Bibliografías temáticas / Signal processing

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Literatura académica sobre el tema "Signal processing"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 9 de junio de 2025

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Artículos de revistas sobre el tema "Signal processing"

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Borawake, Prof Dr M. P. "Audio Signal Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1495–96. http://dx.doi.org/10.22214/ijraset.2022.44063.

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Abstract: Audio Signal Processing is also known as Digital Analog Conversion (DAC). Sound waves are the most common example of longitudinal waves. The speed of sound waves is a particular medium depends on the properties of that temperature and the medium. Sound waves travel through air when the air elements vibrate to produce changes in pressure and density along the direction of the wave’s motion. It transforms the Analog Signal into Digital Signals, and then converted Digital Signals is sent to the Devices. Which can be used in Various things., Such as audio signal, RADAR, speed processing, voice recognition, entertainment industry, and to find defected in machines using audio signals or frequencies. The signals pay important role in our day-to-day communication, perception of environment, and entertainment. A joint time-frequency (TF) approach would be better choice to effectively process this signal. The theory of signal processing and its application to audio was largely developed at Bell Labs in the mid-20th century. Claude Shannon and Harry Nyquist’s early work on communication theory and pulse-code modulation (PCM) laid the foundations for the field.
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Sharma, Sushma, Hitesh Kumar, and Charul Thareja. "Digital Signal Processing Over Analog Signal Processing." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 1, no. 2 (February 28, 2014): 01–02. http://dx.doi.org/10.53555/nneee.v1i2.255.

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This paper provides a survey of digital signal processing over analog signal processing. Initially digital signal processing is developed to replace limited application based analog signal processing (ASP) of high cost. This paper describes the comparison of analog signal processing (ASP) and digital signal processing, technology under digital signal processing , application of digital signal processing, new technology of digital signal processing (DSP). This paper also focuses on the future scope of digital signal processing (DSP).
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Smolarik, Lukas, Dusan Mudroncik, and Lubos Ondriga. "ECG Signal Processing." Advanced Materials Research 749 (August 2013): 394–400. http://dx.doi.org/10.4028/www.scientific.net/amr.749.394.

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Electrocardiography (ECG) is a diagnostic method that allows sensing and record the electric activity of heart [. The measurement of electrical activity is used as a standard twelve-point system. At each of these leads to measure the useful signal and interference was measured. The intensity of interference depends on the artefacts (electrical lines, brum, motion artefacts, muscle, interference from the environment, etc.). For correct evaluation of measured signal there is a need to processing the measured signal to suitable form. At present, the use of electrocardiograms with sensors with contact scanning are difficult to set a time so we decided to use the principle of non-contact sensing. Such a device to measure the ECG was constructed under the project. The disadvantage of such devices is a problem with a high level of noise, which degrades a useful signal. The aim of this article is to pre-process the signals obtained from non-contact sensing. The contactless devices are powered from the network and battery. The electrodes were connected by way of Eithoven bipolar leads. Signals were pre-treated with suitable filters so that they are also appropriate for their subsequent analysis. In the filtration ECG signals was used as a method of linear (low pass filter, high pass, IIR (Infinite Impulse Response) peak, notch filter. The results of many signals clearly demonstrate removing noise in the ECG signals to the point that is also suitable for their analysis.
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Shelishiyah, R., M. Bharani Dharan, T. Kishore Kumar, R. Musaraf, and Thiyam Deepa Beeta. "Signal Processing for Hybrid BCI Signals." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2318/1/012007.

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Abstract The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.
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Minasian, R. A. "Photonic signal processing of microwave signals." IEEE Transactions on Microwave Theory and Techniques 54, no. 2 (February 2006): 832–46. http://dx.doi.org/10.1109/tmtt.2005.863060.

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Lessard, Charles S. "Signal Processing of Random Physiological Signals." Synthesis Lectures on Biomedical Engineering 1, no. 1 (January 2006): 1–232. http://dx.doi.org/10.2200/s00012ed1v01y200602bme001.

