Relevant bibliographies by topics / Speech processing systems. Signal processing

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

Academic literature on the topic 'Speech processing systems. Signal processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Speech processing systems. Signal processing"

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Dasarathy, Belur V. "Robust speech processing." Information Fusion 5, no. 2 (June 2004): 75. http://dx.doi.org/10.1016/j.inffus.2004.02.002.

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Delic, Vlado, Darko Pekar, Radovan Obradovic, and Milan Secujski. "Speech signal processing in ASR&TTS algorithms." Facta universitatis - series: Electronics and Energetics 16, no. 3 (2003): 355–64. http://dx.doi.org/10.2298/fuee0303355d.

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Speech signal processing and modeling in systems for continuous speech recognition and Text-to-Speech synthesis in Serbian language are described in this paper. Both systems are fully developed by the authors and do not use any third party software. Accuracy of the speech recognizer and intelligibility of the TTS system are in the range of the best solutions in the world, and all conditions are met for commercial use of these solutions.
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de Abreu, Caio Cesar Enside, Marco Aparecido Queiroz Duarte, Bruno Rodrigues de Oliveira, Jozue Vieira Filho, and Francisco Villarreal. "Regression-Based Noise Modeling for Speech Signal Processing." Fluctuation and Noise Letters 20, no. 03 (January 30, 2021): 2150022. http://dx.doi.org/10.1142/s021947752150022x.

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Speech processing systems are very important in different applications involving speech and voice quality such as automatic speech recognition, forensic phonetics and speech enhancement, among others. In most of them, the acoustic environmental noise is added to the original signal, decreasing the signal-to-noise ratio (SNR) and the speech quality by consequence. Therefore, estimating noise is one of the most important steps in speech processing whether to reduce it before processing or to design robust algorithms. In this paper, a new approach to estimate noise from speech signals is presented and its effectiveness is tested in the speech enhancement context. For this purpose, partial least squares (PLS) regression is used to model the acoustic environment (AE) and a Wiener filter based on a priori SNR estimation is implemented to evaluate the proposed approach. Six noise types are used to create seven acoustically modeled noises. The basic idea is to consider the AE model to identify the noise type and estimate its power to be used in a speech processing system. Speech signals processed using the proposed method and classical noise estimators are evaluated through objective measures. Results show that the proposed method produces better speech quality than state-of-the-art noise estimators, enabling it to be used in real-time applications in the field of robotic, telecommunications and acoustic analysis.
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Hu, J., C. C. Cheng, and W. H. Liu. "Processing of speech signals using a microphone array for intelligent robots." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 219, no. 2 (March 1, 2005): 133–43. http://dx.doi.org/10.1243/095965105x9461.

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For intelligent robots to interact with people, an efficient human-robot communication interface is very important (e.g. voice command). However, recognizing voice command or speech represents only part of speech communication. The physics of speech signals includes other information, such as speaker direction. Secondly, a basic element of processing the speech signal is recognition at the acoustic level. However, the performance of recognition depends greatly on the reception. In a noisy environment, the success rate can be very poor. As a result, prior to speech recognition, it is important to process the speech signals to extract the needed content while rejecting others (such as background noise). This paper presents a speech purification system for robots to improve the signal-to-noise ratio of reception and an algorithm with a multidirection calibration beamformer.
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M Tasbolatov, N. Mekebayev, O. Mamyrbayev, M. Turdalyuly, D. Oralbekova,. "Algorithms and architectures of speech recognition systems." Psychology and Education Journal 58, no. 2 (February 20, 2021): 6497–501. http://dx.doi.org/10.17762/pae.v58i2.3182.

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Digital processing of speech signal and the voice recognition algorithm is very important for fast and accurate automatic scoring of the recognition technology. A voice is a signal of infinite information. The direct analysis and synthesis of a complex speech signal is due to the fact that the information is contained in the signal. Speech is the most natural way of communicating people. The task of speech recognition is to convert speech into a sequence of words using a computer program. This article presents an algorithm of extracting MFCC for speech recognition. The MFCC algorithm reduces the processing power by 53% compared to the conventional algorithm. Automatic speech recognition using Matlab.
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Zheng, Jian, and Tian De Gao. "A Dual-DSP Sonobuoy Signal Processing System." Applied Mechanics and Materials 571-572 (June 2014): 873–77. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.873.

