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检索条件"主题词=Detection Algorithms"

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Automatic Modulation detection in Non-orthogonal Multiple Access Systems

Automatic Modulation Detection in Non-orthogonal Multiple Ac...

引用

International Symposium on Telecommunications, IST

作者： Fatemeh Shabanali Mehrdad Ardebilipour Department of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

Increasing demand and spectrum limitations pose ongoing challenges in telecommunications that constantly engage researchers in this field. Recently, various wireless systems have been developed, including Non-Orthogonal Multiple Access (NOMA), which is proposed for 5G and 6G networks. NOMA improves capacity while maintaining spectral resources by allowing the transmitter to send predefined user symbols with user-specific power over the same resources. The conventional NOMA receiver, Serial Interference Cancellation (SIC), requires knowledge of the modulation types to separate user signals. To improve spectral efficiency and reduce signaling overhead issues, an Automatic Modulation detection algorithm has been proposed. The algorithm enables the receiver to automatically identify the modulation type, eliminating the need for the transmitter to provide this information. This paper presents a novel automatic modulation detection algorithm for a two-user NOMA system that uses feature extraction and deep learning to achieve high detection rates while maintaining manageable computational complexity. The proposed algorithm outperforms previous methods, achieving 93% detection rate at 5 dB signal-to-noise ratio, compared to 78.36% reported in previous studies.

关键词： Deep learning Wireless communication NOMA Interference cancellation Transmitters Modulation Receivers Feature extraction Telecommunications detection algorithms

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Electric Energy Meter Reading detection Algorithm Based on Improved YOLOv8n Model

Electric Energy Meter Reading Detection Algorithm Based on I...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Youhu Wang Zijing Wang Jianhua Shi Peiyong Yuan Bobo Xue Shunde Liu Kang Ma Xining Jiuzheng Electronic Technology Co. Ltd Xining Qinghai China Computer Department of North China Electric Power University Baoding Hebei China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

In smart grid systems, automated electric meter reading technology is crucial for optimizing the management and distribution of electricity resources. In this study, an optimized algorithm based on the YOLOv8n model is introduced for electric meter reading recognition, with the goal of improving both the precision and performance of automated meter reading systems. This algorithm incorporates the SPDConv as a substitute for the conventional convolution layers in the original model, which significantly minimizes information loss while boosting the model's feature extraction efficiency. Moreover, the backbone network integrates a lightweight channel attention mechanism known as ECA, which enhances attention to critical local features and improves the detection performance for small objects. The experimental evaluation on a tailored electric meter dataset reveals that the enhanced algorithm surpasses the YOLOv8n model, achieving a 4.6% improvement in mAP, along with reductions of 10.6% in parameter count, 10% in model size, and 7.4% in computational cost.

关键词： Attention mechanisms Accuracy Convolution Computational modeling Interference Feature extraction Boosting Meter reading Smart grids detection algorithms

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Design of a Precision Resolver Signal Measurement Algorithm based on Phase Sensitive detection

Design of a Precision Resolver Signal Measurement Algorithm ...

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URSI Atlantic Radio Science Conference (URSI AT-RASC)

作者： Li You Haibin Jin Yalu Li Min Liu Zhenyu Guan Beijing Orient Institute of Measurement and Test Beijing China Beihang University Beijing China

ISBN: (数字)9789463968102

ISBN: (纸本)9798350360257

The article introduce a precision resolver signal measurement algorithm based on phase sensitive detection, which can be applied to the development of resolver calibration systems for angle position indicators or resolver simulators. Based on universal digitizers we realized resolver signal measurement with 0.0001° resolution and $0.001^{\circ} (k=2)$ uncertainty.

关键词： Phase measurement Accuracy Uncertainty Measurement uncertainty Position measurement Calibration detection algorithms

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Coherent Integration detection of Maneuvering Targets Using Non-Uniformly Resampling Trilinear Autocorrelation Function and KT

Coherent Integration Detection of Maneuvering Targets Using ...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Liang Qiao Hongmeng Chen Ming Li Lei Zuo National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Beijing Institute of Radio Measurement Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

The presence of range migration (RM) and Doppler migration (DM) leads to the degradation of performance in coherent integration detection of maneuvering targets. This article proposed a coherent integration detection method for third-order maneuvering targets based on the non-uniformly resam-pling trilinear autocorrelation function (NRTAF) and keystone transform (KT). Firstly, The NRTAF is introduced to address DM, as well as second-order and third-order RM, while first-order RM is eliminated using KT. Subsequently, the energy of signal is accumulated through fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT). The proposed algorithm completely avoids searching for motion parameters, resulting in lower computational complexity. It can effectively suppress cross terms, making it suitable for multi-objective scenarios. Additionally, it is appropriate for high-speed target scenarios. The effectiveness of the algorithm has been confirmed through simulation and measured data.

