Готові списки джерел за темами / Signals classification

Добірка наукової літератури з теми "Signals classification"

Автор: Grafiati
Опубліковано: 7 вересня 2022
Оновлено: 18 вересня 2022

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Статті в журналах з теми "Signals classification"

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Fikri, Muhammad Rausan, Indah Soesanti, and Hanung Adi Nugroho. "ECG Signal Classification Review." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 1 (June 18, 2021): 15. http://dx.doi.org/10.22146/ijitee.60295.

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The heart is an important part of the human body, functioning to pump blood through the circulatory system. Heartbeats generate a signal called an ECG signal. ECG signals or electrocardiogram signals are basic raw signals to identify and classify heart function based on heart rate. Its main task is to analyze each signal in the heart, whether normal or abnormal. This paper discusses some of the classification methods which most frequently used to classify ECG signals. These methods include pre-processing, feature extraction, and classification methods such as MLP, K-NN, SVM, CNN, and RNN. There were two stages of ECG classification, the feature extraction stage and the classification stage. Before ECG features were extracted, raw ECG signal data first processed in the pre-processing stage because ECG signals were not necessarily free of noise. Noise will cause a decrease in accuracy during the classification process. After features were extracted, ECG signals were then classified with the classification method. Neural Network methods such as CNN and RNN are best to use since they can give better accuracy. For further research, the machine learning method needs to be improved to get high accuracy and high precision in the ECG signals classification.
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2

Chen, Shichuan, Kunfeng Qiu, Shilian Zheng, Qi Xuan, and Xiaoniu Yang. "Radio–Image Transformer: Bridging Radio Modulation Classification and ImageNet Classification." Electronics 9, no. 10 (October 9, 2020): 1646. http://dx.doi.org/10.3390/electronics9101646.

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Radio modulation classification is widely used in the field of wireless communication. In this paper, in order to realize radio modulation classification with the help of the existing ImageNet classification models, we propose a radio–image transformer which extracts the instantaneous amplitude, instantaneous phase and instantaneous frequency from the received radio complex baseband signals, then converts the signals into images by the proposed signal rearrangement method or convolution mapping method. We finally use the existing ImageNet classification network models to classify the modulation type of the signal. The experimental results show that the proposed signal rearrangement method and convolution mapping method are superior to the methods using constellation diagrams and time–frequency images, which shows their performance advantages. In addition, by comparing the results of the seven ImageNet classification network models, it can be seen that, except for the relatively poor performance of the architecture MNASNet1_0, the modulation classification performance obtained by the other six network architectures is similar, indicating that the proposed methods do not have high requirements for the architecture of the selected ImageNet classification network models. Moreover, the experimental results show that our method has good classification performance for signal datasets with different sampling rates, Orthogonal Frequency Division Multiplexing (OFDM) signals and real measured signals.
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3

Ting, Evon Lim Wan, Almon Chai, and Lim Phei Chin. "A Review on EMG Signal Classification and Applications." International Journal of Signal Processing Systems 9, no. 1 (March 2022): 1–6. http://dx.doi.org/10.18178/ijsps.10.1.1-6.

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Анотація:
Electromyography (EMG) signals are muscles signals that enable the identification of human movements without the need of complex human kinematics calculations. Researchers prefer EMG signals as input signals to control prosthetic arms and exoskeleton robots. However, the proper algorithm to classify human movements from raw EMG signals has been an interesting and challenging topic to researchers. Various studies have been carried out to produce EMG-based human movement classification that gives high accuracy and high reliability. In this paper, the methods used in EMG signal acquisition and processing are reviewed. The different types of feature extraction techniques preferred by researchers are also discussed, including some combination and comparison of feature extraction techniques. This paper also reviews the different types of classifiers favored by researchers to recognize human movements based on EMG signals. The current applications of EMG signals are also reviewed.
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4

Pah, Nemuel D., and Dinesh Kant Kumar. "Thresholding Wavelet Networks for Signal Classification." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 03 (September 2003): 243–61. http://dx.doi.org/10.1142/s0219691303000220.

