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Journal articles on the topic 'Frequency domain approach'

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Author: Grafiati

Published: 14 September 2024

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1

Morkoc, F., J. W. Biggar, R. H. Shumway, and D. R. Nielsen. "River Quality Modeling: Frequency Domain Approach." Journal of Irrigation and Drainage Engineering 115, no. 6 (December 1989): 1008–17. http://dx.doi.org/10.1061/(asce)0733-9437(1989)115:6(1008).

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2

Kastrinaki, Zafeira, and Paul Stoneman. "Merger Cycles: A Frequency Domain Approach*." Oxford Bulletin of Economics and Statistics 75, no. 2 (January 25, 2012): 259–75. http://dx.doi.org/10.1111/j.1468-0084.2012.00691.x.

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3

LlU, G. P. "Frequency-domain approach for critical systems." International Journal of Control 52, no. 6 (December 1990): 1507–19. http://dx.doi.org/10.1080/00207179008953607.

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4

Harrison, J. "A frequency-domain approach to frequency-weighted balanced realization." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 50, no. 5 (May 2003): 655–62. http://dx.doi.org/10.1109/tcsi.2003.811021.

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5

Qureshi, M. Umar, Mitchel J. Colebank, David A. Schreier, Diana M. Tabima, Mansoor A. Haider, Naomi C. Chesler, and Mette S. Olufsen. "Characteristic impedance: frequency or time domain approach?" Physiological Measurement 39, no. 1 (January 31, 2018): 014004. http://dx.doi.org/10.1088/1361-6579/aa9d60.

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6

Singh, T., and S. R. Vadali. "Robust time-optimal control - Frequency domain approach." Journal of Guidance, Control, and Dynamics 17, no. 2 (March 1994): 346–53. http://dx.doi.org/10.2514/3.21204.

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7

Fong, K. F., A. P. Loh, and W. W. Tan. "A frequency domain approach for fault detection." International Journal of Control 81, no. 2 (February 2008): 264–76. http://dx.doi.org/10.1080/00207170701536122.

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8

HANNA, S. A. "Frequency-domain maximum likelihood pitch determination approach." International Journal of Electronics 73, no. 6 (December 1992): 1185–99. http://dx.doi.org/10.1080/00207219208925788.

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9

Aravena, Jorge L., and Vidya Venkatachalam. "Pseudo-power scale signatures: frequency domain approach." Journal of the Franklin Institute 337, no. 4 (July 2000): 389–401. http://dx.doi.org/10.1016/s0016-0032(00)00026-0.

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10

Marquez, H. J. "A frequency domain approach to state estimation." Journal of the Franklin Institute 340, no. 2 (March 2003): 147–57. http://dx.doi.org/10.1016/s0016-0032(03)00017-6.

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11

Krajewski, Wieslaw, Antonio Lepschy, Stefano Miani, and Umberto Viaro. "Frequency-domain approach to robust PI control." Journal of the Franklin Institute 342, no. 6 (September 2005): 674–87. http://dx.doi.org/10.1016/j.jfranklin.2005.04.003.

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12

Scheding, S., E. Nebot, and H. Durrant-Whyte. "High-integrity navigation: a frequency-domain approach." IEEE Transactions on Control Systems Technology 8, no. 4 (July 2000): 676–94. http://dx.doi.org/10.1109/87.852913.

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13

Hägglund, T., and K. J. Åström. "A Frequency Domain Approach to Adaptive Control." IFAC Proceedings Volumes 23, no. 8 (August 1990): 421–26. http://dx.doi.org/10.1016/s1474-6670(17)52045-5.

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14

Yanushevsky, Rafael. "Frequency domain approach to guidance systen design." IEEE Transactions on Aerospace and Electronic Systems 43, no. 99 (2007): 1544–52. http://dx.doi.org/10.1109/taes.2007.4407476.

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15

Yanushevsky, R. "Frequency domain approach to guidance system design." IEEE Transactions on Aerospace and Electronic Systems 43, no. 4 (October 2007): 1544–52. http://dx.doi.org/10.1109/taes.2007.4441757.

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16

CHOU, HSEN–HSIN, BOR–SEN CHEN, and YU–PING LIN. "Robust pole placement: a frequency-domain approach." International Journal of Systems Science 21, no. 2 (February 1990): 317–33. http://dx.doi.org/10.1080/00207729008910364.

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17

Ferreres, G., and J. M. Biannic. "Frequency domain curve fitting: a generalized? approach." International Journal of Adaptive Control and Signal Processing 16, no. 4 (2002): 273–88. http://dx.doi.org/10.1002/acs.700.

