Relevant bibliographies by topics / Signal processing applications

Academic literature on the topic 'Signal processing applications'

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Published: 25 May 2024

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Journal articles on the topic "Signal processing applications"

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Jin Chen, Huai Li, Kaihua Sun, and B. Kim. "Signal processing applications - How will bioinformatics impact signal processing research?" IEEE Signal Processing Magazine 20, no. 6 (November 2003): 16–26. http://dx.doi.org/10.1109/msp.2003.1253551.

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Bourennane, Salah, Julien Marot, Caroline Fossati, Ahmed Bouridane, and Klaus Spinnler. "Multidimensional Signal Processing and Applications." Scientific World Journal 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/365126.

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Cruz, J. "Applications of digital signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 2 (April 1985): 487. http://dx.doi.org/10.1109/tassp.1985.1164563.

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Duarte Ortigueira, Manuel, and J. A. Tenreiro Machado. "Fractional signal processing and applications." Signal Processing 83, no. 11 (November 2003): 2285–86. http://dx.doi.org/10.1016/s0165-1684(03)00181-6.

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Ortigueira, Manuel D., Clara M. Ionescu, J. Tenreiro Machado, and Juan J. Trujillo. "Fractional signal processing and applications." Signal Processing 107 (February 2015): 197. http://dx.doi.org/10.1016/j.sigpro.2014.10.002.

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Tibbitts, J., and Yibin Lu. "Forensic applications of signal processing." IEEE Signal Processing Magazine 26, no. 2 (March 2009): 104–11. http://dx.doi.org/10.1109/msp.2008.931099.

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Wang, Hanbo. "Compressed Sensing: Theory and Applications." Journal of Physics: Conference Series 2419, no. 1 (January 1, 2023): 012042. http://dx.doi.org/10.1088/1742-6596/2419/1/012042.

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Abstract Compressed sensing is a new technique for solving underdetermined linear systems. Because of its good performance, it has been widely used in academia. It is applied in electrical engineering to recover sparse signals, especially in signal processing. This technique exploits the signal’s sparse nature, allowing the original signals to recover from fewer samples. This paper discusses the fundamentals of compressed sensing theory, the research progress in compressed sensing signal processing, and the applications of compressed sensing theory in nuclear magnetic resonance imaging and seismic exploration acquisition. Compressed sensing allows for the digitization of analogue data with inexpensive sensors and lowers the associated costs of processing, storage, and transmission. Behind its sophisticated mathematical expression, compressed sensing theory contains a subtle idea. Compressed sensing is a novel theory that is an ideal complement and improvement to conventional signal processing. It is a theory with a high vitality level, and its research outcomes may substantially influence signal processing and other fields.
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Kandle. "A Systolic Signal Processor for Signal-Processing Applications." Computer 20, no. 7 (July 1987): 94–95. http://dx.doi.org/10.1109/mc.1987.1663626.

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Sinha, Pankaj Kumar, and Preetha Sharan. "Multiplexer Based Multiplications for Signal Processing Applications." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (March 1, 2018): 583. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp583-586.

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<p>In signal processing, Filter is a device that removes the unwanted signals. In any electronic circuits, Filters are widely used in the fundamental hands on tool. The basic function of the filter is to selectively allow the desired signal to pass through and /or control the undesired signal based on the frequency. A signal processing filter satisfies a set of requirements which are realization and improvement of the filter. A filter system consists of an analog to digital converter is used to sample the input signal, traced by a microprocessor and some components such as memory to store the data and filter coefficients. Filters can easily be designed to be “linear phase” and it is easy to implement. In this paper, the birecoder multiplier (BM) is designed in terms of VLSI design environment. The proposed multiplier is implemented by using VHDL language and Xilinx ISE for synthesis. The multiplier is mainly used for image processing applications as well as signal processing applications.</p>
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Volić, Ismar. "Topological Methods in Signal Processing." B&H Electrical Engineering 14, s1 (October 1, 2020): 14–25. http://dx.doi.org/10.2478/bhee-2020-0002.

