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检索条件"主题词=Detection Algorithms"

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Yoga Pose detection using MobileNetV3+pose comparison and instruction

Yoga Pose Detection using MobileNetV3+pose comparison and in...

引用

Information Technology and Contemporary Sports (TCS), International Conference on

作者： Xinyan Xie School of Internationa Education Guangxi University of Science and Technology Liu Zhou China

ISBN: (数字)9798331529864

ISBN: (纸本)9798331529871

In the contemporary era, the ancient practice of yoga has witnessed a remarkable resurgence, transcending cultural and geographical boundaries to become a global phenomenon. The ascent of yoga as a global wellness practice underscores the need for accurate yoga pose execution, particularly among novices and those practicing without direct supervision. Leveraging recent advancements in pose detection algorithms, this study proposes a novel framework utilizing MobileNetV3 and Convolutional Neural Networks (CNNs) to detect and classify yoga poses in real-time, coupled with immediate instructional feedback [4] . This integration aims to enhance practitioners’ experience by ensuring correct pose alignment, thereby making yoga more accessible and maximizing its health benefits. A comprehensive dataset, inclusive of diverse yoga styles and practitioner demographics, underpins the system’s development, ensuring broad applicability and effectiveness.

关键词： Accuracy Real-time systems Cultural differences Convolutional neural networks Information technology detection algorithms Sports

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Adaptive Malware detection In Windows Portable Executables (Wpes)

Adaptive Malware Detection In Windows Portable Executables (...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： A Sathish N.Babu M Bhavani K Divya B Jhoshna T Hemavathi Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Andra Pradesh India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

It's crucial to comprehend the characteristics of malware variants that enable them to elude malware detection methods as they are created with the intention of avoiding detection by means of a variety of evasion strategies. It is done by a framework designed to generate undetectable malware samples on Windows executable files by incorporating various techniques such as adding benign sections and obfuscating code. Creating undetectable malware variants using a framework can successfully evaded numerous anti-malware programs, showing significantly higher evasiveness than both the original malware and a previously suggested solution. By examining various perturbations and their efficacy, our results contribute to the improvement of malware detection algorithms by providing a better knowledge of the categories to which more evasive malware variants may belong and how they might be created. To create anti-malware programmed that are capable of successfully identifying malware varieties that have not been observed before, experimental findings and observations would be helpful.

关键词： Technological innovation Codes Social networking (online) Computer hacking Perturbation methods Market research Malware Computational efficiency detection algorithms Monitoring

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Accurate detection of Superspreaders on Multi-Pipeline Switches

Accurate Detection of Superspreaders on Multi-Pipeline Switc...

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International Conference on Computer and Communications (ICCC)

作者： Cenman Wang Bowen Zheng Fan Yang Ye Tian Anhui Key Laboratory on High Performance Computing School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In various network monitoring tasks, the detection of superspreaders has become increasingly crucial. However, latest superspreader detection algorithms and data structures assume the target switch with a single pipeline, while state-of-the-art programmable switches actually comprise multiple independent pipelines. In current method for deploying super-spreader detection applications on multi-pipeline switches, a separate measurement data structure instance is employed for each pipeline. However, under multi-path routing, this simplistic approach leads to lower detection accuracy due to hash collisions. To address this issue, in this paper, we present PipelineAlloc, a HyperLogLog-based superspreader detection system designed for multi-pipeline switches. In order to reduce hash collisions in measurement data structures, PipelineAlloc assigns an egress pipeline as the monitor pipeline for each source IP address. PipelineAlloc introduces novel measurement data structures in the ingress pipelines for temporarily storing statistics of the source and destination IP addresses of packets, and uses packets to carry these measurement data to egress pipelines, which serve as the monitor pipelines. The HyperLogLog-based measurement data structures in egress pipelines are updated using data carried by packets. Experiments driven by real-world traffic traces show that PipelineAlloc outperforms existing solutions in terms of detection accuracy.

关键词： Accuracy Scalability Current measurement Pipelines Switches Data structures Routing IP networks detection algorithms Monitoring

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PDAST: Paradigm Towards Weather Domain Adaptation for 3D detection Based on Density Aware Pooling and Self-Training

PDAST: Paradigm Towards Weather Domain Adaptation for 3D Det...

