Relevant bibliographies by topics / Signals classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Signals classification'

Author: Grafiati
Published: 7 September 2022
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Signals classification.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Signals classification"

1

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 (2021): 15. http://dx.doi.org/10.22146/ijitee.60295.

Full text
Abstract:
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. Ther
APA, Harvard, Vancouver, ISO, and other styles
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 (2020): 1646. http://dx.doi.org/10.3390/electronics9101646.

Full text
Abstract:
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 modulatio
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
Abstract:
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 differ
APA, Harvard, Vancouver, ISO, and other styles
4

Kim, Ji-Hyeon, Soon-Young Kwon, and Hyoung-Nam Kim. "Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments." Sensors 24, no. 23 (2024): 7612. http://dx.doi.org/10.3390/s24237612.

Full text
Abstract:
Accurately classifying and deinterleaving overlapping radar signals presents a significant challenge in complex environments, such as electronic warfare. Traditional methods, such as spectrogram-based analysis, often struggle to differentiate radar signals with similar scan patterns, particularly under low signal-to-noise ratio (SNR) conditions. To address these limitations, we propose a novel two-stage classification framework that combines amplitude pattern (AMP) analysis and visibility graphs to enhance the accuracy and efficiency of radar signal classification. In the first stage, AMP anal
APA, Harvard, Vancouver, ISO, and other styles
5

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 (2022): 1–6. http://dx.doi.org/10.18178/ijsps.10.1.1-6.

Full text
Abstract:
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 pr
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Jinghui, Li Ke, Qiang Du, Xiaodi Ding, and Xiangmin Chen. "Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS." Applied Sciences 12, no. 22 (2022): 11762. http://dx.doi.org/10.3390/app122211762.

Full text
Abstract:
Because a cardiac function signal cannot reflect cardiac health in all directions, we propose a classification method using ECG and PCG signals based on BiLSTM-GoogLeNet-DS. The electrocardiogram (ECG) and phonocardiogram (PCG) signals used as research objects were collected synchronously. Firstly, the obtained ECG and PCG signals were filtered, and then the ECG and PCG signals were fused and classified by using a bi-directional long short-term memory network (BiLSTM). After that, the time-frequency processing was performed on the filtered ECG and PCG signals to obtain the time-frequency diagr
APA, Harvard, Vancouver, ISO, and other styles
7

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 (2002): 1273–93. http://dx.doi.org/10.1142/s021812740200508x.

Full text
Abstract:
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 Bayes
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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 v
APA, Harvard, Vancouver, ISO, and other styles
9

Duan, Li, Jianxian Cai, Juan Liang, Danqi Chen, and Xiaoye Sun. "Identification and Analysis of Non-Stationary Time Series Signals Based on Data Preprocessing and Deep Learning." Traitement du Signal 39, no. 5 (2022): 1703–9. http://dx.doi.org/10.18280/ts.390528.

Full text
Abstract:
Deep learning is not the most accurate way for recognizing time series signals, and it is unable to identify non-stationary time series signals with numerous chaotic classes. Moreover, the signal detection benefits from data preprocessing have gone unnoticed. Therefore, this paper investigates the detection and analysis of non-stationary time series signals using deep learning and data preprocessing. The fitting model of the historical stationarity index is built based on the Gaussian mixture model of single Gaussian models, and the change point of the non-stationary time series signal is dete
APA, Harvard, Vancouver, ISO, and other styles
10

Melinda, Melinda, Filbert H. Juwono, I. Ketut Agung Enriko, Maulisa Oktiana, Siti Mulyani, and Khairun Saddami. "Application of continuous wavelet transform and support vector machine for autism spectrum disorder electroencephalography signal classification." Radioelectronic and Computer Systems, no. 3 (September 29, 2023): 73–90. http://dx.doi.org/10.32620/reks.2023.3.07.