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Abstract This article gives an overview of the applications of algebraic topology methods in signal processing. We explain how the notions and invariants such as (co)chain complexes and (co)homology of simplicial complexes can be used to gain insight into higher-order interactions of signals. The discussion begins with some basic ideas in classical circuits, continues with signals over graphs and simplicial complexes, and culminates with an overview of sheaf theory and the connections between sheaf cohomology and signal processing.
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Dissertations / Theses on the topic "Signal processing applications"

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Gudmundson, Erik. "Signal Processing for Spectroscopic Applications." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120194.

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Spectroscopic techniques allow for studies of materials and organisms on the atomic and molecular level. Examples of such techniques are nuclear magnetic resonance (NMR) spectroscopy—one of the principal techniques to obtain physical, chemical, electronic and structural information about molecules—and magnetic resonance imaging (MRI)—an important medical imaging technique for, e.g., visualization of the internal structure of the human body. The less well-known spectroscopic technique of nuclear quadrupole resonance (NQR) is related to NMR and MRI but with the difference that no external magnetic field is needed. NQR has found applications in, e.g., detection of explosives and narcotics. The first part of this thesis is focused on detection and identification of solid and liquid explosives using both NQR and NMR data. Methods allowing for uncertainties in the assumed signal amplitudes are proposed, as well as methods for estimation of model parameters that allow for non-uniform sampling of the data. The second part treats two medical applications. Firstly, new, fast methods for parameter estimation in MRI data are presented. MRI can be used for, e.g., the diagnosis of anomalies in the skin or in the brain. The presented methods allow for a significant decrease in computational complexity without loss in performance. Secondly, the estimation of blood flow velo-city using medical ultrasound scanners is addressed. Information about anomalies in the blood flow dynamics is an important tool for the diagnosis of, for example, stenosis and atherosclerosis. The presented methods make no assumption on the sampling schemes, allowing for duplex mode transmissions where B-mode images are interleaved with the Doppler emissions.
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Zhao, Wentao. "Genomic applications of statistical signal processing." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2952.

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Xu, Luzhou. "Growth curve models in signal processing applications." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015020.

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Ko, Ming-Yung. "Integrated software synthesis for signal processing applications." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3459.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Peterson, Krystal, Samuel Richter, Adam Schafer, Steve Grant, and Kurt Kosbar. "DISTRIBUTED COMPUTING PROCESSOR FOR SIGNAL PROCESSING APPLICATIONS." International Foundation for Telemetering, 2016. http://hdl.handle.net/10150/624191.

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Many signal processing, data analysis and graphical user interface algorithms are computationally intensive. This paper investigates a method of off-loading some of the calculations to remotely located processors. Inexpensive, commercial off the shelf processors are used to perform operations such as fast Fourier transforms and other numerically intensive algorithms. The data is passed to the processors, and results collected, using conventional network interfaces such as TCP/IP. This allows the processors to be located at any location, and also allows potentially large caches of processors to be shared between multiple applications.
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Naulleau, Patrick. "Optical signal processing and real world applications /." Online version of thesis, 1993. http://hdl.handle.net/1850/12136.

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Ghaderi, Foad. "Signal processing techniques for extracting signals with periodic structure : applications to biomedical signals." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/55183/.