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Sonobuoy is used as aviation antisubmarine device to detect submarines, and its wireless communication mechanism would introduce radio interference. The speech signal needs to be identified from the submarine noise in order to facilitate sonar signal processing system to do further processing of the signal. This paper presents a TS-201 based dual-DSP sonobuoy signal processing system, and proposes an algorithm using Cubic Spline Interpolation and Pearson correlation coefficient to identify the speech signal from submarine radiated noise signal. This article describes the specific signal processing algorithm of the system, the hardware and software design of the system. This article uses a large number of data from experiments to test the hardware and software systems separately. The results of tests are analyzed, which indicate that the system function well in identifying speech signal from submarine radiated noise signal to, with a percentage of 98% correct rate.
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Hills, A., and K. Scott. "Perceived degradation effects in packet speech systems." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 5 (May 1987): 699–701. http://dx.doi.org/10.1109/tassp.1987.1165187.

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Chen, Tsuhan. "Video signal processing systems and methods utilizing automated speech analysis." Journal of the Acoustical Society of America 112, no. 2 (2002): 368. http://dx.doi.org/10.1121/1.1507005.

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Varga, A., and F. Fallside. "A technique for using multipulse linear predictive speech synthesis in text-to-speech type systems." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 4 (April 1987): 586–87. http://dx.doi.org/10.1109/tassp.1987.1165151.

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LÉVY-VÉHEL, JACQUES. "FRACTAL APPROACHES IN SIGNAL PROCESSING." Fractals 03, no. 04 (December 1995): 755–75. http://dx.doi.org/10.1142/s0218348x95000679.

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Some recent advances in the application of fractal tools for studying complex signals are presented. The first part of the paper is devoted to a brief description of the theoretical methods used. These essentially consist of generalizations of previous techniques that allow us to efficiently handle real signals. We present some results dealing with the multifractal analysis of sequences of Choquet capacities, and the possibility of constructing such capacities with prescribed spectrum. Related results concerning the pointwise irregularity of a continuous function at each point are given in the frame of iterated functions systems. Finally, some results on a particular stochastic process are sketched: the multifractional Brownian motion, which is a generalization of the classical fractional Brownian motion, where the parameter H is replaced by a function. The second part consists of the description of selected applications of current interest, in the fields of image analysis, speech synthesis and road traffic modeling. In each case we try to show how a fractal approach provides new means to solve specific problems in signal processing, sometimes with greater success than classical methods.
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Dissertations / Theses on the topic "Speech processing systems. Signal processing"

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Morris, Robert W. "Enhancement and recognition of whispered speech." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180338/unrestricted/morris%5frobert%5fw%5f200312%5fphd.pdf.

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Little, M. A. "Biomechanically informed nonlinear speech signal processing." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:6f5b84fb-ab0b-42e1-9ac2-5f6acc9c5b80.

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Linear digital signal processing based around linear, time-invariant systems theory finds substantial application in speech processing. The linear acoustic source-filter theory of speech production provides ready biomechanical justification for using linear techniques. Nonetheless, biomechanical studies surveyed in this thesis display significant nonlinearity and non-Gaussinity, casting doubt on the linear model of speech production. In order therefore to test the appropriateness of linear systems assumptions for speech production, surrogate data techniques can be used. This study uncovers systematic flaws in the design and use of exiting surrogate data techniques, and, by making novel improvements, develops a more reliable technique. Collating the largest set of speech signals to-date compatible with this new technique, this study next demonstrates that the linear assumptions are not appropriate for all speech signals. Detailed analysis shows that while vowel production from healthy subjects cannot be explained within the linear assumptions, consonants can. Linear assumptions also fail for most vowel production by pathological subjects with voice disorders. Combining this new empirical evidence with information from biomechanical studies concludes that the most parsimonious model for speech production, explaining all these findings in one unified set of mathematical assumptions, is a stochastic nonlinear, non-Gaussian model, which subsumes both Gaussian linear and deterministic nonlinear models. As a case study, to demonstrate the engineering value of nonlinear signal processing techniques based upon the proposed biomechanically-informed, unified model, the study investigates the biomedical engineering application of disordered voice measurement. A new state space recurrence measure is devised and combined with an existing measure of the fractal scaling properties of stochastic signals. Using a simple pattern classifier these two measures outperform all combinations of linear methods for the detection of voice disorders on a large database of pathological and healthy vowels, making explicit the effectiveness of such biomechanically-informed, nonlinear signal processing techniques.
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Coetzee, H. J. "The development of a new objective speech quality measure for speech coding applications." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/15474.

