检索结果-内蒙古大学图书馆

IEEE INTERNET OF THINGS JOURNAL 2023年第13期10卷 11938-11939页

作者： Kang, Jae-Mo Kyungpook Natl Univ Dept Artificial Intelligence Daegu 41566 South Korea

Long range (LoRa) is a widely adopted Internet of Things (IoT) technique, but its fatal limit is a low-data rate. In this letter, we propose a new index modulation for LoRa to increase the data rate, in which multiple quasi-orthogonal chirps modulated under different spreading factors (SFs) are concurrently transmitted, thereby leveraging the SF domain as a means to carry more information. Both the coherent and noncoherent detection algorithms for the proposed index modulation are also developed in efficient forms. Numerical results demonstrate that the proposed scheme notably outperforms the state-of-the-art techniques.

关键词： Chirp Modulation Indexes Signal to noise ratio detection algorithms Symbols Sensitivity Chirp spread spectrum index modulation Internet of Things (IoT) long-range (LoRa)

来源：评论

学校读者我要写书评

暂无评论

Sensitivity and Specificity of Wearables for Atrial Fibrillation in Elderly Populations: A Systematic Review

引用

CURRENT CARDIOLOGY REPORTS 2023年第7期25卷 761-779页

作者： Babar, Faiza Cheema, Abdul Manan Ahmad, Zubair Sarfraz, Azza Sarfraz, Zouina Ashraff, Huma Tayyab, Hamnah Bajwa, Ali Mustafa Ali, Haider Sarfraz, Muzna Shahzad, Anum Sabzwari, Syed Rafay Ali CMH Lahore Med Coll Lahore Pakistan Sargodha Med Coll Sargodha Pakistan Independent Med Coll Faisalabad Pakistan Aga Khan Univ Karachi Pakistan Fatima Jinnah Med Univ Lahore Pakistan Shalamar Med & Dent Coll Lahore Pakistan King Edward Med Univ Lahore Pakistan Bahria Univ Karachi Pakistan Albert Einstein Med Ctr Philadelphia PA USA Univ Colorado Aurora CO USA

Purpose of ReviewThis study aims to evaluate the sensitivity and specificity of wearable devices for AF detection in older adults, as well as examine the incidence of AF across various studies, contextual factors impacting AF detection, and safety and adverse events associated with wearable *** FindingsA systematic search of three databases identified 30 studies on wearables for AF detection in older adults, encompassing 111,798 participants. Both PPG-based and single-lead electrocardiography-based wearables show scalable potential for screening and managing *** results of this systematic review demonstrate that wearable devices, such as smartwatches, can effectively identify arrhythmias like AF in older adults, with scalable potential in PPG-based and single-lead electrocardiography-based wearables. As wearable technologies continue to gain prominence in healthcare, it is crucial to understand their challenges and incorporate them as preventative and monitoring tools for AF detection in elderly populations to improve patient care and prevention techniques.

关键词： Atrial fibrillation Wearable technology Elderly population Sensitivity and specificity detection algorithms Cardiovascular disease Stroke risk Electrocardiogram Systematic review Adverse events

来源：评论

学校读者我要写书评

暂无评论

Trained and Robust Parameter Based Path Sampling for Low Complexity MIMO detection in 5G-NR 95

Trained and Robust Parameter Based Path Sampling for Low Com...

引用

IEEE 95th Vehicular Technology Conference: (VTC-Spring)

作者： Qian, Jing Wang, Hao Huawei Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665482431

ISBN: (纸本)9781665482431

In this paper, a low complexity MIMO detection algorithm based on path sampling is proposed to achieve near-optimal performance with limited number of paths. Conventional list sphere decoders (LSD) have good detection performance but at cost of high computation complexity, while the random sampling (RS) based MIMO detectors may suffer from inevitable performance loss and sample repetition. To overcome these issues, we first propose a data-driven method, called the trained parameter based path sampling (TPbPS), to optimize the parameter of sampling distribution, and design an approach to improve the generalization performance. Further, we take advantages of the well-trained sampling distribution and derandomize the RS to develop the robust parameter based path sampling (RPbPS), while all the sampling paths are determined by the trained sampling distribution as well as the fixed and uniformly-distributed numbers. Combining these techniques yields an enhanced MIMO detection design that non-trivially advances the state-of-the-art, and can provide significant performance and complexity gains over the traditional LSD and RS methods.