引用

IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Yonghui Huang Xi Zhang Chuan Hu Yanhao Liu Yakang Wang School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

Most 3D detection algorithms are developed and validated on datasets collected during clear weather, while performance significantly deteriorates under severe weather conditions such as snowy days. To address this problem, we propose PDAST, an unsupervised pipeline for weather domain adaptation. PDAST involves point density dealing (PDD), a data augmentation method, and a density-aware pooling module (PDV), both of which are plug-and-play modules to enhance algorithmic robustness in snowy weather conditions along with the self-training [1] strategy. The data augmentation method PDD randomly samples and perturbs the ground truth point database in the point cloud. Meanwhile, the PDV module combines voxel features and density-based positional encoding features as inputs to a multi-head self-attention module, facilitating the capture of global features comprising density features. In this paper, we validate the effectiveness of PDAST on a typical voxel-based 3D detection baseline, SecondIOU [2], and conduct experiments on STF [3] datasets with clear and snowy datasets. Our approach PDAST achieves a performance enhancement of around 14.9% ~ 25.5% for $AP_{3D}$ (Average Precision Of 3D Boundingboxes) and 17.5% ~ 29.3% for $AP_{AOS}$ (Average Precision Of Orientation Similarity) compared to the baseline detector on clear-to-snowy conditions. Furthermore, PDAST's performance in unsupervised clear-to-snowy conditions matches or exceeds the baseline model fully supervised on snowy conditions. Our PDAST alleviates the performance degradation caused by severe weather, such as snowy days, and is of great significance for all-weather perception.

关键词： Point cloud compression Degradation Three-dimensional displays Pipelines Imaging Detectors Data augmentation Robustness detection algorithms Meteorology

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PSF3-YOLOv8:Low resolution vehicle detection algorithm based on improved YOLOv8

PSF3-YOLOv8:Low resolution vehicle detection algorithm based...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： YiYuan Cheng ZhuoHao Ning ZhuoHao Deng Kuan Li JianPing Yin School of Computer Science and Technology Dongguan University of Technology Dong Guan China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

Detecting vehicles at low resolution is essential for traffic surveillance and autonomous driving. Traditional high-resolution detection methods often struggle with low-resolution images, but super-resolution techniques can recover fine details, thereby significantly improving detection performance. In this paper, a target detection method that is based on YOLOv8s is presented, utilizing the ADown convolution module in place of the Conv module to reduce parameters and improve efficiency. Additionally, the PSFM deep semantic fusion module is incorporated into the detection head to enhance feature fusion, while the Dattention mechanism is integrated into the C2f module to capture complex details. Experimental results demonstrate that the PSF3-YOLOv8 model outperforms other YOLO networks in key metrics, achieving improvements of 5.5% in Precision, 0.1% in Recall, 0.9% in mAP50, and 3.5% in mAP50-95.

关键词： YOLO Measurement Head Surveillance Superresolution Semantics Traffic control Feature extraction detection algorithms Information systems

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Real-Time R-Wave detection Algorithm for ECG Signals Based on Feature Enhancement

Real-Time R-Wave Detection Algorithm for ECG Signals Based o...

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Electronics and Communication, Network and Computer Technology (ECNCT), International Conference on

作者： Sikai Guo Huibin Wang Ke Xu Wenping Chen College of Information Science and Engineering Hohai University HHU Nanjing China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

With advancing technology, real-time monitoring of electrocardiogram (ECG) signals has transitioned from clinical settings to daily life. To meet the growing demand for real-time R-wave detection, this paper proposes a feature-enhanced algorithm. The signal undergoes low-pass filtering followed by first-order backward differentiation and cumulative multiplication to enhance R-wave detection. Adaptive thresholding and a retrospective missed detection mechanism are then applied. Experimental validation using MIT-BIH and NSTDB databases shows high P+ and Se rates of 99.80% and 99.70%, 98.28% and 99.90%, respectively. Compared to existing methods, the algorithm offers high detection accuracy, simplicity, and real-time capability, effectively meeting the requirements for real-time R-wave detection scenarios.

关键词： Sensitivity Filtering Noise Interference Electrocardiography Feature extraction Prediction algorithms Real-time systems detection algorithms Monitoring

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Lightweight Traffic Accident detection Algorithm Based on Attention Mechanism

Lightweight Traffic Accident Detection Algorithm Based on At...

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Intelligent Computing and Control (IC&C), International Conference on

作者： XiaoYan Hou ShiHuan Wen ShengFu Chen JianHe Li Kai Xu ZhenYu Wang WenHan Wu South China University of Technology GuangZhou China PSBC Consumer Finance Company Limited GuangZhou China Tianjin University of Science & Technology TianJin China

