Joint Multi-User Detection with Weighting Factors for Unsourced Multiple Access

Multi-user detection techniques are currently being studied as highly promising technologies for improving the performance of unsourced multiple access systems. In this paper, we propose joint multi-user detection schemes with weighting factors for unsourced multiple access. First, we introduce bidirectional weighting factors in the extrinsic information passing process between the multi-user detector based on belief propagation (BP) and the LDPC decoder. Second, we incorporate bidirectional weighting factors in the message passing process between the MAC nodes and the user variable nodes in BP- based multi-user detector. The proposed schemes select the optimal weighting factors through simulations. The simulation results demonstrate that the proposed schemes exhibit significant performance improvements in terms of block error rate (BLER) compared to traditional schemes. .

Complex field network-coded cooperation based on multi-user detection in wireless networks

Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.

Wavelet Packet Domain LMS Based Multi-User Detection

An improved wavelet packet domain least mean square （IWPD-LMS） based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the best wavelet packet basis according to a novel convergence contribution function rather than the conventional Shannon entropy. The theoretic analyses show that the inadequacy of the eigenvalue spread of the tap-input correlation matrix is ameliorated, thus the convergence performance is improved greatly. The simulation result of convergence performance and bit error rate（BER） performance as a function of the signal power to noise power ratio（SNR） are presented finally to prove the validity of the proposed algorithm.

RLS and LMS blind adaptive multi-user detection method and comparison in acoustic communication

RLS and LMS blind adaptive multi-user detection algorithm and multi-user detector was proposed to solve the problem of multi-user signal detection problem encountered in underwater acoustic communication networks.In simulation analysis,RLS and the LMS blind adaptive multi-user detector were designed and tested for synchronous and asynchronous multi-user communication process.The results of SIR comparison and MMSE comparison show that,both of the two methods can realize blind adaptive detection when any user change in multi-user communication,during this process,the training communication sequences are not needed.The RLS algorithm has about 5 dB higher in SIR compared with LMS algorithm,and the convergence velocity of RLS algorithm is also higher than LMS algorithm when the communication users change.RLS algorithm has better ability in multi-user detection than that of LMS algorithm,and it has great attraction and guiding significance for solving the problem of multiple access interference(MAI) in multi-user communication.

Study of multi-rate multi-user detection based on supervision decision

Multi-user detection (MUD) based on multirate transmission in code division multiple access (CDMA) system is discussed. Under the requirement of signal interference ratio (SIR) detection at base station and framework with parallel interference cancellation, a supervision decision algorithm based on pre-decision of probabilistic data association (PDA) and hard decision is proposed. The detection performance is analyzed and simulation is implemented to show that the supervision decision algorithm improves the detection performance effectively.

Joint Channel and Multi-User Detection Empowered with Machine Learning

The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

ROLS-AWS algorithm used in RBF neural network for multi-user detection

To improve the computational speed, the ROLS-AWS algorithm was employed in the RBF based MUD receiver. The radial basis function was introduced into the multi-user detection （MUD） firstly. Then a three-layer neural network demodulation spread-spectrum signal model in the synchronous Gauss channel was given and the multi-user detection receiver was analyzed intensively. Simulations by computer illustrate that the proposed RBF based MUD receiver employing the ROKS-AWS algorithm is better than conventional detectors and common BP neural network based MUD receivers on suppressing multiple access interference and near-far resistance.

A New Approach to Multi-user Detection in DS-CDMA

A graph model is constructed for the Multi-user Detection of DS-CDMA system. Based on it, a Hopfield-like algorithm is put forward for the implementation of optimum receiver. Compared with the Hopfield approach, it has a higher computational complexity but better performance.