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18

Poornima, B. V., and S. Srinath. "Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures." Indian Journal Of Science And Technology 17, no. 7 (February 15, 2024): 660–69. http://dx.doi.org/10.17485/ijst/v17i7.2836.

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Objectives: The objective of this paper is to introduce and demonstrate an innovative approach for the recognition of Indian sign language gestures, with a focus on bridging communication gap between the deaf and hearing communities. The goal is to contribute to the development of effective tools and technologies that facilitate seamless communication between individuals using sign language and the people with no knowledge about sign language. Methods: The methodology consists of three key steps. First, data pre-processing involves resizing and contours extraction. Next, feature extraction employs Fourier descriptors for frequency domain analysis and gray-level-co-occurrence matrix for spatial domain analysis. Finally, various machine learning models including SVM, Random Forest, Logistic Regression, K-Nearest Neighbor and Naive Bayes are trained on a standard dataset. Findings: In our controlled experimental setup, we applied a diverse set of machine learning classifiers to evaluate the proposed approach for gesture recognition. Among the classifiers tested, K-Nearest Neighbors demonstrated the highest accuracy, achieving 99.82%. To validate the robustness of our approach, we employed k-fold cross-validation with 5 folds. Novelty: This study presents an innovative method for sign language recognition by employing a dual-domain fusion strategy that prominently emphasizes the frequency domain. Through the integration of Fourier descriptors, the research conducts a detailed frequency domain analysis to characterize the contour shapes of sign language gestures. The synergy with gray-level co-occurrence matrix texture features in the spatial domain analysis, contributes to the creation of a comprehensive feature vector. The proposed approach ensures a thorough exploration of gesture features, there by advancing the precision and efficacy of sign language recognition. Keywords: Indian Sign Language (ISL), Sign Language Recognition (SLR), Frequency domain, Spatial domain, Fourier descriptors, Gray level cooccurrence matrix (GLCM), K Fold

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19

Marc, Berneman, Pintelon Rik, and Lataire John. "A Frequency Domain Approach to Model Reference Control." IFAC-PapersOnLine 54, no. 7 (2021): 216–21. http://dx.doi.org/10.1016/j.ifacol.2021.08.361.

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20

Bergamasco, Marco, Andrea Ragazzi, and Marco Lovera. "Rotorcraft system identification: a time/frequency domain approach." IFAC Proceedings Volumes 47, no. 3 (2014): 8861–66. http://dx.doi.org/10.3182/20140824-6-za-1003.02755.

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21

D'Amico, M. B., J. L. Moiola, and E. E. Paolini. "Hopf bifurcation for maps: a frequency-domain approach." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 49, no. 3 (March 2002): 281–88. http://dx.doi.org/10.1109/81.989161.

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22

NYMAN, P. O. "A simplified frequency-domain approach to super-optimization." International Journal of Control 62, no. 6 (December 1995): 1453–69. http://dx.doi.org/10.1080/00207179508921608.

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23

Thompson, P. J., and M. T. Manry. "A frequency domain weighting function approach to extrapolation." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 8 (1990): 1395–402. http://dx.doi.org/10.1109/29.57574.

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24

Deutscher, J., and P. Hippe. "A Frequency Domain Approach to Complete Modal Synthesis." IFAC Proceedings Volumes 33, no. 13 (June 2000): 55–60. http://dx.doi.org/10.1016/s1474-6670(17)37165-3.

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25

Vinagre, B. M., I. Podlubny, L. Dorcak, and V. Feliu. "On Fractional PID Controllers: A Frequency Domain Approach." IFAC Proceedings Volumes 33, no. 4 (April 2000): 51–56. http://dx.doi.org/10.1016/s1474-6670(17)38220-4.

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26

Sun, Lianming, and Akira Sano. "Frequency domain identification approach to closed-loop systems." IFAC Proceedings Volumes 37, no. 12 (August 2004): 859–64. http://dx.doi.org/10.1016/s1474-6670(17)31578-1.

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27

Ho, W. K., C. C. Hang, W. Wojsznis, and Q. H. Tao. "Frequency domain approach to self-tuning PID control." Control Engineering Practice 4, no. 6 (June 1996): 807–13. http://dx.doi.org/10.1016/0967-0661(96)00071-8.

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28

Manton, J. H., and Yingbo Hua. "A frequency domain deterministic approach to channel identification." IEEE Signal Processing Letters 6, no. 12 (December 1999): 323–26. http://dx.doi.org/10.1109/97.803436.

