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检索条件"主题词=detection Algorithms"

共 7428 条记录，以下是681-690 订阅

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System Architecture Design for Spoofing detection Based on Baseband Information

System Architecture Design for Spoofing Detection Based on B...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Zhen Peng Ao Peng Jianghong Shi School of Information Science and Engineering Xiamen University Xiamen China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

This paper evaluates the performance of two anti-spoofing algorithms, studies the performance boundary of anti-spoofing technology under multiple parameters, verifies its reliability and effectiveness in the face of multiple spoofing attack ways, and provides a judgment basis for scheme selection. Moreover, combining the complementarity of advantages of different spoofing detection technologies, the detection probability in joint detection scenarios is calculated, it provides effective support for various spoofing scenarios. This paper then proposes an anti-spoofing system architecture based on baseband information. The architecture utilizes multi-dimensional and multi-feature signal analysis, and combines various spoofing parameters using specific group strategies, so as to obtain a more comprehensive reference for detection effects.

关键词： Global navigation satellite system Baseband Systems architecture Computer architecture Prediction algorithms Probability distribution Reliability Signal analysis detection algorithms Faces

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Lightweight Pedestrian detection Algorithm Based on Improved YoLov8s

Lightweight Pedestrian Detection Algorithm Based on Improved...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Chenhao Zhang Weidong Li Xiaojuan Li Hebei University of Economics and Business Shijiazhuang China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

To address the high computational complexity and parameter redundancy in pedestrian detection models, this paper proposes a lightweight YOLOv8s-based algorithm optimized for resource-constrained devices. The improvements include three key components: (1) Integration of RepViT blocks into the backbone network to replace traditional bottlenecks, where the Squeeze- and-Excitation (SE) attention mechanism is selectively removed from shallow layers to reduce redundancy while retaining it in deeper layers for enhanced feature representation. (2) Replacement of standard convolutions with Group Spatial Convolution (GSConv) to minimize parameters and computational costs. (3) Introduction of the Shape-IoU loss function, which incorporates shape and scale alignment to improve bounding box accuracy for irregular pedestrian contours. Experiments on the WiderPerson dataset demonstrate that the proposed model achieves S9.5 % mAP@0.5 and 63.7% mAP@0.5:0.95, comparable to the original YOLOv8s, while reducing parameters and computational effort by 36.6% (to 7.1 MB) and 32.6% (to 19.4 GFLOPs), respectively. The model also outperforms YOLOv5n, YOLOv6n, and YOLOv8n in recall (8O.8%) and precision (S7.2 % ), achieving an optimal balance between efficiency and accuracy. These advancements enable practical deployment in intelligent transportation, surveillance, and edge-computing scenarios.

关键词： Pedestrians Accuracy Shape Computational modeling Surveillance Image edge detection Redundancy Transportation detection algorithms Standards

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FFT Nuttall Window Four Spectral Line Interpolation Correction Harmonic detection Algorithm

FFT Nuttall Window Four Spectral Line Interpolation Correcti...

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Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Jianchao Li Jiayi Sun Jilei Wan Xifeng Liu Lina Cao Bo Yang Danwen Yu Wanyue Zhang Weida Cao Fang Chen State Grid Liaocheng Power Supply Company Liaocheng China

ISBN: (数字)9798331504076

ISBN: (纸本)9798331504083

When using traditional FT for harmonic signal analysis, it is difficult to strictly achieve synchronous sampling and full period truncation, which inevitably leads to spectral leakage and barrier effects, reducing the accuracy of detection results. In order to improve the accuracy of detection results, based on FFT (Fast Fourier Transformation), Hann window Blackman window Blackmanharis window Nuttall's four line interpolation algorithm was studied, and correction formulas for amplitude, frequency, and phase corresponding to different spinor window four line interpolation were derived through polynomial fitting. And simulation experiments were conducted on different four line interpolation algorithms of the complementary window in Matlab, and compared and analyzed with the corresponding three line interpolation algorithm of the complementary window. The results showed that the four line interpolation algorithm of the fourth-order Nuttall window had higher amplitude accuracy, phase accuracy, and frequency accuracy compared to other algorithms, better sidelobe performance, and can better suppress spectral leakage and barrier effects.

关键词： Interpolation Accuracy Fast Fourier transforms Fitting Power system harmonics Harmonic analysis Polynomials Steady-state MATLAB detection algorithms

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Hash-Prune-Invert: Improved Differentially Private Heavy-Hitter detection in the Two-Server Model

Hash-Prune-Invert: Improved Differentially Private Heavy-Hit...

