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检索条件"主题词=Detection algorithms"

共 7359 条记录，以下是261-270 订阅

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Enhanced signal processing for conventional and unconventional PD measuring methods: Wavelet de-noising, automatic detection algorithms and averaging for arrival time-based PD location in transformers and power cables

Enhanced signal processing for conventional and unconvention...

引用

International Conference on Condition Monitoring and Diagnosis, CMD

作者： Sacha Markalous Thomas Strehl Christoph Herold Thomas Leibfried LDIC GmbH Kesselsdorf Germany Institute of Electricaal Energy Systems and High Voltage Technology University of Karlsruhe Karlsruhe Germany

ISBN: (纸本)9781424416219

Two major tasks of partial discharges (PD) measurements may be distinguished, (i) providing general evidence and the type of PD (detection) and (ii) the location of the PD. Dependent on the type of device under test the two issues have changing priority. For the on-line/on-site PD location in power transformers unconventional PD measuring methods like acoustic ultra-sonic measurements or electromagnetic measurements up to the UHF (ultra high frequencies) range are performed, while the Time Domain Reflectometry (TDR) of electric PD signals is a standard technique for the location of PD in power cables.

关键词： Signal processing algorithms Noise reduction detection algorithms Partial discharges Acoustic measurements Electromagnetic measurements Power transformers Power measurement Frequency measurement Electric variables measurement

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Synthesis of Signal detection algorithms Under Conditions of Aprioristic Uncertainty

Synthesis of Signal Detection Algorithms Under Conditions of...

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2020 IEEE Ukrainian Microwave Week (UkrMW)

作者： Igor Prokopenko Igor Omelchuk Maryna Maloyed National Aviation University Kyiv Ukraine

ISBN: (数字)9781728173139

ISBN: (纸本)9781728173146

Ensuring the accuracy of signal processing in the context of incomplete a priori information is one of the primary problems of applied theory and technique of radio reception. In this paper two problems are solved. Using a "randomization" method, the random process models are created, the statistical parameters of which are random as well with known probability distribution. On the basis of this, the optimal detection algorithms by one received sample or by a pack of samples are synthesized using the likelihood ratio and the Wald randomization. Efficiency and robustness of synthesized algorithms to non-gaussian interference are investigated.

关键词： Microwave measurement Uncertainty Shape measurement Signal processing algorithms Random processes detection algorithms Task analysis

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Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities

Assessment of waist-worn tri-axial accelerometer based fall-...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alan K. Bourke Pepijn van de Ven Mary Gamble Raymond O'Connor Kieran Murphy Elizabeth Bogan Eamonn McQuade Paul Finucane Gearóid ÓLaighin John Nelson Department of Electronic and Computer Engineering Faculty of Science and Engineering University of Limerick Ireland Graduate Medical School Faculty of Education and Health Science University of Limerick Ireland Cois Abhann Primary Care Team Mid Western Health Board Limerick Ireland Electrical & Electronic Engineering Department National University of Ireland Ireland

This study aims to evaluate a variety of existing and novel fall detection algorithms, for a waist mounted accelerometer based system. algorithms were tested against a comprehensive data-set recorded from 10 young healthy subjects performing 240 falls and 120 activities of daily living and 10 elderly healthy subjects performing 240 scripted and 52.4 hours of continuous unscripted normal activities. Results show that using a simple algorithm employing Velocity+Impact+Posture can achieve a low false-positive rate of less than 1 FP/day* (0.94FPs/day*) with a sensitivity of 94.6% and a specificity of 100%. The algorithms were tested using unsupervised continuous activities performed by elderly subjects living in the community, which is the target environment for a fall detection device.

