Beamforming Design for IRS-and-UAV-aided Two-way Amplify-and-Forward Relay Networks

As a promising solution to improve communication quality, unmanned aerial vehicle (UAV) has been widely integrated into wireless networks. In this paper, for the sake of enhancing the message exchange rate between User1 (U1) and User2 (U2), an intelligent reflective surface (IRS)-and-UAV- assisted two-way amplify-and-forward (AF) relay wireless system is proposed, where U1 and U2 can communicate each other via a UAV-mounted IRS and an AF relay. Besides, an optimization problem of maximizing minimum rate is casted, where the variables, namely AF relay beamforming matrix and IRS phase shifts of two time slots, need to be optimized. To achieve a maximum rate, a low-complexity alternately iterative (AI) scheme based on zero forcing and successive convex approximation (LC-ZF-SCA) algorithm is put forward, where the expression of AF relay beamforming matrix can be derived in semi-closed form by ZF method, and IRS phase shift vectors of two time slots can be respectively optimized by utilizing SCA algorithm. To obtain a significant rate enhancement, a high-performance AI method based on one step, semidefinite programming and penalty SCA (ONS-SDP-PSCA) is proposed, where the beamforming matrix at AF relay can be firstly solved by singular value decomposition and ONS method, IRS phase shift matrices of two time slots are optimized by SDP and PSCA algorithms. Simulation results present that the rate performance of the proposed LC-ZF-SCA and ONS-SDP-PSCA methods surpass those of random phase and only AF relay. In particular, when total transmit power is equal to 30dBm, the proposed two methods can harvest more than 68.5% rate gain compared to random phase and only AF relay. Meanwhile, the rate performance of ONS-SDP-PSCA method at cost of extremely high complexity is superior to that of LC-ZF-SCA method

Statistical Inference for Panel Dynamic Simultaneous Equations Models

Abstract We study the identi…cation and estimation of panel dynamic simultaneous equations models. We show that the presence of time-persistent individual-speci…c e¤ects does not lead to changes in the identi…cation conditions of traditional Cowles Commission dynamic simultaneous equations models. However, the limiting properties of the estimators depend on the way the cross-section dimension, N , or the time series dimension, T , goes to in…nity. We propose three limited information estimator: panel simple instrumental variables (PIV), panel generalized two stage least squares (PG2SLS), and panel limited information maximum likelihood estimation (PLIML). We show that they are all asymptotically unbiased independent of the way of how N or T tends to in…nity. Monte Carlo studies are conducted to compare the performance of the PLIML, PIV, PG2SLS, the Arellano-Bond type generalized method of moments and the Akashi-Kunitomo least variance ratio estimator and to demonstrate the sensitivity of statistical inference to the asymptotic bias of an estimator

Noise-to-State Stability in Probability for Random Complex Dynamical Systems on Networks

This paper studies noise-to-state stability in probability (NSSP) for random complex dynamical systems on networks (RCDSN). On the basis of Kirchhoff’s matrix theorem in graph theory, an appropriate Lyapunov function which combines with every subsystem for RCDSN is established. Moreover, some sufficient criteria closely related to the topological structure of RCDSN are given to guarantee RCDSN to meet NSSP by means of the Lyapunov method and stochastic analysis techniques. Finally, to show the usefulness and feasibility of theoretical findings, we apply them to random coupled oscillators on networks (RCON), and some numerical tests are given

Multi-Sensor Fusion Self-Supervised Deep Odometry and Depth Estimation

This paper presents a new deep visual-inertial odometry and depth estimation framework for improving the accuracy of depth estimation and ego-motion from image sequences and inertial measurement unit (IMU) raw data. The proposed framework predicts ego-motion and depth with absolute scale in a self-supervised manner. We first capture dense features and solve the pose by deep visual odometry (DVO), and then combine the pose estimation pipeline with deep inertial odometry (DIO) by the extended Kalman filter (EKF) method to produce the sparse depth and pose with absolute scale. We then join deep visual-inertial odometry (DeepVIO) with depth estimation by using sparse depth and the pose from DeepVIO pipeline to align the scale of the depth prediction with the triangulated point cloud and reduce image reconstruction error. Specifically, we use the strengths of learning-based visual-inertial odometry (VIO) and depth estimation to build an end-to-end self-supervised learning architecture. We evaluated the new framework on the KITTI datasets and compared it to the previous techniques. We show that our approach improves results for ego-motion estimation and achieves comparable results for depth estimation, especially in the detail area

