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Articles de revues sur le sujet « Signal processing applications »

Auteur : Grafiati

Publié le 25 mai 2024

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1

Jin Chen, Huai Li, Kaihua Sun, and B. Kim. "Signal processing applications - How will bioinformatics impact signal processing research?" IEEE Signal Processing Magazine 20, no. 6 (2003): 16–26. http://dx.doi.org/10.1109/msp.2003.1253551.

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Bourennane, Salah, Julien Marot, Caroline Fossati, Ahmed Bouridane, and Klaus Spinnler. "Multidimensional Signal Processing and Applications." Scientific World Journal 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/365126.

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3

Cruz, J. "Applications of digital signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 2 (1985): 487. http://dx.doi.org/10.1109/tassp.1985.1164563.

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Duarte Ortigueira, Manuel, and J. A. Tenreiro Machado. "Fractional signal processing and applications." Signal Processing 83, no. 11 (2003): 2285–86. http://dx.doi.org/10.1016/s0165-1684(03)00181-6.

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Ortigueira, Manuel D., Clara M. Ionescu, J. Tenreiro Machado, and Juan J. Trujillo. "Fractional signal processing and applications." Signal Processing 107 (February 2015): 197. http://dx.doi.org/10.1016/j.sigpro.2014.10.002.

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Tibbitts, J., and Yibin Lu. "Forensic applications of signal processing." IEEE Signal Processing Magazine 26, no. 2 (2009): 104–11. http://dx.doi.org/10.1109/msp.2008.931099.

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7

Wang, Hanbo. "Compressed Sensing: Theory and Applications." Journal of Physics: Conference Series 2419, no. 1 (2023): 012042. http://dx.doi.org/10.1088/1742-6596/2419/1/012042.

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Abstract Compressed sensing is a new technique for solving underdetermined linear systems. Because of its good performance, it has been widely used in academia. It is applied in electrical engineering to recover sparse signals, especially in signal processing. This technique exploits the signal’s sparse nature, allowing the original signals to recover from fewer samples. This paper discusses the fundamentals of compressed sensing theory, the research progress in compressed sensing signal processing, and the applications of compressed sensing theory in nuclear magnetic resonance imaging and seismic exploration acquisition. Compressed sensing allows for the digitization of analogue data with inexpensive sensors and lowers the associated costs of processing, storage, and transmission. Behind its sophisticated mathematical expression, compressed sensing theory contains a subtle idea. Compressed sensing is a novel theory that is an ideal complement and improvement to conventional signal processing. It is a theory with a high vitality level, and its research outcomes may substantially influence signal processing and other fields.

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Kandle. "A Systolic Signal Processor for Signal-Processing Applications." Computer 20, no. 7 (1987): 94–95. http://dx.doi.org/10.1109/mc.1987.1663626.

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9

Sinha, Pankaj Kumar, and Preetha Sharan. "Multiplexer Based Multiplications for Signal Processing Applications." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 583. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp583-586.

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<p>In signal processing, Filter is a device that removes the unwanted signals. In any electronic circuits, Filters are widely used in the fundamental hands on tool. The basic function of the filter is to selectively allow the desired signal to pass through and /or control the undesired signal based on the frequency. A signal processing filter satisfies a set of requirements which are realization and improvement of the filter. A filter system consists of an analog to digital converter is used to sample the input signal, traced by a microprocessor and some components such as memory to store the data and filter coefficients. Filters can easily be designed to be “linear phase” and it is easy to implement. In this paper, the birecoder multiplier (BM) is designed in terms of VLSI design environment. The proposed multiplier is implemented by using VHDL language and Xilinx ISE for synthesis. The multiplier is mainly used for image processing applications as well as signal processing applications.</p>

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10

Volić, Ismar. "Topological Methods in Signal Processing." B&H Electrical Engineering 14, s1 (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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11

Cruces, Sergio, Rubén Martín-Clemente, and Wojciech Samek. "Information Theory Applications in Signal Processing." Entropy 21, no. 7 (2019): 653. http://dx.doi.org/10.3390/e21070653.

