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Journal articles on the topic 'Signal processing Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

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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2

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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3

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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4

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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8

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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9

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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10

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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Courcier, Thierry, Patrick Pittet, Paul G. Charette, Vincent Aimez, and Guo Neng Lu. "BQJ Photodetector Signal Processing." Key Engineering Materials 605 (April 2014): 91–94. http://dx.doi.org/10.4028/www.scientific.net/kem.605.91.

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We propose a signal processing method for the CMOS Buried Quad Junction (BQJ) photodetector employed for multi-label fluorescence detection. It serves to quantify label components in an arbitrary mixture with improved signal-to-noise ratio. The proposed method includes least squares optimization and statistical data preprocessing based on Principal Component Analysis (PCA). The method was applied to the BQJ as well as to Buried Double Junction (BDJ) and Buried Triple Junction (BTJ) detectors. The obtained results show that BQJ case achieves best accuracy in label quantification compared to BDJ and BTJ detectors in any tested configurations. The statistical data preprocessing approach was also evaluated: 5dB SNR improvements for an example case of two-label mixture (Green-Red excitation with optical power over 28pW).

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12

Le Guernic, P., A. Benveniste, P. Bournai, and T. Gautier. "Signal--A data flow-oriented language for signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 2 (April 1986): 362–74. http://dx.doi.org/10.1109/tassp.1986.1164809.

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Amin, Farhan, Omar M. Barukab, and Gyu Sang Choi. "Big Data Analytics Using Graph Signal Processing." Computers, Materials & Continua 74, no. 1 (2023): 489–502. http://dx.doi.org/10.32604/cmc.2023.030615.

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Staszewski, W. J., and Karen M. Holford. "Wavelet Signal Processing of Acoustic Emission Data." Key Engineering Materials 204-205 (April 2001): 351–58. http://dx.doi.org/10.4028/www.scientific.net/kem.204-205.351.

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15

Changyou Guo. "Radar Signal Processing Algorithm Using Data Filter." Journal of Convergence Information Technology 6, no. 6 (June 30, 2011): 376–82. http://dx.doi.org/10.4156/jcit.vol6.issue6.38.

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16

Cull, J. P. "Signal Processing Concepts for Airborne Sirotem Data." Exploration Geophysics 22, no. 1 (March 1991): 97–100. http://dx.doi.org/10.1071/eg991097.

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Gust, Devens, Joakim Andréasson, Uwe Pischel, Thomas A. Moore, and Ana L. Moore. "Data and signal processing using photochromic molecules." Chem. Commun. 48, no. 14 (2012): 1947–57. http://dx.doi.org/10.1039/c1cc15329c.

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Conway, T., R. Conway, and S. Tosi. "Signal processing for multitrack digital data storage." IEEE Transactions on Magnetics 41, no. 4 (April 2005): 1333–39. http://dx.doi.org/10.1109/tmag.2005.845394.

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19

Gaudiot, J. L. "Data-driven multicomputers in digital signal processing." Proceedings of the IEEE 75, no. 9 (1987): 1220–34. http://dx.doi.org/10.1109/proc.1987.13875.

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20

Shtrauss, Vairis. "Digital signal processing for relaxation data conversion." Journal of Non-Crystalline Solids 351, no. 33-36 (September 2005): 2911–16. http://dx.doi.org/10.1016/j.jnoncrysol.2005.04.087.

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21

Baura, G. D. "Listen to your data [signal processing applications]." IEEE Signal Processing Magazine 21, no. 1 (January 2004): 21–25. http://dx.doi.org/10.1109/msp.2004.1267045.

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22

Tianxing, Cai, and Cai Tianfang. "Signal Processing of High-Noisy Chaotic Data." Physica Scripta 61, no. 1 (January 1, 2000): 46–48. http://dx.doi.org/10.1238/physica.regular.061a00046.

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23

Baziw, Erick, and Gerald Verbeek. "Signal Processing Challenges When Processing DST and CST Seismic Data Containing TIRs." Geotechnical Testing Journal 37, no. 3 (March 24, 2014): 20130122. http://dx.doi.org/10.1520/gtj20130122.

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Serej, Michał, and Maria Skublewska - Paszkowska. "The methods of EMG data processing." Journal of Computer Sciences Institute 3 (March 30, 2017): 38–45. http://dx.doi.org/10.35784/jcsi.591.

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The article presents both the methods of data processing of electromyography (EMG), and EMG signal analysis using the implemented piece of software. This application is used to load the EMG signal stored in a file with the .C3D extension. The analysis was conducted in terms of the highest muscles activaton during exercise recorded with Motion Capture technique.

