Gotowa bibliografia na temat „Signal detection”
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Artykuły w czasopismach na temat "Signal detection"
Gudiškis, Andrius. "HEART BEAT DETECTION IN NOISY ECG SIGNALS USING STATISTICAL ANALYSIS OF THE AUTOMATICALLY DETECTED ANNOTATIONS / ŠIRDIES DŪŽIŲ NUSTATYMAS IŠ IŠKRAIPYTŲ EKG SIGNALŲ ATLIEKANT AUTOMATIŠKAI APTIKTŲ ATSKAITŲ STATISTINĘ ANALIZĘ". Mokslas – Lietuvos ateitis 7, nr 3 (13.07.2015): 300–303. http://dx.doi.org/10.3846/mla.2015.787.
Pełny tekst źródłaThompson, William Forde, i Max Coltheart. "The role of signal detection and amplification in the induction of emotion by music". Behavioral and Brain Sciences 31, nr 5 (październik 2008): 597–98. http://dx.doi.org/10.1017/s0140525x08005529.
Pełny tekst źródłaPark, Do-Hyun, Min-Wook Jeon, Da-Min Shin i Hyoung-Nam Kim. "LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function". Sensors 23, nr 20 (18.10.2023): 8564. http://dx.doi.org/10.3390/s23208564.
Pełny tekst źródłaEgberts, Toine C. G. "Signal Detection". Drug Safety 30, nr 7 (2007): 607–9. http://dx.doi.org/10.2165/00002018-200730070-00006.
Pełny tekst źródłaCheng, Yu-Chung Norman, i E. Mark Haacke. "Signal Detection". Current Protocols in Magnetic Resonance Imaging 00, nr 1 (marzec 2001): B2.1.1—B2.1.10. http://dx.doi.org/10.1002/0471142719.mib0201s00.
Pełny tekst źródłaCheng, Yu-Chung Norman, i E. Mark Haacke. "Signal Detection". Current Protocols in Magnetic Resonance Imaging 13, nr 1 (kwiecień 2005): B2.1.1—B2.1.10. http://dx.doi.org/10.1002/0471142719.mib0201s13.
Pełny tekst źródłaKumar, Anoop, i Henna Khan. "Signal Detection and their Assessment in Pharmacovigilance". Open Pharmaceutical Sciences Journal 2, nr 1 (17.12.2015): 66–73. http://dx.doi.org/10.2174/1874844901502010066.
Pełny tekst źródłaLiu, Shuai, Xiang Chen, Ying Li i Xiaochun Cheng. "Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis". Symmetry 11, nr 12 (3.12.2019): 1471. http://dx.doi.org/10.3390/sym11121471.
Pełny tekst źródłaKhudov, Hennadii, Serhii Yarosh, Oleksandr Kostyria, Oleksandr Oleksenko, Mykola Khomik, Andrii Zvonko, Bohdan Lisohorskyi, Petro Mynko, Serhii Sukonko i Taras Kravets. "Improving a method for non-coherent processing of signals by a network of two small-sized radars for detecting a stealth unmanned aerial vehicle". Eastern-European Journal of Enterprise Technologies 1, nr 9 (127) (28.02.2024): 6–13. http://dx.doi.org/10.15587/1729-4061.2024.298598.
Pełny tekst źródłaWang, Liwei, Senxiang Lu, Xiaoyuan Liu i Jinhai Liu. "Two-Stage Ultrasound Signal Recognition Method Based on Envelope and Local Similarity Features". Machines 10, nr 12 (23.11.2022): 1111. http://dx.doi.org/10.3390/machines10121111.
Pełny tekst źródłaRozprawy doktorskie na temat "Signal detection"
Park, Subok. "Signal detection with random backgrounds and random signals". Diss., The University of Arizona, 2004. http://hdl.handle.net/10150/280729.
Pełny tekst źródłaLie, Celia, i n/a. "Punishment and human signal detection". University of Otago. Department of Psychology, 2007. http://adt.otago.ac.nz./public/adt-NZDU20071004.134135.