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29

Ohbuchi, Ryutarou, Akio Mukaiyama, and Shigeo Takahashi. "A Frequency-Domain Approach to Watermarking 3D Shapes." Computer Graphics Forum 21, no. 3 (September 2002): 373–82. http://dx.doi.org/10.1111/1467-8659.00597.

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30

Ohbuchi, Ryutarou, Akio Mukaiyama, and Shigeo Takahashi. "A Frequency-Domain Approach to Watermarking 3D Shapes." Computer Graphics Forum 21, no. 3 (September 2002): 373–82. http://dx.doi.org/10.1111/1467-8659.t01-1-00597.

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31

ITO, Akihiro, and Masatake SHIRAISHI. "New Design Method of Observer. Frequency Domain Approach." Journal of the Japan Society for Precision Engineering 67, no. 5 (2001): 781–85. http://dx.doi.org/10.2493/jjspe.67.781.

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32

Soverini, Umberto, and Torsten Söderström. "Frequency domain EIV identification: a Frisch Scheme approach." IFAC Proceedings Volumes 47, no. 3 (2014): 4631–36. http://dx.doi.org/10.3182/20140824-6-za-1003.00220.

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33

Pokrajac, Ivan, and Desimir Vucic. "Wideband cyclic music algorithms: A frequency-domain approach." Facta universitatis - series: Electronics and Energetics 23, no. 3 (2010): 367–78. http://dx.doi.org/10.2298/fuee1003367p.

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In this paper, we propose two spectral cyclic MUSIC DOA estimation algorithms for wideband cyclostationary signals: the wideband spectral cyclic conjugateMUSIC (WSCCM) algorithm and the extended wideband spectral cyclicMUSIC (EWSCM) algorithm. The proposed algorithms can be applied to estimate the spectral cyclic conjugate correlation matrix and the extended spectral cyclic correlation matrix of wideband cyclostationary signals. The presented algorithms do not require any knowledge of the optimal time lag parameter, which appears in the similar algorithms in time domain. In the proposed algorithms, the DOAs are estimated at each spectral component of the selected frequency band for a cyclic frequency of interest. This can be used to form the azimuth cyclic frequency diagram, similar to the azimuth frequency diagram in wideband direction-finding systems. Some simulation results are provided to confirm the theoretical findings.

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34

Yang, Bo. "High-Order Consensus Seeking: A Frequency Domain Approach." Applied Mathematics & Information Sciences 8, no. 4 (July 1, 2014): 1829–35. http://dx.doi.org/10.12785/amis/080440.

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35

Pandey, Divyanshu, Adithya Venugopal, and Harry Leib. "Multi-Domain Communication Systems and Networks: A Tensor-Based Approach." Network 1, no. 2 (July 7, 2021): 50–74. http://dx.doi.org/10.3390/network1020005.

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Most modern communication systems, such as those intended for deployment in IoT applications or 5G and beyond networks, utilize multiple domains for transmission and reception at the physical layer. Depending on the application, these domains can include space, time, frequency, users, code sequences, and transmission media, to name a few. As such, the design criteria of future communication systems must be cognizant of the opportunities and the challenges that exist in exploiting the multi-domain nature of the signals and systems involved for information transmission. Focussing on the Physical Layer, this paper presents a novel mathematical framework using tensors, to represent, design, and analyze multi-domain systems. Various domains can be integrated into the transceiver design scheme using tensors. Tools from multi-linear algebra can be used to develop simultaneous signal processing techniques across all the domains. In particular, we present tensor partial response signaling (TPRS) which allows the introduction of controlled interference within elements of a domain and also across domains. We develop the TPRS system using the tensor contracted convolution to generate a multi-domain signal with desired spectral and cross-spectral properties across domains. In addition, by studying the information theoretic properties of the multi-domain tensor channel, we present the trade-off between different domains that can be harnessed using this framework. Numerical examples for capacity and mean square error are presented to highlight the domain trade-off revealed by the tensor formulation. Furthermore, an application of the tensor framework to MIMO Generalized Frequency Division Multiplexing (GFDM) is also presented.

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36

Shwetal Raipure. "An Efficient Cross-Domain Recommendation System Based on Clustering Approach and User-Score." Journal of Electrical Systems 20, no. 3 (April 30, 2024): 739–49. http://dx.doi.org/10.52783/jes.2998.

