The fractional Fourier transform as a biomedical signal and image processing tool: A review
This work presents a literature review of the fractional Fourier transform (FrFT) investigations and applications in the biomedical field. The FrFT is a time-frequency analysis tool that has been used for signal and image processing due to its capability in capturing the non-stationary characteristi...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udem.edu.co:11407/6031
- Acceso en línea:
- http://hdl.handle.net/11407/6031
- Palabra clave:
- Biomedical signal processing
Fractional Fourier transform
Non-stationary signals
Time-frequency analysis
calculation
extraction
filtration
fractional Fourier transform
frequency analysis
image processing
review
signal detection
- Rights
- License
- http://purl.org/coar/access_right/c_16ec
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| dc.title.none.fl_str_mv |
The fractional Fourier transform as a biomedical signal and image processing tool: A review |
| title |
The fractional Fourier transform as a biomedical signal and image processing tool: A review |
| spellingShingle |
The fractional Fourier transform as a biomedical signal and image processing tool: A review Biomedical signal processing Fractional Fourier transform Non-stationary signals Time-frequency analysis calculation extraction filtration fractional Fourier transform frequency analysis image processing review signal detection |
| title_short |
The fractional Fourier transform as a biomedical signal and image processing tool: A review |
| title_full |
The fractional Fourier transform as a biomedical signal and image processing tool: A review |
| title_fullStr |
The fractional Fourier transform as a biomedical signal and image processing tool: A review |
| title_full_unstemmed |
The fractional Fourier transform as a biomedical signal and image processing tool: A review |
| title_sort |
The fractional Fourier transform as a biomedical signal and image processing tool: A review |
| dc.subject.spa.fl_str_mv |
Biomedical signal processing Fractional Fourier transform Non-stationary signals Time-frequency analysis |
| topic |
Biomedical signal processing Fractional Fourier transform Non-stationary signals Time-frequency analysis calculation extraction filtration fractional Fourier transform frequency analysis image processing review signal detection |
| dc.subject.keyword.eng.fl_str_mv |
calculation extraction filtration fractional Fourier transform frequency analysis image processing review signal detection |
| description |
This work presents a literature review of the fractional Fourier transform (FrFT) investigations and applications in the biomedical field. The FrFT is a time-frequency analysis tool that has been used for signal and image processing due to its capability in capturing the non-stationary characteristics of real signals. Most biomedical signals are an example of such non-stationarity. Thus, the FrFT-based solutions can be formulated, aiming to enhance the health technology. As the literature review indicates, common applications of the FrFT involves signal detection, filtering and features extraction. Establishing adequate solutions for these tasks requires a proper fractional order estimation and implementing the suitable numeric approach for the discrete FrFT calculation. Since most of the reports barely describe the methodology on this regard, it is important that future works include detailed information about the implementation criteria of the FrFT. Although the applications in biomedical sciences are not yet among the most frequent FrFT fields of action, the growing interest of the scientific community in the FrFT, supports its practical usefulness for developing new biomedical tools. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2021-02-05T14:58:54Z |
| dc.date.available.none.fl_str_mv |
2021-02-05T14:58:54Z |
| dc.date.none.fl_str_mv |
2020 |
| dc.type.eng.fl_str_mv |
Review |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_efa0 |
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info:eu-repo/semantics/review |
| dc.identifier.issn.none.fl_str_mv |
2085216 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/11407/6031 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.bbe.2020.05.004 |
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2085216 10.1016/j.bbe.2020.05.004 |
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http://hdl.handle.net/11407/6031 |
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eng |
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eng |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086895645&doi=10.1016%2fj.bbe.2020.05.004&partnerID=40&md5=1782ceab0532fe652250b662d292534a |
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Escabí, M., Chapter 11 – biosignal processing. (2012) Introduction to biomedical engineering, pp. 667-746. , J.D. Enderle J.D. Bronzino Third ed. Biomedical Engineering Academic Press Tabar, Y.R., Halici, U., A novel deep learning approach for classification of EEG motor imagery signals (2016) J Neural Eng, 14 (1), p. 16003 Huang, J., Chen, B., Yao, B., He, W., ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network (2019) IEEE Access, 7, pp. 92871-92880 Tsipouras, M.G., Spectral information of EEG signals with respect to epilepsy classification (2019) Eurasip J Adv Signal Process, 2019 (1) Boashash, B., Time-frequency signal analysis and processing: a comprehensive reference (2015), Academic Press Healy, J.J., Kutay, M.A., Ozaktas, H.M., Sheridan, J.T., (2015) Linear canonical transforms: theory and applications, 198. , Springer Almeida, L.B., The fractional fourier transform and time-frequency representations (1994) IEEE Trans Signal Process, 42 (11), pp. 3084-3091 Man'ko, M.A., Man'ko, V.I., Mendes, R.V., Tomograms and other transforms: a unified view (2001) J Phys A: Math Gen, 34 (40), pp. 8321-8332 Mendlovic, D., Ozaktas, H.M., Fractional Fourier transforms and their optical implementation I (1993) J Opt Soc Am A, 10 (12), pp. 1875-1881 Ozaktas, H.M., Mendlovic, D., Fractional Fourier transform and their optical implementation. II (1993) J Opt Soc Am A, 10 (12), pp. 2522-2531 Bernardo, L.M., Soares, O.D., Fractional Fourier transforms and optical systems (1994) Optics Commun, 110 (5-6), pp. 517-522 Sejdić, E., Djurović, I., Stanković, L., Fractional Fourier transform as a signal processing tool: an overview of recent developments (2011) Signal Process, 91 (6), pp. 1351-1369 