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In this dissertation some advanced methods for extracting sources from single and multichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the periodic behaviour is not detectable from their time representation or even Fourier transform of the signal. For the case of single channel recordings a method based on singular spectrum anal ysis (SSA) of the signal is proposed. The proposed method is utilized in localizing heart sounds in respiratory signals, which is an essential pre-processing step in most of the heart sound cancellation methods. Artificially mixed and real respiratory signals are used for evaluating the method. It is shown that the performance of the proposed method is superior to those of the other methods in terms of false detection. More over, the execution time is significantly lower than that of the method ranked second in performance. For multichannel data, the problem is tackled using two approaches. First, it is assumed that the sources are periodic and the statistical characteristics of periodic sources are exploited in developing a method to effectively choose the appropriate delays in which the diagonalization takes place. In the second approach it is assumed that the sources of interest are cyclostationary. Necessary and sufficient conditions for extractability of the sources are mathematically proved and the extraction algorithms are proposed. Ballistocardiogram (BCG) artifact is considered as the sum of a number of independent cyclostationary components having the same cycle frequency. The proposed method, called cyclostationary source extraction (CSE), is able to extract these components without much destructive effect on the background electroencephalogram (EEG)
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Auyeung, Cheung. "Optimal constraint-based signal restoration and its applications." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/15785.

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Kuchler, Ryan J. "Theory of multirate statistical signal processing and applications." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Sep%5FKuchler%5FPhD.pdf.

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Liu, Haibo. "SEED devices used in optical signal processing applications." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq25657.pdf.

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Books on the topic "Signal processing applications"

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Digital signal processing: Fundamentals and applications. Amsterdam: Academic Press, 2008.

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William, Walker, ed. Digital Signal Processing and Applications. 2nd ed. San Diego: Newnes [Imprint], 2004.

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Brook, D. Signal processing: Principles and applications. London: E. Arnold, 1988.

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Alexander, S. Thomas. Adaptive signal processing: Theoryand applications. New York: Springer-Verlag, 1986.

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Antonia, Papandreou-Suppappola, ed. Applications in time-frequency signal processing. Boca Raton: CRC Press, 2003.

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Black, J. L., Ph.D. and Ledwidge T. J, eds. Signal processing for industrial diagnostics. Chichester: Wiley, 1996.

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Li, Jian, Robert Hummel, Petre Stoica, and Edmund G. Zelnio, eds. Radar Signal Processing and Its Applications. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-6342-3.

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Jian, Li, ed. Radar signal processing and its applications. Boston: Kluwer Academic Publishers, 2003.

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Binh, Le Nguyen. Photonic signal processing: Techniques and applications. Boca Raton, Fla: CRC Press, 2008.

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Das, Pankaj K. Acousto-optic signal processing: Fundamentals & applications. Boston: Artech House, 1991.

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Book chapters on the topic "Signal processing applications"

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Emerging Applications." In Digital Signal Processing, 329–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_8.

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Zhang, Fuxue, Wei Zhang, and Guosheng Wang. "Signal Processing." In Non-driven Micromechanical Gyroscopes and Their Applications, 285–323. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54045-9_10.

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Ahmad, Khalil, and Abdullah. "Applications in Signal Processing." In Forum for Interdisciplinary Mathematics, 131–201. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0268-8_5.

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Floyd, Robert W. "Biosonar Signal Processing Applications." In Animal Sonar, 773–83. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-7493-0_81.

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Tobin, Paul. "Digital Signal Processing Applications." In PSpice for Digital Signal Processing, 89–107. Cham: Springer International Publishing, 2007. http://dx.doi.org/10.1007/978-3-031-79767-5_5.

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Singh, Anuj Kumar, and Ankit Garg. "Applications of Signal Processing." In Machine Learning in Signal Processing, 73–95. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003107026-4.

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Apte, Shaila Dinkar. "Applications of Random Signal Processing." In Random Signal Processing, 415–30. Boca Raton : CRC Press, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155357-10.

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Alexander, S. Thomas. "Applications of the LMS Algorithm." In Adaptive Signal Processing, 87–98. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4978-8_6.

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Richter, Michael M., Sheuli Paul, Veton Këpuska, and Marius Silaghi. "Signal Processing Background." In Signal Processing and Machine Learning with Applications, 39–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-45372-9_2.

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Richter, Michael M., Sheuli Paul, Veton Këpuska, and Marius Silaghi. "Adaptive Signal Processing." In Signal Processing and Machine Learning with Applications, 131–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-45372-9_6.

