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检索条件"主题词=detection algorithms"

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Accurate Vital Signs detection Based on FMCW Radar in Dynamic Environments

Accurate Vital Signs Detection Based on FMCW Radar in Dynami...

引用

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

作者： Hongtao Zhao Minglei Yang Ziyang Xing Ziyi Xing Xiuping Li Xueyi Wang National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Non-contact vital signs detection plays an important role in smart healthcare. However, most of the existing algorithms focus on stationary environments and the proposed algorithms are computationally intensive. To address the existing problems, this paper proposes the envelope fitting-adaptive fast ensemble empirical mode decomposition (EF-AFEEMD) algorithm. The time-varying DC offset is estimated by fitting the envelope of the signal. Afterwards, the demodulated signal is decomposed into multiple IMFs by adaptively determining multiple parameters of the decomposition. Finally, the appropriate IMFs corresponding to the vital signs signals are selected by calculating the correlation coefficient and local energy ratio (LER) of each IMF. Simulation has validated the effectiveness of the proposed algorithm. Moreover, experimental results have shown that compared to EEMD and FEEMD, the proposed algorithm can ensure accuracy while greatly reducing computation, which is less than half of the computation time of other algorithms.

关键词： Correlation coefficient Empirical mode decomposition Accuracy Heuristic algorithms Fitting Radar detection Radar Medical services Data processing detection algorithms

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A Deep Learning-Based Algorithm for Multi-Feature Processing and Signal detection

A Deep Learning-Based Algorithm for Multi-Feature Processing...

引用

International Conference on Computer and Communications (ICCC)

作者： Haoran Ji Lei Wang Shuhao Zhang Chi Yang Wenjie Zhou Cong Peng College of Electrical Information Engineering Hunan University Changsha China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In the field of signal detection, traditional signal processing algorithms encounter challenges in effectively integrating multiple features for joint detection. To address this issue, we propose a multi-feature fusion detection algorithm. This algorithm processes various signal features, including matched filtering results, Short-Time Fourier Transform (STFT) features, and wavelet transform features. For the matched filtering results, we employ a Long Short-Term Memory (LSTM) network, while a convolutional neural network (CNN) is utilized for processing time-frequency domain features. After processing multiple features and performing a weighted summation of the results, the algorithm yields the final detection outcome. Simulation results indicate that the algorithm demonstrates a high level of detection accuracy.

关键词： Wavelet transforms Time-frequency analysis Filtering Simulation Signal processing algorithms Feature extraction Convolutional neural networks detection algorithms Signal detection Long short term memory

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Lane Line detection Algorithm Based on Global Attention and Reconstruction of Spatial Pyramid Pooling

Lane Line Detection Algorithm Based on Global Attention and ...

引用

Electronic Information Engineering and Computer Technology (EIECT), International Conference on

作者： Hongchang Zhang Jianwei Song Jiali Hu Juan Zeng Wuhan University of Technology Wuhan China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

To address the key issues present in traditional lane line detection methods, such as high computational cost, neglect of visual cues, and insufficient handling of occluded lane lines, this paper optimizes and improves the UFLDV2 (Ultra Fast Deep Lane detection with Hybrid Anchor Driven Ordinal Classification) network model. The backbone network is replaced with DenseNet-121 to enhance feature reuse, and atrous depthwise separable convolution (Atrous DSC) is employed for network lightweighting by expanding the receptive field and strengthening information aggregation. Additionally, a lightweight global attention mechanism (GAM) that incorporates depthwise separable convolution (DSC) is introduced into the feature extraction network, improving the ability to recognize lane lines. The spatial pyramid pooling structure (SPPCSPC) is reconstructed to retain more detailed lane line feature information. The L-Boundary (Lane Boundary loss) function is used to focus more on lane line edge information. These mechanisms help mitigate the issues of information loss during convolution and pooling in the UFLDV2 algorithm. Experimental results show that the detection accuracy on the TuSimple dataset reaches 97.34 % , while the F1 score on the CUlane dataset is 76.7%, indicating an improvement in recognition rates and demonstrating competitive performance.

关键词： Visualization Attention mechanisms Accuracy Laser radar Convolution Lane detection Computational modeling Image edge detection Computational efficiency detection algorithms

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Enhanced Pneumothorax detection in Chest Radiographs via a Deep Learning Framework DENSE Network Fusion with Yolov8

Enhanced Pneumothorax Detection in Chest Radiographs via a D...