Ozaktas, H.M., Kutay, M.A., The fractional Fourier transform (2001) 2001 European Control Conference (ECC), pp. 1477-1483 Ozaktas, H.M., Kutay, M.A., Mendlovic, D., Introduction to the fractional Fourier transform and its applications (1999) Adv Imaging Electron Phys, 106, pp. 239-291 Moody, G.B., Mark, R.G., The impact of the MIT-BIH arrhythmia database (2001) IEEE Eng Med Biol Mag, 20 (3), pp. 45-50 Santhanam, B., McClellan, J., The discrete rotational Fourier transform (1996) IEEE Trans Signal Process, 44 (4), pp. 983-987 Dickinson, B.W., Steiglitz, K., Eigenvectors and functions of the discrete Fourier transform (1982) IEEE Trans Acoust Speech Signal Process, 30 (1), pp. 25-31 Pei, S.C., Yeh, M.H., Improved discrete fractional Fourier transform (1997) Optics Lett, 22 (14), pp. 1047-1049 Candan, Ç., Kutay, M.A., Ozaktas, H.M., The discrete fractional Fourier transform (2000) Water Resour Manag, 32 (12), pp. 3887-3902 Ozaktas, H.M., Ankan, O., Kutay, M.A., Bozdagt, G., Digital computation of the fractional Fourier transform (1996) IEEE Trans Signal Process, 44 (9), pp. 2141-2150 Zhang, Y.D., Wang, S.H., Yang, J.F., Zhang, Z., Phillips, P., Sun, P., A comprehensive survey on fractional Fourier transform (2017) Fundam Inform, 151 (1-4), pp. 1-48 Su, X., Tao, R., Kang, X., Analysis and comparison of discrete fractional Fourier transforms (2019) Signal Process, 160, pp. 284-298 Pei, S.C., Ding, J.J., Closed-form discrete fractional and affine Fourier transforms (2000) IEEE Trans Signal Process, 48 (5), pp. 1338-1353 Pei, S.C., Tseng, C.C., Yeh, M.H., A new discrete fractional Fourier transform based on constrained eigendecomposition of DFT matrix by largrange multiplier method (1999) IEEE Trans Circuits Syst II: Analog Digit Signal Process, 46 (9), pp. 1240-1245 Pei, S.C., Yeh, M.H., Tseng, C.C., Discrete fractional Fourier transform based on orthogonal projections (1999) IEEE Trans Signal Process, 47 (5), pp. 1335-1348 Hanna, M.T., Seif, N.P.A., Ahmed, W.A.E.M., Hermite-Gaussian-like eigenvectors of the discrete Fourier transform matrix based on the singular-value decomposition of its orthogonal projection matrices (2004) IEEE Trans Circuits Syst I: Regular Pap, 51 (11), pp. 2245-2254 Pei, S.C., Hsue, W.L., Ding, J.J., Discrete fractional Fourier transform based on new nearly tridiagonal commuting matrices (2006) IEEE Trans Signal Process, 54 (10), pp. 3815-3828 Candan, Ç., On higher order approximations for Hermite-Gaussian functions and discrete fractional Fourier transforms (2007) IEEE Signal Process Lett, 14 (10), pp. 699-702 Hanna, M.T., Direct batch evaluation of optimal orthonormal eigenvectors of the DFT matrix (2008) IEEE Trans Signal Process, 56 (5), pp. 2138-2143 Pei, S.C., Hsue, W.L., Ding, J.J., DFT-commuting matrix with arbitrary or infinite order second derivative approximation (2009) IEEE Trans Signal Process, 57 (1), pp. 390-394 Serbes, A., Durak-Ata, L., Efficient computation of DFT commuting matrices by a closed-form infinite order approximation to the second differentiation matrix (2011) Signal Process, 91 (3), pp. 582-589 Serbes, A., Durak-Ata, L., The discrete fractional Fourier transform based on the DFT matrix (2011) Signal Process, 91 (3), pp. 571-581 Candan, Ç., On the eigenstructure of DFT matrices (2011) IEEE Signal Process Mag, 28 (2), pp. 105-108 Hanna, M.T., Direct sequential evaluation of optimal orthonormal eigenvectors of the discrete Fourier transform matrix by constrained optimization (2012) Digit Signal Process: Rev J, 22 (4), pp. 681-689 Hanna, M.T., The direct batch generation of Hermite-Gaussian-like eigenvectors of the DFT matrix using the notion of matrix pseudoinverse (2013) ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, IEEE, pp. 6063-6067 Kuznetsov, A., Explicit hermite-type eigenvectors of the discrete Fourier transform (2015) SIAM J Matrix Anal Appl, 36 (4), pp. 1443-1464 De Oliveira Neto, J.R., Lima, J.B., Discrete fractional fourier transforms based on closed-form Hermite-Gaussian-like DFT eigenvectors (2017) IEEE Trans Signal Process, 65 (23), pp. 6171-6184 De Oliveira Neto, J.R., Lima, J.B., da Silva, G.J., Campello de Souza, R.M., Computation of an eigendecomposition-based discrete fractional Fourier transform with reduced arithmetic complexity (2019) Signal Process, 165, pp. 72-82 Santhanam, B., McClellan, J., The DRFT-a rotation in time-frequency space (1995) 1995 International Conference on Acoustics, Speech, and Signal Processing, IEEE, pp. 921-924 Cariolaro, G., Erseghe, T., Kraniauskas, P., Laurenti, N., A unified framework for the fractional Fourier transform (1998) IEEE Trans Signal Process, 46 (12), pp. 3206-3219 Richman, M.S., Parks, T.W., Understanding discrete rotations (1997) ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, vol. 3, IEEE, pp. 2057-2060 Deng, X., Li, Y., Fan, D., Qiu, Y., A fast algorithm for fractional Fourier transforms (1997) Optics Commun, 138 (4-6), pp. 270-274 Ikram, M.Z., Abed-Meraim, K., Hua, Y., Fast quadratic phase transform for estimating the parameters of multicomponent chrip signals (1997) Digit Signal Process: Rev J, 7 (2), pp. 127-135 Bi, G., Wei, Y., Li, G., Wan, C., Si, B., Radix-2 DIF fast algorithms for polynomial time-frequency transforms (2006) IEEE Trans Aerosp Electron Syst, 42 (4), pp. 1540-1546 Ju, Y., Bi, G., Generalized fast algorithms for the polynomial time-frequency transform (2007) IEEE Trans Signal Process, 55 (10), pp. 4907-4915 Bi, G., Ju, Y., Li, X., Fast algorithms for polynomial time-frequency transforms of real-valued sequences (2008) IEEE Trans Signal Process, 56 (5), pp. 1905-1915 Ozaktas, H.M., Mendlovic, D., Onural, L., Barshan, B., Convolution, filtering, and multiplexing in fractional Fourier domains and their relation to chirp and wavelet transforms (1994) J Opt Soc Am A, 11 (2), pp. 547-559 Ozaktas, H.M., Barshan, B., Mendlovic, D., Convolution and filtering in fractional Fourier domains (1994) Opt Rev, 1 (1), pp. 15-16 Kutay, M.A., Ozaktas, H.M., Ankan, O., Optimal filtering in fractional Fourier domains (1997) IEEE Trans Signal Process, 45 (5), pp. 1129-1143 Erden, M.F., Kutay, M.A., Ozaktas, H.M., Applications of the fractional Fourier transform to filtering, estimation and restoration (1999) NSIP, pp. 481-485 Durak, L., Aldirmaz, S., Adaptive fractional Fourier domain filtering (2010) Signal Process, 90 (4), pp. 1188-1196 Kumar, S., Saxena, R., ϕFrMF: fractional Fourier matched filter (2018) Circuits Syst Signal Process, 37 (1), pp. 49-80 