Power Efficient Visible Light Communication (VLC) with Unmanned Aerial Vehicles (UAVs)

A novel approach that combines visible light communication (VLC) with unmanned aerial vehicles (UAVs) to simultaneously provide flexible communication and illumination is proposed. To minimize the power consumption, the locations of UAVs and the cell associations are optimized under illumination and communication constraints. An efficient sub-optimal solution that divides the original problem into two sub-problems is proposed. The first sub-problem is modeled as a classical smallest enclosing disk problem to obtain the optimal locations of UAVs, given the cell association. Then, assuming fixed UAV locations, the second sub-problem is modeled as a min-size clustering problem to obtain the optimized cell association. In addition, the obtained UAV locations and cell associations are iteratively optimized multiple times to reduce the power consumption. Numerical results show that the proposed approach can reduce the total transmit power consumption by at least 53.8% compared to two baseline algorithms with fixed UAV locations.Comment: 4 pages, 4 figures. Accepted for publication in IEEE Communications Letter

Assessing biogeochemical effects and best management practice for a wheat–maize cropping system using the DNDC model

Contemporary agriculture is shifting from a single-goal to a multi-goal strategy, which in turn requires choosing best management practice (BMP) based on an assessment of the biogeochemical effects of management alternatives. The bottleneck is the capacity of predicting the simultaneous effects of different management practice scenarios on multiple goals and choosing BMP among scenarios. The denitrification–decomposition (DNDC) model may provide an opportunity to solve this problem. We validated the DNDC model (version 95) using the observations of soil moisture and temperature, crop yields, aboveground biomass and fluxes of net ecosystem exchange of carbon dioxide, methane, nitrous oxide (N2O), nitric oxide (NO) and ammonia (NH3) from a wheat–maize cropping site in northern China. The model performed well for these variables. Then we used this model to simulate the effects of management practices on the goal variables of crop yields, NO emission, nitrate leaching, NH3 volatilization and net emission of greenhouse gases in the ecosystem (NEGE). Results showed that no-till and straw-incorporated practices had beneficial effects on crop yields and NEGE. Use of nitrification inhibitors decreased nitrate leaching and N2O and NO emissions, but they significantly increased NH3 volatilization. Irrigation based on crop demand significantly increased crop yield and decreased nitrate leaching and NH3 volatilization. Crop yields were hardly decreased if nitrogen dose was reduced by 15% or irrigation water amount was reduced by 25%. Two methods were used to identify BMP and resulted in the same BMP, which adopted the current crop cultivar, field operation schedules and full straw incorporation and applied nitrogen and irrigation water at 15 and 25% lower rates, respectively, than the current use. Our study indicates that the DNDC model can be used as a tool to assess biogeochemical effects of management alternatives and identify BMP

Deep Learning with S-shaped Rectified Linear Activation Units

Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.Comment: Accepted by AAAI-1

A Novel Received Signal Strength Assisted Perspective-three-Point Algorithm for Indoor Visible Light Positioning

In this paper, a received signal strength assisted Perspective-three-Point positioning algorithm (R-P3P) is proposed for visible light positioning (VLP) systems. The basic idea of R-P3P is to joint visual and strength information to estimate the receiver position using 3 LEDs regardless of the LEDs' orientations. R-P3P first utilizes visual information captured by the camera to estimate the incidence angles of visible lights. Then, R-P3P calculates the candidate distances between the LEDs and the receiver based on the law of cosines and the Wu-Ritt's zero decomposition method. Based on the incidence angles, the candidate distances and the physical characteristics of the LEDs, R-P3P can select the exact distances from all the candidate distances. Finally, the linear least square (LLS) method is employed to estimate the position of the receiver. Due to the combination of visual and strength information of visible light signals, R-P3P can achieve high accuracy using 3 LEDs regardless of the LEDs' orientations. Simulation results show that R-P3P can achieve positioning accuracy within 10 cm over 70% indoor area with low complexity regardless of LEDs orientations.Comment: arXiv admin note: substantial text overlap with arXiv:2004.0629

