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检索条件"主题词=detection algorithms"

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Smartwatch Based Atrial Fibrillation detection from Photoplethysmography Signals 41

Smartwatch Based Atrial Fibrillation Detection from Photople...

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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Bashar, Syed Khairul Han, Dong Ding, Eric Whitcomb, Cody McManus, David D. Chon, Ki H. Univ Connecticut Biomed Engn Dept Storrs CT 06269 USA Univ Massachusetts Div Cardiol Med Sch Worcester MA 01655 USA

ISBN: (纸本)9781538613115

Atrial fibrillation (AF) detection from wristwatch is important as it can lead to non-invasive, long-term and continuous monitoring of AF from photoplethysmogram (PPG) signal. In this paper, we propose a novel method not only to detect AF from wristwatch PPG, but also to automatically distinguish between clean and corrupted PPG segments. We use accelerometer data as well as variable frequency complex demodulation based time-frequency analysis of the PPG signal to detect motion and noise artifacts in the PPG signal waveform. Next, root mean square of successive differences and sample entropy are extracted from the beat-to-beat intervals of the clean detected PPG signals, which we use to separate AF from normal sinus rhythm. UMass dataset consisting of 20 subjects has been used in this study to test the efficacy of the proposed algorithm. Our method achieves sensitivity, specificity and accuracy of 96.15%, 97.37% and 97.11%, respectively, which shows the potential of a practical and reliable AF monitoring scheme.

关键词： detection algorithms Motion segmentation Accelerometers Heart rate Time-frequency analysis Electrocardiography Monitoring

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Blackleg detection in Potato Plants using Convolutional Neural Networks 6th

Blackleg Detection in Potato Plants using Convolutional Neur...

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6th International-Federation-of-Automatic-Control (IFAC) Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)

作者： Afonso, Manya Blok, Pieter M. Polder, Gerrit van der Wolf, Jan M. Kamp, Jan Wageningen Univ & Res Wageningen Netherlands

Potato blackleg is a tuber-borne bacterial disease caused by species within the genera Dickeya and Pectobacterium that can cause decay of plant tissue and wilting through the action of cell wall degrading enzymes released by the pathogen. In case of serious infections, tubers may rot before emergence. Management is largely based on the use of pathogen-free seed potato tubers. For this, fields are visually monitored both for certification and also to take out diseased plants to avoid spread to neighboring plants. Imaging potentially offers a quick and non-destructive way to inspect the health of potato plants in a field. Early detection of blackleg diseased plants with modern vision techniques can significantly reduce costs. In this paper, we studied the use of deep learning for detecting blackleg diseased potato plants. Two deep convolutional neural networks were trained on RGB images with healthy and diseased plants. One of these networks (ResNetl8) was experimentally found to produce a precision of 95 % and recall of 91 % for the disease class. These results show that convolutional neural networks can be used to detect blackleg diseased potato plants. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Neural networks Machine learning Image processing detection algorithms Agriculture

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detection of MIMO Systems Based on Dynamic Search for High-Order Modulations 90

Detection of MIMO Systems Based on Dynamic Search for High-O...

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90th IEEE Vehicular Technology Conference (IEEE VTC-Fall)

作者： Wu, Pei-Hua Chang, Ming-Xian Natl Cheng Kung Univ Inst Comp & Commun Engn Tainan Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan

ISBN: (纸本)9781728112206

The typical sphere decoding (SD) algorithm can efficiently achieve the ML detection for the multiple-input multiple-output (MIMO) system. However, the complexity of the SD algorithm increases for high-order modulations, like 16-QAM and 64-QAM. In this work, we propose an efficient detection algorithm for the MIMO system based on the SD algorithm with dynamic search. During the tree search process, we calculate the soft value of each visited node based on the path from the root to this node. The expansion range of each visited node is determined by the soft value and a branch list. With the soft values, we can reduce the ranges of nodes to be searched while maintaining the performance of near-ML detection. For a given branch list, we also give an upper bound of the total number of visited nodes. The channel inversion or channel sorting preprocessing is not necessary. We also consider an improved scheme with the dynamic branch list. For the proposed algorithm, the simulation results show that we can achieve the bit-error rate (BER) close to the optimal ML detection with much lower complexity than the typical SD algorithm.

关键词： Heuristic algorithms Complexity theory MIMO communication Measurement Modulation Decoding detection algorithms

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Evaluation of Communities from Exploratory Evolutionary Compression of Weighted Graphs

Evaluation of Communities from Exploratory Evolutionary Comp...