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Conference papers on the topic "Signal processing applications"

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"Signal processing and applications." In 2014 24th International Conference Radioelektronika (RADIOELEKTRONIKA). IEEE, 2014. http://dx.doi.org/10.1109/radioelek.2014.6828465.

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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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"ISETC 2018 Adaptive Signal Processing and Digital Signal Processing Applications." In 2018 International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2018. http://dx.doi.org/10.1109/isetc.2018.8584030.

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Sarkar, Tapan K. "Application of signal processing algorithms in microwave applications." In 26th European Microwave Conference, 1996. IEEE, 1996. http://dx.doi.org/10.1109/euma.1996.337614.

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Buddha, S., H. Braun, V. Krishnan, C. Tepedelenlioglu, A. Spanias, T. Yeider, and T. Takehara. "Signal processing for photovoltaic applications." In 2012 IEEE International Conference on Emerging Signal Processing Applications (ESPA 2012). IEEE, 2012. http://dx.doi.org/10.1109/espa.2012.6152459.

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Kurschl, Werner, Stefan Mitsch, and Johannes Schoenboeck. "Modeling Distributed Signal Processing Applications." In Implantable Body Sensor Networks Conference (BSN). IEEE, 2009. http://dx.doi.org/10.1109/bsn.2009.20.

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Frankenstein, B., K. J. Froehlich, D. Hentschel, and G. Reppe. "Microsystem for signal processing applications." In Nondestructive Evaulation for Health Monitoring and Diagnostics, edited by Norbert Meyendorf, George Y. Baaklini, and Bernd Michel. SPIE, 2005. http://dx.doi.org/10.1117/12.602108.

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Azghani, Masoumeh, and Farokh Marvasti. "Applications of sparse signal processing." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7906061.

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Dantas, Pierre V., Celso B. Carvalho, and Waldir S. S. Junior. "Turning Digital Signal Processing into Graph Signal Processing: Overview and Applications." In 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2020. http://dx.doi.org/10.1109/icce-taiwan49838.2020.9258083.

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Andonovic, Ivan, Brian Culshaw, and Mohammed Shabeer. "Fibre Optic Signal Processing." In Optical Fibers and Their Applications V, edited by Ryszard S. Romaniuk and Mieczyslaw Szustakowski. SPIE, 1990. http://dx.doi.org/10.1117/12.952941.

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Reports on the topic "Signal processing applications"

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Zakhor, Avideh. Representation Issues in Signal Processing Applications. Fort Belvoir, VA: Defense Technical Information Center, June 1995. http://dx.doi.org/10.21236/ada295921.

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Zakhor, Avideh. Representation Issues in Signal Processing Applications. Fort Belvoir, VA: Defense Technical Information Center, June 1996. http://dx.doi.org/10.21236/ada311599.

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Warde, Cardinal. Thin-Film Optics for Signal Processing Applications. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada205141.

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Biglieri, Ezio, and Michele Elia. Applications of Signal Processing in Digital Communications. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada190420.

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Elia, Michele. Applications of Signal Processing in Digital Communications. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada190422.

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Tran, Merry. Applications of Digital Signal Processing with Cardiac Pacemakers. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6466.

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Moore, Frank, Brendan Babb, Steven Becke, Heather Koyuk, Earl Lamson, Wedge III, and Christopher. Genetic Algorithms Evolve Optimized Transforms for Signal Processing Applications. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada437529.

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May, Marvin, Alison Brown, and Barry Tanju. Applications of Digital Storage Receivers for Enhanced Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada444472.

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Casey, Stephen D. Multichannel Deconvolution with Applications to Signal and Image Processing. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada303433.

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Kailath, Thomas. Systematic Methods for Design of VLSI Signal Processing Arrays for Communications Applications. Fort Belvoir, VA: Defense Technical Information Center, July 1989. http://dx.doi.org/10.21236/ada293120.

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