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检索条件"主题词=detection algorithms"

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Automatic detection of Characteristic Waves in Electrocardiogram

Automatic Detection of Characteristic Waves in Electrocardio...

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Computers in Cardiology (CinC)

作者： Lucia Billeci Lorenzo Bachi Maurizio Varanini Institute of Clinical Physiology National Research Council of Italy (IFC-CNR) Pisa Italy

ISBN: (数字)9781728173825

ISBN: (纸本)9781728111056

The goal of automatic ECG analysis is to assess the clinical status of the heart system as accurately as possible, and the identification of P and T waves plays a significant role in this matter. This works presents original algorithms for the detection of P and T waves. These algorithms are based on the morphological and temporal characteristics of the electrocardiogram. To test and compare the algorithms' performance, we considered the QTDB and MIT-BIH Arrhythmia annotated databases. The developed algorithms obtained a good performance for the detection of both peaks. In particular, in both the QTDB and MIT-BITH database the P wave detection algorithm obtained considerably higher performance than those presented in the literature (QTDB: 95.87% vs 89.05%; MIT-BITH: 84.65% vs 83.36% for Lead 1). The T wave detection algorithm, achieved best performance than those in literature in the QTDB (89.05% vs 87.49%) while in the MIT-BITH database results were almost comparable to those reported in the literature. These findings suggest the high potential of the proposed simple algorithms for P and T wave detection in ECG.

关键词： Heart Electric potential Databases Electrocardiography Lead Cardiology detection algorithms

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Federated Learning for Arrhythmia detection of Non-IID ECG

Federated Learning for Arrhythmia Detection of Non-IID ECG

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International Conference on Computer and Communications (ICCC)

作者： Mufeng Zhang Yining Wang Tao Luo Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

In this paper, a distributed arrhythmia detection algorithm based on electrocardiogram (ECG) is proposed for auxiliary diagnosis and treatment. ECG that contains tremendous cardiac rhythm information plays an important role in clinical treatment. Machine learning (ML) algorithms can effectively build the relationship between ECG and the underlying arrhythmia in it. Due to the privacy sensitivity of the ECG, we introduced a federated learning (FL)-based distributed algorithm that enables each medical institution to cooperatively train a arrhythmia detection algorithm locally. Compared with the traditional centralized ML algorithms, the use of FL-based algorithm does not need to collect all the local ECG of each medical institution to an external platform to perform centralized learning, and hence preventing the privacy from leakage. However, ECG collected from different medical institution is non-independent and identically distributed (non-IID) in reality, which will lead to non convergence of the FL-based algorithm. To address this challenge, we optimize the FL-based algorithm using a sharing strategy for partial ECG data of each medical institution combined with elastic weight consolidation (EWC) algorithm. Here, the sharing strategy, which makes each medical institution share ECG data to the central server while not share to other clients, could help build an initial FL model and EWC algorithm make the accuracy of the model trained by each medical institution not decline, therefore the proposed FL algorithm can achieve a trade-off between the privacy and model performance. The experiment results show that, compared with baseline FedAvg algorithm and FedCurv algorithm, the optimized FL-based algorithm is faster in convergence for IID ECG and achieves signicant improvement in terms of both recall and precision for non-IID ECG.

关键词： Privacy Data privacy Electrocardiography Prediction algorithms Data models detection algorithms Medical diagnostic imaging

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Improved Peak detection Algorithm for Photoplethysmographic Signals

Improved Peak Detection Algorithm for Photoplethysmographic ...

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National Conference with International Participation (TELECOM)

作者： Valentina Ilieva Markova Kalin Boyanov Kalinkov Todor Dimitrov Ganchev Faculty of Computer Sciences and Automation Technical University of Varna 1 Studentska str Varna Bulgaria

ISBN: (数字)9781728187174

ISBN: (纸本)9781728187181

We present a computationally efficient non-parametric algorithm for the automated detection of systolic peaks in photoplethysmography (PPG) signal that does not require preprocessing for artifact elimination, signal filtering, or detrending. It is validated in an experimental setup based on the publicly available CLAS dataset. The experimental results show that it outperforms two well-known methods in terms of detection accuracy and computational demands. We report a very high detection accuracy, with an error rate below 0.5%, on good quality signals and below 13% on very low-quality PPG signals. The proposed algorithm is characterized with very short processing times and on a low-cost laptop computer requires approximately 0.000012 real-time for the processing of a 60-seconds recording.

