Relevant bibliographies by topics / Signal processing Data processing

Contents

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

Academic literature on the topic 'Signal processing Data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Signal processing Data processing"

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Stevens, N. "Processing of sar data: fundamentals, signal processing, interferometry." Photogrammetric Record 19, no. 108 (December 2004): 419–20. http://dx.doi.org/10.1111/j.0031-868x.2004.295_5.x.

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Xing, Mengdao, Zhong Lu, and Hanwen Yu. "InSAR Signal and Data Processing." Sensors 20, no. 13 (July 7, 2020): 3801. http://dx.doi.org/10.3390/s20133801.

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I. S. Amiri, I. S. Amiri, and J. Ali J. Ali. "Data signal processing via manchester coding-decoding method using chaotic signals generated by PANDA ring resonator." Chinese Optics Letters 11, no. 4 (2013): 041901–41904. http://dx.doi.org/10.3788/col201311.041901.

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Wei, Bo, Kai Li, Chengwen Luo, Weitao Xu, Jin Zhang, and Kuan Zhang. "No Need of Data Pre-processing." ACM Transactions on Internet of Things 2, no. 4 (November 30, 2021): 1–26. http://dx.doi.org/10.1145/3467980.

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Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and
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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 (
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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 Y
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Berroth, M., V. Hurm, M. Lang, Z. Lao, A. Thiede, Z. G. Wang, A. Bangert, et al. "Hemt circuits for signal/data processing." Solid-State Electronics 41, no. 10 (October 1997): 1407–12. http://dx.doi.org/10.1016/s0038-1101(97)00083-x.

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Chakrabarti, Satyananda, Donald E. Shaw, Dale E. Stephenson, and B. V. K. Vijaya Kumar. "Digital Signal Processing of Geotechnical Data." Journal of Engineering Mechanics 112, no. 1 (January 1986): 70–83. http://dx.doi.org/10.1061/(asce)0733-9399(1986)112:1(70).

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Müller-Trapet, Markus, and Michael Vorländer. "Signal processing for hemispherical measurement data." Journal of the Acoustical Society of America 133, no. 5 (May 2013): 3525. http://dx.doi.org/10.1121/1.4806341.

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Grydeland, T., F. D. Lind, P. J. Erickson, and J. M. Holt. "Software Radar signal processing." Annales Geophysicae 23, no. 1 (January 31, 2005): 109–21. http://dx.doi.org/10.5194/angeo-23-109-2005.

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Abstract. Software infrastructure is a growing part of modern radio science systems. As part of developing a generic infrastructure for implementing Software Radar systems, we have developed a set of reusable signal processing components. These components are generic software-based implementations for use on general purpose computing systems. The components allow for the implementation of signal processing chains for radio frequency signal reception, correlation-based data processing, and cross-correlation-based interferometry. The components have been used to implement the signal processing n
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Dissertations / Theses on the topic "Signal processing Data processing"

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Bañuelos, Saucedo Miguel Angel. "Signal and data processing for THz imaging." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/signal-and-data-processing-for-thz-imaging(58a646f3-033b-4771-b1dc-d1f9fc6dfbf0).html.

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This thesis presents the research made on signal and data processing for THz imaging, with emphasis in noise analysis and tomography in amplitude contrast using a THz time-domain spectrometry system. A THz computerized tomography system was built, tested and characterized. The system is controlled from a personal computer using a program developed ad hoc. Detail is given on the operating principles of the system’s numerous optical and THz components, the design of a computer-based fast lock-in amplifier, the proposal of a local apodization method for reducing spurious oscillations in a THz spe
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Varnavas, Andreas Soteriou. "Signal processing methods for EEG data classification." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/11943.

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Ma, Ding. "Miniature data acquisition system for multi-channel sensor arrays." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Thesis/Spring2010/d_ma_042610.pdf.

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Thesis (M.S. in electrical engineering)--Washington State University, May 2010.<br>Title from PDF title page (viewed on July 23, 2010). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 55-57).
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Cena, Bernard Maria. "Reconstruction for visualisation of discrete data fields using wavelet signal processing." University of Western Australia. Dept. of Computer Science, 2000. http://theses.library.uwa.edu.au/adt-WU2003.0014.

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The reconstruction of a function and its derivative from a set of measured samples is a fundamental operation in visualisation. Multiresolution techniques, such as wavelet signal processing, are instrumental in improving the performance and algorithm design for data analysis, filtering and processing. This dissertation explores the possibilities of combining traditional multiresolution analysis and processing features of wavelets with the design of appropriate filters for reconstruction of sampled data. On the one hand, a multiresolution system allows data feature detection, analysis and fil
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T, N. Santhosh Kumar, K. Abdul Samad A, and M. Sarojini K. "DSP BASED SIGNAL PROCESSING UNIT FOR REAL TIME PROCESSING OF VIBRATION AND ACOUSTIC SIGNALS OF SATELLITE LAUNCH VEHICLES." International Foundation for Telemetering, 1995. http://hdl.handle.net/10150/608530.

