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    dc.contributor.authorMendoza, Luis Enrique-
    dc.date.accessioned2022-09-28T22:11:10Z-
    dc.date.available2021-
    dc.date.available2022-09-28T22:11:10Z-
    dc.date.issued2021-
    dc.identifier.citationMendoza, L. (2021). Procesamiento de Datos Discretos en 1D y 2D: Fourier, Coseno y Wavelet. Sello Editorial Universidad de Pamplona. http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3072es_CO
    dc.identifier.isbn978-958-53020-3-7.-
    dc.identifier.urihttp://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3072-
    dc.descriptionEl procesamiento de datos, en la actualidad, está siendo una de las áreas que más importancia y más crecimiento ha tenido en los últimos años, esto es debido a sus múltiples aplicaciones. Es así, como se puede decir que el procesamiento de datos es una herramienta fundamental y transversal para las áreas que actualmente tienen auge. El procesamiento de datos se puede ver, de manera sencilla, como una forma o método de obtener, conseguir o visualizar información relevante, que en el dominio del tiempo es poco probable observarla, como la frecuencia de una señal. En este libro se presenta, los métodos matemáticos en 1D y 2D, de herramientas matemáticas como: La transformada de Fourier, la transformada discreta del coseno y la transformada wavelet discreta, así mismo este libro presenta resultados de aplicaciones como: eliminación de ruido, compresión y cifrado.es_CO
    dc.format.extent269es_CO
    dc.format.mimetypeapplication/pdfes_CO
    dc.language.isoeses_CO
    dc.publisherUniversidad de Pamplona, Sello Editorial – Facultad de Ingenierías y Arquitectura – Tecnología e Ingenierías.es_CO
    dc.subjectProcesamiento.es_CO
    dc.subjectDatos.es_CO
    dc.titleProcesamiento de Datos Discretos en 1D y 2D: Fourier, Coseno y Wavelet.es_CO
    dc.typehttp://purl.org/coar/resource_type/c_2f33es_CO
    dc.date.accepted2022-09-28-
    dc.relation.referencesStark, R. W., & Heckl, W. M. (2000). Fourier transformed atomic force microscopy: tapping mode atomic force microscopy beyond the Hookian approximation. Surface Science, 457(1-2), 219-228.es_CO
    dc.relation.referencesRockley, M. G. (1979). Fourier-transformed infrared photoacoustic spectroscopy of polystyrene film. Chemical Physics Letters, 68(2-3), 455-456.es_CO
    dc.relation.referencesOzaktas, H. M., & Kutay, M. A. (2001, September). The fractional Fourier transform. In 2001 European Control Conference (ECC)(pp. 1477-1483). IEEE.es_CO
    dc.relation.referencesOzaktas, H. M., Arikan, O., Kutay, M. A., & Bozdagt, G. (1996). Digital computation of the fractional Fourier transform. IEEE Transactions on signal processing, 44(9), 2141-2150.es_CO
    dc.relation.referencesWeisstein, E. W. (2015). Fast fourier transform.es_CO
    dc.relation.referencesWeller, H. (2015). Fourier analysis.es_CO
    dc.relation.referencesCroft, A. (2017). Engineering mathematics. Pearson education.es_CO
    dc.relation.referencesAntoniou, A. (2016). Digital signal processing. McGraw-Hill.es_CO
    dc.relation.referencesHowell, K. B. (2016). Principles of Fourier analysis. CRC Press.es_CO
    dc.relation.referencesSogge, C. D. (2017). Fourier integrals in classical analysis (Vol. 210). Cambridge University Press.es_CO
    dc.relation.referencesBracewell, R. N., & Bracewell, R. N. (1986). The Fourier transform and its applications (Vol. 31999). New York: McGraw-Hill.es_CO
    dc.relation.referencesCruz Rodríguez, V. (2012). Diseño de un codificador de imágenes adaptativo multitransformada mediante el uso de la transformada Karhunen-Loève (Bachelor's thesis).es_CO
    dc.relation.referencesAmer, I., Hishmat, P., Badawy, W., & Jullien, G. (2010). Comparisons and Analysis of DCTbased Image Watermarking Algorithms. In Advanced Techniques in Computing Sciences and Software Engineering (pp. 55- 58). Springer, Dordrecht.es_CO
    dc.relation.referencesSoria Lorente, A., Cumbrera González, R. A., & Fonseca Reyna, Y. (2016). Algoritmo esteganográfico de clave privada en el dominio de la transformada discreta del coseno. Revista Cubana de Ciencias Informáticas, 10(2), 116-131.es_CO