关键词： Accelerometers Senior citizens detection algorithms Sensitivity Sensors Algorithm design and analysis Aging

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Comparisonof local anomaly detection algorithms based on statistical hypothesis tests

Comparisonof local anomaly detection algorithms based on sta...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Alexis Huck Mireille Guillaume Guillaume Oller Manuel Grizonnet Magellium Toulouse France Fresnel Institute Marseilles France CNES Toulouse France

We present some results about the comparison of two families of anomaly detection algorithms, specific to hyperspectral images analysis, both based on local statistical Hypothesis Testing (HT). The study has involved the RX and the Gauss-Markov Random Fields (GMRF) anomaly detectors. These algorithms share a quite similar sliding window strategy and the approach of statistical HT but they differ notably in the way data are modeled and the parameters estimated. An underlying main question is actually the trade-off between describing the background clutter with many parameters (case of RX), harder to locally estimate, and considering a model with fewer parameters (case of GMRF), thus easier to estimate from the local clutter only. The study includes the issue of reducing the spectral dimension with linear second order and higher order statistics based methods. As an output of the study, RX will be included in the next release of the Orfeo Toolbox (OTB), an open source remote sensing image processing library developed by the CNES.

关键词： Covariance matrix Vectors Clutter Hyperspectral imaging detection algorithms Estimation

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A Review of One-Stage detection algorithms in Autonomous Driving

A Review of One-Stage Detection Algorithms in Autonomous Dri...

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Conference on Vehicle Control and Intelligence (CVCI)

作者： Jiaqi Fan Tianjiao Huo Xin Li Jilin University College of Automotive Engineering Changchun China

ISBN: (数字)9781728184975

ISBN: (纸本)9781728184982

With the deep research on autonomous driving, the target detection algorithms based on 2D images have become a hot topic in recent years. In this paper, we mainly study the six mainstream deep learning detection algorithms, namely YOLO, YOLOv2, YOLOv3, YOLOv4, SSD and RetinaNet, which are used as the representative algorithms of the one-stage object detection methods. This paper is a review of the working principles of these six detection algorithms in deep learning. Firstly, this paper explains and compares the detection network structure, loss function and improvements of these six detection algorithms in detail according to the order in which they were presented. Especially the YOLOv3, YOLOv4, SSD and RetinaNet algorithms, which are the most common used algorithms at presented with a very high detection speed and detection precision for real-time detection of the autonomous driving. Secondly, by comparing the detection accuracy and speed of the YOLOv3, YOLOv4, SSD and RetinaNet algorithms, this paper gives their respective suitable application scenarios.

关键词： Deep learning Roads Two dimensional displays Object detection Real-time systems detection algorithms Autonomous vehicles

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Health Care Fraud detection with Community detection algorithms

Health Care Fraud Detection with Community Detection Algorit...

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International Conference on Smart Computing (SMARTCOMP)

作者： Aryya Gangopadhyay Song Chen Inf. Syst. Univ. of Maryland Baltimore County MD USA Information Systems University of Maryland Baltimor County

ISBN: (纸本)9781509008995

Fraud detection is interesting research topic and it not only needs data mining techniques but also needs a lot of inputs from domain experts. In health care claims, relationships between physicians and patients form complex communities structures and these communities could lead to potential fraud discoveries. Traditionally, researchers have focused on clustering physicians and patients and tried to find the suspicious communities. In this paper, we studied and discussed different types of relationships and focus on small but exclusive relationships that are suspicious and may indicate potential health care frauds. We developed two algorithms to detect these small and exclusive communities. These algorithms can be applied to larger dataset and are highly scalable. We tested these algorithms with a set of synthesized datasets. These synthesized datasets were created to resemble the real health care claims datasets and used to test the fraud detection algorithms. The test results show the these algorithms are very efficient and can evaluate the communities structures of 50,000 providers in about 1 minute.

关键词： Clustering algorithms Matrix converters detection algorithms Insurance Algorithm design and analysis Drugs

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Cooperative robust sequential detection algorithms for Spectrum Sensing in Cognitive Radio

Cooperative robust sequential detection algorithms for Spect...

