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40th Annual Meeting on Computing in Cardiology (CinC)

作者： Wang, John Philips Healthcare Andover MA 01810 USA

ISBN: (纸本)9781479908844

Several arrhythmia performance measures as specified in the current AAMI recommended practice document do not adequately reflect actual clinical experience in real-time patient monitoring. Additional reporting requirements that are more clinical relevant are thus needed: 1) Due to the large number of QRS complexes that need to be analyzed, even an algorithm with high specificity will generate a large number of false positives, which is not directly reflected by the reported false positive rate. A new recommendation is to report the actual number of false positives. 2) Due to the low PVC prevalence in most monitored patients, the high positive predictive values (PPV) reported using databases with high PVC prevalence are not clinically relevant. A proposed recommendation is to report PPVs at much lower PVC rates. 3) Arrhythmia performance measures as specified in the recommended practice do not include performance bounds. A new proposal is to use the bootstrap method with sample replacement to generate the mean values and 95% confident intervals for all the reported arrhythmia performance measures. Conclusions: Additional performance measures are proposed to further improve the clinical relevance of the reported results. These measures should be considered for inclusion as part of the standard reporting.

关键词： Cardiac Arrhythmias False-positives bootstrapping PIGMENT VOLUME CONCENTRATION Polyvinyl Chloride detection algorithms p-phenyl vinyl sampling with replacement

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A Comprehensive Review of Driver Behavior Analysis Utilizing Smartphones

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第10期21卷 4444-4475页

作者： Chan, Teck Kai Chin, Cheng Siong Chen, Hao Zhong, Xionghu Newcastle Univ Singapore Fac Sci Agr & Engn Singapore 599493 Singapore Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Human factors are the primary catalyst for traffic accidents. Among different factors, fatigue, distraction, drunkenness, and/or recklessness are the most common types of abnormal driving behavior that leads to an accident. With technological advances, modern smartphones have the capabilities for driving behavior analysis. There has not yet been a comprehensive review on methodologies utilizing only a smartphone for drowsiness detection and abnormal driver behavior detection. In this paper, different methodologies proposed by different authors are discussed. It includes the sensing schemes, detection algorithms, and their corresponding accuracy and limitations. Challenges and possible solutions such as integration of the smartphone behavior classification system with the concept of context-aware, mobile crowdsensing, and active steering control are analyzed. The issue of model training and updating on the smartphone and cloud environment is also included.

关键词： Vehicles Smart phones Sensors Monitoring Acceleration Electrodes Electrocardiography Driving behavior analysis detection algorithms drowsiness detection abnormal driving detection smartphone telematics solution

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A UAV detection Algorithm Combined With Lightweight Network

A UAV Detection Algorithm Combined With Lightweight Network

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Hong Li Jinhui Yang Yao Mao Qintao Hu Yunyan Du Jinjin Peng Chao Liu Key Laboratory of Optical Engineering Chinese Academy of Science Chengdu China Institute of Optics and Electronics Chinese Academy of Science Chengdu China University of Chinese Academy of Science Beijing China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Unmanned Aerial Vehicles (UAVs) are characterized by the small scale and fast flying speed, which brings certain challenges to the detection and supervision of UAVs. In response to this problem, this paper proposes an improved detection algorithm combined with a lightweight network for objects with fast speed. First of all, the original three feature scales that the You Only Look Once(YOLO) v3 algorithm detects are extended to five feature scales for detection, which improves the detection performance of scenes of small objects such as UAVs. Secondly, combined with the Ghost module in the lightweight network, a lightweight feature extraction network is constructed, and a series of cheap operations like linear transformations are applied to generate more feature maps with fewer parameters. To further improve the detection performance of the network, the channel attention mechanism is added to suppress the adverse information. Besides, a UAV dataset with an urban background for training and testing is made. The experimental results show that the improved method proposed in this paper effectively improves the detection accuracy of UAVs in a complex background of cities, meets the real-time requirements, and realizes the lightweight of the object detection algorithm, which provides the possibility to realize the object detection on the embedded platform.