关键词： Resistance Parameter estimation Fast Fourier transforms Noise Signal processing algorithms Transforms Doppler effect Autocorrelation Computational complexity detection algorithms

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Enhanced Chipless RFID detection Algorithm for Dense Multipath Channels based on Calibration Equalization Techniques

Enhanced Chipless RFID Detection Algorithm for Dense Multipa...

引用

International Conference on Electromagnetics in Advanced Applications (ICEAA)

作者： Mohamed El Hadidy Ines Bakri RheinMain University of Applied Science Rüsselsheim Germany

ISBN: (数字)9798350360974

ISBN: (纸本)9798350360981

Main objective of this paper is to introduce an enhanced detection algorithm for Frequency Coded (FC) Chipless RFID tags within dense multipath channels based on Calibration Equalization Techniques (CET). In real-world multipath channels, few resolvable paths between the transmitter and the receiver of the reader are hitting the chipless RFID tag or the frequency selective surface, while many other paths are directly linked between the transmitter and the receiver without passing the tag and unfortunately those have in most cases the major contribution of the received power as illustrated in equation 1

关键词： Frequency selective surfaces Transmitters Chipless RFID Receivers Calibration detection algorithms Electromagnetics Multipath channels

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DED-YOLOv8:Dense pedestrian detection algorithm based on YOLOv8

DED-YOLOv8:Dense pedestrian detection algorithm based on YOL...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Xiaolin Li Lishun Ma School of Communications and Information Engineering Chongqing University of Posts and Telecommunications China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

Aiming at the problem that pedestrian occlusion and scale changes in dense scenes make the model prone to missed detection and false detection, a dense pedestrian detection algorithm improved from YOLOv8n is proposed: DED-YOLOv8. Firstly, the backbone network improved by Deformable ConvNets_V2 (Dcnv2) is employed to boost the capacity of extracting pedestrian features. Then, the Efficient Multi-Scale Attention (EMA) module is introduced into the baseline model. Finally, The Dynamic Head (Dyhead) is brought in to heighten the attention towards multi-scale pedestrian features and strengthen the expression capacity of the detection head. Through the analysis of experimental results, on the WiderPerson public dense pedestrian dataset, in comparison with the model of YOLOv8n, the mAP@0.5 and mAP@0.5:0.95 of DED-YOLOv8 have risen respectively by 2.5% and 1.7%. It has been greatly improved, and it also has shown strong competitiveness when compared with other advanced human detection models.

关键词： Performance evaluation Information science Analytical models Pedestrians Accuracy Computational modeling Object detection Feature extraction Complexity theory detection algorithms

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CrossAlert: Enhancing Multi-Stage Attack detection through Semantic Embedding of Alerts Across Targeted Domains

CrossAlert: Enhancing Multi-Stage Attack Detection through S...

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IEEE Conference on Communications and Network Security (CNS)

作者： Nadia Niknami Vahid Mahzoon Jie Wu Center for Networked Computing Temple University USA

ISBN: (数字)9798350375961

ISBN: (纸本)9798350375978

With the continuous evolution of attack methods, cyber attacks have become more distributed and complex, employing techniques such as multi-stage network attacks (MSA). Monitoring tools generate numerous alerts during these attacks, but the high dimensionality and diverse features of alert data often result in poor detection performance. Manual analysis of MSAs is time-consuming, leading to limited labeled data. Additionally, changes in attack types and new domains cause Intrusion detection Systems (IDSs) to perform poorly, presenting a significant challenge known as domain shift. In this paper, we address these issues by proposing a multi-stage network attack detection algorithm that enhances MSA detection through the analysis of high-dimensional alerts and the integration of various alert aspects. Our algorithm incorporates multiple facets including semantic similarity, anomaly scores, and feature extraction to identify relevant entities, detect intricate relationships, and uncover hidden patterns, enhancing the detection of multi-stage network attacks. The model was tested successfully using the DARPA 2000 and ISCX 2012 datasets, with completeness and soundness measured to evaluate its effectiveness.

