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Thematische Bibliographien / Signal processing / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Signal processing“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 9. Juni 2025

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1

Borawake, Prof Dr M. P. "Audio Signal Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1495–96. http://dx.doi.org/10.22214/ijraset.2022.44063.

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Abstract: Audio Signal Processing is also known as Digital Analog Conversion (DAC). Sound waves are the most common example of longitudinal waves. The speed of sound waves is a particular medium depends on the properties of that temperature and the medium. Sound waves travel through air when the air elements vibrate to produce changes in pressure and density along the direction of the wave’s motion. It transforms the Analog Signal into Digital Signals, and then converted Digital Signals is sent to the Devices. Which can be used in Various things., Such as audio signal, RADAR, speed processing, voice recognition, entertainment industry, and to find defected in machines using audio signals or frequencies. The signals pay important role in our day-to-day communication, perception of environment, and entertainment. A joint time-frequency (TF) approach would be better choice to effectively process this signal. The theory of signal processing and its application to audio was largely developed at Bell Labs in the mid-20th century. Claude Shannon and Harry Nyquist’s early work on communication theory and pulse-code modulation (PCM) laid the foundations for the field.

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2

Sharma, Sushma, Hitesh Kumar, and Charul Thareja. "Digital Signal Processing Over Analog Signal Processing." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 1, no. 2 (February 28, 2014): 01–02. http://dx.doi.org/10.53555/nneee.v1i2.255.

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This paper provides a survey of digital signal processing over analog signal processing. Initially digital signal processing is developed to replace limited application based analog signal processing (ASP) of high cost. This paper describes the comparison of analog signal processing (ASP) and digital signal processing, technology under digital signal processing , application of digital signal processing, new technology of digital signal processing (DSP). This paper also focuses on the future scope of digital signal processing (DSP).

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3

Smolarik, Lukas, Dusan Mudroncik, and Lubos Ondriga. "ECG Signal Processing." Advanced Materials Research 749 (August 2013): 394–400. http://dx.doi.org/10.4028/www.scientific.net/amr.749.394.

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Electrocardiography (ECG) is a diagnostic method that allows sensing and record the electric activity of heart [. The measurement of electrical activity is used as a standard twelve-point system. At each of these leads to measure the useful signal and interference was measured. The intensity of interference depends on the artefacts (electrical lines, brum, motion artefacts, muscle, interference from the environment, etc.). For correct evaluation of measured signal there is a need to processing the measured signal to suitable form. At present, the use of electrocardiograms with sensors with contact scanning are difficult to set a time so we decided to use the principle of non-contact sensing. Such a device to measure the ECG was constructed under the project. The disadvantage of such devices is a problem with a high level of noise, which degrades a useful signal. The aim of this article is to pre-process the signals obtained from non-contact sensing. The contactless devices are powered from the network and battery. The electrodes were connected by way of Eithoven bipolar leads. Signals were pre-treated with suitable filters so that they are also appropriate for their subsequent analysis. In the filtration ECG signals was used as a method of linear (low pass filter, high pass, IIR (Infinite Impulse Response) peak, notch filter. The results of many signals clearly demonstrate removing noise in the ECG signals to the point that is also suitable for their analysis.

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4

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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5

Minasian, R. A. "Photonic signal processing of microwave signals." IEEE Transactions on Microwave Theory and Techniques 54, no. 2 (February 2006): 832–46. http://dx.doi.org/10.1109/tmtt.2005.863060.

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6

Lessard, Charles S. "Signal Processing of Random Physiological Signals." Synthesis Lectures on Biomedical Engineering 1, no. 1 (January 2006): 1–232. http://dx.doi.org/10.2200/s00012ed1v01y200602bme001.

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7

Birdsall, Theodore G., Kurt Metzger, and Matthew A. Dzieciuch. "Signals, signal processing, and general results." Journal of the Acoustical Society of America 96, no. 4 (October 1994): 2343–52. http://dx.doi.org/10.1121/1.410106.

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8

Afanasiev, D. S. "Digital Chirp Processing." LETI Transactions on Electrical Engineering & Computer Science 15, no. 4 (2022): 44–48. http://dx.doi.org/10.32603/2071-8985-2022-15-4-44-48.

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Algorithms for digital signal processing with linear frequency modulation LFM have been developed. A method for calibrating several chirp signals for their subsequent joint processing, an algorithm for shifting a signal in time, compensating for compression or stretching of a signal in time, and determining the start time of a signal are considered, digital signal processing.

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9

Dewhurst, David J. "Signal processing." Journal of the Acoustical Society of America 89, no. 5 (May 1991): 2481. http://dx.doi.org/10.1121/1.400842.

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10

Katkovnik, V. "Signal Processing." Signal Processing 59, no. 2 (June 1997): 251–52. http://dx.doi.org/10.1016/s0165-1684(97)89502-3.

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11

Laguna, P. "Signal Processing." Yearbook of Medical Informatics 11, no. 01 (August 2002): 427–30. http://dx.doi.org/10.1055/s-0038-1638133.