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Birdsall, Theodore G., Kurt Metzger, and Matthew A. Dzieciuch. "Signals, signal processing, and general results." Journal of the Acoustical Society of America 96, no. 4 (October 1994): 2343–52. http://dx.doi.org/10.1121/1.410106.

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Afanasiev, D. S. "Digital Chirp Processing." LETI Transactions on Electrical Engineering & Computer Science 15, no. 4 (2022): 44–48. http://dx.doi.org/10.32603/2071-8985-2022-15-4-44-48.

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Algorithms for digital signal processing with linear frequency modulation LFM have been developed. A method for calibrating several chirp signals for their subsequent joint processing, an algorithm for shifting a signal in time, compensating for compression or stretching of a signal in time, and determining the start time of a signal are considered, digital signal processing.
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Dewhurst, David J. "Signal processing." Journal of the Acoustical Society of America 89, no. 5 (May 1991): 2481. http://dx.doi.org/10.1121/1.400842.

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Katkovnik, V. "Signal Processing." Signal Processing 59, no. 2 (June 1997): 251–52. http://dx.doi.org/10.1016/s0165-1684(97)89502-3.

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Tesis sobre el tema "Signal processing"

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Östlund, Nils. "Adaptive signal processing of surface electromyogram signals." Doctoral thesis, Umeå universitet, Strålningsvetenskaper, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-743.

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Electromyography is the study of muscle function through the electrical signals from the muscles. In surface electromyography the electrical signal is detected on the skin. The signal arises from ion exchanges across the muscle fibres’ membranes. The ion exchange in a motor unit, which is the smallest unit of excitation, produces a waveform that is called an action potential (AP). When a sustained contraction is performed the motor units involved in the contraction will repeatedly produce APs, which result in AP trains. A surface electromyogram (EMG) signal consists of the superposition of many AP trains generated by a large number of active motor units. The aim of this dissertation was to introduce and evaluate new methods for analysis of surface EMG signals. An important aspect is to consider where to place the electrodes during the recording so that the electrodes are not located over the zone where the neuromuscular junctions are located. A method that could estimate the location of this zone was presented in one study. The mean frequency of the EMG signal is often used to estimate muscle fatigue. For signals with low signal-to-noise ratio it is important to limit the integration intervals in the mean frequency calculations. Therefore, a method that improved the maximum frequency estimation was introduced and evaluated in comparison with existing methods. The main methodological work in this dissertation was concentrated on finding single motor unit AP trains from EMG signals recorded with several channels. In two studies single motor unit AP trains were enhanced by using filters that maximised the kurtosis of the output. The first of these studies used a spatial filter, and in the second study the technique was expanded to include filtration in time. The introduction of time filtration resulted in improved performance, and when the method was evaluated in comparison with other methods that use spatial and/or temporal filtration, it gave the best performance among them. In the last study of this dissertation this technique was used to compare AP firing rates and conduction velocities in fibromyalgia patients as compared with a control group of healthy subjects. In conclusion, this dissertation has resulted in new methods that improve the analysis of EMG signals, and as a consequence the methods can simplify physiological research projects.
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Östlund, Nils. "Adaptive signal processing of surface electromyogram signals /." Umeå : Department of Radiation Sciences, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-743.

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Lee, Li 1975. "Distributed signal processing." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86436.