关键词： Vehicular and wireless technologies Costs Detectors Performance gain Parity check codes Decoding detection algorithms

来源：评论

学校读者我要写书评

暂无评论

MMW-YOLOv5: A Multi-Scale Enhanced Traffic Sign detection Algorithm

引用

IEEE ACCESS 2024年 12卷 148880-148892页

作者： Wang, Tong Zhang, Juwei Ren, Bingyi Liu, Bo Henan Univ Sci & Technol Coll Informat Engn Luoyang 471023 Henan Peoples R China Henan New Energy Vehicle Power Elect & Elect Drive Luoyang 471023 Peoples R China Henan Univ Sci & Technol Sch Mechatron Engn Luoyang 471003 Henan Peoples R China Zhengzhou Univ Aeronaut Sch Elect & Informat Zhengzhou 450046 Henan Peoples R China

Traffic sign detection is a crucial component of the autonomous driving field, where real-time performance and accuracy play a significant role in ensuring vehicle safety. This paper aims to improve the detection performance of multi-scale traffic sign targets and proposes an enhanced multi-scale traffic sign detection algorithm MMW-YOLOv5 based on the YOLOv5 algorithm. The algorithm first uses a multi-scale fusion network (MSFNet) on the neck, which significantly enhances the algorithm's fusion capabilities for multi-scale features and its ability to detect small-sized targets. Secondly, the C3 bottleneck structure in the trunk and neck used to process small-scale feature maps is replaced with the multi-scale feature extraction bottleneck module (MSFEBM) to obtain rich multi-scale feature information and facilitate multi-scale target detection. Finally, the positioning regression function Wise-MPDIoU (WMPDIoU) is used to further improve the overall accuracy of the model and accelerate the convergence speed of the network. Experimental results show that the detection accuracy of the MMW-YOLOv5 algorithm on the TT100K data set reached 87.1% mAP@0.5 and 53.7% mAP@0.5:0.95, which were improved by 6.6% and 5.1% respectively compared with the baseline model.

关键词： Feature extraction YOLO Accuracy detection algorithms Convolutional neural networks Real-time systems Convergence Roads Optimization Traffic control Object detection Traffic sign detection deep learning YOLOv5 multi-scale fusion object detection

来源：评论

学校读者我要写书评

暂无评论

Hey ChatGPT, is this message phishing? 22

Hey ChatGPT, is this message phishing?

引用

22nd Mediterranean Communication and Computer Networking Conference (MedComNet)

作者： De Rosa, Sara Gringoli, Francesco Bellicini, Gabriele Univ Brescia Dept Informat Engn Brescia Italy Verxo SrL Brescia Italy

ISBN: (纸本)9798350390476;9798350390483

This paper investigates the effectiveness of AI-based tools, with a focus on utilizing ChatGPT as a test platform, in email phishing detection, addressing the pressing need to combat this pervasive threat in the digital landscape. With phishing attacks causing substantial financial losses amounting to billions of dollars annually, innovative approaches are essential to mitigate their impact. We propose leveraging ChatGPT for email phishing detection, capitalizing on its machine learning and natural language processing capabilities to classify emails based on nuanced patterns and variations in phishing techniques. Unlike traditional rule-based approaches, AI-based tools offer a promising avenue for enhanced detection accuracy. Through rigorous testing focusing on binary mail classification, incorporating metrics such as accuracy, specificity, sensitivity, precision, and the F1 Score, ChatGPT demonstrates commendable performance, achieving an overall accuracy of 75.75% and an impressive sensitivity of 98.4%. These findings underscore the potential of AI-based tools as valuable assets in mitigating phishing threats. Comparison with FortiSandbox, a widely-used solution, reveals promising potential for AI-based tools, albeit with recognition of the need for further development and refinement. Our evaluation, encompassing various aspects of .eml file analysis, highlights both the strengths and limitations of AI-based tools in identifying phishing emails. While this research lays a strong foundation for leveraging AI-based tools in email phishing detection, there exist opportunities for improvement. Addressing identified limitations and exploring future research directions will be crucial in advancing the efficacy of AI-based tools and enhancing email security measures against cyber threats.