    dc.relation.referencesLloris, A., Fernández, P. G., & Ramírez, J. (2001). Procesamiento de imágenes utilizando la transformada discreta coseno. Revista española de electrónica, (558), 72-75.es_CO
    dc.relation.referencesRamos, A. I. C., Riverón, E. M. F., Ramírez, P. M., & Pogrebnyak, O. B. (2016). Filtro de restauración de imágenes basado en la transformada discreta del coseno y el análisis de componentes principales. Research in Computing Science, 120, 169-178.es_CO
    dc.relation.referencesPortocarrero Rodriguez, M. A. (2018). Diseño de la arquitectura de transformada discreta directa e inversa del coseno para un decodificador HEVC.es_CO
    dc.relation.referencesSubasi, A., & Yaman, E. (2019, May). EMG Signal Classification Using Discrete Wavelet Transform and Rotation Forest. In International Conference on Medical and Biological Engineering(pp. 29-35). Springer, Cham.es_CO
    dc.relation.referencesCheca, H., & Andrés, M. (2017). Diseño e implementación de un prototipo para adquisición y compresión de señales ECG con filtros coseno modulado (Bachelor's thesis, Universidad de las Fuerzas Armadas ESPE. Carrera de Ingeniería en Electrónica, Automatización y Control.).es_CO
    dc.relation.referencesReyes Rodriguez, V. (2015). Study of accuracy and hardware performance in discrete transforms and their fast algorithms (Doctoral dissertation).es_CO
    dc.relation.referencesAvila-Domenech, E. (2018). Marca de agua digital basada en DWT-DCT para imágenes de documentos manuscritos: optimización contra ataques de compresión JPEG. Revista Cubana de Ciencias Informáticas, 12(2), 30- 43.es_CO
    dc.relation.referencesAhuja, S., & Mehan, A. (2018, April). Design of Orthogonal Wavelet for Human Palmprint Recognition. In 2018 International Conference on Intelligent Circuits and Systems (ICICS) (pp. 265-270). IEEE.es_CO
    dc.relation.referencesMoya, S., Hadad, M., Funes, M., Donato, P., & Carrica, D. (2017, September). Different alternatives for the use of Cosine Transform in OFDM systems. In 2017 XVII Workshop on Information Processing and Control (RPIC) (pp. 1-5). IEEE.es_CO
    dc.relation.referencesHernández, J. L., Bautista, C. V., Miyatake, M. N., & Meana, H. P. (2015). Algoritmo Esteganografico Robusto a Compresión JPEG Usando DCT. Instituto Politécnico Nacional, 6.es_CO
    dc.relation.referencesGarcía-Pinzón, J. A., Mendoza, L. E., & Flórez, E. G. (2015). Electronic control arm using electromyographic signals. Facultad de Ingeniería, 24(39), 71-84.es_CO
    dc.relation.referencesGarcía-Pinzón, J. A., Mendoza, L. E., & Flórez, E. G. (2015). Control de brazo electrónico usando señales electromiográficas. Facultad de Ingeniería, 24(39), 71-84.es_CO
    dc.relation.referencesGamboa Córdova, R. G. (2017). Diseño e implementación de un sistema MIMO Fast OFDM en módulos NI-USRP (Bachelor's thesis).es_CO
    dc.relation.referencesAlfonte Zapana, R. (2018). Reducción de la dimensionalidad de series temporales climáticas usando Deep Multi-Layer Autoencoder.es_CO
    dc.relation.referencesMoreno-Alvarado, R., Pérez-Meana, H., Nakano-Miyatake, M., & Robles-Camarillo, D. (2019). Método de compresión de electrocardiogramas basado en muestreo compresivo.es_CO
    dc.relation.referencesKrivoshein, A., Protasov, V., & Skopina, M. A. (2016). Multivariate wavelet frames (p. 182). Singapore: Springer.es_CO
    dc.relation.referencesAvila-Domenech, E. (2018). Marca de agua digital basada en DWT-DCT para imágenes de documentos manuscritos: optimización contra ataques de compresión JPEG. Revista Cubana de Ciencias Informáticas, 12(2), 30- 43.es_CO
    dc.relation.referencesCoy, L., Orjuela, L., & Jiménez, F. (2018). Compresión de video en Streaming usando transformadas Wavelet y DCT. Infometric@- Serie Ingeniería, Básicas y Agrícolas, 1(2).es_CO
    dc.relation.referencesSivakumar, R., & Mohan, E. (2018). High Resolution Satellite Image Enhancement Using Discrete Wavelet Transform. International Journal of Applied Engineering Research, 13(11), 9811-9815.es_CO
