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A Low Complexity Symbol-Wise ML detection Algorithm for User-Centric C-RAN

IEEE COMMUNICATIONS LETTERS 2022年第5期26卷 1057-1061页

作者： Wang, Xinyi Liu, Wenlong Jin, Minglu Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China

This letter considers an optimal signal detection problem for uplink user-centric cloud radio access network (C-RAN). In user-centric C-RAN, users communicate with nearby remote radio heads (RRHs), and RRHs are connected to baseband unit (BBU) pool. Then, the baseband processing functionalities are migrated to the BBU pool to provide joint signal detection for a number of users. However, joint signal processing will encounter high computational complexity as the network scale grows. Especially for optimal signal detection, the computational complexity increases exponentially as the number of users increases. To address this issue, we propose a symbol-wise maximum likelihood (ML) detection algorithm to achieve optimal performance with low complexity. The key to realize the symbol-by-symbol separate detection is that it makes full use of the sparsity of the channel matrix in user-centric C-RAN. We further provide the theoretical derivation to clarify the superiority of the proposed algorithm in this sparse channel scenario. Numerical experiments show that the proposed algorithm can achieve optimal performance with low complexity in user-centric C-RAN.

关键词： detection algorithms Sparse matrices Signal processing algorithms Signal detection Detectors Iterative algorithms Computational complexity C-RAN user-centric signal detection ML detector optimal performance

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Rhythm-based Accuracy Improvement of Heart Beat detection algorithms 41

Rhythm-based Accuracy Improvement of Heart Beat Detection Al...

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41st Computing in Cardiology Conference (CinC)

作者： Gilian, Zoltan Kovacs, Peter Samiee, Kaveh Eotvos Lorand Univ Dept Numer Anal 1-C Pazmany Peter Setany H-1117 Budapest Hungary Tampere Univ Technol Dept Signal Proc FI-33101 Tampere Finland

ISBN: (纸本)9781479943463

Aims: Our aim is to improve the accuracy of existing heart beat detection algorithms in order to provide reliable heart beat locations in a multi-modal beat detection scheme. Methods: A rhythm-based algorithm is presented which on top of a base beat detection method processes the detected beats by rejecting annotations and filling in gaps while minimizing a deviation score. A novel beat detection method based on rational modelling of ECG signals is also presented as a base algorithm. Results: The rhythm-correction algorithm applied to Sachin Vernekar's phase 11 entry was submitted to the third phase of the PhysioNetlCinC Challenge 2014 contest. The algorithm has 99.98% gross and average sensitivity and 99.96% gross and average positive predictivity compared to 99.92% and 99.94%, respectively, of the base algorithm. Due to run-time performance problems, the rational algorithm was not able to qualify in the contest. Conclusions: The rhythm-based method improves the results of the base algorithm on the training data set. The hidden records are not yet available at the time of writing of this paper;therefore we are not able to report the final performance of the algorithm. Run-time improvement of the rational algorithm remains future work.

关键词： electrocardiography medical signal processing rational functions ECG signal rational modelling heart beat detection method heart beat locations multimodal beat detection scheme rhythm-based accuracy improvement rhythm-based method training data set Abstracts detection algorithms Heart beat Noise measurement Prediction algorithms Reliability Heart beat detection algorithms Prediction algorithms Noise measurement medical signal processing rational functions Electrocardiography algorithms Dataset

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Real-time Image Edge detection System Design and algorithms for Artificial Intelligence FPGAs

Real-time Image Edge Detection System Design and Algorithms ...

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2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs)

作者： Caizeng Ye Shandong Institute of Commerce and Technology Jinan China

Extraction of image edges is the basis of image processing class algorithms, for image edge extraction is of great significance. Nowadays, video image data is greatly increases the amount of image data and the processing difficulty. FPGA technology stands out for its parallel processing method and pipeline structure, which enables high-speed processing of large amounts of image data, and its high processing speed ensures real-time image data processing. The application of FPGA technology in the field of image data processing will be unstoppable. The main objective of this paper is to investigate the design and algorithms of a real-time image edge detection system using artificial intelligence and FPGAs. Median filtering and edge detection algorithms in video acquisition and pre-processing systems are an important part of complex algorithms in the field of target recognition and tracking, and the quality of image pre-processing plays an important role in the high accuracy of subsequent algorithms. This paper presents an in-depth study and analysis of the hardware implementation of the HD video capture and pre-processing system, combining the characteristics of FPGA parallel high-speed and pipeline structure, and completes the system design and verification on the hardware platform, fully proving the feasibility and correctness of the system.

