User Detection Research Articles

Research on improved multiple users detection algorithm in pattern division multiple access system

Receiver detection algorithm has always been a research hotspot in communication system and the key point to determine the performance of 5G system. Both industry and academia have been committed to finding detection algorithms with lower complexity and higher detection performance. This paper will study how to solve the problems of low detection performance and high complexity of classical algorithms. Taking Pattern Division Multiple Access (PDMA) as an example, this paper improves the existing receiver detection algorithms such as Minimum Mean Square Error-Serial Interference Cancellation (MMSE-SIC), Message Passing Algorithm (MPA) and Expectation Propagation Algorithm (EPA), and puts forward corresponding improved algorithms with specific formulas, i.e. Enhanced-MMSE-SIC, Turbo-like MPA (MPA-Turbo) and Turbo-like EPA with hybrid parallel interference cancellation (EPA-Turbo-PIC). Link-level simulation results and analysis show that the proposed improved algorithms have performance advantages over the corresponding traditional algorithms.

MmWave extra-large-scale MIMO based active user detection and channel estimation for high-speed railway communications

The current High-Speed Railway (HSR) communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs. To this end, this paper investigates millimeter-Wave (mmWave) extra-large scale (XL)-MIMO-based massive Internet-of-Things (IoT) access in near-field HSR communications, and proposes a block simultaneous orthogonal matching pursuit (B-SOMP)-based Active User Detection (AUD) and Channel Estimation (CE) scheme by exploiting the spatial block sparsity of the XL-MIMO-based massive access channels. Specifically, we first model the uplink mmWave XL-MIMO channels, which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays. By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels, the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing (MMV-CS) problem. Based on the designed sensing matrix, a B-SOMP algorithm is proposed to achieve joint AUD and CE. Finally, simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.

CNN-SVM Spectrum Sensing in Cognitive Radio Based on Signal Covariance Matrix

Among the basic components of cognitive radio technology is spectrum sensing technology. Cooperative spectrum sensing is also a crucial factor research directions of spectrum sensing. Due to the problems of channel fading and hidden nodes, the spectrum sensing based on a single node cannot meet the requirements of the cognitive radio system for the primary user (PU) detection performance. A spectrum sensing algorithm is based on CNN-SVM and covariance matrix. In this algorithm, the covariance matrix of the signal is input into the convolutional neural network for feature extraction. Secondly, the result of the fully connected layer is taken as the input of the support vector machine for training to obtain a classifier, and finally, spectrum sensing is performed. The simulation outcomes demonstrate that the algorithm suggested in this research works outperform other algorithms in this paper. When the signal-to-noise ratio is -14 dB, the false alarm probability is 0.1, the number of secondary users is 32, and the detection probability reaches 0.81.

Cyberattack detection model using community detection and text analysis on social media

Online social media such as Twitter has been used as an important source for predicting, detecting, or analyzing critical social phenomena such as elections, disease outbreaks, and cyberattacks. In this study, we propose a cyberattack detection model on social media. First, we conduct community detection of users related to cyberattacks on Twitter to identify the most relevant group to the cyberattacks. Second, to effectively identify the tweets related to cyberattacks, we conduct a textual similarity analysis between the tweet and the cyberattack relevant keywords, which overcomes the limitation of lexical analysis of tweets such as keyword-based filtering and frequency of keywords. Finally, we propose a novel cyberattack detection model by integrating both text- and graph-based models. Our methodology has a distinguishing feature from the existing studies in that we incorporate the semantics in Tweets to evaluate the relevance with cyberattacks and employ community detection to identify the most relevant group to the cyberattacks. Through extensive experiments, we show the effectiveness of the proposed model. First, we show that the text analysis in the proposed model outperforms detection accuracy of the keyword frequency-based analysis by up to 29.46%. Second, the community detection improves the detection accuracy by 28.89∼35.56% compared to the baseline criteria to select relevant users to the cyberattacks. Through two experiments to measure the relevancy of detected communities to the cyberattack, the results consistently show that the highest relevant community by our community detection shows the highest relevancy.