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International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada<br>Measurement of vibration and acoustic signals at various locations in the launch vehicle is important to establish the vibration and acoustic environment encountered by the launch vehicle during flight. The vibration and acoustic signals are wideband and require very large telemetry bandwidth if directly transmitted to ground. The DSP based Signal Processing Unit is designed to measure and analyse acoustic and vibration signals onboard the launch vehicle and transmit the
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Roberts, G. "Some aspects seismic signal processing and analysis." Thesis, Bangor University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379692.

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Sungoor, Ala M. H. "Genomic signal processing for enhanced microarray data clustering." Thesis, Kingston University, 2009. http://eprints.kingston.ac.uk/20310/.

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Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies for enhanced genetic data analysis. Microarray is a well known technology for the evaluation of thousands of gene expression profiles. By considering these profiles as digital signals, the power of DSP methods can be applied to produce robust and unsupervised clustering of microarray samples. This can be achieved by transferring expression profiles into spectral components which are interpreted as a measure of profile similarity. This thesis introduces enhanced signal process
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Hloupis, Georgios. "Seismological data acquisition and signal processing using wavelets." Thesis, Brunel University, 2009. http://bura.brunel.ac.uk/handle/2438/3470.

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This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite lin
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Kolb, John. "SIGNAL PROCESSING ABOUT A DISTRIBUTED DATA ACQUISITION SYSTEM." International Foundation for Telemetering, 2002. http://hdl.handle.net/10150/605610.

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International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California<br>Because modern data acquisition systems use digital backplanes, it is logical for more and more data processing to be done in each Data Acquisition Unit (DAU) or even in each module. The processing related to an analog acquisition module typically takes the form of digital signal conditioning for range adjust, linearization and filtering. Some of the advantages of this are discussed in this paper. The next stage is powerful processing boards within DA
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Chen, Siheng. "Data Science with Graphs: A Signal Processing Perspective." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/724.

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A massive amount of data is being generated at an unprecedented level from a diversity of sources, including social media, internet services, biological studies, physical infrastructure monitoring and many others. The necessity of analyzing such complex data has led to the birth of an emerging framework, graph signal processing. This framework offers an unified and mathematically rigorous paradigm for the analysis of high-dimensional data with complex and irregular structure. It extends fundamental signal processing concepts such as signals, Fourier transform, frequency response and filtering,
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Books on the topic "Signal processing Data processing"

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L, Horner Joseph, ed. Optical signal processing. San Diego: Academic Press, 1987.

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Underwater signal and data processing. Boca Raton, Fla: CRC Press, 1989.

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Technologies, Signal Processing. Signal processing technologies: Data book. Colorado Springs: Signal Processing Technologies, 1993.

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Optical signal processing: Fundamentals. Berlin: Springer-Verlag, 1991.

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Naidu, Prabhakar S. Sensor array signal processing. 2nd ed. Boca Raton: CRC Press, 2009.

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Sensor array signal processing. Boca Raton, FL: CRC Press, 2001.

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Das, Pankaj K. Optical Signal Processing: Fundamentals. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991.

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VanderLugt, Anthony. Optical signal processing. New York: Wiley, 1992.

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Technologies, Signal Processing. Signal Processing Technologies 1995 data book. Colorado Springs: Signal Processing Technologies, 1995.

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Merchant, S. N., Krishna Warhade, and Debashis Adhikari, eds. Advances in Signal and Data Processing. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8391-9.

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Book chapters on the topic "Signal processing Data processing"

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Havskov, Jens, and Lars Ottemöller. "Signal Processing." In Routine Data Processing in Earthquake Seismology, 83–99. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8697-6_4.

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Hein, Achim. "SAR-signal processing." In Processing of SAR Data, 99–150. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09457-0_3.

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Hein, Achim. "Signal processing algorithms." In Processing of SAR Data, 151–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09457-0_4.

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Barksdale, William J. "Communications Signals and Signal Processing." In Practical Computer Data Communications, 23–71. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4684-5164-1_3.

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Quinto, Eric Todd. "Limited Data Tomography in Non-Destructive Evaluation." In Signal Processing, 347–54. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4684-7095-6_18.

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Bricogne, Gerard, and Richard Tolimieri. "Two Dimensional FFT Algorithms on Data Admitting 90°-Rotational Symmetry." In Signal Processing, 25–35. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4684-6393-4_3.