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12

Erskine, R. L. "Signal processing." Chemometrics and Intelligent Laboratory Systems 2, no. 1-3 (August 1987): 6–8. http://dx.doi.org/10.1016/0169-7439(87)80079-5.

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13

Xiaofeng, Lu, Li Zan, and Cai Jueping. "Signal Processing." European Transactions on Telecommunications 20, no. 4 (June 2009): 403–12. http://dx.doi.org/10.1002/ett.1296.

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14

MONACU, Larisa, and Titus BĂLAN. "SDR SYSTEM FOR GNSS SIGNAL PROCESSING." Review of the Air Force Academy 16, no. 3 (December 19, 2018): 77–84. http://dx.doi.org/10.19062/1842-9238.2018.16.3.9.

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15

Asada, Kohei. "HEADPHONE DEVICE, SIGNAL PROCESSING DEVICE, AND SIGNAL PROCESSING METHOD." Journal of the Acoustical Society of America 133, no. 1 (2013): 605. http://dx.doi.org/10.1121/1.4774147.

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16

Maruko, Tsuguto, and Naotaka Saito. "ACOUSTIC SIGNAL PROCESSING APPARATUS AND ACOUSTIC SIGNAL PROCESSING METHOD." Journal of the Acoustical Society of America 132, no. 1 (2012): 569. http://dx.doi.org/10.1121/1.4734253.

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17

Sakurai, Masaru. "Video Signal Processing LSI. Signal Processing LSIs for HDTV." Journal of the Institute of Television Engineers of Japan 48, no. 1 (1994): 25–30. http://dx.doi.org/10.3169/itej1978.48.25.

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18

Venkatachalam, K. L., Joel E. Herbrandson, and Samuel J. Asirvatham. "Signals and Signal Processing for the Electrophysiologist." Circulation: Arrhythmia and Electrophysiology 4, no. 6 (December 2011): 965–73. http://dx.doi.org/10.1161/circep.111.964304.

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19

Venkatachalam, K. L., Joel E. Herbrandson, and Samuel J. Asirvatham. "Signals and Signal Processing for the Electrophysiologist." Circulation: Arrhythmia and Electrophysiology 4, no. 6 (December 2011): 974–81. http://dx.doi.org/10.1161/circep.111.964973.

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20

Lu, Jie, Naveen Verma, and Niraj K. Jha. "Compressed Signal Processing on Nyquist-Sampled Signals." IEEE Transactions on Computers 65, no. 11 (November 1, 2016): 3293–303. http://dx.doi.org/10.1109/tc.2016.2532861.

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21

Ask, Per. "Ultrasound imaging. Waves, signals and signal processing." Ultrasound in Medicine & Biology 28, no. 3 (March 2002): 401–2. http://dx.doi.org/10.1016/s0301-5629(01)00520-8.

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22

Vosvrda, Miloslav S. "Discrete random signals and statistical signal processing." Automatica 29, no. 6 (November 1993): 1617. http://dx.doi.org/10.1016/0005-1098(93)90033-p.

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23

Kale, Uma, and Edward Voigtman. "Signal processing of transient atomic absorption signals." Spectrochimica Acta Part B: Atomic Spectroscopy 50, no. 12 (October 1995): 1531–41. http://dx.doi.org/10.1016/0584-8547(95)01380-6.

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24

Volić, Ismar. "Topological Methods in Signal Processing." B&H Electrical Engineering 14, s1 (October 1, 2020): 14–25. http://dx.doi.org/10.2478/bhee-2020-0002.

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Abstract This article gives an overview of the applications of algebraic topology methods in signal processing. We explain how the notions and invariants such as (co)chain complexes and (co)homology of simplicial complexes can be used to gain insight into higher-order interactions of signals. The discussion begins with some basic ideas in classical circuits, continues with signals over graphs and simplicial complexes, and culminates with an overview of sheaf theory and the connections between sheaf cohomology and signal processing.

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25

M, Sankar, Narendra Babu J, Swati S. Halunde, and Maduri B. Mulik. "Brain Signal Processing: Analysis, Technologies and Application." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (December 20, 2019): 69–74. http://dx.doi.org/10.5373/jardcs/v11i12/20193213.

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26

Zou, JinFeng, Yi-an Cui, and Jing Xie. "Self-potential signal processing based on NMF." Journal of Physics: Conference Series 2895, no. 1 (November 1, 2024): 012023. https://doi.org/10.1088/1742-6596/2895/1/012023.

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Abstract In recent years, new algorithms have been continuously applied in the field of geophysical data processing, all of which have achieved good results. However, there is currently no dedicated signal separation method for self-potential field signal processing. In this paper, we propose a self-potential signal separation algorithm based on non-negative matrix factorization (NMF) to perform blind source signal separation. We aim to separate different self-potential signals from the collected mixed signals, laying the foundation for subsequent work such as feature recognition. We utilized analytical formulas of simple polarization bodies and forward modeling procedures to generate a series of self-potential signal data. Subsequently, we conducted numerical simulation experiments for signal separation. The numerical simulation results demonstrate that the proposed algorithm is capable of separating self-potential signals of different models from mixed signals.