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Anderson, David Verl. "Audio signal enhancement using multi-resolution sinusoidal modeling." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15394.

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Kale, Kaustubh R. "Low complexity, narrow baseline beamformer for hand-held devices." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0001223.

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Hild, Kenneth E. "Blind separation of convolutive mixtures using Renyi's divergence." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0002387.

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Sukittanon, Somsak. "Modulation scale analysis : theory and application for nonstationary signal classification /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5875.

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Ikram, Muhammad Zubair. "Multichannel blind separation of speech signals in a reverberant environment." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/15023.

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Smith, Daniel. "An analysis of blind signal separation for real time application." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20070815.152400/index.html.

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Otterson, Scott. "Use of speaker location features in meeting diarization /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/15463.

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Books on the topic "Speech processing systems. Signal processing"

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Signal processing of speech. Basingstoke, [England]: Macmillan, 1993.

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Saitō, Shūzō. Fundamentals of speech signal processing. New York: Academic Press, 1985.

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Kazuo, Nakata, ed. Fundamentals of speech signal processing. New York: Academic Press, 1985.

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Signal processing, speech, and music. Lisse [Netherlands]: Swets & Zeitlinger Publishers, 1996.

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Nelson, Morgan, ed. Speech and audio signal processing: Processing and perception of speech and music. New York: John Wiley, 2000.

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Gold, Bernard. Speech and audio signal processing: Processing and perception of speech and music. 2nd ed. Hoboken, N.J: Wiley, 2011.

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Benesty, Jacob. Microphone array signal processing. Berlin: Springer, 2008.

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Discrete-time speech signal processing: Principles and practice. Upper Saddle River, NJ: Prentice Hall, 2002.

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Bhattacharyya, Shuvra S. Handbook of Signal Processing Systems. 2nd ed. New York, NY: Springer New York, 2013.

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Deller, John R. Discrete-time processing of speech signals. New York: Institute of Electrical and Electronics Engineers, 2000.

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Book chapters on the topic "Speech processing systems. Signal processing"

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Frerking, Marvin E. "Speech Processing." In Digital Signal Processing in Communication Systems, 490–547. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-4990-8_9.

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Sinha, Priyabrata. "Signal Processing Fundamentals." In Speech Processing in Embedded Systems, 9–36. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_2.

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Man, K. F., K. S. Tang, and S. Kwong. "Genetic Algorithms in Speech Recognition Systems." In Advanced Textbooks in Control and Signal Processing, 199–257. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0577-0_8.

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Gierlich, H. W. "Methods of Determining the Communicational Quality of Speech Transmission Systems." In Handbook of Signal Processing in Acoustics, 831–52. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_44.

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Shanbhag, Naresh, Andrew Singer, and Hyeon-min Bae. "Signal Processing for High-Speed Links." In Handbook of Signal Processing Systems, 69–101. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6345-1_4.

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Shanbhag, Naresh, Andrew Singer, and Hyeon-Min Bae. "Signal Processing for High-Speed Links." In Handbook of Signal Processing Systems, 315–48. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6859-2_11.