Pełny tekst źródłaHuang, Wensheng. "Wavelet Transform Adaptive Signal Detection". NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19991104-151423.
Pełny tekst źródłaWavelet Transform Adaptive Signal Detection is a signal detection method that uses the Wavelet Transform Adaptive Filter (WTAF). The WTAF is the application of adaptive filtering on the subband signals obtained by wavelet decomposition and reconstruction. The WTAF is an adaptive filtering technique that leads to good convergence and low computational complexity. It can effectively adapt to non-stationary signals, and thus could find practical use for transient signals. Different architectures for implementing the WTAF were proposed and studied in this dissertation. In terms of the type of the wavelet transform being used, we presented the DWT based WTAF and the wavelet tree based WTAF. In terms of the position of the adaptive filter in the signal paths of the system, we presented the Before-Reconstruction WTAF, in which the adaptive filter is placed before the reconstruction filter; and the After-Reconstruction WTAF, in which the adaptive filter is placed after the reconstruction filter. This could also be considered as implementing the adaptive filtering in different domains, with the Before-Reconstruction structure corresponding to adaptive filtering in the scale-domain, and the After-Reconstruction structure corresponding to adaptive filtering in the time-domain. In terms of the type of the error signal used in the WTAF, we presented the output error based WTAF and the subband error based WTAF. In the output error based WTAF, the output error signal is used as input to the LMS algorithm. In the subband error based WTAF, the error signal in each subband is used as input to the LMS algorithm. The algorithms for the WTAF were also generalized in this work. In order to speed up the calculation, we developed the block LMS based WTAF, which modifies the weights of the adaptive filter block-by-block instead of sample-by-sample. Experimental studies were performed to study the performance of different implementation schemes for the WTAF. Simulations were performed on different WTAF algorithms with a sinusoidal input and with a pulse input. The speed and stability properties of each structure were studied experimentally and theoretically. It was found that different WTAF structures had different tradeoffs in terms of stability, performance, computational complexity, and convergence speed. The WTAF algorithms were applied to an online measurement system for fabric compressional behavior and they showed encouraging results. A 3-stage DWT based WTAF and a block WTAF based on a 3-stage DWT was employed to process the noisy force-displacement signal acquired from the online measurement system. The signal-to-noise ratio was greatly increased by applying these WTAFs, which makes a lower sampling rate a possibility. The reduction of the required time for data sampling and processing greatly improves the system speed to meet faster testing requirements. The WTAF algorithm could also be used in other applications requiring fast processing, such as in the real-time applications in communications, measurement, and control.
Krause, Michael. "Signal Detection for Overloaded Receivers". Thesis, University of Canterbury. Department of Electrical and Computer Engineering, 2009. http://hdl.handle.net/10092/2959.
Pełny tekst źródłaArslanian, A. S. "Spectral techniques for signal detection". Thesis, University of Strathclyde, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372465.
Pełny tekst źródłaShikhaliev, Azer P. "Techniques for Adaptive Signal Detection". The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1610123085674943.
Pełny tekst źródłaGallas, Brandon Dominic. "Signal detection in lumpy backgrounds". Diss., The University of Arizona, 2001. http://hdl.handle.net/10150/290090.
Pełny tekst źródłaZhang, Hongbin. "Signal detection in medical imaging". Diss., The University of Arizona, 2001. http://hdl.handle.net/10150/290512.
Pełny tekst źródłaMabrouk, Mohamed Hussein Emam Mabrouk. "Signal Processing of UWB Radar Signals for Human Detection Behind Walls". Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31945.
Pełny tekst źródłaPike, Cameron M. "Multipath signal detection using the bispectrum". Ohio : Ohio University, 1990. http://www.ohiolink.edu/etd/view.cgi?ohiou1183467926.
Pełny tekst źródłaKsiążki na temat "Signal detection"
Tuzlukov, Vyacheslav P. Signal Detection Theory. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8.
Pełny tekst źródłaTuzlukov, V. P. Signal Detection Theory. Boston, MA: Birkhäuser Boston, 2001.
Znajdź pełny tekst źródłaBarkat, Mourad. Signal detection and estimation. Boston: Artech House, 1991.