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International Conference on Ultra Modern Telecommunications and Workshops (ICUMT)

作者： ArunKumar Jayaprakasam Vinod Sharma Electrical Communication Engineering Department Indian Institute of Science Bangalore India

We consider the problem of spectrum sensing in cognitive radio networks. In our previous work we have developed DualCUSUM, a distributed algorithm for change detection and used it for cooperative spectrum sensing. The algorithm is based on sequential change detection techniques which optimally use the past observations. But DualCUSUM requires the knowledge of the channel gains for each of the secondary users. In this work we modify DualCUSUM to develop GLR-CUSUM which can work with imprecise estimates of the channel gains. Next we extend the algorithm to the case where the receiver noise power is also not known exactly. We show that the SNR wall problem encountered in this scenario is not experienced by our algorithm. We also analyze these algorithms theoretically.

关键词： detection algorithms Cognitive radio Uncertainty Change detection algorithms Signal to noise ratio Noise robustness White spaces Signal processing algorithms Radiofrequency identification TV broadcasting

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Landmark Extraction from Echocardiography Sequence Based on Corner detection algorithms Using Product Moment Coefficient Matcher

Landmark Extraction from Echocardiography Sequence Based on ...

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International Conference on Signal Processing Systems, ICSPS

作者： Meysam Siyah Mansoory Hamid Behnam Emad Fatemi Zadeh Bio Medical Engineering Department Science and Research Branch Islamic Azad University슠 Tehran Iran Bio Medical Engineering Group Electrical Engineering Department Iran University of Science and Technology Tehran Iran Bio Medical Signal and Image Processing Laboratory School of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781424442133

Landmark extraction is used as the first step of many vision tasks such as tracking, image registration, localization, image matching and recognition. Furthermore, landmarks are used to reduce the data flow and consequently the computational costs. In this paper we extracted landmarks from echocardiography sequence, our algorithm is based on corner extraction, and then we evaluate our algorithm with applying some test. For this purpose, we evaluated the detectors according to their repeatability, stability and landmark localization under changes in noise.

关键词： Echocardiography detection algorithms Data mining Image registration Image matching Image recognition Computational efficiency Testing Detectors Stability

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A Comparative Study of Various Pitch detection algorithms

A Comparative Study of Various Pitch Detection Algorithms

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IEEE International Conference on Computing, Communication and Security (ICCCS)

作者： Prajwal S Rao S Khoushikh Sriram Ravishankar R Advaith Ananthkrishnan K Balachandra B.M.S. College of Engineering Bengaluru India

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

Digital Signal Processing deals with performing varied mathematical operations on different types of signals and in this paper, we study pitch estimation algorithms applied on music signals. We give a detailed study of time domain and frequency domain pitch detection algorithms namely Modified Autocorrelation, Average Magnitude Difference function, Yin, Cepstrum. The algorithms have been tested with real and synthesized music signals and the performances were compared based on time complexity and error obtained.

关键词： Signal processing algorithms Music Multiple signal classification Security detection algorithms Time-domain analysis Time complexity

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Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass detection algorithms

Automatic Tuning of MST Segmentation of Mammograms for Regis...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Mariusz Bajger Fei Ma Murk J. Bottema School of Computer Science Engineering and Mathematics Flinders University Adelaide SA Australia

A technique utilizing an entropy measure is developed for automatically tuning the segmentation of screening mammograms by minimum spanning trees (MST). The lack of such technique has been a major obstacle in previous work to segment mammograms for registration and applying mass detection algorithms. The proposed method is tested on two sets of mammograms: a set of 55 mammograms chosen from a publicly available Mini-MIAS database, and a set of 37 mammograms selected from a local database. The method performance is evaluated in conjunction with three different preprocessing filters: gaussian, anisotropic and neutrosophic. Results show that the automatic tuning has the potential to produce state-of-the art segmentation of mass-like objects in mammograms. The neutrosophic filtering provided the best performance.

关键词： detection algorithms Image segmentation Cancer Databases Coronary arteriosclerosis Humans Image registration Breast tissue Muscles Diseases

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