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Eldar, Yonina Chana 1973. "Quantum signal processing." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/16805.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2002.<br>Includes bibliographical references (p. 337-346).<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Quantum signal processing (QSP) as formulated in this thesis, borrows from the formalism and principles of quantum mechanics and some of its interesting axioms and constraints, leading to a novel paradigm for signal processing with applications in areas ranging from frame theory, quantization and sampling methods to detection, parameter estimation, covariance shaping and multiuser wireless communication systems. The QSP framework is aimed at developing new or modifying existing signal processing algorithms by drawing a parallel between quantum mechanical measurements and signal processing algorithms, and by exploiting the rich mathematical structure of quantum mechanics, but not requiring a physical implementation based on quantum mechanics. This framework provides a unifying conceptual structure for a variety of traditional processing techniques, and a precise mathematical setting for developing generalizations and extensions of algorithms. Emulating the probabilistic nature of quantum mechanics in the QSP framework gives rise to probabilistic and randomized algorithms. As an example we introduce a probabilistic quantizer and derive its statistical properties. Exploiting the concept of generalized quantum measurements we develop frame-theoretical analogues of various quantum-mechanical concepts and results, as well as new classes of frames including oblique frame expansions, that are then applied to the development of a general framework for sampling in arbitrary spaces. Building upon the problem of optimal quantum measurement design, we develop and discuss applications of optimal methods that construct a set of vectors.<br>(cont.) We demonstrate that, even for problems without inherent inner product constraints, imposing such constraints in combination with least-squares inner product shaping leads to interesting processing techniques that often exhibit improved performance over traditional methods. In particular, we formulate a new viewpoint toward matched filter detection that leads to the notion of minimum mean-squared error covariance shaping. Using this concept we develop an effective linear estimator for the unknown parameters in a linear model, referred to as the covariance shaping least-squares estimator. Applying this estimator to a multiuser wireless setting, we derive an efficient covariance shaping multiuser receiver for suppressing interference in multiuser communication systems.<br>by Yonina Chana Eldar.<br>Ph.D.
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Chan, M. K. "Adaptive signal processing algorithms for non-Gaussian signals." Thesis, Queen's University Belfast, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269023.

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Bland, Denise. "Alias-free signal processing of nonuniformly sampled signals." Thesis, University of Westminster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322992.

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Hannaske, Roland. "Fast Digitizing and Digital Signal Processing of Detector Signals." Forschungszentrum Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:d120-qucosa-27888.

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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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Case, David Robert. "Real-time signal processing of multi-path video signals." Thesis, University of Salford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334170.

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Haghighi-Mood, Ali. "Analysis of phonocardiographic signals using advanced signal processing techniques." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321465.

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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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Libros sobre el tema "Signal processing"

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Auslander, L., F. Alberto Grünbaum, J. William Helton, T. Kailath, Pramod P. Khargonekar, and S. Mitter, eds. Signal Processing. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4684-7095-6.

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Mohanty, Nirode. Signal Processing. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-011-7044-4.

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Auslander, Louis, F. A. Grünbaum, J. W. Helton, Tom Kailath, P. Khargonekar, and Sanjoy K. Mitter, eds. Signal Processing. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4684-6393-4.

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Louis, Auslander, Grünbaum F. Alberto, Helton J. William 1944-, Khargonekar P, and University of Minnesota. Institute for Mathematics and Its Applications., eds. Signal processing. New York: Springer-Verlag, 1990.

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

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Jean-Louis, Lacoume, Durrani Tariq S, Stora Raymond 1930-, Université scientifique et médicale de Grenoble., and NATO Advanced Study Institute, eds. Traitement du signal =: Signal processing. Amsterdam: North-Holland, 1987.

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Rangarao, Kaluri. Digital Signal Processing. New York: John Wiley & Sons, Ltd., 2006.

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Braun, Simon. Discover Signal Processing. New York: John Wiley & Sons, Ltd., 2008.

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Mulgrew, Bernard. Digital Signal Processing. London: Macmillan Education UK, 2003. http://dx.doi.org/10.1057/978-1-137-08351-7.

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Apte, Shaila Dinkar. Random Signal Processing. Boca Raton : CRC Press, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155357.

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Capítulos de libros sobre el tema "Signal processing"

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Wang, K. C. "Signals and Signal Processing." In Systems Programming in Unix/Linux, 205–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92429-8_6.

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Wang, K. C. "Signals and Signal Processing." In Design and Implementation of the MTX Operating System, 257–71. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17575-1_9.

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Au, Whitlow W. L. "Signal Processing and Signal Processing Models." In The Sonar of Dolphins, 216–41. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4612-4356-4_10.

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Challis, John H. "Signal Processing." In Experimental Methods in Biomechanics, 45–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52256-8_4.

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Gomez, Claude, Carey Bunks, Jean-Philippe Chancelier, François Delebecque, Maurice Goursat, Ramine Nikoukhah, and Serge Steer. "Signal Processing." In Engineering and Scientific Computing with Scilab, 209–46. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1584-4_7.