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This paper reports a new signal classification tool, a modified wavelet network called Thresholding Wavelet Networks (TWN). The network is designed for the purposes of classifying signals. The philosophy of the technique is that often the difference between signals may not lie in the spectral or temporal region where the signal strength is high. Unlike other wavelet networks, this network does not concentrate necessarily on the high-energy region of the input signals. The network iteratively identifies the suitable wavelet coefficients (scale and translation) that best differentiate the different signals provided during training, irrespective of the ability of these coefficients to represent the signals. The network is not limited to the changes in temporal location of the signal identifiers. This paper also reports the testing of the network using simulated signals.
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5

RAPP, P. E., T. A. A. WATANABE, P. FAURE, and C. J. CELLUCCI. "NONLINEAR SIGNAL CLASSIFICATION." International Journal of Bifurcation and Chaos 12, no. 06 (June 2002): 1273–93. http://dx.doi.org/10.1142/s021812740200508x.

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In this contribution, we show that the incorporation of nonlinear dynamical measures into a multivariate discrimination provides a signal classification system that is robust to additive noise. The signal library was composed of nine groups of signals. Four groups were generated computationally from deterministic systems (van der Pol, Lorenz, Rössler and Hénon). Four groups were generated computationally from different stochastic systems. The ninth group contained inter-decay interval sequences from radioactive cobalt. Two classification criteria (minimum Mahalanobis distance and maximum Bayesian likelihood) were tested. In the absence of additive noise, no errors occurred in a within-library classification. Normally distributed random numbers were added to produce signal to noise ratios of 10, 5 and 0 dB. When the minimum Mahalanobis distance was used as the classification criterion, the corresponding error rates were 2.2%, 4.4% and 20% (Expected Error Rate = 89%). When Bayesian maximum likelihood was the criterion, the error rates were 1.1%, 4.4% and 21% respectively. Using nonlinear measures an effective discrimination can be achieved in cases where spectral measures are known to fail. Most classification errors occurred at low signal to noise ratios when a stochastic signal was misclassified into a different group of stochastic signals. When the within-library classification exercise is limited to the four groups of deterministic signals, no classification errors occurred with clean data, at SNR = 10 dB, or at SNR = 5 dB. A single classification error (Observed Error Rate = 2.5%, Expected Error Rate = 75%) occurred with both classification criteria at SNR = 0 dB.
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6

Matz, Vaclav, Marcel Kreidl, and Radislav Smid. "Classification of ultrasonic signals." International Journal of Materials and Product Technology 27, no. 3/4 (2006): 145. http://dx.doi.org/10.1504/ijmpt.2006.011267.

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7

Abdullah, A. R., N. A. Abidullah, N. H. Shamsudin, N. H. H. Ahmad, and M. H. Jopri. "Power Quality Signals Classification System Using Time-Frequency Distribution." Applied Mechanics and Materials 494-495 (February 2014): 1889–94. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1889.

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Анотація:
Power quality signals are an important issue to electricity consumers. The signals will affect manufacturing process, malfunction of equipment and economic losses. Thus, an automated monitoring system is required to identify and classify the signals for diagnosis purposes. This paper presents the development of power quality signals classification system using time-frequency analysis technique which is spectrogram. From the time-frequency representation (TFR), parameters of the signal are estimated to identify the characteristics of the signals. The signal parameters are instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion, total harmonic distortion and total non harmonic distortion. In this paper, major power quality signals are focused based on IEEE Std. 1159-2009 such as swell, sag, interruption, harmonic, interharmonic, and transient. An automated signal classification system using spectrogram is developed to identify, classify as well as provide the information of the signal.
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8

Duan, Li Juan, Xue Bin Wang, Zhen Yang, Hai Yan Zhou, Chun Peng Wu, Qi Zhang, and Jun Miao. "EEG Signal Classification by Global Field Power." Applied Mechanics and Materials 128-129 (October 2011): 1434–37. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1434.

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Our project focuses on the emotional face evoked EEG signal recognition. Since EEG signals contain enough information to separate different emotional facial expressions. Thus we propose a new approach which is based on global field power on EEG signal classification. In order to perform this result, firstly, we gather a dataset with EEG signals. This is done by measuring EEG signals from people aged 20-30 that are stimulated by emotional facial expressions (Happy, Neutral, Sad). Secondly, the collected EEG signals are preprocessed through using noise reduction method. And then select features by principal component analysis (PCA) to filter out redundant information. Finally, using fisher classifier and a 10-fold cross validation method for training and testing, a good classification rate is achieved when combination local max global field power EEG signals. The rate is 90.49%.
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9

Abdullah, Abdul Rahim, N. A. Abidullah, N. H. Shamsudin, N. H. H. Ahmad, and M. H. Jopri. "Performance Verification of Power Quality Signals Classification System." Applied Mechanics and Materials 752-753 (April 2015): 1158–63. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1158.