Dynamic multi-user detection scheme based on CVA-SSAOMP algorithm in uplink grant-free NOMA

In the uplink grant-free non-orthogonal multiple access(NOMA)scenario,since the active user at the sender has a structured sparsity transmission characteristic,the compressive sensing recovery algorithm is initially applied to the joint detection of the active user and the transmitted data.However,the existing compressed sensing recovery algorithms with unknown sparsity often require noise power or signal-to-noise ratio(SNR)as the priori conditions,which greatly reduces the algorithm adaptability in multi-user detection.Therefore,an algorithm based on cross validation aided structured sparsity adaptive orthogonal matching pursuit(CVA-SSAOMP)is proposed to realize multi-user detection in dynamic change communication scenario of channel state information(CSI).The proposed algorithm transforms the structured sparsity model into a block sparse model,and without the priori conditions above,the cross validation method in the field of statistics and machine learning is used to adaptively estimate the sparsity of active user through the residual update of cross validation.The simulation results show that,compared with the traditional orthogonal matching pursuit(OMP)algorithm,subspace pursuit(SP)algorithm and cross validation aided block sparsity adaptive subspace pursuit(CVA-BSASP)algorithm,the proposed algorithm can effectively improve the accurate estimation of the sparsity of active user and the performance of system bit error ratio(BER),and has the advantage of low-complexity.

Quantum Multi-User Detection Based on Coherent State Signals

Multi-user detection is one of the important technical problems for moderncommunications. In the field of quantum communication, the multi-access channel onwhich we apply the technology of quantum information processing is still an openquestion. In this work, we investigate the multi-user detection problem based on thebinary coherent-state signals whose communication way is supposed to be seen as aquantum channel. A binary phase shift keying model of this multi-access channel isstudied and a novel method of quantum detection proposed according to the conclusionof the quantum measurement theory. As a result, the average interference betweendeferent users is presented and the average error probability of the quantum detection isderived theoretically. Finally, we show the maximum channel capacity of this effectivedetection for a two-access quantum channel.

Multi-User Detection for Spatial Modulation toward 5G Wireless Communications

Spatial modulation(SM) is a class of novel multiple-input multiple-output(MIMO) techniques toward future wireless communications,which activates only one transmit antenna in each time slot,so as to reduce the number of RF chains for saving the implement cost.Meanwhile,considering its application in 5G systems with multiple users,the detection of multi-user spatial modulation has drawn greater attention.In this paper,a pair of efficient detectors are proposed for multi-user spatial modulation.Specially,a threshold-aided approximate message passing(T-AMP) detector is proposed with the purpose of reducing the computational complexity of traditional structured approximate message passing(Str-AMP) detector.In addition,a novel probability sorting aided approximate message passing detector,called probability-ranking-aided AMP detector(P-AMP),is also proposed with the purpose of improving the performance.Simulation results show that the proposed T-AMP detector is able to achieve similar performance as traditional StrAMP with lower complexity,while the proposed P-AMP detector exhibits a better symbol error rate(SER) performance with similar complexity.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

Intelligent multi-user detection using an artificial immune system

Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

The Applicative Investigation of Adaptive BP Networks for Multi-user Detection in Asynchronous DS-CDMA Mobile Communications

Three-layer Adaptive Back-Propagation Neural Networks(TABPNN) are employed for the demodulation of spread spectrum signals in a multiple-access environment. A configuration employing three-layer adaptive Back-propagation neural networks is put forward for the demodulation of spread-spectrum signals in asynchronous Gaussian channels. The theoretical arguments and practical performance based on the neural networks are analyzed. The results show that whether the resistance to the multiple access interference or the robust to near-far effects, the proposed detector significantly outperforms not only the conventional detector but also the BP neural networks detector and is comparable to the optimum detector.

Iterative multi-user detection and decoding for space-time block coding systems

To restrain the interference of co-channel users using space-time block coding （STBC）, the proposed Gaussian-forcing soft decision multi-user detection （GFSDMUD） algorithm is applied in fiat-fading channels by using the relation among the users＇ signals, which can enhance the capacity by introducing co-channel users. During iterations, extrinsic information is calculated and exchanged between a soft multi-user detector and a bank of turbo decoders to achieve refined estimates of the users＇ signals. The simulations show that the proposed iterative receiver techniques provide significant performance improvement around 2 dB over conventional noniterative methods. Furthermore, iterative multi-user space-time processing techniques offer substantial performance gains around 8 dB by adding the number of receiver antennas from 4 to 6, and the system performance can be enhanced by using this strategy in multi-user STBC systems, which is very important for enlarging the system capacity.

Esophageal cancer screening,early detection and treatment:Current insights and future directions

Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7

For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.

Multimodal Social Media Fake News Detection Based on Similarity Inference and Adversarial Networks

As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.