Zhang, X.Z., Ling, B.W.K., Dam, H.H., Teo, K.L., Wu, C., Optimal joint design of discrete fractional Fourier transform matrices and mask coefficients for multichannel filtering in fractional Fourier domains (2018) IEEE Trans Signal Process, 66 (22), pp. 6016-6030 Zhao, Y., Yu, H., Wei, G., Ji, F., Chen, F., Parameter estimation of wideband underwater acoustic multipath channels based on fractional Fourier transform (2016) IEEE Trans Signal Process, 64 (20), pp. 5396-5408 Lu, Y., Kasaeifard, A., Oruklu, E., Saniie, J., Fractional Fourier transform for ultrasonic chirplet signal decomposition (2012) Adv Acoust Vib, , 2012 Bhalke, D.G., Rao, C.B., Bormane, D.S., Automatic musical instrument classification using fractional Fourier transform based- MFCC features and counter propagation neural network (2016) J Intell Inf Syst, 46 (3), pp. 425-446 Shi, Q., Li, W., Tao, R., Classification in remote sensing imagery (2018) 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), IEEE, pp. 1-5 Gu, F.C., Chen, H.C., Chen, B.Y., A fractional Fourier transform-based approach for gas-insulated switchgear partial discharge recognition (2019) J Electr Eng Technol, 14 (5), pp. 2073-2084 Kumar, S., Saxena, R., Singh, K., Fractional Fourier transform and fractional-order calculus-based image edge detection (2017) Circuits Syst Signal Process, 36 (4), pp. 1493-1513 Saxena, N., Sharma, K.K., Pansharpening scheme using filtering in twodimensional discrete fractional Fourier transform (2018) IET Image Process, 12 (6), pp. 1013-1019 Qiu, F., Liu, Z., Liu, R., Quan, X., Tao, C., Wang, Y., Fluid flow signals processing based on fractional Fourier transform in a stirred tank reactor (2019) ISA Trans, 90, pp. 268-277 Chen, H., Liu, Z., Chen, Q., Blondel, W., Varis, P., Color image cryptosystem using Fresnel diffraction and phase modulation in an expanded fractional Fourier transform domain (2018) Laser Phys, 28 (5) Liu, Z., Chen, H., Blondel, W., Shen, Z., Liu, S., Image security based on iterative random phase encoding in expanded fractional Fourier transform domains (2018) Optics Lasers Eng, 105 (December 2017), pp. 1-5 Chen, H., Liu, Z., Zhu, L., Tanougast, C., Blondel, W., Asymmetric color cryptosystem using chaotic Ushiki map and equal modulus decomposition in fractional Fourier transform domains (2019) Optics Lasers Eng, 112 (August 2018), pp. 7-15 Liansheng, S., Xiao, Z., Chongtian, H., Ailing, T., Krishna Asundi, A., Silhouette-free interference-based multiple-image encryption using cascaded fractional Fourier transforms (2019) Optics Lasers Eng, 113 (September 2018), pp. 29-37 Yu, S.S., Zhou, N.R., Gong, L.H., Nie, Z., Optical image encryption algorithm based on phase-truncated short-time fractional Fourier transform and hyper-chaotic system (2020) Optics Lasers Eng, 124 (July 2019) Farah, M.A., Guesmi, R., Kachouri, A., Samet, M., A novel chaos based optical image encryption using fractional Fourier transform and DNA sequence operation (2020) Optics Laser Technol, 121 (April 2019), p. 105777 Pedersen, A.F., Simons, H., Detlefs, C., Poulsen, H.F., The fractional Fourier transform as a simulation tool for lens-based X-ray microscopy (2018) J Synchrotron Radiat, 125 (3), pp. 717-728 Yang, L., Guo, P., Yang, A., Qiao, Y., Blind third-order dispersion estimation based on fractional Fourier transformation for coherent optical communication (2018) Optics Laser Technol, 99, pp. 86-90 Habibi, F., Moradi, M., Propagation of an airy beam through atmospheric turbulence with optical vortex under fractional Fourier transforms (2018) Optics Laser Technol, 107, pp. 313-324 Habibi, F., Moradi, M., Ansari, A., Study on the Mainardi beam through the fractional Fourier transforms system (2018) Comput Optics, 42 (5), pp. 751-757 Saad, F., Ebrahim, A.A., Khouilid, M., Belafhal, A., Fractional Fourier transform of double-half inverse Gaussian hollow beams (2018) Opt Quantum Electron, 50 (2), pp. 1-12 Sreekumar, G., Mary, L., Unnikrishnan, A., Performance analysis of fractional Fourier domain beam-forming methods for sensor arrays (2018) Smart Sci, 7 (1), pp. 28-38 Hanbali, S.B.S., Kastantin, R., Fractional Fourier transform-based chirp radars for countering self-protection frequency-shifting jammers (2017) Int J Microw Wirel Technol, 9 (8), pp. 1687-1693 Wang, F., Wang, Y., Liu, J., Wang, Y., Optical excitation fractional Fourier transform (FrFT) based enhanced thermal-wave radar imaging (TWRI) (2018) Optics Express, 26 (17), pp. 21403-21417 Tang, Z., Bao, Q., Chen, Z., Lin, C., Wang, S., A new target detection method for noncooperative bistatic radar based on fractional Fourier transform and wavelet transform (2018) Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, IEEE, pp. 834-838 Fang, X., Cao, Z., Min, R., Pi, Y., Radar maneuvering target detection based on two steps scaling and fractional Fourier transform (2019) Signal Process, 155, pp. 1-13 Gaglione, D., Clemente, C., Ilioudis, C.V., Persico, A.R., Proudler, I.K., Soraghan, J.J., Waveform design for communicating radar systems using fractional Fourier transform (2018) Digit Signal Process: Rev J, 80, pp. 57-69 Ali, M., Ahn, C.W., Pant, M., An efficient lossless robust watermarking scheme by integrating redistributed invariant wavelet and fractional Fourier transforms (2018) Multimed Tools Appl, 77 (10), pp. 11751-11773 Abdelhakim, A.M., Saad, M.H., Sayed, M., Saleh, H.I., Optimized SVD-based robust watermarking in the fractional Fourier domain (2018) Multimed Tools Appl, 77 (21), pp. 27895-27917 Zhang, X.Z., Li, Y., Ling, B.W.K., Song, C., Teo, K.L., Spread spectrum compressed sensing magnetic resonance imaging via fractional Fourier transform (2017) 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 90-93 Naveen Kumar, R., Jagadale, B.N., Bhat, J.S., A lossless image compression algorithm using wavelets and fractional Fourier transform (2019) SN Appl Sci, 1 (3), pp. 1-8 Gong, L., Deng, C., Pan, S., Zhou, N., Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform (2018) Optics Laser Technol, 103, pp. 48-58 Zhang, D., Liao, X., Yang, B., Zhang, Y., A fast and efficient approach to color-image encryption based on compressive sensing and fractional Fourier transform (2018) Multimed Tools Appl, 77 (2), pp. 2191-2208 Chen, B., Yu, M., Tian, Y., Li, L., Wang, D., Sun, X., Multiple-parameter fractional quaternion Fourier transform and its application in colour image encryption (2018) IET Image Process, 12 (12), pp. 2238-2249 Li, Y., Song, Z., Sha, X., The multi-weighted type fractional fourier transform scheme and its application over wireless communications (2018) Eurasip J Wirel Commun Netw 2018, 1 Li, J., Sha, X., Fang, X., Mei, L., Dxwkru, F., Vkd, H., 8-Weighted-type fractional Fourier transform based three-branch transmission method (2018) China Commun, 15 (9), pp. 147-159 Ni, L., Da, X., Hu, H., Liang, Y., Xu, R., PHY-aided secure communication via weighted fractional Fourier transform (2018) Wirel Commun Mob Comput 2018 Zhou, L., Zhao, Q., Chi, S., Li, Y., Liu, L., Yu, Q., A fractional Fourier transform-based channel estimation algorithm in single-carrier direct sequence code division multiple access underwater acoustic communication system (2019) Int J Distrib Sens Netw, 15 (1) Zhang, Y., Zhang, Q., Wu, S., Biomedical signal detection based on fractional Fourier transform (2008) 5th Int. Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in Conjunction With 2nd Int. Symposium and Summer School on Biomedical and Health Engineering, IS3BHE, 2008, pp. 349-352 Iwai, R., Yoshimura, H., A new method for improving robustness of registered fingerprint data using the fractional Fourier transform (2010) Int J Commun Netw Syst Sci, 3 (9), pp. 722-729 Iwai, R., Yoshimura, H., New method for increasing matching accuracy and reducing process time of fingerprint data by the fractional Fourier transform (2010) Proceedings – International Conference on Image Processing, ICIP, pp. 3061-3064 Iwai, R., Yoshimura, H., Matching accuracy analysis of fingerprint templates generated by data processing method using the fractional Fourier transform (2011) Int J Commun Netw Syst Sci, 4 (1), pp. 24-32 Iwai, R., Yoshimura, H., Accuracy analysis in fingerprint authentication using the fractional Fourier transform without misalignment correction of scanned images (2012) Int J Commun Netw Syst Sci, 5 (3), pp. 178-186 Guerrero-Mosquera, C., Verleysen, M., Navia Vazquez, A., EEG feature selection using mutual information and support vector machine: a comparative analysis (2010) 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, IEEE, pp. 4946-4949 Subramaniam, S.R., Hon, T.K., Georgakis, A., Papadakis, G., Fractional fourier-based filter for denoising elastograms (2010) 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 4028-4031 Madhu, A., Jayasree, V.K., Thomas, V., Seizure detection in epileptic EEGs using short time fractional Fourier transform (2011) Int J Adv Eng Emerg Technol (IJAEET), 2 (2), pp. 9-16 Rizwan-I-Haque, I., Khan, M.F., Saleem, M., Rao, N.I., Network weight adjustment in a fractional fourier transform based multi-channel brain computer interface for person authentication (2012) 2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA), IEEE, pp. 900-905 Zheng, L., Shi, D., Maximum amplitude method for estimating compact fractional Fourier domain (2010) IEEE Signal Process Lett, 17 (3), pp. 293-296 Gencer, M., Bilgin, G., Aydin, N., Embolic Doppler ultrasound signal detection via fractional Fourier transform (2013) 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3050-3053 Wang, S., Zhang, Y., Yang, X., Sun, P., Dong, Z., Liu, A., Pathological brain detection by a novel image feature-fractional Fourier entropy (2015) Entropy, 17 (12), pp. 8278-8296 Zhang, Y.D., Chen, S., Wang, S.H., Yang, J.F., Phillips, P., Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine (2015) Int J Imaging Syst Technol, 25 (4), pp. 317-327 Zhang, Y.D., Sun, Y., Phillips, P., Liu, G., Zhou, X., Wang, S., A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy (2016) J Med Syst, 40 (7) Zhang, Y.D., Wang, S.H., Liu, G., Yang, J., Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform (2016) Adv Mech Eng, 8 (2), pp. 1-11 Wang, S., Yang, M., Zhang, Y., Li, J., Zou, L., Lu, S., Detection of left-sided and right-sided hearing loss via fractional Fourier transform (2016) Entropy, 18 (5), pp. 1-10 Sud, S., Blind separation of twin fetal heartbeats in an electrocardiogram using the fractional Fourier transform (2016) Int J Eng Res Appl, 6 (4), pp. 14-18 Kumar, P., Kansal, S., Noise removal in speech signal using fractional Fourier transform (2017) 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), pp. 1-4 Fei, K., Wang, W., Yang, Q., Tang, S., Chaos feature study in fractional Fourier domain for preictal prediction of epileptic seizure (2017) Neurocomputing, 249, pp. 290-298 Awal, M.A., Ouelha, S., Dong, S., Boashash, B., A robust high-resolution time-frequency representation based on the local optimization of the short-time fractional Fourier transform (2017) Digit Signal Process: Rev J, 70, pp. 125-144 Belousov, Y.M., Elkin, N.N., Man'ko, V.I., Popov, E.G., Revenko, S.V., Tomographic representation of electrocardiogram signals (2018) J Russ Laser Res, 39 (3), pp. 302-313 Zhang, Y., Hu, Q., Guo, Z., Xu, J., Xiong, K., Multi-class brain images classification based on reality-preserving fractional Fourier transform and adaboost (2018) 2018 3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018, vol. 2, IEEE, pp. 444-447 Keshishzadeh, S., Fallah, A., Rashidi, S., Electroencephalogram based biometrics: a fractional Fourier transform approach (2018) Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications, Association for Computing Machinery, pp. 1-5 Guo, Z.P., Xin, Y., Zhao, Y.Z., Cancer classification using entropy analysis in fractional Fourier domain of gene expression profile (2018) Biotechnol Biotechnol Equip, 32 (4), pp. 1042-1046 Gupta, V., Mittal, M., A comparison of ECG signal pre-processing using FrFT (2019) FrWT IPCA Improv Anal Irbm, 40 (3), pp. 145-156 Mendlovic, D., Zalevsky, Z., Mas, D., García, J., Ferreira, C., Fractional wavelet transform (1997) Appl Optics, 36 (20), pp. 4801-4806 Yang, Q., Wu, Y., Cao, R., Brain–computer interface system of steady-state visual evoked potentials based on fractional domain features (2019) Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019, IEEE, pp. 1128-1131 Bajaj, A., Kumar, S., QRS complex detection using fractional Stockwell transform and fractional Stockwell Shannon energy (2019) Biomed Signal Process Control, 54, p. 101628 Wang, T., Liu, N., Su, Z., Li, C., A new time-frequency feature extraction method for action detection on artificial knee by fractional Fourier transform (2019) Micromachines, 10 (5) Abduh, Z., Nehary, E.A., Wahed, M.A., Kadah, Y.M., Classification of heart sounds using fractional Fourier transform based mel-frequency spectral coefficients and stacked autoencoder deep neural network (2019) J Med Imaging Health Inform, 9 (1), pp. 1-8 Abduh, Z., Nehary, E.A., Abdel Wahed, M., Kadah, Y.M., Classification of heart sounds using fractional Fourier transform based mel-frequency spectral coefficients and traditional classifiers (2020) Biomed Signal Process Control, 57, p. 101788 Bultheel, A., Martínez Sulbaran, H.E., Computation of the fractional Fourier transform (2004) Appl Comput Harmonic Anal, 16 (3), pp. 182-202 Sreekumar, G., Mary, L., Unnikrishnan, A., Beam-forming of broadband QFM signals using generalized time-frequency transform (2019) Int J Electron, 0 (0), p. 1 Xu, X., Wang, Y., Chen, S., Medical image fusion using discrete fractional wavelet transform (2016) Biomed Signal Process Control, 27, pp. 103-111 Abdelliche, F., Charef, A., Ladaci, S., Complex fractional and complex morlet wavelets for QRS complex detection (2014) 2014 International Conference on Fractional Differentiation and Its Applications, ICFDA 2014, IEEE, pp. 1-5 |