关键词： Task analysis Image edge detection detection algorithms Error analysis Electrocardiography Computational efficiency Signal processing algorithms

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Onset detection of Tabla Strokes using LP Analysis

Onset detection of Tabla Strokes using LP Analysis

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International Conference on Signal Processing and Communications, SPCOM

作者： R Gowriprasad. K.Sri Rama Murty Indian Institute of Technology Hyderabad India

ISBN: (数字)9781728188959

ISBN: (纸本)9781728188966

Onset detection is an important first step in music analysis. We propose a pre-processing scheme for improved onset detection of complex strokes of Indian percussion instruments with resonance characteristics. In this work, we explore the onset detection of Tabla (Indian percussion instrument) strokes. The resonance characteristics of tabla strokes poses challenges to onset detection. In such cases, the energy-based and spectral flux-based onset detectors are often inaccurate on the raw signal. We propose an onset detection algorithm addressing these challenges using Linear Prediction (LP) analysis and Hilbert envelope (HE) in tandem. Tabla signal is modeled using LP, and its residual highlights the onset time instances very well. Unipolar nature of HE on top of LP residual further enhances the onset instances. Onset detection is performed using energy based, spectral flux based and the state of the art Machine Learning based onset detectors on the Hilbert envelope of LP residual (HELP). Experiments were performed on tabla solo played at various tempi and the results show that the HELP based approach provides 12% relative improvement in F-measures compared to the performance on raw tabla signal.

关键词： Instruments Detectors Music detection algorithms Task analysis Time-domain analysis Frequency-domain analysis

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Human Fall detection Algorithm Based on YOLOv3

Human Fall Detection Algorithm Based on YOLOv3

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International Conference on Image, Vision and Computing (ICIVC)

作者： Xiang Wang Kebin Jia Faculty of Information Technology Beijing University of Technology Beijing Peoples Republic of China

ISBN: (数字)9781728166612

ISBN: (纸本)9781728112350

With the increase of the elderly population, the phenomenon of the elderly falling at home or out is more and more common. Therefore, fall detection is of great significance for the health protection of the elderly. Throughout the research of fall detection at home and abroad, most of the fall detection based on video monitoring is complex and redundant, which affects the real-time and accuracy of detection. In view of the above problems, this paper proposes a fall detection method based on video in complex environment, aiming to detect fall behavior more accurately and quickly. The main work of this paper is as follows: firstly, YOLOv3 network model is proposed for detection algorithm. Secondly, the human fall detection data set is constructed by referring to Pascal VOC data set format. Then, the algorithm model is optimized and trained in GPU (graphic processing unit) deep learning server. Finally, comparison of test results with our YOLOv3 network model and other detection algorithms shows that our detection algorithm has a good recognition effect.

关键词： Training Feature extraction detection algorithms Machine learning Convolution Senior citizens Object detection

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Overlapping Community detection based On Maximal Clique and Multi-objective Ant Colony Optimization

Overlapping Community Detection based On Maximal Clique and ...

引用

Chinese Control and Decision Conference, CCDC

作者： Ping Ji Shanxin Zhang ZhiPing Zhou School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Community detection is an important technique of complex network, which has great influence in understanding the structure of networks. There are much attention has been paid to detect separated communities, where one node must belong to one community. However, it is more necessary for us to detect the overlapping communities, which often appear in many real-world networks. In this paper, we proposed a multi-objective overlapping community detection algorithm called MCMOCD-ACO, which combing with the multiobjective ant colony optimization(mACO) and clique-based representation scheme. In this algorithm, a new representation scheme based on the introduced maximal-clique graph is presented. Since the maximal clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Under this representation scheme, mACO is allowed to handle the separated community in a way similar to that of the separated community detection which can simplified the overlapping community detection optimization problems. The experimental results on synthetic and real networks demonstrate the effective performance of our algorithm over four state-of-the-art overlapping community methods.

关键词： Optimization Image edge detection Linear programming Clustering algorithms Internet of Things Complex networks detection algorithms

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Fast Bug detection Algorithm for Identifying Potential Vulnerabilities in Juliet Test Cases

Fast Bug Detection Algorithm for Identifying Potential Vulne...