Deep Learning-Based Joint NOMA Signal Detection and Power Allocation in Cognitive Radio Networks

Presently, Non-Orthogonal Multiple Access (NOMA) frequently uses Successive Interference Cancellation (SIC) with channel estimation to detect the receivers’ signal successfully. However, it’s a slow and sophisticated process. Consequently, the decoding precision of previous Primary Users (PUs) / Secondary Users (SUs) is degraded due to error propagation. These shortcomings are mitigated by the proposed Deep Learning (DL) schemes for signal detection and Power Allocation (PA) in NOMA-based futuristic Cognitive Radio Networks (CRNs). Hence, proposed technique is optimized jointly and determines the desired solution in one step without channel estimation through skilled Deep Neural Network (DNN) approach. It can understands the things because it is the core (so-called heart) of DL, which integrates user detection with channel estimation and PA. The optimization problem for fair PA is planned by combining proposed technique for optimizing systems threshold throughput. In addition, DNN training algorithms is also analyzed, and used to retrieve users’ information from the aired signal directly during the testing stage. The real-time experimental results and analytic outcomes are investigated and validate the supremacy of the proposed scheme over prevailing techniques, respectively. Furthermore, the robustness of the DL technique is additionally evaluated for its dominance over existing approaches.

Research on Application of Computer Artificial Intelligence Technology in Sports Economic Big Data Analysis

People's fitness concept is becoming more and more mature. The general public has also begun to pursue a higher quality of healthy life. The improvement of physical fitness has become an important part of a healthy life. Sports shoes and clothing, exercise equipment, health management, etc. have become people's daily sports and fitness consumption. At the same time, "intelligence + sports economy" makes more and more people look forward to sports. Intelligent sports that are in line with emerging technologies enter people's lives. Various sports apps, wearable smart devices, etc. not only make people Sports information is digitized, and big data technology can be used to analyze the collected data. For example, the detection of user's fat consumption and sleep quality provides scientific support for users' physical fitness. Sports consumption has become a driving force for the upgrading of the sports industry. Important grip.

The detection of vulnerable road users by younger and older drivers

The detection of vulnerable road users (VRUs), especially under time constraints, may be impaired in elderly drivers, due to their visual and cognitive decline. This represents a major concern for road safety. The objective of the present study was to investigate the effect of aging on the detection of VRUs. A further aim was to investigate the impact of external factors on VRU detection. Twenty-two young adults, 20 younger-old adults, and 32 older-old adults were included in the study. A series of photographs were displayed for 500 ms. Participants were asked to detect the VRU, which could be a motorcyclist, a cyclist, or a pedestrian. The VRU was located at one of two distances (near/far), one of two locations (off-centered/centered), and in two car-DRL (daytime running lights) environments (on/off). The ability to correctly detect a VRU was measured. An eye-tracker was used to record eye movements. The main findings showed that VRU-detection performance decreased with aging, even in the younger-old group (ages 55–68). The ability to correctly detect a VRU by older-old adults was poorer particularly when the VRU was a motorcyclist or a cyclist. As a whole, the older-old adults made more fixations to correctly detect the VRU than the other two age groups did. Moreover, the visual angle between the gaze of the participant’s last fixation and the target in the older-old group was lower than in the other two groups, particularly when the VRU was off-centered. This finding suggests that older-old adults compensated for their visual-field decline by doing more visual exploration than the other two groups did to correctly detect the VRU. The results are discussed with regards to age-related cognitive and visual deficits.Word count: 279.

