Bibliografías temáticas / Signal processing Data processing

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Literatura académica sobre el tema "Signal processing Data processing"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 29 de enero de 2023

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Artículos de revistas sobre el tema "Signal processing Data processing"

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 exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

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Shelishiyah, R., M. Bharani Dharan, T. Kishore Kumar, R. Musaraf, and Thiyam Deepa Beeta. "Signal Processing for Hybrid BCI Signals." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2318/1/012007.

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Abstract The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.

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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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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 necessary for incoherent scatter radar signal reception and processing as part of the latest version of the Millstone Hill Data Acquisition System (MIDAS-W). Several hardware realizations with varying capabilities have been created, and these have been used successfully with different radars. We discuss the signal processing components in detail, describe the software patterns in which they are used, and show example data from the Millstone Hill, EISCAT Svalbard, and SOUSY Svalbard radars.

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Tesis sobre el tema "Signal processing Data processing"

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 spectrum, and the use of a parabolic interpolation of integrated signals as a method for estimating THz pulse delay. It is shown that our system can achieve a signal-to-noise ratio of 60 dB in spectrometry tests and 47 dB in tomography tests. Styrofoam phantoms of different shapes and up to 50x60 mm is size are used for analysis. Tomographic images are reconstructed at different frequencies from 0.2 THz to 2.5 THz, showing that volume scattering and edge contrast increase with wavelength. Evidence is given that refractive losses and surface scattering are responsible of high edge contrast in THz tomography images reconstructed in amplitude contrast. A modified Rayleigh roughness factor is proposed to model surface transmission scattering. It is also shown that volume scattering can be modelled by the material’s attenuation coefficient. The use of 4 mm apertures as spatial filters is compared against full beam imaging, and the limitations of Raleigh range are also addressed. It was estimated that for some frequencies between 0.5 THz and 1 THz the Rayleigh range is enough for the tested phantoms. Results on the influence of attenuation and scattering at different THz frequencies can be applied to the development of THz CW imaging systems and as a point of departure for the development of more complex scattering models.

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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 filtering. Wavelets have already been proven successful in these tasks. On the other hand, a choice of discrete filter which converges to a continuous basis function under iteration permits efficient and accurate function representation by providing a “bridge” from the discrete to the continuous. A function representation method capable of both multiresolution analysis and accurate reconstruction of the underlying measured function would make a valuable tool for scientific visualisation. The aim of this dissertation is not to try to outperform existing filters designed specifically for reconstruction of sampled functions. The goal is to design a wavelet filter family which, while retaining properties necessary to preform multiresolution analysis, possesses features to enable the wavelets to be used as efficient and accurate “building blocks” for function representation. The application to visualisation is used as a means of practical demonstration of the results. Wavelet and visualisation filter design is analysed in the first part of this dissertation and a list of wavelet filter design criteria for visualisation is collated. Candidate wavelet filters are constructed based on a parameter space search of the BC-spline family and direct solution of equations describing filter properties. Further, a biorthogonal wavelet filter family is constructed based on point and average interpolating subdivision and using the lifting scheme. The main feature of these filters is their ability to reconstruct arbitrary degree piecewise polynomial functions and their derivatives using measured samples as direct input into a wavelet transform. The lifting scheme provides an intuitive, interval-adapted, time-domain filter and transform construction method. A generalised factorisation for arbitrary primal and dual order point and average interpolating filters is a result of the lifting construction. The proposed visualisation filter family is analysed quantitatively and qualitatively in the final part of the dissertation. Results from wavelet theory are used in the analysis which allow comparisons among wavelet filter families and between wavelets and filters designed specifically for reconstruction for visualisation. Lastly, the performance of the constructed wavelet filters is demonstrated in the visualisation context. One-dimensional signals are used to illustrate reconstruction performance of the wavelet filter family from noiseless and noisy samples in comparison to other wavelet filters and dedicated visualisation filters. The proposed wavelet filters converge to basis functions capable of reproducing functions that can be represented locally by arbitrary order piecewise polynomials. They are interpolating, smooth and provide asymptotically optimal reconstruction in the case when samples are used directly as wavelet coefficients. The reconstruction performance of the proposed wavelet filter family approaches that of continuous spatial domain filters designed specifically for reconstruction for visualisation. This is achieved in addition to retaining multiresolution analysis and processing properties of wavelets.

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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 computed spectrum to ground through centralised baseband telemetry system. The analysis techniques employed are power spectral density (PSD) computations using Fast Fourier Transform (FFT) and 1/3rd octave analysis using digital Infinite Impulse Response (IIR) filters. The programmability of all analysis parameters is achieved using EEPROM. This paper discusses the details of measurement and analysis techniques, design philosophy, tools used and implementation schemes. The paper also presents the performance results of flight models.

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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 processing algorithms for robust clustering of micro array gene expression samples. The main aim of the research is to design and validate novel genomic signal processing methodologies for micro array data analysis based on different DSP methods. More specifically, clustering algorithms based on Linear prediction coding, Wavelet decomposition and Fractal dimension methods combined with Vector quantisation algorithm are applied and compared on a set of test microarray datasets. These techniques take as an input microarray gene expression samples and produce predictive coefficients arrays associated to the microarray data that are quantised in discrete levels, and consequently used for sample clustering. A variety of standard micro array datasets are used in this work to validate the robustness of these methods compared to conventional methods. Two well known validation approaches, i.e. Silhouette and Davies Bouldin index methods, are applied to evaluate internally and externally the genomic signal processing clustering results. In conclusion, the results demonstrate that genomic signal processing based methods outperform traditional methods by providing more clustering accuracy. Moreover, the study shows that the local features of the gene expression signals are better clustered using wavelets compared to the other DSP methods.

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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 links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed.

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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 DAUs for data reduction and third-party algorithm development. Once data is being written to and from powerful processing modules an obvious next step is networking and decom-less access to data. This paper discusses some of the issues related to these types of processing.

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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, from signals residing on regular lattices, which have been studied by the classical signal processing theory, to data residing on general graphs, which are called graph signals. In this thesis, we consider five fundamental tasks on graphs from the perspective of graph signal processing: representation, sampling, recovery, detection and localization. Representation, aiming to concisely model shapes of graph signals, is at the heart of the proposed techniques. Sampling followed by recovery, aiming to reconstruct an original graph signal from a few selected samples, is applicable in semi-supervised learning and user profiling in online social networks. Detection followed by localization, aiming to identify and localize targeted patterns in noisy graph signals, is related to many real-world applications, such as localizing virus attacks in cyber-physical systems, localizing stimuli in brain connectivity networks, and mining traffic events in city street networks, to name just a few. We illustrate the power of the proposed tools on two real-world problems: fast resampling of 3D point clouds and mining of urban traffic data.

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Libros sobre el tema "Signal processing Data processing"

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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Capítulos de libros sobre el tema "Signal processing Data processing"

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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Actas de conferencias sobre el tema "Signal processing Data processing"

"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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Informes sobre el tema "Signal processing Data processing"

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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