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Power quality has become a greater concern nowadays. The increasing number of power electronics equipment contributes to the poor quality of electrical power supply. The power quality signals will affect manufacturing process, malfunction of equipment and economic losses. This paper presents the verification analysis of power quality signals classification system. The developed system is based on linear time-frequency distribution (TFD) which is spectrogram that represents the signals jointly in time-frequency representation (TFR). The TFD is very appropriate to analyze power quality signals that have magnitude and frequency variations. Parameters of the signal such as root mean square (RMS) and fundamental RMS, total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) of voltage signal are estimated from the TFR to identify the characteristics of the signal. Then, the signal characteristics are used as input for signal classifier to classify power quality signals. In addition, standard power line measurements are also calculated from voltage and current such as RMS and fundamental RMS voltage and current, real power, apparent power, reactive power, frequency and power factor. The power quality signals focused are swell, sag, interruption, harmonic, interharmonic, and transient based on IEEE Std. 1159-2009. The power quality analysis has been tested using a set of data and the results show that, the spectrogram gives high accuracy measurement of signal characteristics. However, the system offers lower accuracy compare to simulation due to the limitation of the system.
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10

Abed, Sawsan Hadi, and Nidaa A. Abbas. "Gender Classification of Mixing and De-mixing Speech." Webology 19, no. 1 (January 20, 2022): 5353–68. http://dx.doi.org/10.14704/web/v19i1/web19359.

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Gender classification is growing in popularity due to the variety of fields in which it can be used. It can be employed in various fields, including criminal investigations and security and authentication services. Gender Classifying speech for different speakers is still a demanding and challenging task for recognizing overlapped speech and building a robust prediction model. The paper provides a gender classification system that uses Independent Component Analysis (ICA) and several machine learning algorithms to identify mixing and de-mixing speech signals. ICA is employed to separate the mixed signal into their source signals. The system consists of two stages: the first stage is the mixing and separating process for signals. The second stage involves combining feature extraction and constructing a classification model to determine whether a signal is male or female based on its acoustic attributes. The system will evaluate the efficacy and significance of machine learning algorithms for selecting the optimal method to identify the speaker's gender with the most excellent efficiency and accuracy. Experimentation shows that the best accuracy value for an SVM model with mixing speeches is 87.1 %, and the best accuracy value for a Neural Net and SVM model with de-mixing speeches is 97.8 %.
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Дисертації з теми "Signals classification"

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Rida, Imad. "Temporal signals classification." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMIR01/document.

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De nos jours, il existe de nombreuses applications liées à la vision et à l’audition visant à reproduire par des machines les capacités humaines. Notre intérêt pour ce sujet vient du fait que ces problèmes sont principalement modélisés par la classification de signaux temporels. En fait, nous nous sommes intéressés à deux cas distincts, la reconnaissance de la démarche humaine et la reconnaissance de signaux audio, (notamment environnementaux et musicaux). Dans le cadre de la reconnaissance de la démarche, nous avons proposé une nouvelle méthode qui apprend et sélectionne automatiquement les parties dynamiques du corps humain. Ceci permet de résoudre le problème des variations intra-classe de façon dynamique; les méthodes à l’état de l’art se basant au contraire sur des connaissances a priori. Dans le cadre de la reconnaissance audio, aucune représentation de caractéristiques conventionnelle n’a montré sa capacité à s’attaquer indifféremment à des problèmes de reconnaissance d’environnement ou de musique : diverses caractéristiques ont été introduites pour résoudre chaque tâche spécifiquement. Nous proposons ici un cadre général qui effectue la classification des signaux audio grâce à un problème d’apprentissage de dictionnaire supervisé visant à minimiser et maximiser les variations intra-classe et inter-classe respectivement
Nowadays, there are a lot of applications related to machine vision and hearing which tried to reproduce human capabilities on machines. These problems are mainly amenable to a temporal signals classification problem, due our interest to this subject. In fact, we were interested to two distinct problems, humain gait recognition and audio signal recognition including both environmental and music ones. In the former, we have proposed a novel method to automatically learn and select the dynamic human body-parts to tackle the problem intra-class variations contrary to state-of-art methods which relied on predefined knowledge. To achieve it a group fused lasso algorithm is applied to segment the human body into parts with coherent motion value across the subjects. In the latter, while no conventional feature representation showed its ability to tackle both environmental and music problems, we propose to model audio classification as a supervised dictionary learning problem. This is done by learning a dictionary per class and encouraging the dissimilarity between the dictionaries by penalizing their pair- wise similarities. In addition the coefficients of a signal representation over these dictionaries is sought as sparse as possible. The experimental evaluations provide performing and encouraging results
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2

楊永生 and Yongsheng Yang. "Fuzzy classification of biomedical signals." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31213832.