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Congress on Evolutionary Computation

作者： Emilia Rutkowski James Sargant Sheridan Houghten Joseph Alexander Brown Brock University St. Catharines ON Canada AI in Games Development Lab Innopolis University Innopolis Russia

Contact networks are used as a representation for the modeling of illness transmission. In this study, we represent not only the links of the transmissions but also utilize a weighted graph to represent the probability of transfer. These graphs can be large and complex when taking into account the number of contacts used in tracing. Compression of the graph allows for the development of community detection as well as providing a simpler graph. By examining the contact networks developed by an evolutionary algorithm for compression, it is discovered that the choice of fitness function and the appropriate weighting of edges leads to a different compressed graph, finding different connected communities; this is also true when compared to the communities identified by the Louvain community detection algorithm. This demonstrates the importance of considering weighting in contact networks, and suggests that in the future an understanding of the community structure should be utilized by public health officials.

关键词： Image edge detection Evolutionary computation Public healthcare detection algorithms

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Pre-impact detection algorithm to identify lack of balance due to tripping-like perturbations 41

Pre-impact detection algorithm to identify lack of balance d...

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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Aprigliano, F. Guaitolini, M. Sabatini, A. M. Micera, S. Monaco, V Scuola Super Sant Anna BioRobot Inst Pontedera PI Italy Ecole Polytech Fed Lausanne Bertarelli Fdn Chair Translat NeuroEngn Ctr Neuroprosthet Sch Engn Lausanne Switzerland Ecole Polytech Fed Lausanne Inst Bioengn Sch Engn Lausanne Switzerland Fdn Don Carlo Gnocchi Milan Italy

ISBN: (纸本)9781538613115

This study investigates the performance of an updated version of our pre-impact detection algorithm while parsing out hip kinematics in order to identify unexpected tripping-like perturbations during walking. This approach grounds on the hypothesis that due to unexpected gait disturbances, the cyclic features of hip kinematics are suddenly altered thus promptly highlighting that the balance is challenged. To achieve our goal, hip angles of eight healthy young subjects were recorded while they were managing unexpected tripping trials delivered during the steady locomotion. Results showed that the updated version of our pre-impact detection algorithm allows for identifying a lack of balance due to tripping-like perturbations, after a suitable tuning of the algorithm parameters. The best performance is represented by a mean detection time ranging within 0.8-0.9 s with a low percentage of false alarms (i.e., lower than 10%). Accordingly, we can conclude that the proposed strategy is able to detect lack of balance due to different kinds of gait disturbances (e.g., slippages, tripping) and that it could be easily implemented in lower limb orthoses/prostheses since it only relies on joint angles.

关键词： Perturbation methods Hip Tuning detection algorithms Legged locomotion Foot Kinematics

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Motor-Controlled Spindle (MCS) detection for Primate in BCI System 9

Motor-Controlled Spindle (MCS) Detection for Primate in BCI ...

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9th IEEE/EMBS International Conference on Neural Engineering (NER)

作者： Chen, Duo So, Rosa Ding, Yi Guan, Cuntai Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781538679210

Neuronal activities of two monkeys are analyzed to explore whether moving tasks can trigger specific motor-controlled signal as response. A multi-array contains 96-channel electrodes was used to record the long-term neuronal activity. In the time domain analysis, we found the cues of tasks can stably stimulate some spindle waves which do not exist in the task-free stages. Most spindles sparkled after the participant issued real and correct actions as responses to the cues of tasks, indicating the spindle as a motor-controlled wave-marker. In a further time-frequency analysis, the motor-controlled spindles indicate their high intensity in the spectrogram of beta band. A spindle detection algorithm based on SVM was constructed to annotate the motor-controlled spindles automatically. Considering the individual and the day-to-day differences, the subject-specific model under leave-one-day-out cross-validation was framed to guarantee the model robustness. The algorithm performed well with F1 scores at 83:73 +/- 13:08% and 79:26 +/- 16:95% on the two monkeys, respectively. This finding might be used in the future as a new marker in certain research areas, e.g., mental detection, autism spectrum disorder (ASD).

关键词： Task analysis Time-domain analysis Time-frequency analysis Continuous wavelet transforms Sensitivity Spectrogram detection algorithms

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Determination of Characteristic Points Based on Wavelet Change of Electrocardiogram Signal

Determination of Characteristic Points Based on Wavelet Chan...