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Elsevier Sp. z o.o. |
| dc.publisher.program.spa.fl_str_mv |
Ingeniería de Sistemas |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias Básicas |
| publisher.none.fl_str_mv |
Elsevier Sp. z o.o. |
| dc.source.none.fl_str_mv |
Biocybernetics and Biomedical Engineering |
| institution |
Universidad de Medellín |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad de Medellin |
| repository.mail.fl_str_mv |
repositorio@udem.edu.co |
| _version_ |
1814159230016421888 |
| spelling |
20202021-02-05T14:58:54Z2021-02-05T14:58:54Z2085216http://hdl.handle.net/11407/603110.1016/j.bbe.2020.05.004This work presents a literature review of the fractional Fourier transform (FrFT) investigations and applications in the biomedical field. The FrFT is a time-frequency analysis tool that has been used for signal and image processing due to its capability in capturing the non-stationary characteristics of real signals. Most biomedical signals are an example of such non-stationarity. Thus, the FrFT-based solutions can be formulated, aiming to enhance the health technology. As the literature review indicates, common applications of the FrFT involves signal detection, filtering and features extraction. Establishing adequate solutions for these tasks requires a proper fractional order estimation and implementing the suitable numeric approach for the discrete FrFT calculation. Since most of the reports barely describe the methodology on this regard, it is important that future works include detailed information about the implementation criteria of the FrFT. Although the applications in biomedical sciences are not yet among the most frequent FrFT fields of action, the growing interest of the scientific community in the FrFT, supports its practical usefulness for developing new biomedical tools. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of SciencesengElsevier Sp. z o.o.Ingeniería de SistemasFacultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086895645&doi=10.1016%2fj.bbe.2020.05.004&partnerID=40&md5=1782ceab0532fe652250b662d292534a40310811093Escabí, M., Chapter 11 – biosignal processing. (2012) Introduction to biomedical engineering, pp. 667-746. , J.D. Enderle J.D. Bronzino Third ed. Biomedical EngineeringAcademic PressTabar, Y.R., Halici, U., A novel deep learning approach for classification of EEG motor imagery signals (2016) J Neural Eng, 14 (1), p. 16003Huang, J., Chen, B., Yao, B., He, W., ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network (2019) IEEE Access, 7, pp. 92871-92880Tsipouras, M.G., Spectral information of EEG signals with respect to epilepsy classification (2019) Eurasip J Adv Signal Process, 2019 (1)Boashash, B., Time-frequency signal analysis and processing: a comprehensive reference (2015), Academic PressHealy, J.J., Kutay, M.A., Ozaktas, H.M., Sheridan, J.T., (2015) Linear canonical transforms: theory and applications, 198. , SpringerAlmeida, L.B., The fractional fourier transform and time-frequency representations (1994) IEEE Trans Signal Process, 42 (11), pp. 3084-3091Man'ko, M.A., Man'ko, V.I., Mendes, R.V., Tomograms and other transforms: a unified view (2001) J Phys A: Math Gen, 34 (40), pp. 8321-8332Mendlovic, D., Ozaktas, H.M., Fractional Fourier transforms and their optical implementation I (1993) J Opt Soc Am A, 10 (12), pp. 1875-1881Ozaktas, H.M., Mendlovic, D., Fractional Fourier transform and their optical implementation. II (1993) J Opt Soc Am A, 10 (12), pp. 2522-2531Bernardo, L.M., Soares, O.D., Fractional Fourier transforms and optical systems (1994) Optics Commun, 110 (5-6), pp. 517-522Sejdić, E., Djurović, I., Stanković, L., Fractional Fourier transform as a signal processing tool: an overview of recent developments (2011) Signal Process, 91 (6), pp. 1351-1369Ozaktas, H.M., Kutay, M.A., The fractional Fourier transform (2001) 2001 European Control Conference (ECC), pp. 1477-1483Ozaktas, H.M., Kutay, M.A., Mendlovic, D., Introduction to the fractional Fourier transform and its applications (1999) Adv Imaging Electron Phys, 106, pp. 239-291Moody, G.B., Mark, R.G., The impact of the MIT-BIH arrhythmia database (2001) IEEE Eng Med Biol Mag, 20 (3), pp. 45-50Santhanam, B., McClellan, J., The discrete rotational Fourier transform (1996) IEEE Trans Signal Process, 44 (4), pp. 983-987Dickinson, B.W., Steiglitz, K., Eigenvectors and functions of the discrete Fourier transform (1982) IEEE Trans Acoust Speech Signal Process, 30 (1), pp. 25-31Pei, S.C., Yeh, M.H., Improved discrete fractional Fourier transform (1997) Optics Lett, 22 (14), pp. 1047-1049Candan, Ç., Kutay, M.A., Ozaktas, H.M., The discrete fractional Fourier transform (2000) Water Resour Manag, 32 (12), pp. 3887-3902Ozaktas, H.M., Ankan, O., Kutay, M.A., Bozdagt, G., Digital computation of the fractional Fourier transform (1996) IEEE Trans Signal Process, 44 (9), pp. 2141-2150Zhang, Y.D., Wang, S.H., Yang, J.F., Zhang, Z., Phillips, P., Sun, P., A comprehensive survey on fractional Fourier transform (2017) Fundam Inform, 151 (1-4), pp. 1-48Su, X., Tao, R., Kang, X., Analysis and comparison of discrete fractional Fourier transforms (2019) Signal Process, 160, pp. 284-298Pei, S.C., Ding, J.J., Closed-form discrete fractional and affine Fourier transforms (2000) IEEE Trans Signal Process, 48 (5), pp. 1338-1353Pei, S.C., Tseng, C.C., Yeh, M.H., A new discrete fractional Fourier transform based on constrained eigendecomposition of DFT matrix by largrange multiplier method (1999) IEEE Trans Circuits Syst II: Analog