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3

Alty, Stephen Robert. "The classification of voiceband signals." Thesis, Liverpool John Moores University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242312.

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4

Yang, Yongsheng. "Fuzzy classification of biomedical signals /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19669549.

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5

Proper, Ethan R. "Automated classification of power signals." Thesis, (7 MB), 2008. http://handle.dtic.mil/100.2/ADA488187.

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Анотація:
Thesis (Degrees of Naval Engineer and M.S. in Engineering and Management)--Massachusetts Institute of Technology, June 2008.
"June 2008." Description based on title screen as viewed on August 26, 2009. DTIC Descriptor(s): Reverse Osmosis, Shipboard, Electronic Equipment, Electronics, Waste Disposal, Voltage, Graphical User Interface, Electromagnetic Radiation, Computer Programs, Classification, Measurement, Expert Systems, Transients, Waste Collection. DTIC Identifier(s): Non-Intrusive Load Monitors, Electromagnetic Systems, Electronic Systems, Power Signals, NILM (Non-Intrusive Load Monitor), Shipboard Systems, Spectral Power Envelopes, Prep (Preprocessed Power Data), CHT (Classification Of Waste Disposal). Includes bibliographical references. Also available in CR-ROM format.
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6

Proper, Ethan R. (Ethan Richard). "Automated classification of power signals." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44842.

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Анотація:
Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.
Includes bibliographical references (p. 106-107).
The Non-Intrusive Load Monitor (NILM) is a device that utilizes voltage and current measurements to monitor an entire system from a single reference point. The NILM and associated software convert the V/I signal to spectral power envelopes that can be searched to determine when a transient occurs. The identification of this signal can then be determined by an expert classifier and a series of these classifications can be used to diagnose system failures or improper operation. Current NILM research conducted at Massachusetts Institute of Technology's Laboratory for Electromagnetic and Electronic Systems (LEES) is exploring the application and expansion of NILM technology for the use of monitoring shipboard systems. This thesis presents the ginzu application that implements a detect-classify-verify loop that locates the indexes of transients, identifies them using a decision-tree based expert classifier, and then generates a summary event file containing relevant information. The ginzu application provides a command-line interface between streaming preprocessed power data (PREP) and an included graphical user interface. This software was developed using thousands of hours of archived data from the Coast Guard Cutters ESCANABA (WMEC-907) and SENECA (WMEC-906). A validation of software effectiveness was conducted as the software was installed onboard ESCANABA.
by Ethan R. Proper.
S.M.
Nav.E.
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7

VanDerKamp, Martha M. "Modeling and classification of biological signals." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School; Available from the National Technical Information Service, 1992. http://edocs.nps.edu/npspubs/scholarly/theses/1992/Dec/92Dec_VanDerKamp.pdf.

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8

Atsma, Willem Jentje. "Classification of myoelectric signals using neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29968.pdf.

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9

Ju, Peter M. (Peter Ming-Wei) 1977. "Classification of finger gestures from myoelectric signals." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9074.

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Анотація:
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (p. 73-75).
Electromyographic signals may provide an important new class of user interface for consumer electronics. In order to make such interfaces effective, it will be crucial to map EMG signals to user gestures in real time. The mapping from signals to gestures will vary from user to user, so it must be acquired adaptively. In this thesis, I describe and compare three methods for static classification of EMG signals. I then go on to explore methods for adapting the classifiers over time and for sequential analysis of the gesture stream by combining the static classification algorithm with a hidden Markov model. I conclude with an evaluation of the combined model on an unsegmented stream of gestures.
by Peter M. Ju.
S.B.and M.Eng.
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10

Hannaske, Roland. "Fast Digitizing and Digital Signal Processing of Detector Signals." Forschungszentrum Dresden-Rossendorf, 2009. https://hzdr.qucosa.de/id/qucosa%3A21615.