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Smart City and Informatization (iSCI), IEEE International Conference on

作者： Richard Amankwah Jinfu Chen Alfred Adutwum Amponsah Patrick Kwaku Kudjo Vivienne Ocran Comfort Ofoley Anang Jiangsu University Zhengjiang China Univ. of Prof Std Accra Ghana Presby Coll. Of Education Akropong Ghana

ISBN: (数字)9781665403948

ISBN: (纸本)9781665403955

Automated static analysis tools (ASATs) are one of the most widely used and effective ways of detecting bugs in Java code. ASATs helps to improve the security of software by detecting potential violations without executing the application. We have explored the existing automated static analysis techniques detection capabilities and noticed that, they are deficient in terms of processing time and generation of false warnings. Thus, the study proposed a Fast Bug detection Algorithm (FBDA) to address the aforementioned deficiencies. Furthermore, we compared our results based on the FBDA to the existing automated static analysis tools. The main idea is to reduce the size of the code area to be investigated without compromising on quality and improve the processing time. Additionally, we tested the effectiveness of our framework using a designated subset of the Juliet Test Suite and the results show that our approach achieved a performance gain of 66% and can successfully detect bug patterns than existing static analysis tools. Our experimental analysis further shows that, the percentage of false positive obtained by our framework is 18.5%, which is much less than the percentage of false positive reported by ASATs.

关键词： Smart cities Computer bugs Static analysis Tools Throughput Software detection algorithms

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Study of Active Power Filter Control Strategy based on Improved P-Q Harmonic Current detection

Study of Active Power Filter Control Strategy based on Impro...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Xuexiang Yan Wenquan Shao Yuan Cheng Ning Li School of Electronics and Information Xi’an Polytechnic University Xi’an China School of Electrical Engineering Xi'an University of Technology Xi’an China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

The traditional harmonic current detection algorithm has large compensation current error when the three-phase voltage is asymmetric or the load changes abruptly. In order to solve the problem, an improved P-Q harmonic current detection method is used in this paper, which is suitable for both three-phase voltage symmetrical system and asymmetric system. Based on the analysis that the harmonic current of power system is mainly 6k ± 1 , the controllers of multiple proportional resonance (QPR) and PR+ fast repetitive control are used. The comparison shows that active power filter with improved P-Q harmonic current detection and PR+ fast repetitive control has the advantages of good harmonic compensation effect and fast dynamic response.

关键词： active power filter harmonic compensation proportional resonance control fast repetitive control active power filter harmonic compensation current harmonics three-phase voltage phase voltage asymmetric system Current power detection algorithms Dynamic response

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Community Overlapping detection Algorithm based on Bias-Subordination

Community Overlapping Detection Algorithm based on Bias-Subo...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Zeng Xian Han Bing School of Information Science North China University of Technology Beijing China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

The real-world network structure often presents the characteristics of overlapping communities. High-quality communities are helpful to understand the real complex networks. The discovery of overlapping communities has become a hot spot in current recommendation research algorithms. Regarding the randomness and instability problems in the original overlapping community discovery algorithm, this paper proposes the overlapping community discovery algorithm CODA-BS, which is based on the priority of community degree subordination. In each iteration process, the CODA-BS algorithm uses information entropy to calculate the vertex threshold, uses the weighted function proposed in this paper to optimize the label membership coefficient, and performs label screening and normalization of the membership coefficient according to the label propagation rules to detect a more ideal community. Tests on the benchmark data set and data sets of Chinese Association for Science and Technology scholars, show that the CODA-BS algorithm has higher accuracy and improved stability than COPRA algorithm, and the CODA-BS algorithm is more accurate and more stable than the classic COPRA algorithm.

关键词： Machine learning algorithms Uncertainty Machine learning Entropy Partitioning algorithms Information entropy detection algorithms

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General Obstacle detection by Ground Shape Invariant Features with a Fisheye Camera

General Obstacle Detection by Ground Shape Invariant Feature...

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IEEE International Conference on Systems, Man and Cybernetics

作者： HongFei Yu GuangSheng Zhang XiWang Guo Huan Tian College of Computer and Communication Engineering Liaoning Shihua University Fushun China Intelligent Driving Business Line Neusoft Reach Automotive Technology Co. Ltd Shenyang China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Reliable detection of obstacles around the vehicle is crucial for autonomous cars. We present a novel and robust ground shape invariant feature method for general obstacle detection with a car-mounted monocular fisheye camera. Both stationary and moving obstacles can be detected by our approach without recovering the camera motion. Firstly, In order to compute the ground shape invariant feature, the image is mapped into the top view image. And then feature points are extracted and matched between adjacent frames. Secondly, the points are grouped according to image patch partition. Finally, the ground shape invariant feature is computed for each group of points to detect obstacle points. Extensive experiments have been carried out with prerecorded video sequences including various obstacle types, various scenes and various illumination conditions. The experimental results show promising detection performance of the proposed method.

关键词： Shape Video sequences Feature extraction Cameras Reliability detection algorithms Strain

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