Relevant bibliographies by topics / Signal processing Digital techniques

Contents

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

Academic literature on the topic 'Signal processing Digital techniques'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Signal processing Digital techniques"

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YAN, LIUMING, YUEFEI MA, and JORGE M. SEMINARIO. "TERAHERTZ SIGNAL TRANSMISSION IN MOLECULAR SYSTEMS." International Journal of High Speed Electronics and Systems 16, no. 02 (June 2006): 669–75. http://dx.doi.org/10.1142/s0129156406003928.

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Terahertz signal transmission in DNA is simulated and analyzed using molecular dynamics and digital signal processing techniques to demonstrate that signals encoded in vibrational movements of hydrogen bonds can travel along the backbone of DNA and eventually be recovered and analyzed using digital signal processing techniques.
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Smith, Steward G., Ralph W. Morgan, and Julian Payne. "ASIC techniques for high-performance digital signal processing." Annales des Télécommunications 46, no. 1-2 (January 1991): 40–48. http://dx.doi.org/10.1007/bf02995434.

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Mikkelsen, H. F. "Using digital signal processing techniques in light controllers." IEEE Transactions on Consumer Electronics 39, no. 2 (May 1993): 122–30. http://dx.doi.org/10.1109/30.214817.

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Denisov, V. S., and Yu A. Rogovsky. "Development of Digital Signal Processing Techniques with BPM." SIBERIAN JOURNAL OF PHYSICS 19, no. 2 (July 15, 2024): 23–32. http://dx.doi.org/10.25205/2541-9447-2024-19-2-23-32.

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In order to ensure the continuity of operation of the VEPP-2000 collider, accurate measurement of the betatron frequen- cy is necessary. To do this, this work proposes to use methods that refine the Fourier transform, such as parabola inter- polation (Gassior method), NAFF and window functions. The refined frequency is subsequently used in construction of phase portraits of the beam to control the interference of high-order magnetic fields. In addition, the work discusses methods for extracting a signal from a mixture for subsequent analysis – PCA and ICA. Finally, to improve the accuracy of frequency determination, the paper considers the simplest implementation of a Kalman filter to improve the accuracy of subsequent harmonic analysis. In addition to all of the above, the paper briefly discusses a method for monitoring the operation of the beam position monitors themselves.
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Raghavendra, V., N. Vinay kumar, and Manish Kumar. "Latest advancement in image processing techniques." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 390. http://dx.doi.org/10.14419/ijet.v7i2.12.11357.

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Image processing is method of performing some operations on an image, for enhancing the image or for getting some information from that image, or for some other applications is nothing but Image Processing [1]. Image processing is one sort of signal processing, where input is an image and output may be an image, characteristics of that image or some features that image [1]. Image will be taken as a two dimensional signal and signal processing techniques will be applied to that two dimensional image. Image processing is one of the growing technologies [1]. In many real time applications image processing is widely used. In the field of bio technology, computer science, in medical field, envi-ronmental areas etc., image processing is being used for mankind benefits. The following steps are the basics of image processing:Image is taken as an inputImage will be processed (manipulation, analyzing the image, or as per requirement)Altered image will be the outputImage processing is of two typesAnalog Image Processing:As the name implies, analog image processing is applied on analog signals. Television image is best example of analog signal processing [1].(DIP) Digital Image Processing:DIP techniques are used on images, which are in the format of digital for processing them, and get the required output as per the application. Operations were applied on the digital images for processing [1].In this paper, we will discuss about the technologies or tools for image processing especially by using Open CV. With the help of Open CV image processing will be very easy and efficient. When Open CV is collaborated or integrated with python the results are mind blowing. We will discuss about the process of using python and Open CV.
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Siddiqui, Mohd Maroof, Mohd Suhaib Kidwai, Geetika Srivastava, K. K. Singh, and Piyush Charan. "Analysis of Eeg Data Using Different Techniques of Digital Signal Processing." Biomedical and Pharmacology Journal 17, no. 1 (March 20, 2024): 135–39. http://dx.doi.org/10.13005/bpj/2841.