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27

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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28

SAITO, Minoru. "Signal processing techniques." Journal of the Japan Society for Precision Engineering 54, no. 12 (1988): 2233–37. http://dx.doi.org/10.2493/jjspe.54.2233.

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29

Ruiz, Luana, Luiz F. O. Chamon, and Alejandro Ribeiro. "Graphon Signal Processing." IEEE Transactions on Signal Processing 69 (2021): 4961–76. http://dx.doi.org/10.1109/tsp.2021.3106857.

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30

Dack, David. "Digital Signal Processing." Electronics and Power 31, no. 1 (1985): 86. http://dx.doi.org/10.1049/ep.1985.0061.

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31

Tomaniac, A. P. "Signal processing forum." IEEE Signal Processing Magazine 15, no. 1 (1998): 16. http://dx.doi.org/10.1109/79.647039.

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32

Strauss, W. "Digital signal processing." IEEE Signal Processing Magazine 17, no. 2 (March 2000): 52–56. http://dx.doi.org/10.1109/79.826412.

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33

Anastassiou, D. "Genomic signal processing." IEEE Signal Processing Magazine 18, no. 4 (July 2001): 8–20. http://dx.doi.org/10.1109/79.939833.

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34

Radic, Stojan. "Parametric Signal Processing." IEEE Journal of Selected Topics in Quantum Electronics 18, no. 2 (March 2012): 670–80. http://dx.doi.org/10.1109/jstqe.2011.2121896.

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35

Kulkarni, Abhijit. "AUDIO SIGNAL PROCESSING." Journal of the Acoustical Society of America 133, no. 4 (2013): 2514. http://dx.doi.org/10.1121/1.4800116.

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36

Wang, Wen. "Audio signal processing." Journal of the Acoustical Society of America 128, no. 5 (2010): 3275. http://dx.doi.org/10.1121/1.3525338.

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37

Lawrence, Martin, and Michael Brown. "Optical Signal Processing." Physics Bulletin 37, no. 11 (November 1986): 458–60. http://dx.doi.org/10.1088/0031-9112/37/11/022.

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38

Fenderson, Bruce. "Cellular Signal Processing." Medicine & Science in Sports & Exercise 41, no. 8 (August 2009): 1686. http://dx.doi.org/10.1249/01.mss.0000323502.42316.28.

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39

Long, D. "Array signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 5 (October 1985): 1346. http://dx.doi.org/10.1109/tassp.1985.1164669.

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40

Morgan, D. "Adaptive signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 4 (August 1986): 1017–18. http://dx.doi.org/10.1109/tassp.1986.1164869.

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41

Ritchie, Simon. "Waveguide signal processing." Physics World 2, no. 5 (May 1989): 21–22. http://dx.doi.org/10.1088/2058-7058/2/5/21.

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42

Brewster, R. L. "Adaptive Signal Processing." Electronics and Power 32, no. 7 (1986): 545. http://dx.doi.org/10.1049/ep.1986.0314.

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43

Duncan, G. "Advanced Signal Processing." Electronics and Power 33, no. 7 (1987): 469. http://dx.doi.org/10.1049/ep.1987.0286.

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44

Grant, P. M. "Multirate signal processing." Electronics & Communication Engineering Journal 8, no. 1 (February 1, 1996): 4–12. http://dx.doi.org/10.1049/ecej:19960102.

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45

McBride, Sam. "Biomedical Signal Processing." Journal of Clinical Engineering 13, no. 5 (September 1988): 342–44. http://dx.doi.org/10.1097/00004669-198809000-00006.

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46

Meck, Warren H. "Postreinforcement signal processing." Journal of Experimental Psychology: Animal Behavior Processes 11, no. 1 (1985): 52–70. http://dx.doi.org/10.1037/0097-7403.11.1.52.

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47

PECEN, LADISLAV. "Electrical signal processing." International Journal of Electronics 73, no. 5 (November 1992): 1085–86. http://dx.doi.org/10.1080/00207219208925773.

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48

Vainio, Olli. "Intelligent Signal Processing." Signal Processing 81, no. 12 (December 2001): 2615–16. http://dx.doi.org/10.1016/s0165-1684(01)00152-9.

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49

Astola, Jaakko, Edward Dougherty, Ilya Shmulevich, and Ioan Tabus. "Genomic signal processing." Signal Processing 83, no. 4 (April 2003): 691–94. http://dx.doi.org/10.1016/s0165-1684(02)00467-x.

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50

Trojanowicz, Marek. "Signal Processing Algorithms." Analytica Chimica Acta 248, no. 2 (August 1991): 625–26. http://dx.doi.org/10.1016/s0003-2670(00)84686-3.

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