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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...

引用

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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Onset detection of Tabla Strokes using LP Analysis

Onset detection of Tabla Strokes using LP Analysis

引用

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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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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CNN-based Speed detection Algorithm for Walking and Running using Wrist-worn Wearable Sensors

CNN-based Speed Detection Algorithm for Walking and Running ...

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International Conference on Smart Computing (SMARTCOMP)

作者： Venkata Devesh Reddy Seethi Pratool Bharti Computer Science Northern Illinois University Dekalb USA

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169989

In recent years, there have been a surge in ubiquitous technologies such as smartwatches and fitness trackers that can track the human physical activities effortlessly. These devices have enabled common citizens to track their physical fitness and encourage them to lead a healthy lifestyle. Among various exercises, walking and running are the most common ones people do in everyday life, either through commute, exercise, or doing household chores. If done at the right intensity, walking and running are sufficient enough to help individual reach the fitness and weight-loss goals. Therefore, it is important to measure walking/ running speed to estimate the burned calories along with preventing them from the risk of soreness, injury, and burnout. Existing wearable technologies use GPS sensor to measure the speed which is highly energy inefficient and does not work well indoors. In this paper, we design, implement and evaluate a convolutional neural network based algorithm that leverages accelerometer and gyroscope sensory data from the wrist-worn device to detect the speed with high precision. Data from 15 participants were collected while they were walking/running at different speeds on a treadmill. Our speed detection algorithm achieved 4.2% and 9.8% MAPE (Mean Absolute Error Percentage) value using 70-15-15 train-test-evaluation split and leave-one-out cross-validation evaluation strategy respectively.

关键词： Legged locomotion Neural networks Particle measurements detection algorithms Surges Wearable sensors Injuries

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Series Dc Arc Characterization, Prevention & detection Inaircraft Systems

Series Dc Arc Characterization, Prevention & Detection Inair...

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作者： Eric Charles Bauer The Ohio State University

学位级别：硕士

In airplane power distribution systems, dc bus voltages are expected to rise aselectrication efforts push power demands higher. Higher bus voltages improve systemeffciency and reduce the weight of the cabling system. Despite these advantages,electric arcs become a signicant threat in aircraft when the higher voltage is operatedat altitude. Arcs can occur either in series or in parallel with a load. Parallelarcs become near short circuits that release immense energy and pose a severe hazardto aircraft. The onboard Fault Management System (FMS) needs to be able to reactquickly and accurately to these high energy faults to ensure system *** arcs also need to be detected and isolated by the FMS, but the additionalseries arc impedance results in a load current decrease. Without proper detectionmethods, this fault can be mistaken for a load transient since the current still fallswithin normal operating limits. This thesis gives an overview of the key componentsand objectives of a Fault Management System. This includes an introduction to variousfault detection algorithms, fault isolation schools of thought and recongurationstrategies used in modern microgrids. Further focus will be given for how the FMSmust handle the series arc fault at each stage of the arc timeline. First, series arcmodeling is discussed. Impedance-based prevention is then proposed by utilizing anovel arc energy accumulation mechanism to estimate the critical re-ignition ***, optimal detection threshold selection and effcacy will be addressed.

关键词： Electrical Engineering series arc series dc arc arc prevention arc modeling arc characterization arc detection more electric aircraft steady state model series arc transient energy accumulation detection algorithms

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Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images

Using Machine Learning to Detect Rotational Symmetries from ...

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Koen Ponse Anna V. Kononova Maria Loleyt Bas Van Stein LIACS Leiden University Leiden The Netherlands Ornamika Moscow Russia

ISBN: (纸本)9781728190495

Automated symmetry detection is still a difficult task in 2021. However, it has applications in computer vision, and it also plays an important part in understanding art. This paper focuses on aiding the latter by comparing different state-of-the-art automated symmetry detection algorithms. For one of such algorithms aimed at reflectional symmetries, we propose postprocessing improvements to find localised symmetries in images, improve the selection of detected symmetries and identify another symmetry type (rotational). In order to detect rotational symmetries, we contribute a machine learning model which detects rotational symmetries based on provided reflection symmetry axis pairs. We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.

关键词： Computer vision Machine learning algorithms Art Computational modeling Reflection Classification algorithms detection algorithms

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Performance Improvement of Energy detection in Cognitive Radio Under Noise Uncertainty

Performance Improvement of Energy Detection in Cognitive Rad...