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Kundu, Debasis, and Swagata Nandi. "Real Data Example." In Statistical Signal Processing, 91–99. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0628-6_6.

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Izen, Steven H. "Inversion of the X-ray Transform from Data in a Limited Angular Range." In Signal Processing, 275–84. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4684-7095-6_13.

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Marks, Friedrich, Ursula Klingmüller, and Karin Müller-Decker. "Evolution of Cellular Data Processing." In Cellular Signal Processing, 87–140. Second edition. | New York, NY: Garland Science, 2017.: Garland Science, 2017. http://dx.doi.org/10.4324/9781315165479-3.

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Shi, Xizhi. "Data Analysis and Application Study." In Blind Signal Processing, 301–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11347-5_10.

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Conference papers on the topic "Signal processing Data processing"

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"Signal and data processing." In 2017 IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS). IEEE, 2017. http://dx.doi.org/10.1109/mrrs.2017.8075023.

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"Signal TA1b: Big data signal processing." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094612.

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"Session: Data and signal processing." In 2009 IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2009. http://dx.doi.org/10.1109/idaacs.2009.5342943.

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Nikolic, Milos, Badrish Chandramouli, and Jonathan Goldstein. "Enabling Signal Processing over Data Streams." In SIGMOD/PODS'17: International Conference on Management of Data. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3035918.3035935.

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Kharinov, Mikhail V., and Aleksandr N. Bykov. "Data Structure for Multimodal Signal Processing." In 2019 International Russian Automation Conference. IEEE, 2019. http://dx.doi.org/10.1109/rusautocon.2019.8867769.

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Holmes, C. "Signal Processing of Ultrasonic Array Data." In REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2005. http://dx.doi.org/10.1063/1.1916775.

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Müller-Trapet, Markus, and Michael Vorländer. "Signal processing for hemispherical measurement data." In ICA 2013 Montreal. ASA, 2013. http://dx.doi.org/10.1121/1.4798973.

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Tsihrintzis, George A., and Anthony J. Devaney. "Signal processing of ultrasonic tomographic data." In San Diego '92, edited by Michael A. Fiddy. SPIE, 1992. http://dx.doi.org/10.1117/12.139021.

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Durrani, Tariq S. "Big data — Instrumentation and signal processing." In 2015 IEEE 3rd International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). IEEE, 2015. http://dx.doi.org/10.1109/icsima.2015.7558996.

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Pavaloiu, Ionel-Bujorel, Ana-Maria Neagu, Cristian Mustata, Maria Iuliana Dascalu, George Dragoi, Iuliana Marin, Alexandru Dan Mitrea, and Liviu Mihail Mateescu. "DATA AND SIGNAL PROCESSING FOR BUSINESS." In 11th annual International Conference of Education, Research and Innovation. IATED, 2018. http://dx.doi.org/10.21125/iceri.2018.2443.

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Reports on the topic "Signal processing Data processing"

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Ioup, Juliette W., George E. Ioup, and Joseph S. Wheatley. Wavelet Digital Signal Processing of Undersea Acoustic Data. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada405774.

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Spina, John F. Integrated RF Sensor Signal/Data Processing Information Analysis Center (IAC). Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada401075.

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Onanian, Janice S. A Signal Processing Language for Coarse Grain Data flow Multiprocessors. Fort Belvoir, VA: Defense Technical Information Center, June 1989. http://dx.doi.org/10.21236/ada213863.

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Mullenhoff, C. Signal processing of Shiley heart valve data for fracture detection. Office of Scientific and Technical Information (OSTI), September 1993. http://dx.doi.org/10.2172/10190125.

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Mullenhoff, C. Signal processing of Shiley heart valve data for fracture detection. Office of Scientific and Technical Information (OSTI), April 1993. http://dx.doi.org/10.2172/10177267.

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Gaudiot, J. L. A multi-level data-flow architecture for signal and data processing applications. Final report. Office of Scientific and Technical Information (OSTI), September 1993. http://dx.doi.org/10.2172/10181348.

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Baraniuk, Richard G. Multi-Signal, Multi-Modal Data Acquisition and Processing Based on Compressive Sensing. Fort Belvoir, VA: Defense Technical Information Center, December 2008. http://dx.doi.org/10.21236/ada501161.

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Van Veen, Barry D. Reducing Data Dimension to Lower Signal Processing Computational Requirements and Maximize Performance. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada266606.

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Ramchandran, Kannan, and Kristofer Pister. Sensor Webs of SmartDust: Distributed Signal Processing/Data Fusion/Inferencing in Large Microsensor Arrays. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422190.

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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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