    dc.relation.referencesAlkawaz, M. H., Sulong, G., Saba, T., & Rehman, A. (2018). Detection of copy-move image forgery based on discrete cosine transform. Neural Computing and Applications, 30(1), 183-192.es_CO
    dc.relation.referencesMahmood, T., Mehmood, Z., Shah, M., & Saba, T. (2018). A robust technique for copymove forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. Journal of Visual Communication and Image Representation, 53, 202-214.es_CO
    dc.relation.referencesSiddiqui, M., Siddiqi, I., & Khurshid, K. (2018, March). Feature Extraction for Cursive Language Document Images: Using Discrete Cosine Transform, Discrete Wavelet Transform and Gabor Filter. In Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence (pp. 84- 87). ACM.es_CO
    dc.relation.referencesGong, L., Deng, C., Pan, S., & Zhou, N. (2018). Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform. Optics & Laser Technology, 103, 48-58.es_CO
    dc.relation.referencesLi, X. Z., Chen, W. W., & Wang, Y. Q. (2018). Quantum image compression-encryption scheme based on quantum discrete cosine transform. International Journal of Theoretical Physics, 57(9), 2904-2919.es_CO
    dc.relation.referencesAlotaibi, R. A., & Elrefaei, L. A. (2018). Textimage watermarking based on integer wavelet transform (IWT) and discrete cosine transform (DCT). Applied Computing and Informatics.es_CO
    dc.relation.referencesMiri, A., Sharifian, S., Rashidi, S., & Ghods, M. (2018). Medical image denoising based on 2D discrete cosine transform via ant colony optimization. Optik, 156, 938-948.es_CO
    dc.relation.referencesSmith, J. S., & Wilamowski, B. M. (2018, June). Discrete Cosine Transform Spectral Pooling Layers for Convolutional Neural Networks. In International Conference on Artificial Intelligence and Soft Computing (pp. 235-246). Springer, Cham.es_CO
    dc.relation.referencesDu, R. (2019). Engineering monitoring and diagnosis using wavelet transforms. In Computer-Aided Design, Engineering, and Manufacturing (pp. 312-341). CRC Press.es_CO
    dc.relation.referencesPrabukumar, M., Sawant, S., Samiappan, S., & Agilandeeswari, L. (2018). Threedimensional discrete cosine transform-based feature extraction for hyperspectral image classification. Journal of Applied Remote Sensing, 12(4), 046010.es_CO
    dc.relation.referencesChaddad, A., Daniel, P., & Niazi, T. (2018). Radiomics evaluation of histological heterogeneity using multiscale textures derived from 3D wavelet transformation of multispectral images. Frontiers in oncology, 8, 96.es_CO
    dc.relation.referencesJain, A., Pandey, N., & Jain, P. (2019). FPGABased Architecture for Implementation of Discrete Sine Transform. In Advances in System Optimization and Control (pp. 13-22). Springer, Singapore.es_CO
    dc.relation.referencesTan, E. L., & Gan, W. S. (2015). Perceptual Image Coding with Discrete Cosine Transform. New York: Springer-Verlag Singapur.es_CO
    dc.relation.referencesRao, K. R., & Ochoa-Dominguez, H. (2019). Discrete Cosine Transform. CRC Press.es_CO
    dc.relation.referencesChui, C. K. (2016). An introduction to wavelets. Elsevier.es_CO
    dc.relation.referencesNavarro J. F, Martinez D. El (2010). Introducción a la transformada wavelet continua. Ahmad, K. (2018).es_CO
    dc.relation.referencesApplications in Image Processing. In Wavelet Packets and Their Statistical Applications (pp. 203-224). Springer, Singapore.es_CO
    dc.relation.referencesAddison, P. S. (2017). The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC press.es_CO
    dc.relation.referencesAhmad, K. (2018). Applications in Image Processing. In Wavelet Packets and Their Statistical Applications (pp. 203-224). Springer, Singapore.es_CO
    dc.relation.referencesZhang, D. (2019). Wavelet transform. In Fundamentals of Image Data Mining (pp. 35-44). Springer, Cham.es_CO
    dc.relation.referencesHaldorai, A., & Ramu, A. (2018). An intelligent-based wavelet classifier for accurate prediction of breast cancer. In Intelligent Multidimensional Data and Image Processing (pp. 306-319). IGI Global.es_CO