ISBN: (数字)9798350351873

ISBN: (纸本)9798350351880

Traffic accident detection is a challenging task in intelligent transportation systems. Rapid and accurate detection of traffic accidents can ensure the safety of people's lives and property to the greatest extent. However, the existing accident detection models have some limitations such as slow speed or low precision, which limit the practical application. Aiming at the current low accuracy of rapid traffic accident detection, this paper proposes a traffic accident detection algorithm based on attentional feature fusion, which combines the feature extraction and fusion process with the attention mechanism. Specifically, the MHSA module is integrated into the Backbone stage of the YOLOv5 network, and the CBAM module is integrated into the Neck stage to refine and capture image features and enhance network feature extraction and fusion capabilities. At the same time, in view of the lack of traffic accident image data, this paper establishes a traffic accident image data set. Experiments were carried out on the traffic accident image data set and the results show that, under the condition that the detection speed of each image is guaranteed to be 9.7ms, the accuracy of the proposed algorithm to identify traffic accidents is 4% higher than that of YOLOv5s. It achieves a balance between speed and accuracy of traffic accident detection under limited computational cost.

关键词： YOLO Accuracy Attention mechanisms Computational modeling Feature extraction Neck Safety detection algorithms Intelligent transportation systems Accidents

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Multi-level Fast Screening Loop Closure detection Algorithm that Integrates the Intensity and Geometry Information of Point Clouds

Multi-level Fast Screening Loop Closure Detection Algorithm ...

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China Instrument and Control Society (ACCIS), Academic Conference of

作者： Chuan Zhang Xuefen Zhu Zhengpeng Lu Gangyi Tu School of Instrument Science and Engineering Southeast University Nanjing China School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350350326

ISBN: (纸本)9798350350333

In Simultaneous Localization and Mapping (SLAM) systems, LiDAR odometry inevitably has errors when matching inter-frame point clouds, forming a cumulative error over extended periods of operation to lead to the degradation of localization building accuracy. The closed-loop optimization strategy triggered by the loop closure detection algorithm is a key method to reduce the cumulative error. Therefore, in this paper, a multilevel fast sieve loop closure detection algorithm that synergizes point cloud intensity and geometric information is designed. The global identification of loop closure frames is accelerated by employing dimensionality reduction on the intensity feature of point clouds after filtering out the ground point clouds. Then the local plane key points are extracted from the candidate frames for secondary verification to obtain the best loop closure frame. The experiments on the online unstructured dataset show that the precision-recall (PR) curve of this paper's algorithm covers the widest area, with a maximum F1 score of 0.93, showing a better performance compared with the existing loop closure detection algorithms. The root mean square error (RMSE) of the absolute trajectory error was reduced to 0.68 meters after optimization, representing a performance improvement of approximately 48%, and the results of the point cloud construction are more accurate and precise.

关键词： Point cloud compression Location awareness Simultaneous localization and mapping Accuracy Feature extraction Trajectory Odometry detection algorithms Root mean square Optimization

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Spoofing detection Algorithm for Tightly Coupled INS/GNSS Integration Model with robust Averaging Measurement

Spoofing Detection Algorithm for Tightly Coupled INS/GNSS In...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： He Chengyan Wu Luoyu Zhaolin Zhang Wang Ling Tao Mingliang School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Global Navigation Satellite Systems (GNSS) are susceptible to external signal *** GNSS-INS tightly coupled model exhibits high robustness and can avoid some interference; however, it is ineffective against spoofing interference, especially slow-changing spoofing interference. This paper proposes a robust detection algorithm based on measurement averaging and the IGG-III weighting function to effectively identify and mitigate such interference. By constructing a detection statistic from the measurement-averaged innovation, we theoretically demonstrate its nature as a zero-mean white noise process and design a corresponding adaptive detection strategy. Compared with traditional spoofing interference detection methods, simulation experiments using an open-source dataset show that the proposed algorithm significantly improves the detection rate of slowly varying spoofing signals while maintaining a low false alarm rate, validating its effectiveness and robustness in tightly coupled INS/GNSS systems.

关键词： Weight measurement Technological innovation Global navigation satellite system Simulation Interference White noise Particle measurements Robustness Noise measurement detection algorithms

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Simulation Analysis of CS-ACELP Speech Compression Algorithm Based on Mute detection Technology

Simulation Analysis of CS-ACELP Speech Compression Algorithm...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Bo Zhou Zhaoyang Li Zongju Xiong Wanbin Zhang Communication Systems Department Xichang Satellite Launch Center of China Xichang Sichuan China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Based on the study of CS-ACELP speech compression algorithm and mute detection technology, we propose a new mute detection scheme. Based on short-time average amplitude and short-term average over zero rate, it distinguishes noise, clearing and turbidity through the two-level judgment method. Through simulation analysis, the algorithm is based on CS-ACELP algorithm proposed G.729B, it reduces the complexity and achieves the effect of reducing the code rate.

关键词： Resistance Computer science Codes Turbidity Real-time systems Encoding Complexity theory detection algorithms Compression algorithms Signal to noise ratio

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