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12

Sullivan, Francis J. M., H. Briscoe, R. Estrada, and E. Schmidt. "BBN Butterfly applications to signal processing." Journal of the Acoustical Society of America 78, S1 (1985): S80. http://dx.doi.org/10.1121/1.2023010.

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13

Mitsukura, Yasue. "EEG Signal Processing for Real Applications." Journal of Signal Processing 20, no. 1 (2016): 1–7. http://dx.doi.org/10.2299/jsp.20.1.

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Ferguson, Brian G. "Defense Applications of Acoustic Signal Processing." Acoustics Today 15, no. 1 (2019): 10. http://dx.doi.org/10.1121/at.2019.15.1.12.

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15

Tuan Do-Hong and P. Russer. "Signal processing for wideband array applications." IEEE Microwave Magazine 5, no. 1 (2004): 57–67. http://dx.doi.org/10.1109/mmw.2004.1284944.

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Morawski, Roman Z. "Spectrophotometric applications of digital signal processing." Measurement Science and Technology 17, no. 9 (2006): R117—R144. http://dx.doi.org/10.1088/0957-0233/17/9/r01.

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17

Shamsunder, S. "Signal Processing Applications Of The Bootstrap." IEEE Signal Processing Magazine 15, no. 1 (1998): 38. http://dx.doi.org/10.1109/msp.1998.647041.

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18

Tran, Yvonne. "EEG Signal Processing for Biomedical Applications." Sensors 22, no. 24 (2022): 9754. http://dx.doi.org/10.3390/s22249754.

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19

Zhao, Yifan, Fei He, and Yuzhu Guo. "EEG Signal Processing Techniques and Applications." Sensors 23, no. 22 (2023): 9056. http://dx.doi.org/10.3390/s23229056.

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20

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

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21

Simić, Igor, and Aleksa Zejak. "SAW devices and its signal processing applications." Vojnotehnicki glasnik 47, no. 6 (1999): 50–59. http://dx.doi.org/10.5937/vojtehg9904050s.

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22

Rajesh Kumar Upadhyay. "Digital Signal Processing: From Theory to Practical Applications." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 2311–17. http://dx.doi.org/10.52783/tjjpt.v44.i4.1230.

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Digital Signal Processing (DSP) is a vital technology that bridges the gap between theoretical principles and practical applications in the digital age. This article explores the core components of DSP, emphasizing its theoretical foundations based on mathematical concepts like Fourier analysis, discrete-time signals, and the Nyquist theorem. It further delves into the practical applications of DSP, showcasing its extensive use in audio processing, image manipulation, telecommunications, biomedical diagnostics, and more. The article also outlines the challenges and future directions for DSP, including its integration with machine learning, quantum signal processing, and the development of efficient hardware solutions. DSP's potential in emerging fields like biological signal processing, data privacy, and sustainability is discussed, reflecting the ever-evolving nature of this technology. In conclusion, DSP is not just a technology but a dynamic force that continually reshapes our world by enhancing the quality of life, advancing science, and addressing global challenges.

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23

B, Nagesh, and Dr M. Uttara Kumari. "A Review on Machine Learning for Audio Applications." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 62–70. http://dx.doi.org/10.51201/jusst/21/06508.

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Audio processing is an important branch under the signal processing domain. It deals with the manipulation of the audio signals to achieve a task like filtering, data compression, speech processing, noise suppression, etc. which improves the quality of the audio signal. For applications such as natural language processing, speech generation, automatic speech recognition, the conventional algorithms aren’t sufficient. There is a need for machine learning or deep learning algorithms which can be implemented so that the audio signal processing can be achieved with good results and accuracy. In this paper, a review of the various algorithms used by researchers in the past has been described and gives the appropriate algorithm that can be used for the respective applications.

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24

Du, Haotian. "Application of matrix in signal processing." Applied and Computational Engineering 35, no. 1 (2024): 41–49. http://dx.doi.org/10.54254/2755-2721/35/20230358.