Znajdź pełny tekst źródłaSong, Iickho. Advanced Theory of Signal Detection: Weak Signal Detection in Generalized Observations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002.
Znajdź pełny tekst źródłaJinsoo, Bae, i Ki Sun Yong 1968-, red. Advanced theory of signal detection: Weak signal detection in generalized observations. Berlin: Springer, 2002.
Znajdź pełny tekst źródłaSong, Iickho, Jinsoo Bae i Sun Yong Kim. Advanced Theory of Signal Detection. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04859-7.
Pełny tekst źródłaHart, G. F. Wind propeller signal detection improvements. Fayetteville, Tenn: Tennessee Applied Physics, Inc., 1992.
Znajdź pełny tekst źródłaD, Whalen Anthony, i Whalen Anthony D, red. Detection of signals in noise. Wyd. 2. San Diego: Academic Press, 1995.
Znajdź pełny tekst źródła1945-, Papantoni-Kazakos P., red. Detection and estimation. New York: Computer Science Press, 1990.
Znajdź pełny tekst źródła1925-, Thomas John Bowman, red. Signal detection in non-Gaussian noise. New York: Springer-Verlag, 1988.
Znajdź pełny tekst źródłaCzęści książek na temat "Signal detection"
Elsner, James B., i Anastasios A. Tsonis. "Signal Detection". W Singular Spectrum Analysis, 89–112. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2514-8_7.
Pełny tekst źródłaNahler, Gerhard. "signal detection". W Dictionary of Pharmaceutical Medicine, 169. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1283.
Pełny tekst źródłaEvans, David C. "Signal Detection". W Bottlenecks, 85–94. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2580-6_8.
Pełny tekst źródłaRobinson, Michael. "Detection". W Topological Signal Processing, 85–131. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36104-3_4.
Pełny tekst źródłaTuzlukov, Vyacheslav P. "Detection Performances". W Signal Detection Theory, 541–630. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_7.
Pełny tekst źródłaMohanty, Nirode. "Detection of Signals". W Signal Processing, 457–648. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-011-7044-4_4.
Pełny tekst źródłaTuzlukov, Vyacheslav P. "Introduction". W Signal Detection Theory, 1–9. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_1.
Pełny tekst źródłaTuzlukov, Vyacheslav P. "Classical Signal Detection Theory". W Signal Detection Theory, 11–37. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_2.
Pełny tekst źródłaTuzlukov, Vyacheslav P. "Modern Signal Detection Theory". W Signal Detection Theory, 38–224. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_3.
Pełny tekst źródłaTuzlukov, Vyacheslav P. "Generalized Approach". W Signal Detection Theory, 225–93. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_4.
Pełny tekst źródłaStreszczenia konferencji na temat "Signal detection"
Mitrevski, Jovan. "Low Energy LArTPC Signal Detection Using Anomaly Detection". W Low Energy LArTPC Signal Detection Using Anomaly Detection. US DOE, 2023. http://dx.doi.org/10.2172/2204657.
Pełny tekst źródłaKolodiy, Zenoviy, i Andriy Kolodiy. "Detection of Informational Signal Among Noisy Signals". W 2023 International Conference on Noise and Fluctuations (ICNF). IEEE, 2023. http://dx.doi.org/10.1109/icnf57520.2023.10472749.
Pełny tekst źródłaLei, Zhongding, i Francois Chin. "WiMax signal detection". W MILCOM 2008 - 2008 IEEE Military Communications Conference (MILCOM). IEEE, 2008. http://dx.doi.org/10.1109/milcom.2008.4753616.
Pełny tekst źródłaPlazenet, Thibaud, Thierry Boileau, Cyrille Caironi i Babak Nahid-Mobarakeh. "Signal processing tools for non-stationary signals detection". W 2018 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2018. http://dx.doi.org/10.1109/icit.2018.8352466.
Pełny tekst źródłaKatz, A., X. J. Lu, E. G. Kanterikis, Yao Li, Yan Zhang i N. P. Caviris. "Real-time optoelectronic Gabor detection of transient signals". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.ml6.