Digit Signal Process, 46 (9), pp. 1240-1245Pei, S.C., Yeh, M.H., Tseng, C.C., Discrete fractional Fourier transform based on orthogonal projections (1999) IEEE Trans Signal Process, 47 (5), pp. 1335-1348Hanna, M.T., Seif, N.P.A., Ahmed, W.A.E.M., Hermite-Gaussian-like eigenvectors of the discrete Fourier transform matrix based on the singular-value decomposition of its orthogonal projection matrices (2004) IEEE Trans Circuits Syst I: Regular Pap, 51 (11), pp. 2245-2254Pei, S.C., Hsue, W.L., Ding, J.J., Discrete fractional Fourier transform based on new nearly tridiagonal commuting matrices (2006) IEEE Trans Signal Process, 54 (10), pp. 3815-3828Candan, Ç., On higher order approximations for Hermite-Gaussian functions and discrete fractional Fourier transforms (2007) IEEE Signal Process Lett, 14 (10), pp. 699-702Hanna, M.T., Direct batch evaluation of optimal orthonormal eigenvectors of the DFT matrix (2008) IEEE Trans Signal Process, 56 (5), pp. 2138-2143Pei, S.C., Hsue, W.L., Ding, J.J., DFT-commuting matrix with arbitrary or infinite order second derivative approximation (2009) IEEE Trans Signal Process, 57 (1), pp. 390-394Serbes, A., Durak-Ata, L., Efficient computation of DFT commuting matrices by a closed-form infinite order approximation to the second differentiation matrix (2011) Signal Process, 91 (3), pp. 582-589Serbes, A., Durak-Ata, L., The discrete fractional Fourier transform based on the DFT matrix (2011) Signal Process, 91 (3), pp. 571-581Candan, Ç., On the eigenstructure of DFT matrices (2011) IEEE Signal Process Mag, 28 (2), pp. 105-108Hanna, M.T., Direct sequential evaluation of optimal orthonormal eigenvectors of the discrete Fourier transform matrix by constrained optimization (2012) Digit Signal Process: Rev J, 22 (4), pp. 681-689Hanna, M.T., The direct batch generation of Hermite-Gaussian-like eigenvectors of the DFT matrix using the notion of matrix pseudoinverse (2013) ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, IEEE, pp. 6063-6067Kuznetsov, A., Explicit hermite-type eigenvectors of the discrete Fourier transform (2015) SIAM J Matrix Anal Appl, 36 (4), pp. 1443-1464De Oliveira Neto, J.R., Lima, J.B., Discrete fractional fourier transforms based on closed-form Hermite-Gaussian-like DFT eigenvectors (2017) IEEE Trans Signal Process, 65 (23), pp. 6171-6184De Oliveira Neto, J.R., Lima, J.B., da Silva, G.J., Campello de Souza, R.M., Computation of an eigendecomposition-based discrete fractional Fourier transform with reduced arithmetic complexity (2019) Signal Process, 165, pp. 72-82Santhanam, B., McClellan, J., The DRFT-a rotation in time-frequency space (1995) 1995 International Conference on Acoustics, Speech, and Signal Processing, IEEE, pp. 921-924Cariolaro, G., Erseghe, T., Kraniauskas, P., Laurenti, N., A unified framework for the fractional Fourier transform (1998) IEEE Trans Signal Process, 46 (12), pp. 3206-3219Richman, M.S., Parks, T.W., Understanding discrete rotations (1997) ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, vol. 3, IEEE, pp. 2057-2060Deng, X., Li, Y., Fan, D., Qiu, Y., A fast algorithm for fractional Fourier transforms (1997) Optics Commun, 138 (4-6), pp. 270-274Ikram, M.Z., Abed-Meraim, K., Hua, Y., Fast quadratic phase transform for estimating the parameters of multicomponent chrip signals (1997) Digit Signal Process: Rev J, 7 (2), pp. 127-135Bi, G., Wei, Y., Li, G., Wan, C., Si, B., Radix-2 DIF fast algorithms for polynomial time-frequency transforms (2006) IEEE Trans Aerosp Electron Syst, 42 (4), pp. 1540-1546Ju, Y., Bi, G., Generalized fast algorithms for the polynomial time-frequency transform (2007) IEEE Trans Signal Process, 55 (10), pp. 4907-4915Bi, G., Ju, Y., Li, X., Fast algorithms for polynomial time-frequency transforms of real-valued sequences (2008) IEEE Trans Signal Process, 56 (5), pp. 1905-1915Ozaktas, H.M., Mendlovic, D., Onural, L., Barshan, B., Convolution, filtering, and multiplexing in fractional Fourier domains and their relation to chirp and wavelet transforms (1994) J Opt Soc Am A, 11 (2), pp. 547-559Ozaktas, H.M., Barshan, B., Mendlovic, D., Convolution and filtering in fractional Fourier domains (1994) Opt Rev, 1 (1), pp. 15-16Kutay, M.A., Ozaktas, H.M., Ankan, O., Optimal filtering in fractional Fourier domains (1997) IEEE Trans Signal Process, 45 (5), pp. 1129-1143Erden, M.F., Kutay, M.A., Ozaktas, H.M., Applications of the fractional Fourier transform to filtering, estimation and restoration (1999) NSIP, pp. 481-485Durak, L., Aldirmaz, S., Adaptive fractional Fourier domain filtering (2010) Signal Process, 90 (4), pp. 1188-1196Kumar, S., Saxena, R., ϕFrMF: fractional Fourier matched filter (2018) Circuits Syst Signal Process, 37 (1), pp. 49-80Zhang, X.Z., Ling, B.W.K., Dam, H.H., Teo, K.L., Wu, C., Optimal joint design of discrete fractional Fourier transform matrices and mask coefficients for multichannel filtering in fractional Fourier domains (2018) IEEE Trans Signal Process, 66 (22), pp. 6016-6030Zhao, Y., Yu, H., Wei, G., Ji, F., Chen, F., Parameter estimation of wideband underwater acoustic multipath channels based on fractional Fourier transform (2016) IEEE Trans Signal Process, 64 (20), pp. 5396-5408Lu, Y., Kasaeifard, A., Oruklu, E., Saniie, J., Fractional Fourier transform for ultrasonic chirplet signal decomposition (2012) Adv Acoust Vib, , 2012Bhalke, D.G., Rao, C.B., Bormane, D.S., Automatic musical instrument classification using fractional Fourier transform based- MFCC features and counter propagation neural network (2016) J Intell Inf Syst, 46 (3), pp. 425-446Shi, Q., Li, W., Tao, R., Classification in remote sensing imagery (2018) 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), IEEE, pp. 1-5Gu, F.C., Chen, H.C., Chen, B.Y., A fractional Fourier transform-based approach for gas-insulated switchgear partial discharge recognition (2019) J Electr Eng Technol, 14 (5), pp. 2073-2084Kumar, S., Saxena, R., Singh, K., Fractional Fourier transform and fractional-order calculus-based image edge detection (2017) Circuits Syst Signal Process, 36 (4), pp. 1493-1513Saxena, N., Sharma, K.K., Pansharpening scheme using filtering in twodimensional discrete fractional Fourier transform (2018) IET Image Process, 12 (6), pp. 1013-1019Qiu, F., Liu, Z., Liu, R., Quan, X., Tao, C., Wang, Y., Fluid flow signals processing based on fractional Fourier transform in a stirred tank reactor (2019) ISA Trans, 90, pp. 268-277Chen, H., Liu, Z., Chen, Q., Blondel, W., Varis, P., Color image cryptosystem