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Kamaruddin, Norhaslinda, Abdul Wahab, and Hüseyin Abut. "Driver Emotion Profiling from Speech." In Digital Signal Processing for In-Vehicle Systems and Safety, 21–29. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9607-7_2.

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Krishnamurthy, Nitish, Rosarita Lubag, and John H. L. Hansen. "In-Vehicle Speech and Noise Corpora." In Digital Signal Processing for In-Vehicle Systems and Safety, 145–57. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9607-7_9.

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Kleinschmidt, Tristan, Sridha Sridharan, and Michael Mason. "A Likelihood-Maximizing Framework for Enhanced In-Car Speech Recognition Based on Speech Dialog System Interaction." In Digital Signal Processing for In-Vehicle Systems and Safety, 159–74. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9607-7_10.

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Kim, Lae-Hoon, and Mark Hasegawa-Johnson. "Optimal Multi-Microphone Speech Enhancement in Cars." In Digital Signal Processing for In-Vehicle Systems and Safety, 195–204. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9607-7_13.

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Conference papers on the topic "Speech processing systems. Signal processing"

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"Track B: Image, signal, speech processing." In 2016 2nd International Conference on Communication, Control & Intelligent Systems (CCIS). IEEE, 2016. http://dx.doi.org/10.1109/ccintels.2016.7878198.

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Leh Luoh, Yu-Zhe Su, and Chih-Fan Hsu. "Speech signal processing based emotion recognition." In 2010 International Conference on System Science and Engineering (ICSSE). IEEE, 2010. http://dx.doi.org/10.1109/icsse.2010.5551812.

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"Session MP6b: Speech signal processing and health applications." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869162.

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Rakhashia, Nikhar P., Ankit A. Bhurane, and Vikram M. Gadre. "Teaching Signals and Systems - A First Course in Signal Processing." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054231.

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"Session WA2b: Speech processing." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335704.

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"Track C image, signal, speech processing and communication systems." In 2015 Communication, Control and Intelligent Systems (CCIS). IEEE, 2015. http://dx.doi.org/10.1109/ccintels.2015.7437895.

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Pribic, Radmila, and Ioannis Kyriakides. "Design of sparse-signal processing in radar systems." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854555.

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Blouw, Peter, and Chris Eliasmith. "Event-Driven Signal Processing with Neuromorphic Computing Systems." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053043.

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"BIO-INSPIRED AUDITORY PROCESSING FOR SPEECH FEATURE ENHANCEMENT." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003145800510058.

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"Session WA8b1 Speech Processing." In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. IEEE, 2004. http://dx.doi.org/10.1109/acssc.2004.1399566.

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Reports on the topic "Speech processing systems. Signal processing"

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Liu, Fu-Hua, Pedro J. Moreno, Richard M. Stern, and Alejandro Acero. Signal Processing for Robust Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada457798.

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Vaidyanathan, P. P. Results on Cyclic Signal Processing Systems,. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada349620.

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Vaidyanathan, P. P. Cyclic LTI Systems in Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1998. http://dx.doi.org/10.21236/ada349622.

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Lee, Edward A. Design of Parallel Systems for Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada275490.

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Poor, H. V. Advanced Signal Processing for Multiple Access Communications Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada417402.

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Fainman, Yeshaiahu, and Paul E. Shames. Optoelectronic Systems for Space-Variant Signal and Image Processing. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada358424.

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Rhody, Harvey, David Sher, and James Modestino. Intelligent Signal Processing Techniques for Multi-Sensor Surveillance Systems. Fort Belvoir, VA: Defense Technical Information Center, December 1989. http://dx.doi.org/10.21236/ada218890.

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Cathey, W. T., Gregory Johnson, Sara B. Tucker, and Hans B. Wach. Hybrid Image Acquisition and Signal Processing Systems for High Resolution. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada384424.

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Carin, Lawrence, Nilanjan Dasgupta, and Levi Kennedy. Optimal Sensor Management and Signal Processing for New EMI Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada534216.

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Bilgutay, Nihat M. Computer Facilities for High-Speed Data Acquisition, Signal Processing and Large Scale System Simulation. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada170935.

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