Pełny tekst źródłaHao, Long, Dan Liu, Fei Liu, QingXin Wang, Lin Liang i GuangHua Xu. "Research on the Weak Signal Detection of Bearing Fault Based on Duffing Oscillator". W ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86892.
Pełny tekst źródłaNair, Arya Sukumaran, Peter Hoffrogge, Peter Czurratis, Christian Hollerith, Alexander Roch, Alireza Haghighat, Klaus Pressel, Frank Zudock, Mario Wolf i Elfgard Kühnicke. "1D-ResNet Framework for Ultrasound Signal Classification". W ISTFA 2022. ASM International, 2022. http://dx.doi.org/10.31399/asm.cp.istfa2022p0021.
Pełny tekst źródłaChuang, C. H., i Y. L. Lo. "Heterodyne Detection Signal Analysis in Apertureless Scanning Near-Field Optical Microscopy". W ASME 2008 First International Conference on Micro/Nanoscale Heat Transfer. ASMEDC, 2008. http://dx.doi.org/10.1115/mnht2008-52186.
Pełny tekst źródłaBeck, M., M. E. Anderson i M. G. Raymer. "Imaging through Scattering Media Using Pulsed Homodyne Detection". W Advances in Optical Imaging and Photon Migration. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/aoipm.1994.ci.257.
Pełny tekst źródłaPeng, Chubing, M. Mansuripur, Kenichi Nagata i Takeo Ohta. "Edge detection readout signal and cross-talk in phase-change optical data storage". W Optical Data Storage. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/ods.1998.tub.3.
Pełny tekst źródłaRaporty organizacyjne na temat "Signal detection"
Broder, Bruce, i Stuart Schwartz. Quickest Detection Procedures and Transient Signal Detection. Fort Belvoir, VA: Defense Technical Information Center, listopad 1990. http://dx.doi.org/10.21236/ada230068.
Pełny tekst źródłaHughes, Timothy M. A Signal Energy Detection Implementation. Fort Belvoir, VA: Defense Technical Information Center, grudzień 1999. http://dx.doi.org/10.21236/ada372823.
Pełny tekst źródłaBaker, C. R., M. R. Frey i A. F. Gualtierotti. Some Results on Nongaussian Signal Detection. Fort Belvoir, VA: Defense Technical Information Center, marzec 1989. http://dx.doi.org/10.21236/ada207255.
Pełny tekst źródłaRao, C. R. Some Recent Results in Signal Detection. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1986. http://dx.doi.org/10.21236/ada177197.
Pełny tekst źródłaTEXAS UNIV AT AUSTIN APPLIED RESEARCH LABS. Continuation of Signal Detection Using Polyspectra. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1992. http://dx.doi.org/10.21236/ada280176.
Pełny tekst źródłaSchlesinger, M. E., i T. P. Barnett. On greenhouse gas signal detection strategies. Office of Scientific and Technical Information (OSTI), luty 1989. http://dx.doi.org/10.2172/6282370.
Pełny tekst źródłaForrest, Robert. Convolutional Neural Networks for Signal Detection. Office of Scientific and Technical Information (OSTI), listopad 2020. http://dx.doi.org/10.2172/1813655.
Pełny tekst źródłaZhang, Xin Zhu. Spatial CUSUM for Signal Region Detection. Ames (Iowa): Iowa State University, styczeń 2018. http://dx.doi.org/10.31274/cc-20240624-1317.
Pełny tekst źródłaVALLEY, MICHAEL T., BRUCE D. HANSCHE, THOMAS L. PAEZ, ANGEL URBINA i DENNIS M. ASHBAUGH. Advanced Signal Processing for Thermal Flaw Detection. Office of Scientific and Technical Information (OSTI), wrzesień 2001. http://dx.doi.org/10.2172/787641.
Pełny tekst źródłaForrest, R. N. Active Sonar Detection and Signal Excess Fluctuations. Fort Belvoir, VA: Defense Technical Information Center, listopad 1987. http://dx.doi.org/10.21236/ada200932.
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