using Fresnel diffraction and phase modulation in an expanded fractional Fourier transform domain (2018) Laser Phys, 28 (5)Liu, Z., Chen, H., Blondel, W., Shen, Z., Liu, S., Image security based on iterative random phase encoding in expanded fractional Fourier transform domains (2018) Optics Lasers Eng, 105 (December 2017), pp. 1-5Chen, H., Liu, Z., Zhu, L., Tanougast, C., Blondel, W., Asymmetric color cryptosystem using chaotic Ushiki map and equal modulus decomposition in fractional Fourier transform domains (2019) Optics Lasers Eng, 112 (August 2018), pp. 7-15Liansheng, S., Xiao, Z., Chongtian, H., Ailing, T., Krishna Asundi, A., Silhouette-free interference-based multiple-image encryption using cascaded fractional Fourier transforms (2019) Optics Lasers Eng, 113 (September 2018), pp. 29-37Yu, S.S., Zhou, N.R., Gong, L.H., Nie, Z., Optical image encryption algorithm based on phase-truncated short-time fractional Fourier transform and hyper-chaotic system (2020) Optics Lasers Eng, 124 (July 2019)Farah, M.A., Guesmi, R., Kachouri, A., Samet, M., A novel chaos based optical image encryption using fractional Fourier transform and DNA sequence operation (2020) Optics Laser Technol, 121 (April 2019), p. 105777Pedersen, A.F., Simons, H., Detlefs, C., Poulsen, H.F., The fractional Fourier transform as a simulation tool for lens-based X-ray microscopy (2018) J Synchrotron Radiat, 125 (3), pp. 717-728Yang, L., Guo, P., Yang, A., Qiao, Y., Blind third-order dispersion estimation based on fractional Fourier transformation for coherent optical communication (2018) Optics Laser Technol, 99, pp. 86-90Habibi, F., Moradi, M., Propagation of an airy beam through atmospheric turbulence with optical vortex under fractional Fourier transforms (2018) Optics Laser Technol, 107, pp. 313-324Habibi, F., Moradi, M., Ansari, A., Study on the Mainardi beam through the fractional Fourier transforms system (2018) Comput Optics, 42 (5), pp. 751-757Saad, F., Ebrahim, A.A., Khouilid, M., Belafhal, A., Fractional Fourier transform of double-half inverse Gaussian hollow beams (2018) Opt Quantum Electron, 50 (2), pp. 1-12Sreekumar, G., Mary, L., Unnikrishnan, A., Performance analysis of fractional Fourier domain beam-forming methods for sensor arrays (2018) Smart Sci, 7 (1), pp. 28-38Hanbali, S.B.S., Kastantin, R., Fractional Fourier transform-based chirp radars for countering self-protection frequency-shifting jammers (2017) Int J Microw Wirel Technol, 9 (8), pp. 1687-1693Wang, F., Wang, Y., Liu, J., Wang, Y., Optical excitation fractional Fourier transform (FrFT) based enhanced thermal-wave radar imaging (TWRI) (2018) Optics Express, 26 (17), pp. 21403-21417Tang, Z., Bao, Q., Chen, Z., Lin, C., Wang, S., A new target detection method for noncooperative bistatic radar based on fractional Fourier transform and wavelet transform (2018) Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, IEEE, pp. 834-838Fang, X., Cao, Z., Min, R., Pi, Y., Radar maneuvering target detection based on two steps scaling and fractional Fourier transform (2019) Signal Process, 155, pp. 1-13Gaglione, D., Clemente, C., Ilioudis, C.V., Persico, A.R., Proudler, I.K., Soraghan, J.J., Waveform design for communicating radar systems using fractional Fourier transform (2018) Digit Signal Process: Rev J, 80, pp. 57-69Ali, M., Ahn, C.W., Pant, M., An efficient lossless robust watermarking scheme by integrating redistributed invariant wavelet and fractional Fourier transforms (2018) Multimed Tools Appl, 77 (10), pp. 11751-11773Abdelhakim, A.M., Saad, M.H., Sayed, M., Saleh, H.I., Optimized SVD-based robust watermarking in the fractional Fourier domain (2018) Multimed Tools Appl, 77 (21), pp. 27895-27917Zhang, X.Z., Li, Y., Ling, B.W.K., Song, C., Teo, K.L., Spread spectrum compressed sensing magnetic resonance imaging via fractional Fourier transform (2017) 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 90-93Naveen Kumar, R., Jagadale, B.N., Bhat, J.S., A lossless image compression algorithm using wavelets and fractional Fourier transform (2019) SN Appl Sci, 1 (3), pp. 1-8Gong, L., Deng, C., Pan, S., Zhou, N., Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform (2018) Optics Laser Technol, 103, pp. 48-58Zhang, D., Liao, X., Yang, B., Zhang, Y., A fast and efficient approach to color-image encryption based on compressive sensing and fractional Fourier transform (2018) Multimed Tools Appl, 77 (2), pp. 2191-2208Chen, B., Yu, M., Tian, Y., Li, L., Wang, D., Sun, X., Multiple-parameter fractional quaternion Fourier transform and its application in colour image encryption (2018) IET Image Process, 12 (12), pp. 2238-2249Li, Y., Song, Z., Sha, X., The multi-weighted type fractional fourier transform scheme and its application over wireless communications (2018) Eurasip J Wirel Commun Netw 2018, 1Li, J., Sha, X., Fang, X., Mei, L., Dxwkru, F., Vkd, H., 8-Weighted-type fractional Fourier transform based three-branch transmission method (2018) China Commun, 15 (9), pp. 147-159Ni, L., Da, X., Hu, H., Liang, Y., Xu, R., PHY-aided secure communication via weighted fractional Fourier transform (2018) Wirel Commun Mob Comput 2018Zhou, L., Zhao, Q., Chi, S., Li, Y., Liu, L., Yu, Q., A fractional Fourier transform-based channel estimation algorithm in single-carrier direct sequence code division multiple access underwater acoustic communication system (2019) Int J Distrib Sens Netw, 15 (1)Zhang, Y., Zhang, Q., Wu, S., Biomedical signal detection based on fractional Fourier transform (2008) 5th Int. Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in Conjunction With 2nd Int. Symposium and Summer School on Biomedical and Health Engineering, IS3BHE, 2008, pp. 349-352Iwai, R., Yoshimura, H., A new method for improving robustness of registered fingerprint data using the fractional Fourier transform (2010) Int J Commun Netw Syst Sci, 3 (9), pp. 722-729Iwai, R., Yoshimura, H., New method for increasing matching accuracy and reducing process time of fingerprint data by the fractional Fourier transform (2010) Proceedings – International Conference on Image Processing, ICIP, pp. 3061-3064Iwai, R., Yoshimura, H., Matching accuracy analysis of fingerprint templates generated by data processing method using the fractional Fourier transform (2011) Int J Commun Netw Syst Sci, 4 (1), pp. 24-32Iwai, R., Yoshimura, H., Accuracy analysis in fingerprint authentication using the fractional Fourier transform without misalignment correction of scanned images (2012) Int J Commun