Model-Based Deep Learning for Joint Activity Detection and Channel Estimation in Massive and Sporadic Connectivity

We present two model-based neural network architectures purposed for sporadic user detection and channel estimation in massive machine-type communications. In the scenario under consideration, a base station assigns the users a set of pilot sequences that is linearly dependent, but because user activity is sporadic the detection/estimation problem is amenable to sparse recovery algorithms. Further, we consider a millimeter-wave wireless channel, so that the channel vectors are sparse in a known dictionary. We apply the deep unfolding framework to design custom neural network layers by unrolling two iterative optimization algorithms: (1) linearized alternating direction method of multipliers, which we apply to a constrained convex problem, and (2) vector approximate message passing featuring a novel denoiser based on the iterative shrinkage thresholding algorithm. The networks thus inherit domain knowledge as encapsulated by the signal model, and suitable operations as informed by the algorithms—in the same spirit as convolutional networks that exploit structure inherent in images and audio, except grounded in optimization and statistics. The networks, trained on synthetic data generated from the block-fading millimeter-wave multiple access channel model, offer improved complexity and accuracy relative to their iterative counterparts, and are potentially a boon to cell-free MIMO systems.

Better Adaptive Malicious Users Detection Algorithm in Human Contact Networks

A human contact network (HCN) consists of individuals moving around and interacting with each other. In HCN, it is essential to detect malicious users who break the data delivery through terminating the data delivery or tampering with the data. Since malicious users will pay more but gain less when breaking the data delivery of opportunistic contacts, we focus on the non-opportunistic contacts that occur more frequently and stably. It is observed that people contact with each other more frequently if they have more social features in common. In this paper, we build up topology structure for HCN based on social features, and propose a graph theoretical comparison detection model to perform malicious users detection. Then we present an adaptive detection scheme based on Hamiltonian cycle decomposition. Also, we define comparison-0-string and comparison-1-string to improve the detection efficiency. Moreover, we perform scenario simulations on real data to realize the detected process of malicious users. Experiments show that, when the number of malicious users is bounded by the dimension of HCN, our scheme has a detection rate of 100% with both false positive rate and false negative rate being 0%, and the running cost is also very low when compared to baseline approaches. When the number of malicious users exceeds the bound, the detection rate of our scheme decreases slowly, while the false positive rate and false negative rate increase slowly, but they are still better than the baseline approaches.

Capturing Features and Performing Human Detection from Human Gaits Using RFID.

Recently, radio frequency identification (RFID) sensing has attracted much attention due to its contact-free nature, low cost, light weight and other advantages. RFID-based person detection has also become a hot research topic, but there are still some problems in the existing research. First, most of the current studies cannot identify numerous people at a time well. Second, in order to detect more accurately, it is necessary to evaluate the whole-body activity of a person, which will consume a lot of time to process the data and cannot be applied in time. To solve these problems, in this paper we propose RF-Detection, a person detection system using RFID. First of all, RF-Detection takes step length as the standard for person detection, divides step length into specific sections according to the relationship between step length and height, and achieves high accuracy for new user detection through a large amount of training for a specific step length. Secondly, RF-Detection can better identify the number of people in the same space by segmenting continuous people. Finally, the data collection was reduced by expanding the data set, and the deep learning method was used to further improve the accuracy. The results show that the overall recognition accuracy of RF-Detection is 98.93%.

Quantum Key Distribution for Secure Communications

Data protection and information security have been the essence of communication in today's digital era. Authentication and secrecy of secure communication are achieved using key-based cryptographic primitives; the security of which significantly relies upon the underlying computationally complex mathematics. Moreover, these existing cryptographic primitives are considered to be non-deterministic on the basis of the existing computational capabilities. However, the considerable advancements in the development of quantum computers have significantly enhanced parallel computations; thereby, posing a great threat to these existing encryption primitives. Thus, in the future, the physical manifestation of a large successful quantum computer is likely to break all the existing public-key encryption algorithms in no time. This has led to a remarkable surge of interest in propelling quantum mechanics into existence; subsequently, leading cryptographers to research various viable domains to offer quantum-resistant secure communications. Resultantly, quantum cryptography/quantum key distribution has emerged as a futuristic replacement for classical cryptography as it offers unconditionally secure communication along with the inherent detection of any unintended user. Thus, keeping in view the significance of this relatively newer domain of cryptography, this research focuses on presenting a consolidated review of the various Quantum Key Distribution (QKD) protocols. A comparative analysis of the working mechanism of the prominent QKD protocols is presented along with an overview of the various emerging trends that have been proposed to optimize the implementational efficiency of the BB84 protocol.

Secure Collaborative Platform for Health Care Research in an Open Environment: Perspective on Accountability in Access Control.

BackgroundWith the recent use of IT in health care, a variety of eHealth data are increasingly being collected and stored by national health agencies. As these eHealth data can advance the modern health care system and make it smarter, many researchers want to use these data in their studies. However, using eHealth data brings about privacy and security concerns. The analytical environment that supports health care research must also consider many requirements. For these reasons, countries generally provide research platforms for health care, but some data providers (eg, patients) are still concerned about the security and privacy of their eHealth data. Thus, a more secure platform for health care research that guarantees the utility of eHealth data while focusing on its security and privacy is needed.ObjectiveThis study aims to implement a research platform for health care called the health care big data platform (HBDP), which is more secure than previous health care research platforms. The HBDP uses attribute-based encryption to achieve fine-grained access control and encryption of stored eHealth data in an open environment. Moreover, in the HBDP, platform administrators can perform the appropriate follow-up (eg, block illegal users) and monitoring through a private blockchain. In other words, the HBDP supports accountability in access control.MethodsWe first identified potential security threats in the health care domain. We then defined the security requirements to minimize the identified threats. In particular, the requirements were defined based on the security solutions used in existing health care research platforms. We then proposed the HBDP, which meets defined security requirements (ie, access control, encryption of stored eHealth data, and accountability). Finally, we implemented the HBDP to prove its feasibility.ResultsThis study carried out case studies for illegal user detection via the implemented HBDP based on specific scenarios related to the threats. As a result, the platform detected illegal users appropriately via the security agent. Furthermore, in the empirical evaluation of massive data encryption (eg, 100,000 rows with 3 sensitive columns within 46 columns) for column-level encryption, full encryption after column-level encryption, and full decryption including column-level decryption, our approach achieved approximately 3 minutes, 1 minute, and 9 minutes, respectively. In the blockchain, average latencies and throughputs in 1Org with 2Peers reached approximately 18 seconds and 49 transactions per second (TPS) in read mode and approximately 4 seconds and 120 TPS in write mode in 300 TPS.ConclusionsThe HBDP enables fine-grained access control and secure storage of eHealth data via attribute-based encryption cryptography. It also provides nonrepudiation and accountability through the blockchain. Therefore, we consider that our proposal provides a sufficiently secure environment for the use of eHealth data in health care research.

E-Scooter Rider detection and classification in dense urban environments

Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. Although similar in physical appearance to pedestrians, e-scooter riders follow distinctly different characteristics of movement and can reach speeds of up to 45 kmph. The challenge of detecting e-scooter riders is exacerbated in urban environments where the frequency of partial occlusion is increased as riders navigate between vehicles, traffic infrastructure and other road users. This can lead to the non-detection or mis-classification of e-scooter riders as pedestrians, providing inaccurate information for accident mitigation and path planning in autonomous vehicle applications. This research introduces a novel benchmark for partially occluded e-scooter rider detection to facilitate the objective characterization of detection models. A novel, occlusion-aware method of e-scooter rider detection is presented that achieves a 15.93% improvement in detection performance over the current state of the art.

Illegal drugs and periodontal conditions.