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IEEE Symposium on Security and Privacy

作者： Borja Balle James Bell-Clark Albert Cheu Adria Gascon Jonathan Katz Mariana Raykova Phillipp Schoppmann Thomas Steinke Google DeepMind Google Research Google

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Differentially private (DP) heavy-hitter detection is an important primitive for data analysis. Given a threshold $t$ and a dataset of $n$ items from a domain of size $d$ , such detection algorithms ignore items occurring fewer than $t$ times while identifying items occurring more than $t+\Delta$ times; we call $\Delta$ the error margin. In the central model where a curator holds the entire dataset, $(\varepsilon, \delta)$ -DP algorithms can achieve error margin $\Theta\left(\fracalgorithms{\varepsilon} \log \fracalgorithms{\delta}\right)$ , which is optimal when $d\gg 1/\delta$ . Several works, e.g., Poplar (S&P 2021), have proposed protocols in which two or more non-colluding servers jointly compute the heavy hitters from inputs held by $n$ clients. Unfortunately, existing protocols suffer from an undesirable dependence on Iog $d$ in terms of both server efficiency (computation, communication, and round complexity) and accuracy (i.e., error margin), making them unsuitable for large domains (e.g., when items are kB-long strings, log $d\approx 10^{4}$ ). We present hash-prune-invert (HPI), a technique for compiling any heavy-hitter protocol with the log $d$ dependencies mentioned above into a new protocol with improvements across the board: computation, communication, and round complexity depend (roughly) on log $n$ rather than log $d$ , and the error margin is independent of $d$ . Our transformation preserves privacy against an active adversary corrupting at most one of the servers and any number of clients. We apply HPI to an improved version of Poplar, also introduced in this work, that improves Poplar's error margin by roughly a factor of $\sqrt{n}$ (regardless of $d)$ . Our experiments confirm that the resulting protocol improves efficiency and accuracy for large $d$ .

关键词： Privacy Protocols Accuracy Data analysis Computational modeling Computational efficiency Complexity theory Servers Security detection algorithms

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Lightweight detection method of distracted driving based on YOLO-SSC

Lightweight detection method of distracted driving based on ...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Wuxier Balatijiang Xia Wei College of Electrical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

To address the problems of high device requirements, low detection accuracy, and high miss detection rate of overlapping targets in traditional distracted driving target detection models, a vehicle target detection algorithm called YOLO-SSC, based on the improved YOLOv8, was proposed. First, the main body network was reconstructed by using multi-scale fusion. Then, the StarBlock module was used to improve the C2f module in the neck, achieving multi-scale feature fusion to improve the model's detection accuracy for different size and distance targets. Finally, a lightweight model with shared convolution was used to further reduce the model's parameter number and computational cost. The experimental results show that the mAP50 of the improved model is 96.3%, the number of model parameters and the amount of model calculation are 1.4M and 4.5G, respectively, which are reduced by 53.3%and 44.4%compared with YOLOv8n.

关键词： Accuracy Convolution Computational modeling Object detection Feature extraction Real-time systems Neck detection algorithms Robots Intelligent control

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Multi-Channel Attention Mechanism Hot Spot detection Algorithm Based on YOLOv5

Multi-Channel Attention Mechanism Hot Spot Detection Algorit...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： XiangZhe Xin Ming Li Qingkai Lang Qiong Wu BEIJING GUOWANG FUDA SCIENCE AND TECHNOLOGY DEVELOPMENT CO. LTD. Beijing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

To solve the problems of scarcity of hot spot image data and insufficient feature extraction of photovoltaic modules, a multi-channel attention mechanism hot spot detection algorithm based on YOLOv5 is proposed. The hybrid data augmentation method of grid masking and image elastic deformation is used to effectively expand the data of a small number of existing hot spot datasets, while the multi-channel attention mechanism is used to interact with cross-latitude information to obtain the features of hot spot images. The average precision 50 (AP50) of the improved algorithm is improved by 1.17% compared to the baseline model YOLOv5, and its inference speed reaches 100.12 frames/s. It is demonstrated through attentional ablation experiments that capturing the interaction between channel attentions is important for improving the accuracy of the model.

关键词： YOLO Attention mechanisms Accuracy Deformation Feature extraction Data augmentation Inference algorithms Solar panels detection algorithms Informatics

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Seizure detection Based on Vagus Nerve Activity: Translation to an Implantable Setting

Seizure Detection Based on Vagus Nerve Activity: Translation...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Michel-Antony Ngan Yamb Javier Chávez Cerda Louis Vande Perre Riëm El Tahry Antoine Nonclercq BEAMS department Université Libre de Bruxelles Brussels Belgium Institute of Neuroscience Université Catholique de Louvain Brussels Belgium

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

Epilepsy is a chronic disease that affects fifty million people worldwide. One-third of patients are not responding to antiepileptic drugs. For those patients, if surgery is impossible, vagus nerve stimulation (VNS) can be offered as an adjunctive treatment. As on-demand stimulation improves VNS efficacy, closed-loop VNS – i.e., stimulating at the occurrence of a seizure – emerges as a promising solution for those patients. However, a suitable biomarker for seizure detection is required. Besides, to be clinically valuable, computations related to such a biomarker should be low enough to be embedded in an implant. This work explores the use of vagus nerve electroneurogram (VENG) as a biomarker and compares the performances of various seizure detection algorithms. We analyzed data from six rats that exhibited acute seizures induced by pentylenetetrazol (PTZ), along with three control rats infused with a saline solution. All seizures could be detected with no false positives, even without requiring large computations. This work opens ways to reduce the computation power necessary for VENG-based seizure detection, aiming at embedding a seizure detection algorithm in an implant. Further work should address whether the performance of low computation power algorithms is maintained in a chronic setting, i.e., in the presence of movement artifacts.