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Ernst, Floris. "Signal Processing." In Compensating for Quasi-periodic Motion in Robotic Radiosurgery, 31–63. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1912-9_3.

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Trauth, Martin H. "Signal Processing." In MATLAB® Recipes for Earth Sciences, 215–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46244-7_6.

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Turk, Irfan. "Signal Processing." In Practical MATLAB, 185–207. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5281-9_9.

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Auger, François. "Signal Processing." In Signal Processing with Free Software, 27–66. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118579619.ch3.

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Pajankar, Ashwin, and Sharvani Chandu. "Signal Processing." In GNU Octave by Example, 125–37. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6086-9_7.

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Actas de conferencias sobre el tema "Signal processing"

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Blundell, V., T. Clarke, and D. Williams. "Synthetic signals for signal processing." In Sensor Signal Processing for Defence (SSPD 2010). IET, 2010. http://dx.doi.org/10.1049/ic.2010.0229.

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Minasian, R. A., E. H. W. Chan, and Xiaoke Yi. "Photonic signal processing of microwave signals." In 35th Australian Conference on Optical Fibre Technology (ACOFT 2010). IEEE, 2010. http://dx.doi.org/10.1109/acoft.2010.5929926.

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Kachru, R. "Stimulated Echo Signal Processing." In Spectral Hole-Burning and Luminescence Line Narrowing: Science and Applications. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/shbl.1992.tha3.

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High-speed signal processing is essential in many applications where large amounts of analog or digital data need to be analyzed and processed in real time. The existing techniques, however, suffer from either very limited capacity for storing reference signals or the lack of a rapid reprogramming capability, both of which are of vital importance in high-speed signal processing.
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"Signal processing." In 2016 19th International Multi-Topic Conference (INMIC). IEEE, 2016. http://dx.doi.org/10.1109/inmic.2016.7840160.

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"Signal processing." In 2011 8th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2011). IEEE, 2011. http://dx.doi.org/10.1109/ecticon.2011.5947834.

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"Signal Processing." In 2018 25th International Conference "Mixed Design of Integrated Circuits and System" (MIXDES). IEEE, 2018. http://dx.doi.org/10.23919/mixdes.2018.8436682.

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"Signal processing." In 2017 MIXDES - 24th International Conference "Mixed Design of Integrated Circuits and Systems". IEEE, 2017. http://dx.doi.org/10.23919/mixdes.2017.8005262.

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"Signal Processing." In 2019 MIXDES - 26th International Conference "Mixed Design of Integrated Circuits and Systems". IEEE, 2019. http://dx.doi.org/10.23919/mixdes.2019.8787076.

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"Signal Processing." In 2020 27th International Conference on Mixed Design of Integrated Circuits and System (MIXDES). IEEE, 2020. http://dx.doi.org/10.23919/mixdes49814.2020.9155793.

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"Signal processing." In 2017 Radiation and Scattering of Electromagnetic Waves (RSEMW). IEEE, 2017. http://dx.doi.org/10.1109/rsemw.2017.8103632.

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Informes sobre el tema "Signal processing"

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Easton, Jr., R. Signal processing. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/5071979.

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Miller, Jr, and Willard. Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada206662.

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Basu, Sankar. Multidimensional Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada200954.

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Thomas, J. B., and K. Steiglitz. Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada203744.

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Baraniuk, Richard G. Compressive Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada530830.

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Suter, Bruce W. Wavelets and Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, August 1996. http://dx.doi.org/10.21236/ada324106.

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Van Veen, Barry. Signal Processing in Subspaces. Fort Belvoir, VA: Defense Technical Information Center, March 1997. http://dx.doi.org/10.21236/ada324997.

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Maren, Aliianna J. Signal Processing Chips/Electronics. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada298833.

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Vienneau, Robert. Multichannel Signal Processing Extensions. Fort Belvoir, VA: Defense Technical Information Center, February 1996. http://dx.doi.org/10.21236/ada307110.

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Pflug, Lisa, Jerald W. Caruthers, and Richard R. Slater. ARSRP Signal Processing Software. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada263543.

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