    dc.relation.referencesHuang, Y., De Bortoli, V., Zhou, F., & Gilles, J. (2018). Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets. IET Image Processing, 12(9), 1626-1638.es_CO
    dc.relation.referencesChatterjee, P. (2015). Wavelet analysis in civil engineering. CRC Press.es_CO
    dc.relation.referencesBaleanu, D. (Ed.). (2015). Wavelet Transform and Some of Its Real-World Applications. BoD–Books on Demand.es_CO
    dc.relation.referencesRadhakrishnan, S. (Ed.). (2018). Wavelet Theory and Its Applications. BoD–Books on Demand.es_CO
    dc.relation.referencesMeng, A., Ge, J., Yin, H., & Chen, S. (2016). Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Conversion and Management, 114, 75-88.es_CO
    dc.relation.referencesZhang, Y., Dong, Z., Wang, S., Ji, G., & Yang, J. (2015). Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). Entropy, 17(4), 1795-1813.es_CO
    dc.relation.referencesWang, S., Li, Y., Shao, Y., Cattani, C., Zhang, Y., & Du, S. (2017). Detection of dendritic spines using wavelet packet entropy and fuzzy support vector machine. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders), 16(2), 116-121.es_CO
    dc.relation.referencesBai, Y., Li, Y., Wang, X., Xie, J., & Li, C. (2016). Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmospheric pollution research, 7(3), 557-566.es_CO
    dc.relation.referencesapplications (Vol. 31999). New York: McGraw-Hill.es_CO
    dc.relation.referencesLe Douget, J. E., Fouad, A., Filali, M. M., Pyrzowski, J., & Le Van Quyen, M. (2017, July). Surface and intracranial EEG spike detection based on discrete wavelet decomposition and random forest classification. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 475-478). IEEE.es_CO
    dc.relation.referencesWozniak, M., Napoli, C., Tramontana, E., Capizzi, G., Sciuto, G. L., Nowicki, R. K., & Starczewski, J. T. (2015, July). A multiscale image compressor with rbfnn and discrete wavelet decomposition. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.es_CO
    dc.relation.referencesPhinyomark, A., Nuidod, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification. Elektronika ir Elektrotechnika, 122(6), 27-32.es_CO
    dc.relation.referencesAlickovic, E., Kevric, J., & Subasi, A. (2018). Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomedical Signal Processing and Control, 39, 94-102.es_CO
    dc.relation.referencesGhassemian, H. (2016). A review of remote sensing image fusion methods. Information Fusion, 32, 75-89.es_CO
    dc.relation.referencesZheng, Y., Blasch, E., & Liu, Z. (2018). Multispectral Image Fusion and Colorization. SPIE Press.es_CO
    dc.relation.referencesSingh, A. K., Kumar, B., Dave, M., & Mohan, A. (2015). Multiple watermarking on medical images using selective discrete wavelet transform coefficients. Journal of Medical Imaging and Health Informatics, 5(3), 607- 614.es_CO
    dc.relation.referencesSudarshan, V. K., Mookiah, M. R. K., Acharya, U. R., Chandran, V., Molinari, F., Fujita, H., & Ng, K. H. (2016). Application of wavelettechniques for cancer diagnosis using ultrasound images: A Review. Computers in biology and medicine, 69, 97-111.es_CO
    dc.relation.referencesLai, Z., Qu, X., Liu, Y., Guo, D., Ye, J., Zhan, Z., & Chen, Z. (2016). Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform. Medical image analysis, 27, 93-104.es_CO
    dc.relation.referencesNayak, D. R., Dash, R., & Majhi, B. (2016). Brain MR image classification using twodimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing, 177, 188-197.es_CO
    dc.relation.referencesLi, C., Huang, Y., & Zhu, L. (2017). Color texture image retrieval based on Gaussian copula models of Gabor wavelets. Pattern Recognition, 64, 118-129.es_CO
    dc.relation.referencesOsgood, B. G. (2019). Lectures on the Fourier Transform and Its Applications (Vol. 33). American Mathematical Soc..es_CO
    dc.relation.referencesMughal, B., Muhammad, N., Sharif, M., Saba, T., & Rehman, A. (2017). Extraction of breast border and removal of pectoral muscle in wavelet domain. Biomedical Research, 28(11), 5041-5043.es_CO