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Jamoljon Djumanov Kamola Abdurashidova Farkhat Rajabov Shohida Akbarova Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Republic of Uzbekistan Tashkent State Technical University Named After Islam Karimov Tashkent Republic of Uzbekistan

ISBN: (纸本)9781665432597

In order to analyze and diagnose a biosignal-electrocardiogram (ECG) signal generated during a heartbeat on a computer, it is first necessary to automatically identify and locate its QRS characteristic points. The article deals with the programming of the ECG R tooth detection algorithm in LabView, using the currently popular, powerful mathematical tool - wavelet analysis and synthesis.

关键词： Information science Electrocardiography Programming Wavelet analysis Communications technology Heart rate variability detection algorithms

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Sequential detection of Regime Changes in Neural Data 9

Sequential Detection of Regime Changes in Neural Data

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9th IEEE/EMBS International Conference on Neural Engineering (NER)

作者： Banerjee, Taposh Allsop, Stephen Tye, Kay M. Ba, Demba Tarokh, Vahid Univ Texas San Antonio ECE San Antonio TX 78249 USA Harvard Med Sch Boston MA 02115 USA MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA Harvard Univ SEAS Cambridge MA 02138 USA Duke Univ ECE Durham NC 27706 USA

ISBN: (纸本)9781538679210

The problem of detecting changes in firing patterns in neural data is studied. The problem is formulated as a quickest change detection (QCD) problem. Important algorithms from the literature are reviewed. A new algorithmic technique is discussed to detect deviations from learned baseline behavior. The algorithms studied can be applied to both spike and local field potential data. The algorithms are applied to mice spike data to verify the presence of behavioral learning.

关键词： Change detection algorithms Signal processing algorithms Data models Random variables Hidden Markov models detection algorithms Delays

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A PBNS Based detection Algorithm for Cooperative Wideband Spectrum Sensing Using Hard Combining 89

A PBNS Based Detection Algorithm for Cooperative Wideband Sp...

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89th IEEE Vehicular Technology Conference (VTC Spring)

作者： Captain, Kamal M. Joshi, Manjunath V. DAIICT Gandhinagar 382007 Gujarat India

ISBN: (纸本)9781728112176

Cooperative spectrum sensing (CSS) has been shown as an effective method to improve the detection performance by exploiting the spatial diversity. In this paper, we propose a detection algorithm for cooperative wideband spectrum sensing considering partial band Nyquist sampling (PBNS) in which all the cooperating secondary users (CSUs) posses antenna diversity with two receive antennas (diversity branches). We make use of hard combining at the fusion center (FC) since it requires reduced control channel bandwidth when compared to the soft combining. Novelty of our approach lies in use of hard combining to sense the wide band and making use of PBNS instead of sampling the entire wide band. Complete theoretical analysis of the proposed algorithm is carried out considering Nakagami fading channels. The proposed approach outperforms the ranked channel detection used under no cooperation and the performance is better than both ranked square law combining (R-SLC) and ranked square law selection (R-SLS) algorithms.

关键词： Diversity reception Wideband Sensors Interference detection algorithms Cascading style sheets Fading channels"

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An Efficient Community-based Approach for the Influence Maximization Problem in Social Networks

An Efficient Community-based Approach for the Influence Maxi...

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IEEE International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Ahmed M. Samir Sherine Rady Tarek F. Gharib Faculty of Computer and Information Sciences Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9781665411592

The problem of detecting top influential users in social networks is known as the influence maximization problem. Top social network influencers play a significant role in different fields like virtual marketing, politics, and public health. There are various techniques and methods implemented to identify the top influential users for maximizing the influence within social networks. There exist, however, a real challenge to effectively reach those top influential users with both high accuracy and less computational time. This paper proposes an efficient approach for the problem of influence maximization based on communities’ identification. A proposed approach LCS includes three stages: firstly, the application of the Louvain community detection algorithm to detect the different communities of the social network graph. Secondly, executing the CRANK algorithm to rank and sort the generated communities-based CRANK main four metrics. Finally, the selection of the top seed nodes from the sorted communities using degree centrality. Experimental results conducted on three social network datasets, under an Independent Cascade model for verification, show the effectiveness of the proposed approach compared to the centrality-based heuristics. Additionally, comparable results are achieved against greedy algorithms with far less time complexity.

关键词： Greedy algorithms Measurement Social networking (online) Computational modeling Time complexity Public healthcare detection algorithms

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