关键词： Drugs Surgery Epilepsy Implants Rats detection algorithms

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detection Methodology for Multistage Voltage Sag Based on Residual Analysis

Detection Methodology for Multistage Voltage Sag Based on Re...

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International Conference on Harmonics and Quality of Power

作者： Shunyi Li Ying Wang Huaxi Yu Xianyong Xiao Haishan He College of Elec. Engr. Sichuan University Chengdu China

ISBN: (数字)9798350382563

ISBN: (纸本)9798350382570

The monitoring of voltage sags is a crucial prerequisite for mitigating their harmful effects. With the energy transition in power systems and the integration of a large number of distributed generations, multi-stage voltage sags (MSVS) are becoming increasingly common, causing more severe and profound impacts on the grid and users. Currently, there is a lack of effective description and detection methods for MSVS, posing challenges to their statistics, management, and prevention. Due to the varying residual voltages and multiple transition moments of MSVS, traditional methods struggle to effectively detect them. To address these difficulties, this study proposed a novel detection method based on residual analysis, tailored for the special characteristics of MSVS. First, relevant terms for MSVS were introduced, providing a foundation for the monitoring and management of MSVS. Secondly, a detection algorithm based on residual analysis is proposed, achieving accurate identification of MSVS and precise detection of their mutation moments. The effectiveness and accuracy were validated with data from a 10kV distribution network. The proposed method lays the foundation for improving the power quality of the existing energy transition system.

关键词： Accuracy Voltage fluctuations Prevention and mitigation Power quality Distribution networks Power system harmonics Harmonic analysis Power grids detection algorithms Monitoring

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Classroom Automation System Using Human detection Based on YOLO Algorithm and IoT Technology

Classroom Automation System Using Human Detection Based on Y...

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Electrical and Information Technology (IEIT), International Conference on

作者： Mazaya Muhammad T. S. El Syaff Vincentius Jason Samhadi Brahmantya Aji Pramudita School of Electrical Engineering Telkom University Bandung Indonesia The University Center of Excellence for Intelligent Sensing-IoT Telkom University Bandung Indonesia

ISBN: (数字)9798331516888

ISBN: (纸本)9798331516895

Classroom is an important facility in the school or university to be utilized as a learning space and lessons happen. Students and teachers meet in the classroom on a daily, with different schedules. Hence, every class needs to be monitored and controlled for finding out the class conditions. However, the staffs have to monitor the classroom by checking the classroom one by one. Thus, it is not efficient and increase the workload if there are many classes to be checked. Therefore, automated technology can be implemented to reduces the workload and increases staff efficiency when controlling and monitoring the classroom, especially in the context of time. In this study, an IoT-based system was proposed as a solution to control the classroom condition. This system used class schedules and human presence as a parameter to control the classroom. A camera utilized to detect the object and performed YOLOv8 algorithm to analyze the condition. in this system, firebase was leveraged to store every data from detecting process. Then, the data was used for controlling the actuators, such as door locking, lamp, and fan, which were controlled by ESP32. The result showed that the proposed system could perform with accuracy of 57%, sensitivity of 67%, and positive predictive value of 79%. The proposed system can achieve a success rate of 90% with less than 20 second of time delay which makes this system time effective. But it is not yet known whether this system will work effectively in low light conditions.

关键词： YOLO Schedules Fans Accuracy Sensitivity Prediction algorithms Data transfer Information technology detection algorithms Monitoring

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Ultrasound Image Guided Robotic Puncture System With Needle Tip detection and Deviation Correction Features

Ultrasound Image Guided Robotic Puncture System With Needle ...

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IEEE International Conference on Robotics and Biomimetics

作者： Jinhang Li Zhengnan Jiang Chenyang Li Baoliang Zhac Peng Zhang Pak Kin Wong Ying Hu Department of Electromechanical Engineering University of Macau Macau China Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Robotic needle insertion with ultrasound image guidance can help improve the accuracy of liver cancer biopsy. Most robotic puncture systems with ultrasound guidance utilize dual-arm configurations, which are less efficient in terms of space utilization. To address this, we have developed a compact ultrasound image guided needle puncture mechanism. It consists of an ultrasound probe to provide image feedback and a serial-parallel mechanism together with a range extender to drive the puncture needle. To achieve accurate puncture in the dynamic environment of tissue deformation and needle bending, a needle tip detection algorithm in ultrasound images with YOLO V8 network is proposed, together with needle axis fitting and Kalman Filter to furtherly improve the accuracy. With image feedback, a closed-loop deviation correction controller with fuzzy control algorithm is developed and tested on the robotic puncture system, achieving 0.83mm accuracy, which can meet the clinical requirement.

关键词： YOLO Ultrasonic imaging Accuracy Heuristic algorithms Fitting Needles Robot sensing systems Kalman filters Probes detection algorithms

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