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25

Barnes, Gary, and John Lumley. "Processing gravity gradient data." GEOPHYSICS 76, no. 2 (March 2011): I33—I47. http://dx.doi.org/10.1190/1.3548548.

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As the demand for high-resolution gravity gradient data increases and surveys are undertaken over larger areas, new challenges for data processing have emerged. In the case of full-tensor gradiometry, the processor is faced with multiple derivative measurements of the gravity field with useful signal content down to a few hundred meters’ wavelength. Ideally, all measurement data should be processed together in a joint scheme to exploit the fact that all components derive from a common source. We have investigated two methods used in commercial practice to process airborne full-tensor gravity gradient data; the methods result in enhanced, noise-reduced estimates of the tensor. The first is based around Fourier operators that perform integration and differentiation in the spatial frequency domain. By transforming the tensor measurements to a common component, the data can be combined in a way that reduces noise. The second method is based on the equivalent-source technique, where all measurements are inverted into a single density distribution. This technique incorporates a model that accommodates low-order drift in the measurements, thereby making the inversion less susceptible to correlated time-domain noise. A leveling stage is therefore not required in processing. In our work, using data generated from a geologic model along with noise and survey patterns taken from a real survey, we have analyzed the difference between the processed data and the known signal to show that, when considering the Gzz component, the modified equivalent-source processing method can reduce the noise level by a factor of 2.4. The technique has proven useful for processing data from airborne gradiometer surveys over mountainous terrain where the flight lines tend to be flown at vastly differing heights.

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Roule, Petr, Ondřej Jakubov, Pavel Kovář, Petr Kařmařík, and František Vejražka. "Gnss Signal Processing in Gpu." Artificial Satellites 48, no. 2 (June 1, 2013): 51–61. http://dx.doi.org/10.2478/arsa-2013-0005.

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ABSTRACT Signal processing of the global navigation satellite systems (GNSS) is a computationally demanding task due to the wide bandwidth of the signals and their complicated modulation schemes. The classical GNSS receivers therefore utilize tailored digital signal processors (DSP) not being flexible in nature. Fortunately, the up-to-date parallel processors or graphical processing units (GPUs) dispose sufficient computational power for processing of not only relatively narrow band GPS L1 C/A signal but also the modernized GPS, GLONASS, Galileo and COMPASS signals. The performance improvement of the modern processors is based on the constantly increasing number of cores. This trend is evident not only from the development of the central processing units (CPUs), but also from the development of GPUs that are nowadays equipped with up to several hundreds of cores optimized for video signals. GPUs include special vector instructions that support implementation of massive parallelism. The new GPUs, named as general-purpose computation on graphics processing units (GPGPU), are able to process both graphic and general data, thus making the GNSS signal processing possible. Application programming interfaces (APIs) supporting GPU parallel processing have been developed and standardized. The most general one, Open Computing Language (Open CL), is now supported by most of the GPU vendors. Next, Compute Unified Device Architecture (CUDA) language was developed for NVidia graphic cards. The CUDA language features optimized signal processing libraries including efficient implementation of the fast Fourier transform (FFT). In this paper, we study the applicability of the GPU approach in GNSS signal acquisition. Two common parallel DSP methods, parallel code space search (PCSS) and double-block zero padding (DBZP), have been investigated. Implementations in the C language for CPU and the CUDA language for GPU are discussed and compared with respect to the acquisition time. It is shown that for signals with long ranging codes (with 10230 number of chips - Galileo E5, GPS L5 etc.). Paper presented at the "European Navigation Conference 2012", held in Gdansk, Poland

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Timoshevskaya, Olga, Vladimir Londikov, Dmitry Andreev, Victor Samsonenkov, and Tatyana Klets. "DIGITAL DATA PROCESSING BASED ON WAVELET TRANSFORMS." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 17, 2021): 174–80. http://dx.doi.org/10.17770/etr2021vol2.6634.

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The paper focuses on the main theoretical principles and properties of wavelet transforms. The problem of digital data processing based on wavelet transforms is considered. The analysis and processing of signals and functions that are non-stationary in time and inhomogeneous in space are presented. The authors propose methods of progressive coefficients’ values that combine wavelet decomposition and quantization, the main purpose of which is to convey the most important piece of information about a signal.

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Wu, Yunfeng, Sridhar Krishnan, and Behnaz Ghoraani. "Computational Methods for Physiological Signal Processing and Data Analysis." Computational and Mathematical Methods in Medicine 2022 (August 10, 2022): 1–4. http://dx.doi.org/10.1155/2022/9861801.