Netw Syst Sci, 5 (3), pp. 178-186Guerrero-Mosquera, C., Verleysen, M., Navia Vazquez, A., EEG feature selection using mutual information and support vector machine: a comparative analysis (2010) 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, IEEE, pp. 4946-4949Subramaniam, S.R., Hon, T.K., Georgakis, A., Papadakis, G., Fractional fourier-based filter for denoising elastograms (2010) 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 4028-4031Madhu, A., Jayasree, V.K., Thomas, V., Seizure detection in epileptic EEGs using short time fractional Fourier transform (2011) Int J Adv Eng Emerg Technol (IJAEET), 2 (2), pp. 9-16Rizwan-I-Haque, I., Khan, M.F., Saleem, M., Rao, N.I., Network weight adjustment in a fractional fourier transform based multi-channel brain computer interface for person authentication (2012) 2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA), IEEE, pp. 900-905Zheng, L., Shi, D., Maximum amplitude method for estimating compact fractional Fourier domain (2010) IEEE Signal Process Lett, 17 (3), pp. 293-296Gencer, M., Bilgin, G., Aydin, N., Embolic Doppler ultrasound signal detection via fractional Fourier transform (2013) 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3050-3053Wang, S., Zhang, Y., Yang, X., Sun, P., Dong, Z., Liu, A., Pathological brain detection by a novel image feature-fractional Fourier entropy (2015) Entropy, 17 (12), pp. 8278-8296Zhang, Y.D., Chen, S., Wang, S.H., Yang, J.F., Phillips, P., Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine (2015) Int J Imaging Syst Technol, 25 (4), pp. 317-327Zhang, Y.D., Sun, Y., Phillips, P., Liu, G., Zhou, X., Wang, S., A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy (2016) J Med Syst, 40 (7)Zhang, Y.D., Wang, S.H., Liu, G., Yang, J., Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform (2016) Adv Mech Eng, 8 (2), pp. 1-11Wang, S., Yang, M., Zhang, Y., Li, J., Zou, L., Lu, S., Detection of left-sided and right-sided hearing loss via fractional Fourier transform (2016) Entropy, 18 (5), pp. 1-10Sud, S., Blind separation of twin fetal heartbeats in an electrocardiogram using the fractional Fourier transform (2016) Int J Eng Res Appl, 6 (4), pp. 14-18Kumar, P., Kansal, S., Noise removal in speech signal using fractional Fourier transform (2017) 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), pp. 1-4Fei, K., Wang, W., Yang, Q., Tang, S., Chaos feature study in fractional Fourier domain for preictal prediction of epileptic seizure (2017) Neurocomputing, 249, pp. 290-298Awal, M.A., Ouelha, S., Dong, S., Boashash, B., A robust high-resolution time-frequency representation based on the local optimization of the short-time fractional Fourier transform (2017) Digit Signal Process: Rev J, 70, pp. 125-144Belousov, Y.M., Elkin, N.N., Man'ko, V.I., Popov, E.G., Revenko, S.V., Tomographic representation of electrocardiogram signals (2018) J Russ Laser Res, 39 (3), pp. 302-313Zhang, Y., Hu, Q., Guo, Z., Xu, J., Xiong, K., Multi-class brain images classification based on reality-preserving fractional Fourier transform and adaboost (2018) 2018 3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018, vol. 2, IEEE, pp. 444-447Keshishzadeh, S., Fallah, A., Rashidi, S., Electroencephalogram based biometrics: a fractional Fourier transform approach (2018) Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications, Association for Computing Machinery, pp. 1-5Guo, Z.P., Xin, Y., Zhao, Y.Z., Cancer classification using entropy analysis in fractional Fourier domain of gene expression profile (2018) Biotechnol Biotechnol Equip, 32 (4), pp. 1042-1046Gupta, V., Mittal, M., A comparison of ECG signal pre-processing using FrFT (2019) FrWT IPCA Improv Anal Irbm, 40 (3), pp. 145-156Mendlovic, D., Zalevsky, Z., Mas, D., García, J., Ferreira, C., Fractional wavelet transform (1997) Appl Optics, 36 (20), pp. 4801-4806Yang, Q., Wu, Y., Cao, R., Brain–computer interface system of steady-state visual evoked potentials based on fractional domain features (2019) Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019, IEEE, pp. 1128-1131Bajaj, A., Kumar, S., QRS complex detection using fractional Stockwell transform and fractional Stockwell Shannon energy (2019) Biomed Signal Process Control, 54, p. 101628Wang, T., Liu, N., Su, Z., Li, C., A new time-frequency feature extraction method for action detection on artificial knee by fractional Fourier transform (2019) Micromachines, 10 (5)Abduh, Z., Nehary, E.A., Wahed, M.A., Kadah, Y.M., Classification of heart sounds using fractional Fourier transform based mel-frequency spectral coefficients and stacked autoencoder deep neural network (2019) J Med Imaging Health Inform, 9 (1), pp. 1-8Abduh, Z., Nehary, E.A., Abdel Wahed, M., Kadah, Y.M., Classification of heart sounds using fractional Fourier transform based mel-frequency spectral coefficients and traditional classifiers (2020) Biomed Signal Process Control, 57, p. 101788Bultheel, A., Martínez Sulbaran, H.E., Computation of the fractional Fourier transform (2004) Appl Comput Harmonic Anal, 16 (3), pp. 182-202Sreekumar, G., Mary, L., Unnikrishnan, A., Beam-forming of broadband QFM signals using generalized time-frequency transform (2019) Int J Electron, 0 (0), p. 1Xu, X., Wang, Y., Chen, S., Medical image fusion using discrete fractional wavelet transform (2016) Biomed Signal Process Control, 27, pp. 103-111Abdelliche, F., Charef, A., Ladaci, S., Complex fractional and complex morlet wavelets for QRS complex detection (2014) 2014 International Conference on Fractional Differentiation and Its Applications, ICFDA 2014, IEEE, pp. 1-5Biocybernetics and Biomedical EngineeringBiomedical signal processingFractional Fourier transformNon-stationary signalsTime-frequency analysiscalculationextractionfiltrationfractional Fourier transformfrequency analysisimage processingreviewsignal detectionThe fractional Fourier transform as a biomedical signal and image processing tool: A reviewReviewinfo:eu-repo/semantics/reviewhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_efa0Gómez-Echavarría, A., Facultad de Ciencias Básicas, Universidad de Medellín, Medellín, ColombiaUgarte, J.P., GIMSC, Universidad de San Buenaventura, Medellín, ColombiaTobón, C., Facultad de Ciencias Básicas, Universidad de Medellín, Medellín, Colombiahttp://purl.org/coar/access_right/c_16ecGómez-Echavarría A.Ugarte J.P.Tobón C.11407/6031oai:repository.udem.edu.co:11407/60312021-02-05 09:58:54.266Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co |