IRS辅助的SWIPT系统中基于能效优先的波束成形设计与优化

以智能反射面(intelligent reflecting surface,IRS)辅助的无线携能通信(simultaneous wireless information and power transfer,SWIPT)系统为背景,研究了该系统中基于能效优先的多天线发送端有源波束成形与IRS无源波束成形联合设计与优化方法。以最大化接收端的最小能效为优化目标,构造在发送端功率、接收端能量阈值、IRS相移等多约束下的非线性优化问题,用交替方向乘子法(alternating direction method of multipliers,ADMM)求解。采用Dinkelbach算法转化目标函数,通过奇异值分解(singular value decomposition,SVD)和半定松弛(semi-definite relaxation,SDR)得到发送端有源波束成形向量。采用SDR得到IRS相移矩阵与反射波束成形向量。结果表明,该系统显著降低了系统能量收集(energy harvesting,EH)接收端的能量阈值。当系统总电路功耗为?15 dBm时,所提方案的用户能效为300 KB/J。当IRS反射阵源数与发送天线数均为最大值时,系统可达最大能效。     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

A Photoluminescent Ag10Cu6 Cluster Stablized by a PNNP Ligand and Phenylacetylides Selectively and Reversibly Senses Ammonia in Air and Water

A photoluminescent bimetallic cluster [Ag₁₀Cu₆(bdppthi)₂(C≡CPh)₁₂(MeOH)₂(H₂O)](ClO₄)₄ ( 1 , bdppthi=N,N’-bis(diphenylphosphanylmethyl)-tetrahydroimidazole} was synthesized from the PNNP type ligand bdppthi generated in-situ. Upon excitation at 365 nm, 1 exhibited strong phosphorescent emission at 630 nm, which was selectively quenched by NH₃ in air or water. The sensing of NH₃ was rapid and recoverable, with detection limits of 53 ppm (v/v) in N₂ and 21 μmol/L (0.36 ppm, w/w) for NH₃ ⋅ H₂O in water. Cluster 1 could potentially serve as a bifunctional chemical sensor for the efficient detection of ammonia in waste-gas and waste-water.     

用标准件对梯度磁场定标

就标准厚度卡片在霍尔传感器的定标方面的应用作了有益的探讨。在纵向测量微小长度时,霍尔片处于磁场边缘位置,其对磁场的梯度更大,即灵敏度更高,且靠中心位置的线性更好。     

小型机载激光通信系统设计与验证

针对空空宽带高速通信的需求，设计了小型化机载激光通信系统。仿真分析了300 km、2.5 Gb/s无线激光链路性能，并通过运动仿真台模拟机动环境测试了系统的跟踪与通信性能，其中粗跟踪误差为533.2 μrad(1σ)，精跟踪误差为3.6 μrad(1σ)，测试数据传输240 s，通信误码率为2.82×10-9。仿真与实验验证了该系统用于远距离空空无线激光通信的可行性。     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

Voltammetric Determination of Isoniazid Drug in Various Matrix by Using CuOx Decorated MW‐CNT Modified Glassy Carbon Electrode

Copper oxide decorated multi‐walled carbon nanotube (MWCNT) modified glassy carbon electrode (GCE) was prepared for determination of isoniazid (INZ) in various matrices. The electrochemical behavior of INZ was tested with the aid of Cyclic Voltammetry (CV) and quantitative experiments were performed by using Linear Sweep Voltammetry (LSV). Morphological and structural characterization of the modified electrode was performed by utilizing Scanning Electron Microscopy (SEM), X‐Ray Photoelectron Spectroscopy (XPS) while electrochemical characterization was performed by using CV and Electrochemical Impedance spectroscopy (EIS). The proposed sensor exhibited well defined anodic peak at 0.30 V for INZ at pH 6.0 medium. Under the optimum conditions, a linear relation between INZ concentration and peak current was observed in the range of 2.0×10^?7 to 5.0×10^?5 M. Limit of detection was calculated as 1.0×10^?8 M and repeatability and accuracy was found as 5.60 % and 91.0 % for 5.0 10^?7 M INZ by using 3 successive measurement, respectively. Then, the analytic performance of the electrode developed was tested by analyzing commercial tablets, artificial human serum and urine samples. The results indicated that satisfactory recoveries was observed for all issue.