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Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associated with pathological status. Recent computational methods have greatly improved the effectiveness of signal processing and data analysis, to support the efficient point-of-care diagnosis and accurate medical decision-making. This editorial article highlights the research works published in the special issue of Computational Methods for Physiological Signal Processing and Data Analysis. The context introduces three deep learning applications in epileptic seizure detection, human exercise intensity analysis, and lung nodule CT image segmentation, respectively. The article also summarizes the research works on detection of event-related potential in the single-trial electroencephalogram (EEG) signals during the auditory tests, along with the methodology on estimating the generalized exponential distribution parameters using the simulated and real data produced under the Type I generalized progressive hybrid censoring schemes. The article concludes with perspectives and discussions on future trends in biomedical signal processing and data analysis technologies.

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Qi, Fu Qiang, and Zong Tao Chi. "Data Acquisition and Processing Systems Based on LabVIEW." Applied Mechanics and Materials 602-605 (August 2014): 2736–39. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2736.

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In order to collect and process data effectively, express the advantage of LabVIEW, this paper presents a design method for collecting and processing data in more channel using PXI-5922 and PXI-6733 based on LabVIEW. The system can realize two channel signals generation, data collection and signal analysis.

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Vogel, Ch, St Mendel, P. Singerl, and F. Dielacher. "Digital signal processing for data converters in mixed-signal systems." e & i Elektrotechnik und Informationstechnik 126, no. 11 (November 2009): 390–95. http://dx.doi.org/10.1007/s00502-009-0689-2.

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31

Zhou, Zi Ping, Yu Zhu, and Tian Hao Wang. "The Application of Cross-Correlation Algorithm in CSAMT Received Data Processing." Advanced Materials Research 989-994 (July 2014): 2278–82. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2278.

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Controlled source audio-frequency magnetotelluric method (CSAMT) is an effective frequency domain detecting method in metallic ore exploration. In view of the weak response of a deep target signal and the serious measuring environmental noise,this paper proposes a method to deal with the noise of the CSAMT data based on the theory of cross-correlation algorithm. Emission signal current waveform recorder is designed to record signal parameters.According to cross-correlation technology, this paper uses the characteristics that correlation between emission and received signals is strong, yet the correlation between emission signals and random noise is weak, to deal with emission signals and received signals by cross-correlation ,in order to filter out random noise and other jamming signals.Field exploration test data processing contrast results show that the method can suppress noise signal, and improve the CSAMT measuring accuracy.

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32

Rahman, S. M. "Constraint of Complex Trace Analysis for Seismic Data Processing." Journal of Scientific Research 3, no. 1 (December 19, 2010): 65. http://dx.doi.org/10.3329/jsr.v3i1.2106.

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Time frequency representation is a powerful tool for studying seismic reflection patterns and can thus provide useful information for stratification of the subsurface. Complex trace analysis, one of the geophysical techniques, is being employed for the time frequency analysis of seismic traces as analytic signal for the interpretation of seismic data. The applicability of the complex trace analysis in seismic data processing has been studied in this paper with few synthetic signals. The signals are analyzed with complex trace analysis for time frequency representations and compared with the spectral energy distributions. It is shown that complex trace analysis is not suitable for accurate estimation of time frequency representation of the signals having simultaneous frequencies.Keywords: Time frequency; Complex trace; Analytic signal; Spectral analysis.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.2106 J. Sci. Res. 3 (1), 65-73 (2011)

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Epishkin, D. V. "Improving magnetotelluric data processing methods." Moscow University Bulletin. Series 4. Geology, no. 4 (August 28, 2016): 40–46. http://dx.doi.org/10.33623/0579-9406-2016-4-40-46.

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A magnetotelluric data processing code has been developed, which demonstrates high robustness to intense electromagnetic noise occurring in measured MT data. Key features of the code are specific approach for estimating different transfer functions and capability to utilize all four channels acquired at remote reference station. The code utilizes various techniques to reduce estimate errors, including robust Huber estimator, jackknife approach, improved remote reference technique and compensating for overestimation of power spectra. The proposed code has shown high efficiency in processing of low signal-to-noise data.

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34

Bernaschi, M., A. Di Lallo, A. Farina, R. Fulcoli, Emanuele Gallo, and L. Timmoneri. "Use of a graphics processing unit for passive radar signal and data processing." IEEE Aerospace and Electronic Systems Magazine 27, no. 10 (October 2012): 52–59. http://dx.doi.org/10.1109/maes.2012.6373912.

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Chen, Qunying. "Stepped Frequency Multiresolution Digital Signal Processing." Scientific Programming 2021 (June 8, 2021): 1–13. http://dx.doi.org/10.1155/2021/9081988.