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Growing consumers and online commercial products have put a great challenge on Recommender systems. Abundant browsing and online shopping across the world have provided immense opportunities to E-commercial giants to effectively learn the behaviour and attract users. However, due to the variety of available products and distinct consumers, crucial issues of data sparsity and cold start problems have been introduced. Single-domain recommendation systems are unable to handle such issues and research has been concentrated on developing more sophisticated cross-domain recommendation systems. Concerning other systems, non-overlapping domains form the most difficult part to solve using the cross-domain approach. This article focuses on partially overlapping domains and suggests a simple and efficient approach based on mapping users' interests by clustering books and movies. The cluster information from the auxiliary domain is transferred to the target domain using the user score and mapped properly to recommend books. The user and item scores in both domains are computed using the transfer frequency-inverse document frequency approach. The proposed recommender system offers low computational complexity and requires less time.

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37

Stanković, Ljubiša, Jonatan Lerga, Danilo Mandic, Miloš Brajović, Cédric Richard, and Miloš Daković. "From Time–Frequency to Vertex–Frequency and Back." Mathematics 9, no. 12 (June 17, 2021): 1407. http://dx.doi.org/10.3390/math9121407.

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The paper presents an analysis and overview of vertex–frequency analysis, an emerging area in graph signal processing. A strong formal link of this area to classical time–frequency analysis is provided. Vertex–frequency localization-based approaches to analyzing signals on the graph emerged as a response to challenges of analysis of big data on irregular domains. Graph signals are either localized in the vertex domain before the spectral analysis is performed or are localized in the spectral domain prior to the inverse graph Fourier transform is applied. The latter approach is the spectral form of the vertex–frequency analysis, and it will be considered in this paper since the spectral domain for signal localization is well ordered and thus simpler for application to the graph signals. The localized graph Fourier transform is defined based on its counterpart, the short-time Fourier transform, in classical signal analysis. We consider various spectral window forms based on which these transforms can tackle the localized signal behavior. Conditions for the signal reconstruction, known as the overlap-and-add (OLA) and weighted overlap-and-add (WOLA) methods, are also considered. Since the graphs can be very large, the realizations of vertex–frequency representations using polynomial form localization have a particular significance. These forms use only very localized vertex domains, and do not require either the graph Fourier transform or the inverse graph Fourier transform, are computationally efficient. These kinds of implementations are then applied to classical time–frequency analysis since their simplicity can be very attractive for the implementation in the case of large time-domain signals. Spectral varying forms of the localization functions are presented as well. These spectral varying forms are related to the wavelet transform. For completeness, the inversion and signal reconstruction are discussed as well. The presented theory is illustrated and demonstrated on numerical examples.

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38

Vardy, Mark E., and Timothy J. Henstock. "A frequency-approximated approach to Kirchhoff migration." GEOPHYSICS 75, no. 6 (November 2010): S211—S218. http://dx.doi.org/10.1190/1.3491196.

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The integral solution of the wave equation has long been one of the most popular methods for imaging (Kirchhoff migration) and inverting (Kirchhoff inversion) seismic data. For efficiency, this process is commonly formulated as a time-domain operation on each trace, applying antialiasing through high-cut filtering of the operator or pre-/postmigration dip filtering. Migration in the time domain, however, does not allow for velocity dispersion; standard antialiasing methods assume a flat reflector and tend to overfilter the data. We have recast the Kirchhoff integral in the frequency domain, enabling robust antialias filtering through appropriate dip limiting of each frequency and implicit accommodation of true dispersion. Full frequency decomposition of the input seismogram can be approximated by band-pass filtering (or correlation with band-limited source sweeps for Chirp/Vibroseisdata) into a few narrow-band traces that cumulatively retain the full source bandwidth. From prior knowledge of the source waveform, we have defined suitable bandwidths to describe broadband (3.0 octaves) data using just six frequency bands. Kirchhoff migration of these narrow-band traces using coefficients determined at their central frequencies significantly improves the preservation of higher frequencies and cancellation of steeply dipping aliased energy over traditional time-domain antialiasing methods. If, however, two bands per octave cease to be a robust approach, our frequency-approximated approach provides the processor with ultimate control over the frequency decimation, balancing increased resolution afforded by more bands against computing cost, whereas the number of frequency bands is few enough to permit detailed control over frequency-dependent antialias filtering parameters.

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39

Lee, Joon-Ho, Sung-Woo Cho, Sang-Hong Park, and Kyung-Tae Kim. "PERFORMANCE ANALYSIS OF RADAR TARGET RECOGNITION USING NATURAL FREQUENCY: FREQUENCY DOMAIN APPROACH." Progress In Electromagnetics Research 132 (2012): 315–45. http://dx.doi.org/10.2528/pier12071107.