关键词： Urban areas Object detection Feature extraction Prediction algorithms Unmanned aerial vehicles Real-time systems detection algorithms

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Immature Yuzu Citrus detection Based on DSSD Network with Image Tiling approach

Immature Yuzu Citrus Detection Based on DSSD Network with Im...

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SICE Annual Conference

作者： Yijiu Yang Jun Yu Toru Kurihara School of Information Kochi University of Technology Kochi Japan

The automatic immature yuzu citrus detection algorithm is a potential application for the farmer to provide the leaf-to-fruit ratio and manage fruit thinning. This study aims to develop an efficient and robust algorithm to count and detect immature yuzu citrus under natural illumination conditions. The challenge of yuzu citrus detection is small size objects and leaves occluded. We integrated the robust DSSD network and the image tiling method for yuzu citrus detection to solve the problem. Our proposed algorithm was evaluated in the set of testing images of our database. The experimental results exhibit that our proposed method can offer an efficient way for yuzu citrus detection in natural environments.

关键词： Databases Instruments Lighting Object detection detection algorithms Testing

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A Precise Muscle Activity Onset/Offset detection via EMG Signal

A Precise Muscle Activity Onset/Offset Detection via EMG Sig...

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International Conference on Information Networking

作者： Kimoon Kang Kiwon Rhee Hyun-Chool Shin Soongsil University Seoul Republic of Korea

ISBN: (数字)9781728191010

ISBN: (纸本)9781728191027

In this study, we propose a muscle activity detection algorithm which reflects the activation state. Since muscle in outer part is related to extraction of fingers and inner part controls contraction of fingers, we use two channels of EMG from inner and outer part of wrist for muscle activity detection. By using two channels, we detect a contraction state which we defined as close state. For the muscle activity detection, binarized EMG, and polarized EMG is calculated, and detected through post-processor. For the performance evaluation, we compared the proposed algorithm to conventional method. The proposed algorithm showed 20ms lower estimate error than conventional method.

关键词： Performance evaluation Wrist Fingers Muscles Electromyography detection algorithms

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Study of Algorithm for Aerial Target detection Based on Lightweight Neural Network

Study of Algorithm for Aerial Target Detection Based on Ligh...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Yumin Yang Yurong Liao Shuyan Ni Cunbao Lin Graduate School Space Engineering University Beijing China Engineering University Beijing China

ISBN: (数字)9781728183190

ISBN: (纸本)9781728183206

Real-time detection of targets by video satellites is widely applied for civil and military purposes, but spaceborne platforms are generally limited in memory and computing capacity, with tougher demands on detection algorithms, which can hardly be met by traditional target detection algorithms. Therefore, this paper proposed a lightweight target detection algorithm based on YOLO v3 framework and lightweight neural network MobileNet v3. Compared with YOLO v3, the size of the improved network is reduced by 2.9 times at the same level of detection precision. Experimental results showed that the detection speed of the improved lightweight network could reach up to 40.35FPS, with the mean average precision (mAP) of 87.8%.

关键词： Satellites Neural networks Object detection Streaming media Real-time systems Orbits detection algorithms

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An Optimized Multiple Malicious Node detection method for detection of security attacks in VANETs

An Optimized Multiple Malicious Node Detection method for de...

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Computational Methods in Science & Technology (ICCMST), International Conference on

作者： Sushil Kamboj Kulwinder Singh Mann Sukhpreet Kaur Chandigarh Engineering College IT Guru Nanak Dev Engineering College Ludhiana

ISBN: (纸本)9781665458672

Vehicular Adhoc Networks (VANETs) are used to do communication between different vehicular nodes. But this communication is affected by various security threats which can hamper the performance of network. The proposed research will help in detection of one such attack that is DoS attack (Denial of Service) attack. This will help VANET in detecting flooding in DoS attack. The detection of malicious nodes and invalid networks is based on evaluation of entropy and threshold values. The proposed algorithm that is MMPDA (Multiple Malicious Node detection Algorithm) is evaluated using various parameters like average throughput, packet delivery ratio and end to end delay. It is compared with state-of-art techniques and proved to be much better in detection of malicious nodes.