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A fast-digitizer data acquisition system recently installed at the neutron time-of-flight experiment nELBE, which is located at the superconducting electron accelerator ELBE of Forschungszentrum Dresden-Rossendorf, is tested with two different detector types. Preamplifier signals from a high-purity germanium detector are digitized, stored and finally processed. For a precise determination of the energy of the detected radiation, the moving-window deconvolution algorithm is used to compensate the ballistic deficit and different shaping algorithms are applied. The energy resolution is determined in an experiment with γ-rays from a 22Na source and is compared to the energy resolution achieved with analogously processed signals. On the other hand, signals from the photomultipliers of barium fluoride and plastic scintillation detectors are digitized. These signals have risetimes of a few nanoseconds only. The moment of interaction of the radiation with the detector is determined by methods of digital signal processing. Therefore, different timing algorithms are implemented and tested with data from an experiment at nELBE. The time resolutions achieved with these algorithms are compared to each other as well as to reference values coming from analog signal processing. In addition to these experiments, some properties of the digitizing hardware are measured and a program for the analysis of stored, digitized data is developed. The analysis of the signals shows that the energy resolution achieved with the 10-bit digitizer system used here is not competitive to a 14-bit peak-sensing ADC, although the ballistic deficit can be fully corrected. However, digital methods give better result in sub-ns timing than analog signal processing.
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Книги з теми "Signals classification"

1

Workman, Michael J. Automatic classification of road signals. Birmingham: University of Birmingham, 1991.

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2

Kiasaleh, Kamran. Biological Signals Classification and Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6.

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3

VanDerKamp, Martha M. Modeling and classification of biological signals. Monterey, Calif: Naval Postgraduate School, 1992.

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4

Duzenli, Ozhan. Classification of underwater signals using wavelet-based decompositions. Monterey, Calif: Naval Postgraduate School, 1998.

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5

Bennett, Richard Campbell. Classification of underwater signals using a back-propagation neural network. Monterey, Calif: Naval Postgraduate School, 1997.

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6

Flowers, Nicholas. Remote classification of sea bed material using backscattered acoustic signals. Birmingham: University of Birmingham, 1987.

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7

Paszkiel, Szczepan. Analysis and Classification of EEG Signals for Brain–Computer Interfaces. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30581-9.

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8

Moukadem, Ali, Djaffar Ould Abdeslam, and Alain Dieterlen. Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118908686.

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9

Manfredi, Claudia, ed. Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy. Florence: Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-5518-027-6.

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Анотація:
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference.
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10

Siuly, Siuly, Yan Li, and Yanchun Zhang. EEG Signal Analysis and Classification. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47653-7.

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Частини книг з теми "Signals classification"

1

Lessard, Charles S. "Classification of Signals." In Signal Processing of Random Physiological Signals, 11–18. Cham: Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_3.

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2

Lessard, Charles S. "System Classification." In Signal Processing of Random Physiological Signals, 5–10. Cham: Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_2.

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Kiasaleh, Kamran. "Signal Processing Methods for Biological Signals." In Biological Signals Classification and Analysis, 175–275. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_4.

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Kiasaleh, Kamran. "Biological Signals." In Biological Signals Classification and Analysis, 137–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_3.

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Kiasaleh, Kamran. "Non-Biological Signals." In Biological Signals Classification and Analysis, 1–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_1.

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6

Zieliński, Tomasz P. "Signals: Acquisition, Classification, Sampling." In Starting Digital Signal Processing in Telecommunication Engineering, 1–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49256-4_1.

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Kiasaleh, Kamran. "Signal Decomposition Methods." In Biological Signals Classification and Analysis, 277–376. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_5.

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Layer, Edward, and Krzysztof Tomczyk. "Classification and Parameters of Signals." In Signal Transforms in Dynamic Measurements, 1–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13209-9_1.

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Kiasaleh, Kamran. "Linear and Nonlinear Systems." In Biological Signals Classification and Analysis, 87–135. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_2.

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Kiasaleh, Kamran. "References and Concluding Remarks." In Biological Signals Classification and Analysis, 377–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_6.

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Тези доповідей конференцій з теми "Signals classification"

1

Contreras, Stewart, and V. Sundararajan. "Visual Imagery Classification Using Shapelets of EEG Signals." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71291.