关键词： ChatGPT phishing email Artificial Intelligence cybersecurity machine learning natural language processing detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Fluctuation-enhanced gas sensing by two-dimensional materials

Fluctuation-enhanced gas sensing by two-dimensional material...

引用

IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Smulko, Janusz Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

ISBN: (纸本)9798350380903;9798350380910

The perspectives of gas sensing by flicker noise measurements have been presented for two-dimensional materials characterized by a high surface-to-volume ratio. These materials exhibit a wide range of electrical properties, from almost metallic (e.g., graphene) to semiconducting (e.g., MoS2, WS2, ZrS3, etc.) of various energy bandgaps. Two-dimensional materials exhibit imperfections, partially responsible for the flicker noise generated in their structures. Recent results show how these imperfections can be modulated to advance gas selectivity. Some materials exhibit photocatalytic phenomena induced by irradiation of multiple wavelengths. This effect can adjust the gas sensor to improve its selectivity and sensitivity. Low-frequency noise mechanisms in two-dimensional materials at different operating conditions will be presented, focusing on possible practical applications. The problems of noise measurements will also be enclosed.

关键词： gas sensors two-dimensional materials flicker noise detection algorithms noise measurements

来源：评论

学校读者我要写书评

暂无评论

PDD: Post-Disaster Dataset for Human detection and Performance Evaluation

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Song, Haoqian Song, Weiwei Cheng, Long Wei, Yue Cui, Jinqiang Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen 518107 Peoples R China

Human detection is aimed at automatically labeling specific semantic objects in high-resolution images, which is a key problem in the post-disaster search and rescue (SAR) mission with unmanned aerial vehicles (UAVs). However, for large-scale and efficient search and statistics with UAVs, there is a lack of real-world image datasets of post-disaster ruins, resulting in poor performance in practical applications. In this article, we conducted a comprehensive review of current studies and created a post-disaster dataset (PDD) for human detection first, which contains the real-world post-disaster UAV images from multiple scenes, perspectives, and distances. Second, based on the YOLOv5 algorithm, we verified the validity and superiority of PDD by comparing and analyzing its detection performance with other datasets and on different training proportions. Finally, we compared and evaluated the detection performance of 11 state-of-the-art algorithms on PDD, including faster region-based convolutional neural networks (R-CNN), YOLOv5, YOLOv7, YOLOv8, improved YOLOv5 (im-YOLOv5), neural architecture search (YOLO-NAS), detection transformer (DETR), deformable DETR (DDETR), dynamic anchor box DETR (DAB-DETR), denoising DETR (DN-DETR), and DETR with improved denoising anchor box (DINO), and provided a performance analysis of different deep learning algorithms. The results demonstrate that you only look once (YOLO)-based algorithms have a better real-time statistical performance, while the DETR-based algorithms have more accurate box prediction capabilities. The PDD, codes, and models are available at https://***/HaoqianSong/Post-Disaster-Dataset.

关键词： detection algorithms Remote sensing YOLO Cameras Autonomous aerial vehicles Convolutional neural networks Search problems Human detection multiview image performance evaluation post-disaster ruins scene unmanned aerial vehicle (UAV) search and rescue (SAR)

来源：评论

学校读者我要写书评

暂无评论

Heuristic Optimization based Abnormal Posture detection Algorithm

Heuristic Optimization based Abnormal Posture Detection Algo...