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International Conference on Innovations in Information Technology

作者： Morteza Alijani Anas Osman University of Trento Trento Italy

ISBN: (数字)9781728181844

ISBN: (纸本)9781728181851

Spectrum scarcity has been one of the most conspicuous constraints in networking, amidst the growing demand for 5G and Internet of things (IoT) services. Consequently, a cognitive radio networking system (CRN) presents as a reliable solution to counter the current spectrum limitations. Many spectrum sensing techniques have been integrated with CRN to address the spectrum shortage; through the detection of the frequency band and investigating the status of the primary user (PU). Nonetheless, energy detection approaches are perceived as one of the most effective solutions for spectrum sensing, as they can operate in a non-causal manner. Energy detection works with a single or double threshold. Each threshold plays a fundamental role taking into account the stochastic properties of the detected signal and consequently the calculation of the optimal energy level. Despite various research efforts towards improving the energy detection scheme, the proposed algorithms still struggle in coping with noise uncertainty and signal-to-noise ratio (SNR) wall. The main contribution of this paper is the implementation and comparison between a single (conventional) and double energy detection algorithms, in terms of the characteristics, performance, and simulation results. Finally, we analyze the noise effect on the energy detection approach and present opportunistic potential solutions such as Machine learning (ML) and Graph signal processing (GSP) techniques.

关键词： Sensors Uncertainty Signal to noise ratio Cognitive radio Technological innovation Information technology detection algorithms

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Complex Lane Line detection Under Autonomous Driving

Complex Lane Line Detection Under Autonomous Driving

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International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Huan Shen School of Computer and Information Hohai University Nanjing China

In this paper, we propose an improved lane determination algorithm to increase the accuracy and stability of the driving assistance system in an automatic driving system when determining the position of a lane in a complex driving environment. Based on the proposed single-scale Retinex enhancement and Gamma correction fusion image enhancement algorithm, we introduce a Hidden Markov Model improvement road segmentation algorithm to extract lane feature points by separating the region of interest and at the same time, we design a vanishing line detection technology based on improved V-disparity map. Experimental data show that the proposed algorithm has excellent detection capabilities and can detect main lanes in complex road environments such as shadows, traffic congestion, and uneven lighting.

关键词： Roads Lighting Transforms Feature extraction Stability analysis detection algorithms Image enhancement

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Far Point Algorithm: Active Semi-supervised Clustering for Rare Category detection 2019

Far Point Algorithm: Active Semi-supervised Clustering for R...

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Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

作者： Rohan Loveland Jonathan Amdahl Department of Computer Science Whitman College Walla WA USA Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA

ISBN: (纸本)9781450376259

In some data sets the number of categories (i.e. classes) that are represented is not known in advance. The process of discovering these categories can be difficult, particularly when a data set is skewed, such that the number of data points of some classes may greatly exceed those of other classes. Rare category detection algorithms address this problem by trying to present a user with at least one data point from each category, while minimizing the overall number of data points presented. We present an algorithm based on active and semi-supervised learning that finds category clusters using a query selection strategy that maximizes the distance from a set of already labeled data points to a query data point. We evaluate the algorithm's performance on artificially skewed versions of the MNIST data set as a rare category detection algorithm, investigating differences in performance due to both the effects of relative frequency and inherent class structure differences in feature space.

关键词： machine learning algorithms detection algorithms partitioning algorithms

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Stairway to Abstraction: an Iterative Algorithm for Whisker detection in Video Frames

Stairway to Abstraction: an Iterative Algorithm for Whisker ...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Jan-Harm L.F. Betting Vincenzo Romano Laurens W.J. Bosman Zaid Al-Ars Chris I. De Zeeuw Christos Strydis Erasmus MC Rotterdam The Netherlands

ISBN: (数字)9781728134277

ISBN: (纸本)9781728134284

Automated whisker tracking is important for researching active touch in rodents. Earlier efforts to detect whiskers and represent them in a small set of parameters were either not accurate enough to enable tracking over time, or computationally expensive. In this article we propose an algorithm to cluster whisker centerline points, detected through a curvilinear structure algorithm, using the shape of smaller clusters to form bigger clusters of centerline points. After that, a least-squares approach is used to define each whisker by a set of four parameters. We implemented the algorithm in MATLAB in a parallelized fashion, and found that the processing time per frame is reasonable in MATLAB, and is likely to be short when ported to a lower-level language. When tested on a 33,634-frame segment, 89.2% of the whiskers could be represented in an abstract fashion by four parameters with a mean-squares fitting error of lower than 10 pixels, and visual inspection shows that crossing whiskers are detected and parameterized in an accurate way.

关键词： Clustering algorithms Matlab Merging detection algorithms Shape Mice Visualization

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