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With the rapid development of radar industry technology, the corresponding signal processing technology becomes more and more complex. For the radar with short-range detection function, its corresponding signal mostly presents the characteristics of wide bandwidth and multiresolution. In the traditional data processing process, a large number of signals will interfere with the signal, which makes the final signal processing difficult or even impossible. Based on this problem, this paper proposes a principal component linear prediction processing algorithm based on clutter suppression processing on the basis of traditional signal processing algorithm. According to the curve characteristics of the data returned by the target detected by the signal, through certain image signal measurement and transformation, the clutter can be effectively suppressed and the typical characteristics of the corresponding target curve can be enhanced. For the convergence problem of signal processing and the corresponding image chromatic aberration compensation problem, this paper will realize the chromatic aberration compensation of the corresponding target echo image based on the radial pointing transverse mode algorithm and enhance the convergence speed of the whole algorithm system. In the experimental part of this paper, the optimization algorithm proposed in this paper is compared with the traditional algorithm. The experimental results show that the algorithm proposed in this paper has obvious advantages in the convergence of signal processing and antijamming performance and has the promotion value.

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Oxenlowe, L. K., Hua Ji, M. Galili, Minhao Pu, Hao Hu, H. C. H. Mulvad, K. Yvind, J. M. Hvam, A. T. Clausen, and P. Jeppesen. "Silicon Photonics for Signal Processing of Tbit/s Serial Data Signals." IEEE Journal of Selected Topics in Quantum Electronics 18, no. 2 (March 2012): 996–1005. http://dx.doi.org/10.1109/jstqe.2011.2140093.

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37

Sorzano, C. O. S., M. A. Pérez-de-la-Cruz Moreno, F. R. Martín, C. Montejo, and A. Aguilar-Ros. "A Signal Processing Approach to Pharmacokinetic Data Analysis." Pharmaceutical Research 38, no. 4 (March 22, 2021): 625–35. http://dx.doi.org/10.1007/s11095-021-03000-4.

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Bianchini Ciampoli, Luca, Fabio Tosti, Nikos Economou, and Francesco Benedetto. "Signal Processing of GPR Data for Road Surveys." Geosciences 9, no. 2 (February 19, 2019): 96. http://dx.doi.org/10.3390/geosciences9020096.

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Effective quality assurance and quality control inspections of new roads as well as assessment of remaining service-life of existing assets is taking priority nowadays. Within this context, use of ground penetrating radar (GPR) is well-established in the field, although standards for a correct management of datasets collected on roads are still missing. This paper reports a signal processing method for data acquired on flexible pavements using GPR. To demonstrate the viability of the method, a dataset collected on a real-life flexible pavement was used for processing purposes. An overview of the use of non-destructive testing (NDT) methods in the field, including GPR, is first given. A multi-stage method is then presented including: (i) raw signal correction; (ii) removal of lower frequency harmonics; (iii) removal of antenna ringing; (iv) signal gain; and (v) band-pass filtering. Use of special processing steps such as vertical resolution enhancement, migration and time-to-depth conversion are finally discussed. Key considerations about the effects of each step are given by way of comparison between processed and unprocessed radargrams. Results have proven the viability of the proposed method and provided recommendations on use of specific processing stages depending on survey requirements and quality of the raw dataset.

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Edelmann, Geoffrey F. "Signal Processing: Data analysis, machine learning, and communications." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A133. http://dx.doi.org/10.1121/10.0007877.

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40

Schmidt, U., K. Caesar, and T. Himmel. "Data-driven array processor for video signal processing." IEEE Transactions on Consumer Electronics 36, no. 3 (1990): 327–33. http://dx.doi.org/10.1109/30.103139.

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41

Dong, Yunhan. "Frequency diverse array radar signal and data processing." IET Radar, Sonar & Navigation 12, no. 9 (September 2018): 954–63. http://dx.doi.org/10.1049/iet-rsn.2018.0031.

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Morgan, M. A., and B. W. McDaniel. "Transient electromagnetic scattering: data acquisition and signal processing." IEEE Transactions on Instrumentation and Measurement 37, no. 2 (June 1988): 263–67. http://dx.doi.org/10.1109/19.6063.

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Danaher, Seán, and the ACoRNE Collaboration). "First Data from ACoRNE and Signal Processing Techniques." Journal of Physics: Conference Series 81 (September 1, 2007): 012011. http://dx.doi.org/10.1088/1742-6596/81/1/012011.

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44

Ma, Xuepo, Jian Cui, and Jianqiu Zhang. "Processing methods for signal suppression of FTMS data." Proteome Science 9, Suppl 1 (2011): S2. http://dx.doi.org/10.1186/1477-5956-9-s1-s2.