Full text
Abstract:
The article’s subject matter is to classify Electroencephalography (EEG) signals in Autism Spectrum Disorder (ASD) sufferers. The goal is to develop a classification model using Machine Learning (ML) algorithms that are often implemented in Brain-Computer Interfaces (BCI) technology. The tasks to be solved are as follows: pre-processing the EEG dataset signal to separate the source signal from the noise/artifact signal to produce an observation signal that is free of noise/artifact; obtaining an effective feature comparison to be used as an attribute at the classification stage; and developing
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Signals classification"

1

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

Full text
Abstract:
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 p
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
Abstract:
Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.<br>Includes bibliographical references (p. 106-107).<br>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
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
Abstract:
Thesis (Degrees of Naval Engineer and M.S. in Engineering and Management)--Massachusetts Institute of Technology, June 2008.<br>"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 Monito
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ramakonar, Visalakshi S. "Modulation classification of digital communication signals." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2002. https://ro.ecu.edu.au/theses/752.

Full text
Abstract:
Modulation classification of digital communications signals plays an important role in both military and civilian sectors. It has the potential of replacing several receivers with one universal receiver. An automatic modulation classifier can be defined as a system that automatically identifies the modulation type of the received signal given that the signal exists and its parameters lie in a known range. This thesis addresses the need for a universal modulation classifier capable of classifying a comprehensive list of digital modulation schemes. Two classification approaches are presented: a
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 73-75).<br>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
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Signals classification"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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

Full text
Abstract:
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 (Elsevi
APA, Harvard, Vancouver, ISO, and other styles
10

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Signals classification"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Signals classification"

1

Vermunt, Jim, and Federico Corradi. "FMCW Radar Signal Processing Pipeline for Aircraft Marshalling Signals Classification." In 2024 21st European Radar Conference (EuRAD). IEEE, 2024. http://dx.doi.org/10.23919/eurad61604.2024.10734929.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Carvalho, Itaiara Felix, Edson Guedes Da Costa, Luiz Augusto Medeiros Martins Nobrega, et al. "Classification of Radiometric Partial Discharge Signals Using Signal Conditioning System." In 2024 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). IEEE, 2024. http://dx.doi.org/10.1109/ichve61955.2024.10676295.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Barahona, Jeffrey, Hayley Richardson, Lina Acosta, et al. "Histones Classification Based on EGFET Signals." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782679.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Mehta, Tanay, Bryan Crompton, and Apurva Mody. "Classification of Cochannel Signals using Cyclostationary Signal Processing and Deep Learning." In 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE, 2024. http://dx.doi.org/10.1109/dyspan60163.2024.10632748.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

"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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

OKOPAL, G., and P. LOUGHLIN. "PROPAGATION-INVARIANT CLASSIFICATION OF SHALLOW WATER SONAR SIGNALS." In DETECTION & CLASSIFICATION OF UNDERWATER TARGETS 2007. Institute of Acoustics, 2023. http://dx.doi.org/10.25144/17792.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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 represe
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

"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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Zurovac, Snežana, Nikola Petrović, Vasilija Joksimović, Ivan Pokrajac, Darko Mikanović, and Boban Sazdić-Jotić. "Simple energy detector for two-stage classification for antidrone systems." In 11th International Scientific Conference on Defensive Technologies - OTEX 2024. Military Technical Institute, Belgrade, 2024. http://dx.doi.org/10.5937/oteh24066z.

Full text
Abstract:
Signal detection theory, a fundamental concept in various scientific disciplines, involves mandatory measuring of the signal features. This theory finds applications in telecommunications, radar technology, medical devices, automation and process control, geophysical research, biometric systems, and security systems, emphasizing its broad significance. Likewise, drone detection in the radio-frequency domain is necessary for signal detection and ensures efficient and reliable communication, surveillance, and security. The recent conflict between Russia and Ukraine has underscored the crucial ro
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Signals classification"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Chhan, David, and Vernon Lawhern. An Evaluation of Tabular Neural Network Approaches for Human Affective State Classification from Physiological Signals. DEVCOM Army Research Laboratory, 2022. http://dx.doi.org/10.21236/ad1182171.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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, 2011. http://dx.doi.org/10.5900/su_fens_wp.2011.16498.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Signals classification' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography