High-Performance Beamformer and Low-Complexity Detector for DF-Based Full-Duplex MIMO Relaying Networks

In this paper, we consider a full.duplex multiple.input multiple.output(MIMO) relaying network with the decode.and.forward(DF) protocol. Due to the full.duplex transmissions, the self.interference from the relay transmitter to the relay receiver degrades the system performance. We thus propose an iterative beamforming structure(IBS) to mitigate the self.interference. In this method, the receive beamforming at the relay is optimized to maximize the signal.to.interference.plus.noise.ratio(Max.SINR), while the transmit beamforming at the relay is optimized to maximize the signal.to.leakage.plusnoise.ratio(Max.SLNR). To further improve the performance, the receive and transmit beamforming matrices are optimized between Max.SINR and Max.SLNR in an iterative manner. Furthermore, in the presence of the residual self.interference, a low.complexity whitening.filter(WF) maximum likelihood(ML) detector is proposed. In this detector, a WF is designed to transform a colored interference.plus.noise to a white noise, while the singular value decomposition is used to convert coupled spatial subchannels to parallelindependent ones. From simulations, we find that the proposed IBS performs much better than the existing schemes. Also, the proposed low.complexity detector significantly reduces the complexity of the conventional ML(CML) detector from exponential time(an exponential function of the number of the source transmit antennas) to polynomial one while achieving a slightly better BER performance than the CML due to interference whitening.

A Method for Normal Direction Judge Applied in Electrocardiographic Problem

Boundary Element Method (BEM) is widely used in electrocardiographic (ECG) problem. Formulations of these problems based on mathematical and numerical approximations of the known source in heart and the volume conductor that can transfer voltages on the surface of the body. To analyze the electric potentials on body surface or epicardial surface, a set of discrete equations derived from a boundary integral equations need to be solved. Solving these equations means to get the potential distribution eventually. In the process of solving, transfer matrix of discrete equations has received considerable attention, how to get an appropriate transfer matrix is an important issue. This paper found that the direction of normal vector could affect the results when calculating the transfer matrix and presents a method analogous to Mesh Current Method to deal with this direction problem. Several simulations have been carried out to verify the accurate results with the correct direction of normal vector using new method within a torso model given simultaneous epicardial and body surface potential recordings.

Beamforming design for RIS-aided amplify-and-forward relay networks

The use of a reconfigurable intelligent surface(RIS)in the enhancement of the rate performance is considered to involve the limitation of the RIS being a passive reflector.To address this issue,we propose a RIS-aided amplify-and-forward(AF)relay network in this paper.By jointly optimizing the beamforming matrix at AF relay and the phase-shift matrices at RIS,two schemes are put forward to address a maximizing signal-to-noise ratio(SNR)problem.First,aiming at achieving a high rate,a high-performance alternating optimization(AO)method based on Charnes–Cooper transformation and semidefinite programming(CCT-SDP)is proposed,where the optimization problem is decomposed into three subproblems solved using CCT-SDP,and rank-one solutions can be recovered using Gaussian randomization.However,the optimization variables in the CCT-SDP method are matrices,leading to extremely high complexity.To reduce the complexity,a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation(DT-SCA)is proposed,where the variables are represented in vector form,and the three decoupling subproblems are solved using DT-SCA.Simulation results verify that compared to three benchmarks(i.e.,a RIS-assisted AF relay network with random phase,an AF relay network without RIS,and a RIS-aided network without AF relay),the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance.Furthermore,it is revealed that the rate of the low-complexity DT-SCA method is close to that of the CCT-SDP method.

Panicle-Cloud:An Open and Al-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

Rice(Oryza sativa)is an essential stable food for many rice consumption nations in the world and,thus,the importance to improve its yield production under global climate changes.To evaluate different rice varieties'yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research groups.Nevertheless,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological features.Here,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery.To facilitate the development of Al-powered detection models,we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists;then,we integrated several state-of-the-art deep learning models(including a preferred model called Panicle-AI)into the Panicle-Cloud platform,so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images.We trialed the Al models with images collected at different attitudes and growth stages,through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified.Then,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice varieties.Through correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high accuracy.Hence,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.

Bibliometric analysis on Pythagorean fuzzy sets during 2013-2020

Purpose-The purpose of this paper is to make a comprehensive analysis of 354 publications about Pythagorean fuzzy sets(PFSs)from 2013 to 2020 in order to comprehensively understand their historical progress and current situation,as well as future development trend.Design/methodology/approach-First,this paper describes the fundamental information of these publications on PFSs,including their data information,annual trend and prediction and basic features.Second,the most productive and influential authors,countries/regions,institutions and the most cited documents are presented in the form of evaluation indicators.Third,with the help of VOSviewer software,the visualization analysis is conducted to show the development status of PFSs publications at the level of authors,countries/regions,institutions and keywords.Finally,the burst detection of keywords,timezone review and timeline review are exported from CiteSpace software to analyze the hotspots and development trend on PFSs.Findings-The annual PFSs publications present a quickly increasing trend.The most productive author is Wei Guiwu(China).Wei Guiwu and Wei Cun have the strongest cooperative relationship.Research limitations/implications-The implication of this study is to provide a comprehensive perspective for the scholars who take a fancy to PFSs,and it is valuable for scholars to grasp the hotspots in this field in time.Originality/value-It is the first paper that uses the bibliometric analysis to comprehensively analyze the publications on PFSs.It can help the scholars in the field of PFSs to quickly understand the development status and trend of PFSs.

Weapon-target assignment in unreliable peer-to-peer architecture based on adapted artificial bee colony algorithm

It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the computing nodes on the battlefield are vulnerable to be attacked and the communication environment is usually unreliable.To solve the WTA problems in unreliable environments,this paper proposes a scheme based on decentralized peer-to-peer architecture and adapted artificial bee colony(ABC)optimization algorithm.In the decentralized architecture,the peer computing node is distributed to each weapon units and the packet loss rate is used to simulate the unreliable communication environment.The decisions made in each peer node will be merged into the decision set to carry out the optimal decision in the decentralized system by adapted ABC algorithm.The experimental results demonstrate that the decentralized peer-to-peer architecture perform an extraordinary role in the unreliable communication environment.The proposed scheme preforms outstanding results of enemy residual value(ERV)with the packet loss rate in the range from 0 to 0.9.