关键词： Target tracking Image edge detection Streaming media Real-time systems Hardware detection algorithms Artificial intelligence

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Evaluation Method for Arc Fault detection algorithms 40

Evaluation Method for Arc Fault Detection Algorithms

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40th IEEE Photovoltaic Specialists Conference (PVSC)

作者： McConnell, Stephen Wang, Zhan Balog, Robert S. Johnson, Jay Texas A&M Univ College Stn TX 77840 USA Sandia Natl Labs Albuquerque NM 87185 USA

ISBN: (纸本)9781479943982

Many methods have been proposed to detect arc faults within photovoltaic systems. However, because of the dearth of data surrounding arcs that actually occur in commercial or residential PV systems, a sound method is necessary to systematically check for the effectiveness of algorithms claiming the ability to detect PV arc faults. This method should include data representing actual background PV system noise and seek to quantify the limits of the detection capability for the algorithms of interest.

关键词： arc discharges detection algorithms photovoltaic systems

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Sampling Schemes and detection algorithms for Wideband Spectrum Sensing

Sampling Schemes and Detection Algorithms for Wideband Spect...

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IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN)

作者： Sun, Zhanwei Laneman, J. Nicholas IEEE United States

ISBN: (纸本)9781479926619

This paper establishes a unified model for sub-Nyquist sampling and discusses different aliasing patterns inherent in sub-Nyquist sampling. Several sub-Nyquist sampling schemes are considered within the unified model. This paper also develops multi-channel detection for wideband spectrum sensing following a Bayesian rule based upon the new performance metrics we defined in earlier work. The paper shows that generalized co-prime sampling exhibits similar sensing performance to that of random sampling for the same performance metrics and detection algorithms, and is much easier to implement. Moreover, integer undersampling, which corresponds to the simplest sub-Nyquist sampling scheme and leads to periodic aliasing, appears to be advantageous over the more sophisticated sub-Nyquist sampling schemes in the regime that better protects the primary system.

关键词： detection algorithms Performance metrics primary system broadband Aliasing Scheme Random sampling Sampling

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Rate of Change of Initial Intrinsicoid Deflection Predicts Endocardial Versus Epicardial Ventricular Tachycardia

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JACC-CLINICAL ELECTROPHYSIOLOGY 2024年第6期10卷 1150-1160页

作者： Prisco, Anthony R. Hayase, Justin Olson, Matthew Brigham, Renee C. Ramirez, David A. Iaizzo, Paul A. Shivkumar, Kalyanam Bradfield, Jason Tholakanahalli, Venkat N. Univ Minnesota Cardiol Div 1 Vet Dr Minneapolis MN 55417 USA Univ Minnesota Dept Surg Visible Heart Labs Minneapolis MN USA Univ Calif Los Angeles Cardiac Electrophysiol Los Angeles CA USA United Heart & Vasc Dept Med Div Cardiol Minneapolis MN USA Minneapolis VA Hlth Care Syst Cardiol Div Minneapolis MN USA

BACKGROUND Assessment of origin of ventricular tachycardias (VTs) arising from epicardial vs endocardial sites are largely challenged by the available criteria and etiology of cardiomyopathy. Current electrocardiographic (ECG) criteria based on 12 -lead ECG have varying sensitivity and speci ficity based on site of origin and etiology of cardiomyopathy. OBJECTIVES This study sought to test the hypothesis that epicardial VT has a slower initial rate of depolarization than endocardial VT. METHODS We developed a method that takes advantage of the fact that electrical conduction is faster through the cardiac conduction system than the myocardium, and that the conduction system is primarily an endocardial structure. The technique calculated the rate of change in the initial VT depolarization from a signal -averaged 12 -lead ECG. We hypothesized that the rate of change of depolarization in endocardial VT would be faster than epicardial. We assessed by applying this technique among 26 patients with VT in nonischemic cardiomyopathy patients. RESULTS When comparing patients with VTs ablated using epicardial and endocardial approaches, the rate of change of depolarization was found to be signi ficantly slower in epicardial (6.3 +/- 3.1 mV/s vs 11.4 +/- 3.7 mV/s;P < 0.05). Statistical signi ficance was found when averaging all 12 ECG leads and the limb leads, but not the precordial leads. Follow up analysis by calculation of a receiver -operating characteristic curve demonstrated that this analysis provides a strong prediction if a VT is epicardial in origin (AUC range 0.72-0.88). Slower rate of change of depolarization had high sensitivity and speci ficity for prediction of epicardial VT. CONCLUSIONS This study demonstrates that depolarization rate analysis is a potential technique to predict if a VT is epicardial in nature.