Microstructure damage of directionally solidified alloy turbine blade after service

Turbine blades are the most demanding components in aircraft engines, and their performance is related to the safety of the whole engine. Due to the complex service environment and harsh service conditions of blades, various types of damage cannot be prevented in service. Therefore, it is of great engineering and economic significance to study the service damage of blades. In this paper, the directional solidification alloy turbine blade after actual service was selected as the research object. The cross section position of 80% upper height of the blade was intercepted, and the qualitative and quantitative microstructure analysis was carried out by SEM and EDS analysis. The results show that there are two different types of γ' phases in this leaf. One kind of γ' phase has small size and regular shape, the other has large size and irregular shape. The degree of microscopic damage among different parts of the blade is characterized with the help of dimensional distribution characterization of the γ' phase of each part, combined with the analysis of hardness testing of each part of the cross-section.The results show that the service conditions of different parts are different, and the degree of microstructure damage is different. In addition, matrix crack and coating crack in some parts of blade are summarized and analyzed

Dynamic Kinetic Resolution of Phthalides via Asymmetric Transfer Hydrogenation: A Strategy Constructs 1,3-Distereocentered 3‑(2-Hydroxy-2-arylethyl)isobenzofuran-1(3<i>H</i>)‑one

Dynamic kinetic resolution of phthalides through asymmetric transfer hydrogenation for the construction of 3-(2-hydroxy-2-arylethyl)­isobenzofuran-1­(3<i>H</i>)-one with 1,3-distereocenters has been developed. This procedure is carried out under a mild condition at 40 °C catalyzed with RuCl­[(<i>S</i>,<i>S</i>)-TsDPEN]­(mesitylene) using HCOOH/Et₃N (5:2) as a hydrogen source. A variety of phthalides are smoothly transferred to provide optically pure phthalides with high yields, excellent enantioselectivities, and acceptable diastereomeric ratios

Spatiotemporal Variation of Drought and Associated Multi-Scale Response to Climate Change over the Yarlung Zangbo River Basin of Qinghai–Tibet Plateau, China

Drought is one of the most widespread and threatening natural disasters in the world, which has terrible impacts on agricultural irrigation and production, ecological environment, and socioeconomic development. As a critical ecologically fragile area located in southwest China, the Yarlung Zangbo River (YZR) basin is sensitive and vulnerable to climate change and human activities. Hence, this study focused on the YZR basin and attempted to investigate the spatiotemporal variations of drought and associated multi-scale response to climate change based on the scPDSI (self-calibrating Palmer drought severity index) and CRU (climate research unit) data. Results showed that: (1) The YZR basin has experienced an overall wetting process from 1956 to 2015, while a distinct transition period in the mid 1990s (from wet to dry) was detected by multiple statistical methods. (2) Considering the spatial variation of the scPDSI, areas showing the significantly wetting process with increasing scPDSI values were mostly located in the arid upstream and midstream regions, which accounted for over 48% area of the YZR basin, while areas exhibiting the drying tendency with decreasing scPDSI values were mainly concentrated in the humid southern part of the YZR basin, dominating the transition period from wet to dry, to which more attention should be paid. (3) By using the EEMD (ensemble empirical mode decomposition) method, the scPDSI over the YZR basin showed quasi-3-year and quasi-9-year cycles at the inter-annual scale, while quasi-15-year and quasi-56-year cycles were detected at the inter-decadal scale. The reconstructed inter-annual scale showed a better capability to represent the abrupt change characteristic of drought, which was also more influential to the original time series with a variance contribution of 55.3%, while the inter-decadal scale could be used to portray the long-term drought variation process with a relative lower variance contribution of 29.1%. (4) The multi-scale response of drought to climate change indicated that changes of precipitation (PRE) and diurnal temperature range (DTR) were the major driving factors in the drought variation at different time scales. Compared with potential evapotranspiration (PET), DTR was a much more important climate factor associated with drought variations by altering the energy balance, which is more obvious over the YZR basin distributed with extensive snow cover and glaciers. These findings could provide important implications for ecological environment protection and sustainable socioeconomic development in the YZR basin and other high mountain regions