引用

IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Li, Yufeng Shang, Lin Pan, Peng Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

This paper studies the abnormal posture detection algorithm based on heuristic optimization. Using the data collected by sensors, the features such as acceleration and angular velocity are extracted and put into the classifiers for training. We select the appropriate heuristic algorithm according to different classifier models for optimization. The results demonstrate that, in binary classification experiment, the accuracy ratio of the K-Nearest Neighbor (KNN) model is 99.54%, and the AUC is 0.99. In quad classification experiment, The Support Vector Machine (SVM) model has a 94.32% accuracy ratio and a 0.95 AUC, which has the optimal performance.

关键词： Support vector machines Training Heuristic algorithms Sensor phenomena and characterization Feature extraction Data mining detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Enhanced Leakage detection and Estimation via a Hybrid Genetic Algorithm and High-Order Sliding Modes Observer Approach

引用

IEEE ACCESS 2024年 12卷 147841-147850页

作者： Pumaricra-Rojas, David Perez-Zuniga, Gustavo Sotomayor-Moriano, Javier Pontif Catholic Univ Peru Engn Dept San Miguel 15088 Lima Peru

This paper introduces a hybrid approach designed for both detecting and estimating the magnitude of leaks in oil pipelines. The method integrates a High Order Sliding Mode Observer(HOSMO) with a Super Twisting Algorithm to serve as an observer for state estimation of the system. A parameterized model based on momentum and mass balance equations with discretization is used, where the parameters are the location and magnitude of the leakage. To find these parameters, it incorporates the Genetic Algorithm to solve an optimization problem that relies on a function cost related to the error norm between measurements and states estimation from HOSMO in order to measure the difference between the model with an assumed leakage and the real leakage. The solution of the minimization problem represents the leak position and magnitude. The feasibility and effectiveness of this method are evaluated using a simulation model representing a 306 km sector of the North-Peruvian Oil Pipeline. The results demonstrate its robustness against noise, showcasing a precision of +/- 250 m in pinpointing the location of leaks.

关键词： Pipelines Mathematical models Genetic algorithms Observers Oils Biological cells Voltage measurement Transmitters Prototypes Measurement uncertainty Leak detection detection algorithms Genetic algorithm leakage detection oil leakage sliding mode observer

来源：评论

学校读者我要写书评

暂无评论

A YOLO-Based Intelligent detection Algorithm for Risk Assess-Ment of Construction Sites

Journal of Intelligent Construction

引用

Journal of Intelligent Construction 2025年第4期2卷 1-18页

作者： Ruiyang Feng Yu Miao Junxing Zheng School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

Construction safety accidents have become increasingly frequent in recent years, leading to numerous casualties and substantial property losses. These in-cidents are often attributed to inadequate supervision on construction sites and workers' low safety awareness. Traditional manual management methods, which are labor-intensive and resource-consuming, are no longer effective. Therefore, this study proposes a novel single-stage model based on YOLOv8s, designed for two primary purposes: detecting workers' personal protective equipment and monitoring and recognizing when workers enter hazardous ar-eas. The model provides real-time feedback on detection results to reduce the incidence of construction accidents. Additionally, a brief design for distance calculation was introduced. The model was trained for 200 iterations on a Roboflow dataset comprising 103,500 annotated images. Experimental results showed that YOLOv8s outperformed YOLOv8n, YOLOv5s, and YOLOv5n in de-tection performance, achieving a mean average precision with the intersection over union (IoU) threshold set to 50% (mAP50) of 84.0%, precision of 85.0%, and recall of 60.5% across 9 detection classes. By leveraging artificial intelligence technology, this study aims to offer an effective method for enhancing construction site safety, which can be further improved with additional images and a more robust network architecture.

关键词： YOLO Training Adaptation models Accuracy Surveillance Refining Real-time systems Safety detection algorithms Accidents

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：