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This paper explores the application of digital signal processing (DSP) techniques in the examination of electroencephalogram (EEG) data. DSP encompasses a collection of mathematical algorithms designed to employ signals, such as EEG recordings, and finds application in diverse domains, including sleep medicine, neuroscience, and biomedical engineering. Employing DSP methods for EEG data analysis enables the extraction of pertinent insights from EEG signals, the identification of event-related patterns, and the enhancement of diagnostic and therapeutic practices across various disciplines. This article provides an overview of prevalent DSP methodologies employed in EEG signal processing, encompassing filtering, power spectral analysis, wavelet analysis, independent component analysis, and artifact removal.
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Laddomada, M., G. J. Dolecek, L. Yong Ching, Fa-Long Luo, M. Renfors, and L. Wanhammar. "Editorial: Advanced techniques on multirate signal processing for digital information processing." IET Signal Processing 5, no. 3 (2011): 313. http://dx.doi.org/10.1049/iet-spr.2011.9058.

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Yamamoto, Yutaka, Kaoru Yamamoto, Masaaki Nagahara, and Pramod P. Khargonekar. "Signal processing via sampled-data control theory." Impact 2020, no. 2 (April 15, 2020): 6–8. http://dx.doi.org/10.21820/23987073.2020.2.6.

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Digital sounds and images are used everywhere today, and they are all generated originally by analogue signals. On the other hand, in digital signal processing, the storage or transmission of digital data, such as music, videos or image files, necessitates converting such analogue signals into digital signals via sampling. When these data are sampled, the values from the discrete, sampled points are kept while the information between the sampled points is lost. Various techniques have been developed over the years to recover this lost data, but the results remain incomplete. Professor Yutaka Yamamoto's research is focused on improving how we can recover or reconstruct the original analogue data.
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Li, Yiyang. "Digital signal processing techniques for image enhancement and restoration." Applied and Computational Engineering 17, no. 1 (October 23, 2023): 198–205. http://dx.doi.org/10.54254/2755-2721/17/20230940.

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Digital image processing has become a fundamental tool in modern image processing, including image enhancement and restoration. This paper reviews important image enhancement and restoration techniques in digital image processing. First, some important image enhancement techniques such as histogram equalization are introduced and compared in detail, including their advantages, disadvantages, and application scenarios. Secondly, for image restoration techniques, this paper introduces deblurring techniques such as deconvolution and blind deconvolution, explaining their working principles and application scenarios in detail. Finally, this paper introduces the development and applications of super-resolution technology, and explores their possible future development directions. This review provides comprehensive technical references for researchers in digital image processing.
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Yan, Zheng Guo, and Juan Su. "Through-Casing Resistivity Logging Signal Acquisition and Processing Techniques." Advanced Materials Research 403-408 (November 2011): 2659–62. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2659.

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Weak signal detection is the key technique in developing through-casing resistivity logging tool. In this paper, ultra-low-noise preamplifier, oversampling method, sampling integration and sampling average method, digital phase-sensitive detection technique are applied in detecting logging signals and 30nV is achieved. The indoor calibration test and field experiment of through-casing resistivity logging model machine with those weak signal detection techniques were carried out. The result showed that the measurement range of formation resistivity is 0~200 Ω.m.
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Dissertations / Theses on the topic "Signal processing Digital techniques"

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Kwan, Ching Chung. "Digital signal processing techniques for on-board processing satellites." Thesis, University of Surrey, 1990. http://epubs.surrey.ac.uk/754893/.

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In on-board processing satellite systems in which FDMA/SCPC access schemes are employed. transmultiplexers are required for the frequency demultiplexing of the SCPC signals. Digital techniques for the implementation of the transmultiplexer for such application were examined in this project. The signal processing in the transmultiplexer operations involved many parameters which could be optimized in order to reduce the hardware complexity whilst satisfying the level of performance required of the system. An approach for the assessment of the relationship between the various parameters and the system performance was devised. which allowed hardware requirement of practical system specifications to be estimated. For systems involving signals of different bandwidths a more flexible implementation of the trans multiplexer is required and two computationally efficient methods. the DFT convolution and analysis/synthesis filter bank. were investigated. These methods gave greater flexibility to the input frequency plan of the transmultiplexer. at the expense of increased computational requirements. Filters were then designed to exploit specific properties of the flexible transmultiplexer methods. resulting in considerable improvement in their efficiencies. Hardware implementation of the flexible transmultiplexer was considered and an efficient multi-processor architecture in combination with parallel processing software algorithms for the signal processing operations were designed. Finally. an experimental model of the payload for a land-mobile satellite system proposal. T -SAT. was constructed using general-purpose digital signal processors and the merits of the on-board processing architecture was demonstrated.
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Papaspiridis, Alexandros. "Digital signal processing techniques for gene prediction." Thesis, Imperial College London, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590037.