关键词： detection algorithms endocardial VT epicardial VT signal processing ventricular tachycardia

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Applying Intelligent Reflector Surfaces for Detecting Violent Expiratory Aerosol Cloud Using Terahertz Signals

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IEEE NETWORK 2023年第5期37卷 56-63页

作者： Siljak, Harun Barros, Michael Taynnan D'Arcy, Nathan Martins, Daniel Perez Marchetti, Nicola Balasubramaniam, Sasitharan Trinity Coll Dublin SFI CONNECT Res Ctr Dublin D02PN40 Ireland Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England South East Technol Univ Walton Inst SFI VistaMilk Res Ctr Waterford X91 P20H Ireland Univ Nebraska Sch Comp Lincoln NE 68583 USA

The recent COVID-19 pandemic has driven researchers from different spectrum to develop novel solutions that can improve detection and understanding of SARS-CoV-2 virus. In this article we propose the use of Intelligent Reflector Surface (IRS) and terahertz communication systems to detect violent expiratory aerosol cloud that are secreted from people. Our proposed approach makes use of future IRS infrastructure to extend beyond communication functionality by adding environmental scanning for aerosol clouds. Simulations have also been conducted to analyze the accuracy of aerosol cloud detection based on a signal scanning and path optimization algorithm. Utilizing IRS for detecting violent expiratory aerosol cloud can lead to new added value of telecommunication infrastructures for sensor monitoring data that can be used for public health.

关键词： Cloud computing Aerosols Absorption Sensors Wireless communication Atmospheric modeling Attenuation Terahertz communications Public healthcare COVID-19 Viruses (medical) Path planning Optimization methods detection algorithms

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FAPMIC: Fake Packet and Selective Packet Drops Attacks Mitigation by Merkle Hash Tree in Intermittently Connected Networks

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IEEE ACCESS 2023年 11卷 4549-4573页

作者： Khalid, Waqar Ahmad, Naveed Khan, Suleman Saquib, Najam U. Arshad, Muhammad Shahwar, Duri Wuhan Univ Sch Cyber Sci & Engn Wuhan 430000 Peoples R China Inst Management Sci Peshawar 25000 Pakistan Prince Sultan Univ Dept Comp Sci Riyadh 12435 Saudi Arabia Univ Cent Lancashire Sch Psychol & Comp Sci Preston RR1 2HE England Capital Univ Sci & Technol Islamabad Dept Comp Sci Islamabad 4400 Pakistan Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Inst Management Sci Peshawar Dept Comp Sci Peshawar 25000 Pakistan

Delay/Disruption Tolerant Networks (DTNs) are a special category of IntermittentlyConnectedNetworks (ICNs). It has features such as long-delay, frequent-disruption, asymmetrical-data-rates, and high-bundle-error-rates. DTNs have been mainly developed for planet-to-planet networks, commonly known as Inter-Planetary-Networks (IPNs). However, DTNs have shown undimmed potency in challenged communication networks, such as DakNet, ZebraNet, KioskNet and WiderNet. Due to unique characteristics (Intermittent-connectivity and long-delay) DTNs face tough/several challenges in various research areas i.e bundle-forwarding, key-distribution, privacy, bundle-fragmentation, and malicious/selfish nodes particularly. Malicious/selfish nodes launch various catastrophic attacks, this includes, fake packet attacks, selective packet drops attacks, and denial-of-service/flood attacks. These attacks inevitably consume limited resources (persistent-buffer and bandwidth) in DTNs. Fake-packet and selective-packet-drops attacks are top among the challenging attacks in ICNs. The focus of this article is on critical analyses of fake-packet and selective-packet-drops attacks. The panoramic view on misbehavior nodes mitigation algorithms are analyzed, and evaluated mathematically through several parameters for detection probability/accuracy. This article presents a novel algorithm to detects/mitigates fake-packet and selective-packet-drops attacks. The proposed algorithm uses Merkle-Hash-Tree to detects the aforementioned attacks. The proposed algorithm added root hash along with all packets, when the malicious nodes drop packets or inject fake packets, the algorithm detects malicious nodes. Moreover, trace-driven simulation results show the proposed algorithm of this article accurately (enhanced detection-accuracy, enhanced packet delivery/packet loss ratios, and reduces false-positive/false-negative rates) detects malicious nodes which launch fake-packet and selective-packet-drops attacks, unli