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Kirianaki, N. V., S. Y. Yurish, N. O. Shpak, and V. P. Deynega. "Data Acquisition and Signal Processing for Smart Sensors." Measurement Science and Technology 13, no. 9 (August 14, 2002): 1501. http://dx.doi.org/10.1088/0957-0233/13/9/706.

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Rei, Silviu, Dan Chicea, Beriliu Ilie, and Sorin Olaru. "Dynamic Light Scattering Signal Conditioning for Data Processing." ACTA Universitatis Cibiniensis 69, no. 1 (December 20, 2017): 130–35. http://dx.doi.org/10.1515/aucts-2017-0016.

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Abstract When performing data acquisition for a Dynamic Light Scattering experiment, one of the most important aspect is the filtering and conditioning of the electrical signal. The signal is amplified first and then fed as input for the analog digital convertor. As a result a digital time series is obtained. The frequency spectrum is computed by the logical unit offering the basis for further Dynamic Light Scattering analysis methods. This paper presents a simple setup that can accomplish the signal conditioning and conversion to a digital time series.

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Negi, Sanjay Singh, Nand Kishor, Avinash Kumar, and Kjetil Uhlen. "Signal processing for TFR of synchro-phasor data." IET Generation, Transmission & Distribution 11, no. 16 (November 9, 2017): 3881–91. http://dx.doi.org/10.1049/iet-gtd.2015.1566.

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Brofferio, Sergio, and Giuseppe Mastronardi. "A migrating data driven architecture for signal processing." European Transactions on Telecommunications 1, no. 2 (March 1990): 119–25. http://dx.doi.org/10.1002/ett.4460010207.

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Bellezza, Cinzia, and Flavio Poletto. "Multidimensional deconvolution and processing of seismic-interferometry Arctic data." GEOPHYSICS 79, no. 3 (May 1, 2014): WA25—WA38. http://dx.doi.org/10.1190/geo2013-0297.1.

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Multidimensional deconvolution (MDD) by point-spread-function removes the blurring effects and the spread distortions typically generated in the signal representation by seismic interferometry (SI). Under suitable conditions, the MDD inversion of SI signals reconstructed by the Kirchhoff-Helmholtz integral of crosscorrelation type is a valuable and robust technique to recover the Green’s function of the subsurface. A basic requirement for the effective MDD application to SI data is to know the separated incoming and outcoming wavefields at the receivers illuminated by the real sources. We extended the MDD concept to the virtual reflector (VR) approach by crossconvolution and compared equivalent results obtained with approximations for the propagated wavefields. Examples were discussed with Arctic data of an on-ice shallow-water seismic experiment affected by strong and dispersive flexural ice wave noise. The target was to improve the signal-to-noise ratio by redatuming the sources at the sea bottom. First, we processed the raw input signals recorded by sea-bottom receivers to obtain an approximation of the incoming wavefield from on-ice sources. Then, we processed the data of the whole 2D seismic line in two different ways: applying MDD by crosscorrelation and by crossconvolution to, backward, SI and, forward, VR interferometric results, respectively. The analysis of the signals redatumed to the sea bottom showed that the flexural ice noise was significantly mitigated with respect to the conventional interferometry approach, with improved reflections for seismic investigation purposes. The agreement of the phase in the SI and VR results after MDD inversion confirmed that the approach was robust and enabled us to enhance the signals combining the stacked sections obtained by SI MDD and VR MDD. The resulting MDD SI stacked sections showed an improvement in signal quality especially at low frequencies with respect to the stacked section obtained by conventional processing of the original data.

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Liao, Peng. "Data Processing System Based on Computer." Journal of Physics: Conference Series 2143, no. 1 (December 1, 2021): 012021. http://dx.doi.org/10.1088/1742-6596/2143/1/012021.

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Abstract With the popularization of computer and Internet, software technology, signal processing technology and real-time processing technology have been applied to all aspects of life, which has caused a surge of data. Therefore, big data (hereinafter referred to as BD) has become the focus of attention all over the world, which requires improving data application and processing technology. Through BD, countries can obtain corresponding knowledge, which will improve the software and comprehensive application of BD. With the in-depth study of deep learning algorithm, we can continuously improve the application of BD, which is a Data Processing (hereinafter referred to as DP) method with high precision, fast speed, flexible use and strong scalability. Through the DP system, we can realize the post demodulation and processing of signals in various equipment, which can achieve the state of data availability. In many ways, we can obtain the required knowledge through the DP system, which will improve the processing ability of the computer. Firstly, this paper puts forward the challenges faced by BD. Then, this paper analyzes the algorithms in DP. Finally, this paper designs a DP system.

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