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The purpose of this research is to apply existing Digital Signal Processing techniques to DNA sequences, with the objective of developing improved methods for gene prediction. Sections of DNA sequences are analyzed in the frequency domain and frequency components that distinguish intron regions are identified (21t/lOA). Novel detectors are created using digital filters and auto correlation, capable of identifying the location of intron regions in a sequence. The resulting signal from these detectors is used as a dynamic threshold in existing gene detectors, resulting in an improved accuracy of 12% and 25% respectively. Finally, DNA sequences are analyzed in terms of their amino acid composition, and new gene prediction algorithms are introduced.
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Chan, Tsang Hung. "Digital signal processing in optical fibre digital speckle pattern interferometry." HKBU Institutional Repository, 1996. http://repository.hkbu.edu.hk/etd_ra/269.

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Hamlett, Neil A. "Comparison of multiresolution techniques for digital signal processing." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1993. http://edocs.nps.edu/npspubs/scholarly/theses/1993/Mar/93Mar_Hamlett.pdf.

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Scraggs, David Peter Thomas. "Digital signal processing techniques for semiconductor Compton cameras." Thesis, University of Liverpool, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491364.

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The work presented in this thesis has focused on the development of a low dose Compton camera for nuclear medicine. A Compton camera composed of two high-purity planar germanium orthogonal-strip detectors has been constructed. Fast digital data acquisition has been utilised for the application of pulse shape analysis techniques. A simple back projection imaging code has been developed and validated with a Geant4 radiation transport simulation of the Compton camera configuration. L A 137CS isotropic source and a 22Na anisotropic source have been experimentally reconstructed. Parametric pulse shape analysis was applied to both data sets and has been shown to increase the detector spatial resolution from a raw granularity of 5x5x20mm to a spatial resolution that can be represented by a Gaussian distribution with a standard deviation of 1.5mm < u < 2mm in all dimensions; this result was in-part derived from Geant4 simulations. Qualitatively poor images have been shown to result - based wholly on simulation - from Gaussian spatial-resolution distributions that have a standard deviation of greater than 4mm. A partial experimental basis-data-set has been developed and proved capable of providing 1.9mm FWHM average spatial resolution through the depth axis of a single detector crystal. A novel technique to identify gamma ray scattering within single detector c1osed-face-pixels - hitherto unrecognised - has also been introduced in this thesis. This technique, henceforth known as Digital Compton Suppression (DieS), is based on spectral analysis and has demonstrated the ability of identifying events in which the Compton scattering and photoelectric absorption sites are separated by 13mm in the direction ofthe electric field. Supplied by The British Library - 'The world's knowledge'
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Al-Mbaideen, Amneh Ahmed. "Digital signal processing techniques fpr NIR spectroscopy analysis." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538095.

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Goldfarb, Gilad. "DIGITAL SIGNAL PROCESSING TECHNIQUES FOR COHERENT OPTICAL COMMUNICATION." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2893.