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Signal processing, a foundational discipline in modern technology, encompasses a diverse array of applications, ranging from audio and image processing to communication systems and medical imaging. This review investigates how matrix-based techniques are widely used to advance signal processing methodologies. In order to discretize continuous-time signals for digital processing, which occurs in the first section of the paper, matrices play a crucial role in signal sampling. A key principle, the Nyquist-Shannon Sampling Theorem, directs appropriate sampling rates to prevent aliasing, with matrices permitting effective signal representation. The effectiveness of matrix-based filtering methods for frequency modulation and noise reduction, such as convolution and correlation, is then investigated. By utilising matrix operations, these methods enable real-time signal processing. The Fourier Transform and Wavelet Transform are also featured in matrix-driven signal transformation, providing insights into frequency analysis and non-stationary signal characterization. By reducing noise components, matrix-based approaches, particularly Singular Value Decomposition (SVD) denoising, are essential for improving signal quality. Additionally, image compression employs SVD. Matrix-based compressive sensing revolutionises signal recovery from sparse data and results in data-efficient reconstruction. Signal processing has been transformed by matrix-based approaches, which have enabled previously unheard-of levels of efficiency, accuracy, and adaptability. The review highlights their significant influence on several signal processing fields.

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25

Wang, Zhao, Eng Gee Lim, Yujun Tang, and Mark Leach. "Medical Applications of Microwave Imaging." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/147016.

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Ultrawide band (UWB) microwave imaging is a promising method for the detection of early stage breast cancer, based on the large contrast in electrical parameters between malignant tumour tissue and the surrounding normal breast-tissue. In this paper, the detection and imaging of a malignant tumour are performed through a tomographic based microwave system and signal processing. Simulations of the proposed system are performed and postimage processing is presented. Signal processing involves the extraction of tumour information from background information and then image reconstruction through the confocal method delay-and-sum algorithms. Ultimately, the revision of time-delay and the superposition of more tumour signals are applied to improve accuracy.

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26

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

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27

Nash, J. Greg. "Concurrent VLSI architectures for image and signal processing: Applications in image and signal processing." IEEE Potentials 5, no. 2 (1986): 12–14. http://dx.doi.org/10.1109/mp.1986.6500826.

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28

Aziz, Ashraf. "Applications of Signal Processing in Genomic Research." International Conference on Electrical Engineering 7, no. 7 (2010): 1–17. http://dx.doi.org/10.21608/iceeng.2010.32972.

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Mostafa, M., Fathy Ahmed, and Aly Attallah. "Modern Signal Processing Techniques for GPR Applications." Journal of Engineering Science and Military Technologies 17, no. 17th International Conference (2017): 1–9. http://dx.doi.org/10.21608/ejmtc.2017.21123.

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30

Wilkinson, A. J. "Digital Signal Processing: Principles, Devices and Applications." Computing & Control Engineering Journal 2, no. 5 (1991): 216. http://dx.doi.org/10.1049/cce:19910058.

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31

Creasey, D. J. "Digital Signal Processing: Principles, Devices and Applications." IEE Review 36, no. 7 (1990): 275. http://dx.doi.org/10.1049/ir:19900116.

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32

Behrens, R. T., and L. L. Scharf. "Signal processing applications of oblique projection operators." IEEE Transactions on Signal Processing 42, no. 6 (1994): 1413–24. http://dx.doi.org/10.1109/78.286957.

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Ortega, Antonio, Pascal Frossard, Jelena Kovacevic, Jose M. F. Moura, and Pierre Vandergheynst. "Graph Signal Processing: Overview, Challenges, and Applications." Proceedings of the IEEE 106, no. 5 (2018): 808–28. http://dx.doi.org/10.1109/jproc.2018.2820126.

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Joachim, Dale, and J. R. Deller. "Some Signal Processing Applications of Set Solutions." IFAC Proceedings Volumes 33, no. 15 (2000): 1001–6. http://dx.doi.org/10.1016/s1474-6670(17)39884-1.