关键词： Scalability Semantics Intrusion detection Manuals Network security Feature extraction Reliability detection algorithms Monitoring Cyberattack

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Application of Classification Models on Fraud detection in Retail

Application of Classification Models on Fraud Detection in R...

引用

International Symposium on Computational Intelligence and Informatics

作者： Anna Biceková Natália Onufráková František Babič Department of Cybernetics and Artificial Intelligence Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350353433

ISBN: (纸本)9798350353440

Data-based analytical approach in this area is becoming increasingly important for identification of new methods and approaches helping retail owners protect themselves from financial losses while maintaining customer confidence. The number of self-checkout stations in various retail stores is constantly increasing. This automated process helps avoid long lines and speeds up the paying process for individual customers. On the other hand, a certain level of trust with the customer is required by the retailers, supported by personnel checking the payment from the handheld mobile scanner. This situation requires a good strategy for how to plan enough necessary checks and this number is not annoying for customers. The most important objective is to identify as many false scans as possible. Based on the public data available from Data Mining Cup we have generated a batch of different classification models to find an optimal decision support model from a defined business perspective.

关键词： Hands Data models Real-time systems Fraud Maintenance Security Personnel Object recognition Informatics detection algorithms

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Personnel detection Algorithm Based on Spatiotemporal Mapping Event Frames

Personnel Detection Algorithm Based on Spatiotemporal Mappin...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Jiannan Zhao Lei Zhao Wenyuan Zhang Zhiteng Li Juyi Wu Feng Shuang Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment School of Electrical Engineering Guangxi University Nanning China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

The personnel detection algorithms based on frame-based traditional cameras exhibit low accuracy in harsh lighting conditions or rapidly moving scenes. To address this issue, we propose a personnel detection network called YOLO-SMCA, which utilizes an event camera to capture personnel motion information based on Spatiotemporal Mapping Event Frames. By incorporating temporal information into the event mapping method to generate event frames, we overcome the limitations of traditional event representation methods, which often lead to the loss of temporal information. This enhancement significantly improves the feature representation capability of the input events. Building on this foundation, we employ the YOLOv5n neural network for personnel detection using spatiotemporal mapping event frames. Additionally, we embed the Coordinate Attention (CA) mechanism in the head of the network to enhance the model’s focus on key features during final decision-making. Through comparative and visualization experiments conducted on the PEDRo dataset, the YOLO-SMCA achieved a mean Average Precision (mAP) of 98.1%, outperforming other methods and meeting the accuracy requirements for personnel detection in complex environments.

关键词： Accuracy Face recognition Buildings Cameras Video surveillance Robustness Spatiotemporal phenomena Personnel detection algorithms Testing

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The Enhanced MPPT Using Slope detection Algorithm for PV System Under Partial Shading and Low Irradiation Conditions

The Enhanced MPPT Using Slope Detection Algorithm for PV Sys...

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International Conference on Business and Industrial Research (ICBIR)

作者： Jirada Gosumbonggot Department of Electrical Engineering Faculty of Engineering Thai-Nichi Institute of Technology Bangkok Thailand

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

Photovoltaic (PV) technology, being a prominent renewable energy source primarily due to its environmentally friendly operation and versatile installation options. To improve the efficiency of photovoltaic (PV) systems, it is essential to take into account the impact of weather conditions. Irradiation and temperature are two primary parameters influencing PV-generated power, both of which vary depending on the location where the PV system is installed. Consequently, the issue of irradiation mismatch may arise, posing challenges for the PV system in achieving its maximum potential power generation. This paper presents the maximum power point tracking (MPPT) algorithm that can operates under partial shading and low irradiation conditions. The algorithm operates with a concept of searching the highest mountain's peak point. Case studies using the irradiation dataset from the Department of Alternative Energy Development and Efficiency are input to evaluate the performance of the proposed method under different weather conditions. Graphical and numerical results show the effectiveness of the algorithm, with the searching time under 1.36 seconds and the accuracy at 99.55%.

关键词： Maximum power point trackers Photovoltaic systems Radiation effects Renewable energy sources Temperature dependence Accuracy Estimation Robustness detection algorithms Meteorology

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