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Coherent detection with subsequent digital signal processing (DSP) is developed, analyzed theoretically and numerically and experimentally demonstrated in various fiber‐optic transmission scenarios. The use of DSP in conjunction with coherent detection unleashes the benefits of coherent detection which rely on the preservation of full information of the incoming field. These benefits include high receiver sensitivity, the ability to achieve high spectral‐efficiency and the use of advanced modulation formats. With the immense advancements in DSP speeds, many of the problems hindering the use of coherent detection in optical transmission systems have been eliminated. Most notably, DSP alleviates the need for hardware phase‐locking and polarization tracking, which can now be achieved in the digital domain. The complexity previously associated with coherent detection is hence significantly diminished and coherent detection is once again considered a feasible detection alternative. In this thesis, several aspects of coherent detection (with or without subsequent DSP) are addressed. Coherent detection is presented as a means to extend the dispersion limit of a duobinary signal using an analog decision‐directed phase‐lock loop. Analytical bit‐error ratio estimation for quadrature phase‐shift keying signals is derived. To validate the promise for high spectral efficiency, the orthogonal‐wavelength‐division multiplexing scheme is suggested. In this scheme the WDM channels are spaced at the symbol rate, thus achieving the spectral efficiency limit. Theory, simulation and experimental results demonstrate the feasibility of this approach. Infinite impulse response filtering is shown to be an efficient alternative to finite impulse response filtering for chromatic dispersion compensation. Theory, design considerations, simulation and experimental results relating to this topic are presented. Interaction between fiber dispersion and nonlinearity remains the last major challenge deterministic effects pose for long‐haul optical data transmission. Experimental results which demonstrate the possibility to digitally mitigate both dispersion and nonlinearity are presented. Impairment compensation is achieved using backward propagation by implementing the split‐step method. Efficient realizations of the dispersion compensation operator used in this implementation are considered. Infinite‐impulse response and wavelet‐based filtering are both investigated as a means to reduce the required computational load associated with signal backward‐propagation. Possible future research directions conclude this dissertation.
Ph.D.
Optics and Photonics
Optics and Photonics
Optics PhD
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Erk, Patrick P. (Patrick Peter). "Digital signal processing techniques for laser-doppler anemometry." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/43026.

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Okullo-Oballa, Thomas Samuel. "Systolic realization of multirate digital filters." Thesis, [Hong Kong] : University of Hong Kong, 1988. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12433998.

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Lei, Chi-un, and 李志遠. "VLSI macromodeling and signal integrity analysis via digital signal processing techniques." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45700588.

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Books on the topic "Signal processing Digital techniques"

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1943-, Mullis Clifford T., ed. Digital signal processing. Reading, Mass: Addison-Wesley, 1987.

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Maeda, Wataru. Digital signal processing. Englewood Cliffs, N.J: Prentice Hall, 1993.

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

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Lyons, Richard G. Understanding digital signal processing. Reading, Mass: Addison Wesley Pub. Co., 1997.

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Lyons, Richard G. Understanding digital signal processing. 2nd ed. Upper Saddle River, NJ: Prentice Hall PIR, 2004.

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V, Madisetti, ed. Digital signal processing fundamentals. Boca Raton, FL: CRC Press, 2009.

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Kunt, M. Digital signal processing. Norwood, MA: Artech House, 1986.

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Kunt, M. Digital signal processing. Norwood, MA: Artech House, 1986.

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Madisetti, V. Digital signal processing fundamentals. 2nd ed. Boca Raton, FL: CRC Press, 2010.

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V, Madisetti, ed. Digital signal processing fundamentals. Boca Raton, FL: CRC Press, 2009.

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Book chapters on the topic "Signal processing Digital techniques"

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Simmer, K. Uwe, Joerg Bitzer, and Claude Marro. "Post-Filtering Techniques." In Digital Signal Processing, 39–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04619-7_3.

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Jackson, Leland B. "FIR Filter Design Techniques." In Digital Filters and Signal Processing, 289–321. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2458-5_9.

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Jackson, Leland B. "FIR Filter Design Techniques." In Digital Filters and Signal Processing, 223–48. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4615-3262-0_9.

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Ennser, Karin, Slavisa Aleksic, Franco Curti, D. M. Forin, Michael Galili, M. Karasek, L. K. Oxenløwe, et al. "Optical Signal Processing Techniques for Signal Regeneration and Digital Logic." In Towards Digital Optical Networks, 49–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01524-3_4.

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Purkayastha, Basab Bijoy, and Kandarpa Kumar Sarma. "Modulation Techniques and Signal Processing." In A Digital Phase Locked Loop based Signal and Symbol Recovery System for Wireless Channel, 49–87. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2041-1_3.

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Rawat, Karun, Patrick Roblin, and Shiban Kishen Koul. "Digital Techniques for Broadband and Linearized Transmitters." In Analog Circuits and Signal Processing, 301–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38866-9_5.

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García-Mateo, C., and D. Docampo-Amoedo. "Modeling Techniques for Speech Coding: A Selected Survey." In Digital Signal Processing in Telecommunications, 1–43. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1019-4_1.