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Katsikas, Sokratis K., and Demetrios G. Lainiotis. "Lainiotis filters applications in seismic signal processing." Nonlinear Analysis: Theory, Methods & Applications 30, no. 4 (1997): 2385–95. http://dx.doi.org/10.1016/s0362-546x(97)00281-2.

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Popescu, Theodor D. "Introduction to statistical signal processing with applications." Control Engineering Practice 4, no. 10 (1996): 1484. http://dx.doi.org/10.1016/s0967-0661(96)90047-7.

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Nandi, Asoke K., and Rangaraj M. Rangayyan. "Special issue: Medical applications of signal processing." Journal of the Franklin Institute 344, no. 3-4 (2007): 153. http://dx.doi.org/10.1016/j.jfranklin.2006.12.001.

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38

Allen, Alastair. "Acousto-optic signal processing: Fundamentals and applications." Optics & Laser Technology 25, no. 3 (1993): 211–12. http://dx.doi.org/10.1016/0030-3992(93)90083-r.

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Jarske, Petri. "Introductory digital signal processing with computer applications." Signal Processing 21, no. 3 (1990): 283. http://dx.doi.org/10.1016/0165-1684(90)90094-f.

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Wilson, B. "Digital signal processing applications for hearing accessibility." IEEE Signal Processing Magazine 20, no. 5 (2003): 14–18. http://dx.doi.org/10.1109/msp.2003.1236769.

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

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Bradski, G., and A. Kaehler. "Robot-Vision Signal Processing Primitives [Applications Corner]." IEEE Signal Processing Magazine 25, no. 1 (2008): 130–33. http://dx.doi.org/10.1109/msp.2008.4408449.

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43

Pollak, Ilya. "Incorporating Financial Applications in Signal Processing Curricula." IEEE Signal Processing Magazine 28, no. 5 (2011): 122–25. http://dx.doi.org/10.1109/msp.2011.941844.

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44

Rabenstein, Rudolf, and Jörg Velten. "Special issue on multidimensional signal processing applications." Multidimensional Systems and Signal Processing 25, no. 2 (2014): 245–46. http://dx.doi.org/10.1007/s11045-013-0272-1.

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45

Mostafa, M., Fathy Ahmed, and Aly Attallah. "Modern Signal Processing Techniques for GPR Applications." International Conference on Aerospace Sciences and Aviation Technology 17, AEROSPACE SCIENCES (2017): 1–9. http://dx.doi.org/10.21608/asat.2017.22383.

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46

Shulman, D. "Synchronous SRAM for digital signal processing applications." Electronics Letters 33, no. 7 (1997): 562. http://dx.doi.org/10.1049/el:19970366.

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47

McGrath, Donald T. "Signal processing considerations in power management applications." Digital Signal Processing 1, no. 4 (1991): 245–50. http://dx.doi.org/10.1016/1051-2004(91)90116-3.

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48

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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BERRYMAN, M. J., A. ALLISON, C. R. WILKINSON, and D. ABBOTT. "REVIEW OF SIGNAL PROCESSING IN GENETICS." Fluctuation and Noise Letters 05, no. 04 (2005): R13—R35. http://dx.doi.org/10.1142/s021947750500294x.

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This paper reviews applications of signal processing techniques to a number of areas in the field of genetics. We focus on techniques for analyzing DNA sequences, and briefly discuss applications of signal processing to DNA sequencing, and other related areas in genetics that can provide biologically significant information to assist with sequence analysis.

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Hämäläinen, M., T. Lipping, and Y. Neuvo. "Trends in Nonlinear Signal Processing." Methods of Information in Medicine 33, no. 01 (1994): 4–9. http://dx.doi.org/10.1055/s-0038-1634991.

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Abstract:In this paper, we review several nonlinear filtering methods having desirable complementary properties to those of linear filters. Most of these methods are based on the median filter. Basic properties of these filters as well as some of their applications are reviewed.

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