FeO on substrate molecules