MiR-138 ameliorates myocardial ischemia/reperfusion injury by targeting intercellular cell adhesion molecule 1

Purpose: To explore the effect of miR-138 on regulating intercellular cell adhesion molecule 1 (ICAM-1) expression in endothelial cells to alleviate cardiac ischemia/reperfusion (I/R) injury and its related mechanisms. Methods: The left anterior descending artery of the heart was occluded for 30 min and then perfused for 2 h to induce a rat model of cardiac I/R injury. H9C2 cells were cultured in an anoxic medium without serum to establish the model of hypoxia/reoxygenation (H/R). Triphenyl tetrazolium chloride (TTC) staining was applied to measure myocardial infarction sizes in rat hearts. The mRNA expression levels of miR-138 and ICAM-1 were evaluated by quantitative real-time polymerase chain reaction (qRT-PCR). Dual luciferase reporter assay was used to identify the target of miR-138. The agomiR-138 and miR-138 mimics were transfected into H9C2 cells; exogenous ICAM-1 was also administered, and ROS accumulation, cell viability, and apoptosis were measured. Furthermore, the underlying mechanism was investigated. Results: MiR-138 was downregulated both in vitro and in vivo. AgomiR-138 reduced myocardial infarction area, decreased ROS production and suppressed cell apoptosis in a rat model of cardiac I/R injury. On the other hand, miR-138 mimics increased cell viability, enhanced ROS production and induced cell apoptosis in H/R-induced H9C2 cells. Further analysis verified ICAM-1 as a target of miR- 138. Besides, exogenous ICAM-1 inhibited the protective effect of miR-138 on H/R-induced apoptosis in vitro. Conclusion: MiR-138 may protect against injury of myocardial I/R by targeting ICAM-1. The results also provide insight into miR-138/ICAM-1 axis as new therapeutic targets for myocardial I/R injury. Keywords: Intercellular cell adhesion molecule 1, MicroRNA-138, Myocardial/ischemia reperfusion injury, Reactive oxygen specie

Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems

Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword being assigned to a different observation value. Existing TBMA protocols are based on fixed shared codebooks and on conventional maximum-likelihood or Bayesian decoders, which require knowledge of the distributions of observations and channels. In this letter, we propose a novel design principle for TBMA based on the information bottleneck (IB). In the proposed IB-TBMA protocol, the shared codebook is jointly optimized with a decoder based on artificial neural networks (ANNs), so as to adapt to source, observations, and channel statistics based on data only. We also introduce the Compressed IB-TBMA (CIB-TBMA) protocol, which improves IB-TBMA by enabling a reduction in the number of codewords via an IB-inspired clustering phase. Numerical results demonstrate the importance of a joint design of codebook and neural decoder, and validate the benefits of codebook compression.Comment: 5 pages, 3 figures, accepted by IEEE Signal Processing Letters (SPL

Boundary-Aware Proposal Generation Method for Temporal Action Localization

The goal of Temporal Action Localization (TAL) is to find the categories and temporal boundaries of actions in an untrimmed video. Most TAL methods rely heavily on action recognition models that are sensitive to action labels rather than temporal boundaries. More importantly, few works consider the background frames that are similar to action frames in pixels but dissimilar in semantics, which also leads to inaccurate temporal boundaries. To address the challenge above, we propose a Boundary-Aware Proposal Generation (BAPG) method with contrastive learning. Specifically, we define the above background frames as hard negative samples. Contrastive learning with hard negative mining is introduced to improve the discrimination of BAPG. BAPG is independent of the existing TAL network architecture, so it can be applied plug-and-play to mainstream TAL models. Extensive experimental results on THUMOS14 and ActivityNet-1.3 demonstrate that BAPG can significantly improve the performance of TAL