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Karrenberg, Ulrich. "Digital Transmission Techniques III: Modulation." In An Interactive Multimedia Introduction to Signal Processing, 371–410. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04949-5_14.

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Wölfel, Matthias. "From Signals to Speech Features by Digital Signal Processing." In Techniques for Noise Robustness in Automatic Speech Recognition, 159–92. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118392683.ch7.

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Aguado Bayón, L. E., and P. G. Farrell. "Effective Trellis Decoding Techniques for Block Codes." In Digital Signal Processing for Communication Systems, 37–44. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6119-4_5.

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Conference papers on the topic "Signal processing Digital techniques"

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Kyprianou, Ross, Peter Schachte, and Bill Moran. "Dauphin: A Signal Processing Language - Statistical Signal Processing Made Easy." In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2015. http://dx.doi.org/10.1109/dicta.2015.7371250.

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Kavanagh. "Signal processing techniques for improved digital tachometry." In Proceedings of the IEEE International Symposium on Industrial Electronics ISIE-02. IEEE, 2002. http://dx.doi.org/10.1109/isie.2002.1026342.

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Cote, Daniel R., and Jeanne Lazo-Wasem. "Enhancement Of Optical Registration Signals Through Digital Signal Processing Techniques." In 1988 Microlithography Conferences, edited by Kevin M. Monahan. SPIE, 1988. http://dx.doi.org/10.1117/12.968367.

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Phukan, Ripunjoy, and Subrata Karmakar. "Acoustic Partial Discharge signal analysis using digital signal processing techniques." In 2013 Annual IEEE India Conference (INDICON). IEEE, 2013. http://dx.doi.org/10.1109/indcon.2013.6725936.

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"Digital signal processing techniques for HPGe detectors operation." In 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC). IEEE, 2013. http://dx.doi.org/10.1109/nssmic.2013.6829741.

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Zhongqi Pan, Junyi Wang, and Yi Weng. "Digital signal processing techniques in Nyquist-WDM transmission systems." In 2015 14th International Conference on Optical Communications and Networks (ICOCN). IEEE, 2015. http://dx.doi.org/10.1109/icocn.2015.7203653.

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Tripp, John, and Ping Tcheng. "Aerodynamic stability test instrumentation using digital signal processing techniques." In 25th Plasmadynamics and Lasers Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-2583.

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Haque, Yusuf. "Analog vs Digital Signal Processing (DSP) Techniques for Video." In 1988 IEEE International Solid-State Circuits Conference, 1988 ISSCC. Digest of Technical Papers. IEEE, 1988. http://dx.doi.org/10.1109/isscc.1988.4638697.

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Rady, Radwa Magdy, Ibrahim Mohamed El Akkary, Ahmed Nashaat Haroun, Nader Abd Elmoneum Fasseh, and Mohamed Moustafa Azmy. "Respiratory Wheeze Sound Analysis Using Digital Signal Processing Techniques." In 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN). IEEE, 2015. http://dx.doi.org/10.1109/cicsyn.2015.38.

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Kia, Shahin Hedayati, Humberto Henao, and Gerard-Andre Capolino. "Some digital signal processing techniques for induction machines diagnosis." In 2011 8th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives - (SDEMPED 2011). IEEE, 2011. http://dx.doi.org/10.1109/demped.2011.6063643.

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Reports on the topic "Signal processing Digital techniques"

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Beasley, Joseph N. Digital Signal Processing Techniques for Positioning of Off-Axis Solar Concentrators. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada410937.

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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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Kassam, Saleem A. Statistical Techniques for Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1986. http://dx.doi.org/10.21236/ada185774.

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Kassam, Saleem A. Statistical Techniques for Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada262731.

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Kassam, S. A. Statistical Techniques for Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, May 1985. http://dx.doi.org/10.21236/ada167164.

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Roberts, Richard A. VLSI Implementations for Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada189612.

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Friedlander, Benjamin. Parametric Techniques for Multichannel Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada165649.

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Cunningham, M., and F. Dowla. A Comparison of Digital Signal Extraction Techniques. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/15011424.

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Chung, Y., L. Emery, and J. Kirchman. Digital signal processing for beam position feedback. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/90669.

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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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