引用

International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Imtiaz Ul Haq Khan Saba Firdous Daniyal Ahmad Shazil Adnan Shahbaz Fasih Zaka Rana Hassan Ajmal Department of Computing And IT The Millennium Universal College Faisalabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

Pneumothorax (PTX, sometimes called collapsed lung, is a condition where air is trapped between the chest cavity and the lung. Human breathlessness is a potentially fatal condition known as pneumothorax. Every year, 1.88 million individuals pass away with pneumothorax. To diagnose PTX, chest radiographs, or CXRs, are utilized. Pneumothorax may be diagnosed by X-ray scans, which are a rapid, easy, and affordable procedure that is utilized in hospitals worldwide. The lack of skilled physicians and radiologists is the reason for the need for a technology that identifies PTX from X-ray images at advanced medical institutions. Professionals find it more challenging to handle manually, but it may be resolved automatically, simplifying the medical procedure. This paper proposes a framework for the automated and efficient diagnosis of pneumothorax from CXR utilizing a dense detection algorithm and pretrained YOLOv8. With a 98.72% accuracy rate, our system categorized CXRs as either PTX or healthy. The YOLOv8 detection method and the DensetNet-201 model fused increase classification performance while cutting down on training test time. There is a good chance that the suggested framework will be useful in helping medical professionals diagnose lung diseases.

关键词： Deep learning Training Technological innovation Accuracy Lungs Computer architecture Diagnostic radiography detection algorithms Medical diagnostic imaging X-ray imaging

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Scalable Heavy Hitter detection: A DPDK-based Software Approach with P4 Integration

Scalable Heavy Hitter Detection: A DPDK-based Software Appro...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Samia Choueiri Ali Mazloum Elie F. Kfoury Jorge Crichigno University of South Carolina USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Identifying heavy hitters is vital for applications like Denial of Service (DoS) detection and traffic engineering. Current solutions fall into hardware or software categories. Hardware solutions (e.g., P4 programmable data plane switches) offer high performance but require adding hardware, which may not be ideal for virtualized environments (e.g., cloud). Software solutions are cost-effective and flexible but suffer from performance issues due to the packet processing overhead in the Operating System (OS) kernel. This paper presents a scalable heavy hitter detection algorithm in the software, bypassing the kernel using the Data Plane Development Kit (DPDK). The Count-min Sketch (CMS) data structure is used to estimate the frequency of packets per flow. The system is implemented in P4 and deployed on the P4-DPDK target running on CPU cores. The experiments analyzed the impact of various parameters such as the packet size distribution, the number of CPU cores, and the number of hash functions, on the performance and the accuracy of the detection. The system's performance is further evaluated through comparison with another DPDK-based approach for heavy hitter detection. The results show accurate identification of heavy hitters and improved performance, even at a high traffic rate approaching 100Gbps.

关键词： Hash functions Accuracy System performance Data structures Software Hardware Frequency estimation Global communication Kernel detection algorithms

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Ordinary Tea Shoot detection under Different Lighting Conditions

Ordinary Tea Shoot Detection under Different Lighting Condit...

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Sichen Dong Guangshuai Liu Xurui Li Wenyu Yi Pan Wang School of Mechanical Engineering Southwest Jiaotong University Chengdu China Sichuan Academy of Agricultural Sciences Chengdu China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Tea shoot detection is a crucial step in the automating tea plucking. Tea shoot detection needs to take into account changing light conditions due to unstable outdoor environments. To this end, an ordinary tea shoot detection algorithm under different lighting conditions based on super-green features and threshold segmentation is proposed in this paper. Firstly, the color features of tea shoots were analyzed, and the tea shoot regions was highlighted by the super-green feature index. Next, the tea canopy images were divided into low-light and highlight images based on the average grayscale, and the detection algorithm was performed separately. Finally, experimental results demonstrate the effectiveness of this method in detecting tea shoots under different lighting conditions.

关键词： Image segmentation Image color analysis Lighting Machine learning Gray-scale Feature extraction Solids Robustness Indexes detection algorithms

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Multi-Scale Distribution Network Equipment detection Algorithm Based on Improved YOLOv8

Multi-Scale Distribution Network Equipment Detection Algorit...