In recent years, the practice of dentistry and periodontology has become complicated by several risk factors, including the treatment of an increasing number of patients with substance use disorder. This review presents an update in the current literature of the impact of illegal drug use on periodontal conditions and their possible effect as risk factors or indicators. The main illegal drugs that may have an impact on periodontal health and conditions are described, including their effect, medical manifestations, risks, and the overall effect on oral health and on the periodontium. Where available, data from epidemiologic studies are analyzed and summarized. The clinical management of periodontal patients using illegal drugs is reported in a comprehensive approach inclusive of the detection of illicit drug users, screening, interviewing and counseling, the referral to treatment, and the dental and periodontal management. With regard to the impact of illegal substance use on periodontal conditions, there is moderate evidence that regular long-term use of cannabis is a risk factor for periodontal disease, manifesting as a loss of periodontal attachment, deep pockets, recessions, and gingival enlargements. Limited evidence also shows that the use of cocaine can cause a series of gingival conditions that mostly presents as chemical induced-traumatic lesions (application of cocaine on the gingiva) or necrotizing ulcerative lesions. There is a scarcity of data regarding the impact of other drug use on periodontal health. There is evidence to suggest that regular long-term use of cannabis is a risk factor for periodontal disease and that the use of cocaine can cause a series of periodontal conditions. The dental treatment of subjects that use illegal substances is becoming more common in the daily clinical practice of periodontists and other dental clinicians. When the clinicians encounter such patients, it is essential to manage their addiction properly taking into consideration the impact of it on comprehensive dental treatment. Further studies and clinical observations are required to obtain sound and definitive information.

Weighted Coefficient Firefly Optimization Algorithm and Support Vector Machine for Trust Model and Link Reliability

Cloud computing is widely used by organizations and individuals due to its flexibility and reliability. The trust model is important for cloud computing to detect malicious users and protect user privacy. The existing research faces the issues of local optima trap and overfitting problems when a training user node is idle for more time. This research proposed Weighted Coefficient Firefly Optimization Algorithm (WCFOA) with Support Vector Machine (SVM) for the trust model calculation and identifying paths with better Quality of Services (QoS). The weighted coefficient is added to the FOA model to balance the exploration and exploitation in the search of identifying optimal path based on reliability score. The WC-FOA method measures the link reliability in the model and SVM detects the malicious users in the model. The WC-FOA model selects the optimal path for transmission in terms of trust and efficient QoS parameters. The entropy measure and link reliability are provided as input to the SVM model for the detection of attacks in the network. The WCFOA-SVM model has 96% malicious user detection, whereas the Random Forest Hierarchical Ant Colony Optimization (RF-HEACO) has 92 % accuracy.

A parameterizable influence spread-based centrality measure for influential users detection in social networks

In social network analysis, centrality refers to the relevance of actors or nodes within a social network represented as a graph. Traditional centrality measures are based on topological aspects of the network or the information flow circulating through it. Since 2010, new centrality measures have been proposed based on the influence spread capacity of the actors. One of the purposes of centrality measures is to obtain well-differentiated rankings among the actors. In order to achieve the above, recent experiments suggest that the actors must rely on some neighbors to increase their initial influence spread capacity. This article presents the General Influence Spread Rank (GISR), a generalized and parameterizable centrality measure based on two well-known influence spread models: the Independent Cascade model and the Linear Threshold Model. This measure allows adjusting the depth levels, probability, and directionality of the initial activation of neighbors, to study how these parameters intervene in the actors’ centrality. The experiments validate the importance of the depth level of neighborhoods and reveal that other connectivity aspects are also relevant to the centrality problem. Further, the depth level seems to be the most relevant variable in the influence spread for denser networks. In contrast, for sparser networks, the probability of selecting neighbors seems even more significant than the depth level.

Complementary Coded Identical Code Cyclic Shifted Multiple Access and Its Performance Analysis

The scarcity of available spreading codes poses a hard limitation to user capacity for a code-division multiple access system. In previous work by Liu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2020), the identical code cyclic shift multiple access (ICCSMA) scheme was developed, which allows multiple users to share one spreading code. However, the ICCSMA system uses the basic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> -sequence as the prime code to generate the identical code cyclic shift code, which has a nonideal correlation property and produces a considerable interference in a frequency-selective fading channel. To address this issue, we suggest using the complementary code (CC), which exhibits the perfect correlation property, to suppress the interference, and the proposed scheme is named CC-ICCSMA. Different from the article by Liu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2020), a 3-D transceiver is designed in this article, and the CC set is employed to perform the multiple user detection. The simulation results demonstrate that the CC-ICCSMA strategy achieves a much better bit error rate performance than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> -sequence-based ICCSMA in the frequency-selective fading channels. Also, the outstanding anti-interference capability of CC-ICCSMA can help improve user capacity.