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Анотація:
The goal of this paper is to reconstruct three primitive shapes — rectangular cube, cone and cylinder — by analyzing electrical signals which are emitted by the brain. Three participants are asked to visualize these shapes. During visualization, a 14-channel neuroheadset is used to record electroencephalogram (EEG) signals along the scalp. The EEG recordings are then averaged to increase the signal to noise ratio which is referred to as an event related potential (ERP). Every possible subsequence of each ERP signal is analyzed in an attempt to determine a time series which is maximally representative of a particular class. These time series are referred to as shapelets and form the basis of our classification scheme. After implementing a voting technique for classification, an average classification accuracy of 60% is achieved. Compared to naive classification rate of 33%, we determine that the shapelets are in fact capturing features that are unique in the ERP representation of a unique class.
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2

"Microsleep Detection in Electrophysiological Signals." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001195701020109.

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3

Saraiva, Tatiana, Argentina Leite, E. J. Solteiro Pires, and Rui Faria. "Classification of cardiovascular signals." In 2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, 2021. http://dx.doi.org/10.1109/la-cci48322.2021.9769782.

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4

"Characterising Evoked Potential Signals Using Wavelet Transform Singularity Detection." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001191700030011.

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5

Oner, Poyraz Alper, Serkan Gurkan, Ayhan Istanbullu, and Seydi Dogan. "Digital signal processing and classification study for electrooculogram signals." In 2015 Medical Technologies National Conference (TIPTEKNO). IEEE, 2015. http://dx.doi.org/10.1109/tiptekno.2015.7374538.

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6

Springer, D. B., T. Brennan, L. J. Zuhlke, H. Y. Abdelrahman, N. Ntusi, G. D. Clifford, B. M. Mayosi, and L. Tarassenko. "Signal quality classification of mobile phone-recorded phonocardiogram signals." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853814.

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7

"Automated Recognition of Human Movement States using Body Acceleration Signals." In The Second International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0001225601350143.

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8

Ezzeldin, Yahya H., Christina Fragouli, and Suhas Diggavi. "Quantizing Signals for Linear Classification." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849589.

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Dabas, Harsh, Chaitanya Sethi, Chirag Dua, Mohit Dalawat, and Divyashikha Sethia. "Emotion Classification Using EEG Signals." In the 2018 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3297156.3297177.

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Zernov, Oleg I., and Elena A. Zhilenkova. "Classification Algorithm of Electromyography Signals." In 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2019. http://dx.doi.org/10.1109/eiconrus.2019.8657236.

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Звіти організацій з теми "Signals classification"

1

Hanna, Thomas E. Preliminary Report on Classification of Transient Sonar Signals. Fort Belvoir, VA: Defense Technical Information Center, June 1989. http://dx.doi.org/10.21236/ada211253.

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2

Irizarry, Alfredo V. Optimal Methods for Classification of Digitally Modulated Signals. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada583399.

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3

Chan, A. D., K. Englehart, B. Hudgins, and D. F. Lovely. Hidden Markov Model Classification of Myoelectric Signals in Speech. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada410037.

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4

Margoliash, Daniel. Modeling Temporal Dynamics in the Classification of Auditory Signals. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada267472.

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Pflug, Lisa A., George B. Smith, and Michael K. Broadhead. Blind Deconvolution to Improve Classification of Transient Source Signals in Multipath. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada377973.

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Hurd, Harry L. Workstation Tools for Feature Extraction and Classification for Nonstationary and Transient Signals. Fort Belvoir, VA: Defense Technical Information Center, July 1992. http://dx.doi.org/10.21236/ada255389.

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7

Dao, Minh, and Tung-Duong Tran-Luu. Sparsity-Based Representation for Classification Algorithms and Comparison Results for Transient Acoustic Signals. Fort Belvoir, VA: Defense Technical Information Center, May 2016. http://dx.doi.org/10.21236/ad1009802.

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8

Delgado, Jaime Fernando, and Müjdat Çetin. Modeling differences in the time-frequency representation of EEG signals through HMM’s for classification of imaginary motor tasks. Sabanci University, May 2011. http://dx.doi.org/10.5900/su_fens_wp.2011.16498.

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Schnitta-Israel, B. Robust Detection and Classification of Regional Seismic Signals Using a Two Mode/Two Stage Cascaded Adaptive Arma (CAARMA) Model. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada154710.

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10

Learned, Rachel E., William C. Karl, and Alan S. Willsky. Wavelet Packet Based Transient Signal Classification. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada454915.

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