引用

Electrical Engineering and Automation (IFEEA), International Forum on

作者： Siming He Yong Li Cheng Li Min Xu Xiaofang Yuan Haozhi Xu Sijia Hu Electric Power Research Institute China Southern Power Grid Guangzhou Guangdong China College of Electrical and Information Engineering Hunan University Changsha Hunan China

ISBN: (数字)9798331516611

ISBN: (纸本)9798331516628

This paper proposes a detection algorithm for power distribution network equipment based on an improved YOLOv8 model, to address issues such as low recognition rates and poor detection performance of power distribution network equipment of different scales in the context of power inspection. The improved model introduces an enhanced spatial pyramid module, making multi-scale semantic information fusion more efficient, thereby enhancing the model's semantic representation capability for power distribution network equipment objects of different scales. A feature pyramid network integrating multi-scale location information is proposed to improve the model's localization ability for power distribution network equipment objects of different scales. Additionally, a loss function called Focal SIOU that balances multi-scale objects is introduced, enabling the model to better learn to detect poorly-performing scales of power distribution network equipment objects. Experimental results show that the improved YOLOv8 model exhibits excellent detection performance for equipment of different scales on the power distribution network equipment object detection dataset, with an average detection precision (mAP) increase of 2.4% compared to the original model. The precision for small, medium, and large-scale object detection $(\text{AP}_{\mathrm{S}},\text{AP}_{\mathrm{M}}, \text{AP}_{\mathrm{L}})$ respectively increased by 3.0%, 4.8% and 2.7 % , effectively improving the detection performance for power distribution network equipment objects of all scales.

关键词： Location awareness Electrical engineering Automation Semantics Object detection Distribution networks Inspection Power systems detection algorithms Context modeling

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ELAA Near-Field Localization and Sensing with Partial Blockage detection

ELAA Near-Field Localization and Sensing with Partial Blocka...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hui Chen Pinjun Zheng Yu Ge Ahmed Elzanaty Jiguang He Tareq Y. Al-Naffouri Henk Wymeersch Chalmers University of Technology Sweden King Abdullah University of Science and Technology KSA University of Surrey UK Technology Innovation Institute UAE

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

High-frequency communication systems bring extremely large aperture arrays (ELAA) and large bandwidths, integrating localization and (bi-static) sensing functions without extra infrastructure. Such systems are likely to operate in the near-field (NF), where the performance of localization and sensing is degraded if a simplified far-field channel model is considered. However, when taking advantage of the additional geometry information in the NF, e.g., the encapsulated information in the wavefront, localization and sensing performance can be improved. In this work, we formulate a joint synchronization, localization, and sensing problem in the NF. Considering the array size could be much larger than an obstacle, the effect of partial blockage (i.e., a portion of antennas are blocked) is investigated, and a blockage detection algorithm is proposed. The simulation results show that blockage greatly impacts performance for certain positions, and the proposed blockage detection algorithm can mitigate this impact by identifying the blocked antennas.

关键词： Location awareness Three-dimensional displays Simulation Apertures Sensors Noise measurement Synchronization detection algorithms Uplink Antenna arrays

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Workflow Development of AI Based Spectrogram Analysis with Real-Time Out of Distribution detection

Workflow Development of AI Based Spectrogram Analysis with R...

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International Carpathian Control Conference (ICCC)

作者： Lóránt Szabó Zoltán Weltsch John von Neumann University AI Research Center Kecskemét Hungary University of Györ Györ Huugary

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

The aim of this paper is to investigate possible workflows for OOD pattern recognition in AI-based spectrogram analysis, applied in industrial manufacturing environment. First, we attempt to identify and articulate the challenges associated with OOD recognition in the context of spectrogram analysis, where the acoustic sources are subtle and often complex signals. These deserve particular attention, since the effectivity of OOD detection algorithms are acceptable in case of significant deviations, however, it is questionable for fine anomalies. In addition, it is also discussed here, how OOD records can affect the accuracy and reliability of AI models in terms of equipment failure identification and process inefficiencies. Last, methodes are proposed for OOD-pattern recognition. The integrability of these methods into existing manufacturing workflows in terms of practicality, adaptability and effectiveness are also investigated.

关键词： Training Adaptation models Real-time systems Manufacturing Pattern recognition Reliability detection algorithms

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Application of Classification Models on Fraud detection in Retail

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

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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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