Compressive Sensing in Massive MIMO Array Testing: A Practical Guide

Compressive sensing (CS) has been deployed in a variety of fields including wideband spectrum sensing, active user detection, and antenna arrays. In massive multiple input multiple output (MIMO) arrays, CS has been applied to reduce the number of measurements required to verify the arrays excitation. To date, the literature has concentrated on the various methods of achieving CS and applying them to both linear and 2-D arrays, and aimed at detecting fully failed elements in an array, offering simple pass/fail testing. All follow the general approach of creating the sparsity needed for CS by subtracting the measured far field or near field of the test array from that of a “gold standard” array measured under exactly identical conditions. This article extended this work to the need for rapid but accurate reconstruction of element excitation in a production testing environment for massive MIMO arrays. The aim is to demonstrate that CS can offer accurate reconstruction of array excitation. Particularly, the work addresses the issues of optimal sampling, measurement noise, accuracy of faulty element detection, effects of beam scanning, and physical alignment of the gold standard array with the test array. We have restricted ourselves to considering production standard arrays with failure rates up to around 5% and conclude with a set of proposed modifications to the basic CS process as applied to array excitations that achieve a near 20 dB improvement in the accuracy of the reconstructed array excitation offering mean square errors (MSEs) near to −40 dB, with a sampling strategy of just 1.4% of the Nyquist rate. This is achieved with the number of measurements to array element size ratio of approximately 0.2.

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning.

Cooperative network is a promising concept for achieving a high-accuracy decision of spectrum sensing in cognitive radio networks. It enables a collaborative exchange of the sensing measurements among the network users to monitor the primary spectrum occupancy. However, the presence of malicious users leads to harmful interferences in the system by transmitting incorrect local sensing observations.To overcome this security related problem and to improve the accuracy decision of spectrum sensing in cooperative cognitive radio networks, we proposed a new approach based on two machine learning solutions. For the first solution, a new stacking model-based malicious users detection is proposed, using two innovative techniques, including chaotic compressive sensing technique-based authentication for feature extraction with a minimum of measurements and an ensemble machine learning technique for users classification. For the second solution, a novel deep learning technique is proposed, using scalogram images as inputs for the primary user spectrum’s classification. The simulation results show the high efficiency of both proposed solutions, where the accuracy of the new stacking model reaches 97% in the presence of 50% of malicious users, while the new scalogram technique-based spectrum sensing is fast and achieves a high probability of detection with a lower number of epochs and a low probability of false alarm.

Detecting ShadowsocksR User Based on Intelligence of Cyber Entities

ShadowsocksR (SSR), as a typical emerging anonymous communication tool, may record user information on the SSR client or server, leading to the theft of the user’s privacy, and may be used by attackers to anonymize their internal network environment and organization, which will cause serious damage to data security and bring severe challenges to security defense and threat assessment within organizations. To solve the problem of accurately and effectively discovering SSR users within an organization in a real traffic environment, in this paper, we propose an SSR user detection method based on network entity intelligence as follows: (1) According to the communication characteristics of SSR users, relevant network entity intelligence information from inside and outside the organization is obtained, such as the distribution of IP addresses within and outside the organization, and the differences between SSR and non-SSR users are analyzed to construct a feature space. (2) The communication behaviors of SSR and non-SSR users are further analyzed and features are extracted from the perspective of traffic behavior analysis, and the feature space of the SSR user detection model is expanded. (3) A data-driven machine-learning-based approach is designed and implemented to provide suggestions for the automatic identification of SSR users based on the extracted feature vectors. Results show that the detection method proposed in this paper has a detection accuracy of more than 95% for SSR users in the experimental environment, can accurately distinguish between SSR communication and normal communication, and can achieve accurate SSR user detection.