关键词： Security Computer science Reliability Monitoring Delay effects detection algorithms Bandwidth Connected vehicles Intermittently connected networks delay tolerant networks packet drops attack fake packet attacks misbehaving node selective packet drop attacks

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Cooperative Spectrum Sensing Based on LSTM-CNN Combination Network in Cognitive Radio System

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IEEE ACCESS 2023年 11卷 87615-87625页

作者： Li, Liuwen Xie, Wei Zhou, Xin Natl Univ Def Technol Coll Informat & Commun Wuhan 430000 Hubei Peoples R China

Cognitive radio (CR), as an emerging technology to improve the utilization of radio spectrum, the fundamental of CR technology is spectrum sensing, due to the detection performance being affected by various factors, spectrum sensing is challenging to achieve accurately. In recent years, many spectrum sensing algorithms have been proposed, such as energy detection algorithm, matched filter detection algorithm, cyclic stationary detection algorithm, etc. However, these algorithms are model-driven and require certain prior information. If the model assumptions are inaccurate or the prior information is challenging to obtain, the algorithms' detection performance will be degraded. The development of artificial intelligence technology and deep learning provides a new way to realize spectrum sensing. In this paper, we design a cooperative spectrum sensing model based on the parallel connection of convolutional neural network (CNN) and long-short-term memory (LSTM), which makes full use of the complementary feature extraction capabilities of CNN and LSTM networks. Among them, CNN is used to extract hidden spatial features, and LSTM network is used to extract time features. Both CNN and LSTM can process the original dataset directly avoiding information feature loss when the network is connected serially. Experimental result shows that the detection performance of the proposed algorithm outperforms the conventional cooperative detection algorithm under low SNR condition. For example, when the number of cooperative users is 9 and the transmit power is 10, the detection probability of the proposed algorithm in this paper can reach more than 90%, which is much higher than the detection performance of other spectrum detection algorithms.

关键词： Sensors Feature extraction Convolutional neural networks Radio spectrum management detection algorithms Uncertainty Neural networks Cognitive radio Cooperative spectrum sensing cognitive radio CNN-LSTM combination network

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Credibility-Supervised Dynamic Clustering and Consensus Model Involving Minority Opinions Handling in Social-Network Large-Scale Decision Making

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第10期31卷 3664-3678页

作者： Li, Mengqi Huang, Jing Xu, Yejun Liu, Xia Herrera-Viedma, Enrique Hohai Univ Business Sch Nanjing 211100 Peoples R China Univ Granada Andalusian Res Inst Data Sci & Computat Intelligen Granada 18071 Spain Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Univ Granada Dept Comp Sci & AI Andalusian Res Inst Data Sci & Computat Intelligen Granada 18071 Spain

Existing studies on social-network large-scale decision making (SN-LSDM) have overlooked two vital roles of the information credibility of decision makers (DMs): first, information credibility directly determines the reliability of clustering results and final decision output;second, consideration of minority opinions may hold the truth but may also be risky, while the credibility analysis of minority opinions can help avoid risks. To this end, this article proposes credibility-supervised dynamic clustering and consensus model involving minority opinion processing. First, DMs' information is analyzed and preprocessed to obtain DMs' credibility evaluation. Subsequently, DMs' credibility is used as one of the clustering criteria to supervise and guide the trust relationships and the clustering of large-scale group, which assists in deriving the reliable clustering result. This clustering is dynamic because the clustering result may alter as the DMs' information changes in consensus. Afterwards, an SN-LSDM consensus model with minority opinions settlement is developed. A credibility-based mechanism for identification, DMs' discussion, and weight adjustment of minority opinion subgroup is constructed, including it trust risk measurement method and trust risk level-based weight adjustment. Then, a risk index is defined in feedback adjustment mechanism to adjust DMs' information, and a decision plan with a higher consensus level is obtained. Finally, the feasibility and effectiveness of the proposed model are verified through the case study of "916" Lu County earthquake relief. Discussions and comparisons are conducted by simulation experiments to explore the capabilities and advantages of our proposal.

关键词： Social networking (online) Decision making Reliability Clustering algorithms Indexes Heuristic algorithms detection algorithms Clustering consensus credibility minority opinions social-network large-scale decision making (SN-LSDM)

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