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40

Jung, Do Hyun, and Sung In Bae. "Automotive Component Fatigue Life Estimation by Frequency Domain Approach." Key Engineering Materials 297-300 (November 2005): 1776–83. http://dx.doi.org/10.4028/www.scientific.net/kem.297-300.1776.

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Time domain approach with S-N approach and local strain approach were used for fatigue life estimation. But these days, using PSD (Power Spectral Density) method is highlighted, because of short amount of time in measurement and analysis. Especially, PSD method is useful for analysis of fatigue failure which is caused by vibration damage, also FRF (Frequency Response Function) is useful for efficient prediction of fatigue life when the same product is employing different motor vehicle or test condition. In order to estimate fatigue life of compressor for air conditioning, time domain analysis and frequency domain analysis were performed and the results were compared. As a result, results of analysis in frequency domain and time domain were similar. With this, there is recognition of decreasing the period of measuring and analysis in PSD analysis. Moreover, in case of FRF pursued of a part, using FRF is applicable at fatigue life prediction in different testing condition. There was investigated an analysis method with curtailed analysis period by FRF.

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41

Gradojevic, Nikola, and Eldin Dobardzic. "Causality between regional stock markets: A frequency domain approach." Panoeconomicus 60, no. 5 (2013): 633–47. http://dx.doi.org/10.2298/pan1305633g.

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Using a data set from five regional stock exchanges (Serbia, Croatia, Slovenia, Hungary and Germany), this paper presents a frequency domain analysis of a causal relationship between the returns on the CROBEX, SBITOP, CETOP and DAX indices, and the return on the major Serbian stock exchange index, BELEX 15. We find evidence of a somewhat dominant effect of the CROBEX and CETOP stock indices on the BELEX 15 stock index across a range of frequencies. The results also indicate that the BELEX 15 index and the SBITOP index interact in a bi-directional causal fashion. Finally, the DAX index movements consistently drive the BELEX 15 index returns for cycle lengths between 3 and 11 days without any feedback effect.

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42

Canales, G., and L. Mevel. "Output-only frequency-domain local approach on damage detection." IFAC Proceedings Volumes 42, no. 10 (2009): 197–202. http://dx.doi.org/10.3182/20090706-3-fr-2004.00033.

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43

Shang, Ying. "A Frequency-Domain Approach for Max-Plus Linear Systems." IFAC Proceedings Volumes 43, no. 12 (2010): 394–99. http://dx.doi.org/10.3182/20100830-3-de-4013.00065.

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44

Bernini, R., L. Crocco, A. Minardo, F. Soldovieri, and L. Zeni. "Frequency-domain approach to distributed fiber-optic Brillouin sensing." Optics Letters 27, no. 5 (March 1, 2002): 288. http://dx.doi.org/10.1364/ol.27.000288.

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45

Gangadharan, K. V., C. Sujatha, and V. Ramamurti. "Dynamic response of railroad vehicles: a frequency domain approach." International Journal of Heavy Vehicle Systems 15, no. 1 (2008): 65. http://dx.doi.org/10.1504/ijhvs.2008.017984.

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46

Sha, Shaoshu, Jinghong Chen, and Mingyu Lu. "Efficient Measurement of Impulses Based on Frequency-Domain Approach." IEEE Transactions on Instrumentation and Measurement 61, no. 6 (June 2012): 1757–64. http://dx.doi.org/10.1109/tim.2012.2184012.

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47

Hatanaka, Toshiharu, and Katsuji Uosaki. "Optimal Auxiliary Input for Fault Detection - Frequency Domain Approach -." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1994 (May 5, 1994): 151–56. http://dx.doi.org/10.5687/sss.1994.151.

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48

MORRICE, DOUGLAS J., and LEE W. SCHRUBEN. "A frequency domain metamodeling approach to transient sensitivity analysis." IIE Transactions 33, no. 3 (March 2001): 229–44. http://dx.doi.org/10.1080/07408170108936825.

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49

Rosales, A., Y. Shtessel, L. Fridman, and C. B. Panathula. "Chattering Analysis of HOSM Controlled Systems: Frequency Domain Approach." IEEE Transactions on Automatic Control 62, no. 8 (August 2017): 4109–15. http://dx.doi.org/10.1109/tac.2016.2619559.

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50

Li, Zhihui, Silong Peng, Xiyuan Hu, and Xiaojing Xu. "Antiringing image deblurring approach using frequency domain relative error." Journal of Electronic Imaging 23, no. 1 (February 4, 2014): 013015. http://dx.doi.org/10.1117/1.jei.23.1.013015.

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