关键词： Vehicular ad hoc networks Throughput Entropy Delays Security Floods detection algorithms

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FRN-YOLO: A Feature Re-fusion Network for Remote Sensing Target detection

FRN-YOLO: A Feature Re-fusion Network for Remote Sensing Tar...

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Yu Sun Wenkai Liu Xinghai Hou Fukun Bi North China University of Technology Beijing China

ISBN: (纸本)9781665420648

With the development of artificial intelligence technology, remote sensing target detection has gradually become a hot issue in the field of computer vision, which can be widely used in navigation, exploration, disaster warning, etc., and it has important research significance and application value for remote sensing target detection. However, the scale difference of remote sensing targets makes detection very difficult. Therefore, we propose a feature re-fusion network based on YOLO-FRN-YOLO. Based on the original three detection layers of YOLO, by re-fusing the features of the three output layers of the backbone, each feature layer can be deeply combined with The semantic information before sampling or after sampling, and the depth of the detection layer after feature re-fusion retains the semantic information of targets of different scales, and improves the detection ability of targets of different scales. The results show that on the RSOD datasets, the average precision of our method exceeds YOLOv3, and it is also better than other advanced networks.

关键词： Computer science Computer vision Navigation Semantics Object detection Feature extraction detection algorithms

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Scalable Blockchain Anomaly detection with Sketches

Scalable Blockchain Anomaly Detection with Sketches

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IEEE International Conference on Blockchain (Blockchain)

作者： Tomer Voronov Danny Raz Ori Rottenstreich Technion

ISBN: (纸本)9781665417617

The growing popularity of Blockchain networks attracts also malicious and hacking users. Effectively detecting inappropriate and malicious activity should thus be a top priority for safeguarding blockchain networks and services. Blockchain behavior analysis can be used to detect unusual account activities or time periods with network-wide irregular properties. Thus, optimized anomaly detection based on historical data is an essential task for securing transactions and services. However, processing the complete blockchain history can be slow and costly due to its large size and rapid growth. In this paper we suggest addressing this challenge by analyzing summarized blocks data structures, called sketches, rather than the entire blockchain. Sketches are common data structures used in computer systems and blockchain networks, to allow compact data representation while supporting efficient executions of particular queries. We study how sketches can be used to detect suspicious accounts or time periods without the need to maintain or go through the entire blockchain data. We design solutions for the major known attacks and conduct experiments to evaluate them based on real Ethereum data. We compare the accuracy, run-time and memory usage of our algorithms with traditional detection algorithms relying on the complete blockchain data. Our results indicate that sketch-based anomaly detection methods can provide a practical scalable solution for detecting anomalies in blockchain networks.

关键词： Time-frequency analysis Conferences Data structures Blockchains History Task analysis detection algorithms

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AS-YOLO: An Improved YOLOv4 based on Attention Mechanism and SqueezeNet for Person detection

AS-YOLO: An Improved YOLOv4 based on Attention Mechanism and...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Jiahui Sun Huayong Ge Zhehao Zhang School of information science and technology Donghua University Shanghai China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

With the development of deep learning in the field of object detection, a series of detection algorithms and methods are proposed to improve their performance. YOLOv4 is superior to the early algorithms in detection accuracy and speed. In order to get excellent performance in person detection, the AS-YOLO is proposed based on the YOLOv4 network. An attention mechanism is introduced in YOLOv4 to enhance the detection capability. Residual structure and SqueezeNet structure are also used as reference to deepen the network and reduce parameters. In addition, the activation function of backbone network is replaced by LeakyReLu to accelerate the detection speed. Compared with YOLOv4, the proposed network can decrease the parameters by 20.49% and increase the average precision by 2.02% on INRIA person data set, and the detection speed is also improved by 16.63%. This proves the validity of the proposed AS-YOLO.

关键词： Deep learning Graphics processing units Object detection Predictive models Acceleration Information technology detection algorithms

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