    dc.relation.referencesde A Berger, P., Francisco, A. D. O., do Carmo, J. C., & da Rocha, A. F. (2006). Compression of EMG signals with wavelet transform and artificial neural networks. Physiological measurement, 27(6), 457.es_CO
    dc.relation.referencesDas, D. K., & Dutta, P. K. (2019). Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches. Computers in biology and medicine, 104, 29-42.es_CO
    dc.relation.referencesBascoy, P. G., Quesada-Barriuso, P., Heras, D. B., & Argüello, F. (2019). Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.es_CO
    dc.relation.referencesLi Vigni, M., Prats-Montalban, J. M., Ferrer, A., & Cocchi, M. (2018). Coupling 2D-wavelet decomposition and multivariate image analysis (2D WT-MIA). Journal of Chemometrics, 32(1), e2970.es_CO
    dc.relation.referencesCampos, R. G. (2019). The Ordinary Discrete Fourier Transform. In The XFT Quadrature in Discrete Fourier Analysis (pp. 3-37). Birkhäuser, Cham.es_CO
    dc.relation.referencesSubasi, A., Yaman, E., Somaily, Y., Alynabawi, H. A., Alobaidi, F., & Altheibani, S. (2018). Automated EMG Signal Classification for Diagnosis of Neuromuscular Disorders Using DWT and Bagging. Procedia Computer Science, 140, 230-237.es_CO
    dc.relation.referencesRyan, Ø. (2019). Linear Algebra, Signal Processing and Wavelets – a unified Approach. Python Version. Oslo, Norway: Springer.es_CO
    dc.relation.referencesHariharan. (2019). Wavelet Solutions for Reaction–Diffusion Problems in Science and Engineering. Tamil Nadu, India: Springer International Publishing.es_CO
    dc.relation.referencesBurger, W., & Burge, M. J. (2016). Digital image processing: an algorithmic introduction using Java. Springer.es_CO
    dc.relation.referencesAbood, S. (2020). Digital Signal Processing: A Primer with MATLAB. CRC Press.es_CO
    dc.relation.referencesPanja, M. M., & Mandal, B. N. (2020). Wavelet Based Approximation Schemes for Singular Integral Equations. Kolkata, India: CRC Press.es_CO
    dc.relation.referencesKrantz, S. (2020). Differential Equations: A Modern Approach with Wavelets. New York: Chapman and Hall/CRC.es_CO
    dc.relation.referencesS. Bird, E. Klein y E. Loper, Natural Language Processing with Python, O'Reilly Media, 2009.es_CO
    dc.relation.referencesQ. Nafiul Islam, Mastering PyCharm, Packt Publishing, 2015.es_CO
    dc.relation.references«Python Software Foundation,» 2001-2019. [En línea]. Available: https://www.python.org/downloads/. [Último acceso: 05 Abril 2019].es_CO
    dc.relation.referencesJetBrains s.r.o, «JetBrains s.r.o,» 2000-2019. [En línea]. Available: https://www.jetbrains.com/pycharm/download/# section=windows. [Último acceso: 05 Abril 2019].es_CO
    dc.relation.referencesGrigoryan, A. M., & Grigoryan, M. M. (2016). Brief notes in advanced DSP: Fourier analysis with MATLAB. CRC Press.es_CO
    dc.relation.referencesTravis E., Guide to Numpy, Oliphant , 2006.es_CO
    dc.relation.referencesS. Tosi, Matplotlib for Python Developers, Packt Publishing, 2009.es_CO
    dc.relation.referencesGrigoryan, A. M., & Grigoryan, M. M. (2016). Brief notes in advanced DSP: Fourier analysis with MATLAB. CRC Press.es_CO
    dc.relation.referencesG. Bradski y A. Kaebler, Learning OpenCV Compiter Vision with the OpenCV Library, M. Loakides, Ed., O'Reilly, 2008.es_CO
    dc.relation.referencesA. Mordvintsev y A. K., «OpenCV-Python Tutorials's documentation,» 2013. [En línea]. Available: https://opencv-pythontutroals. readthedocs.io/en/latest/. [Último acceso: 05 Abril 2019].es_CO
    dc.relation.referencesG. Gonzales, «Series de Fourier, Transformadas de Fourier y Aplicaciones,» Divulgaciones Matematicas , vol. 5, nº 1, pp. 43-67, 1997.es_CO
    dc.relation.referencesP. Athanasios, Sistemas Digitales y Analogicos Transformadas de Fourier, Estimación Espectral, Barcelona- Mexico: Marcombo Boixareu Editores, 1986.es_CO
    dc.relation.referencesA. Fournié y G. Boog, «Estudio del Ritmo Cardíaco fetal,» El Sevier, vol. 40, pp. 1-21, 2004.es_CO
    dc.relation.referencesF. Alarid Escudero, Solís Escalante, E. Melgar, R. Valdés Cristerna y Yañez Suarez, «Registro de señales de EEG para aplicaciones de Interfaz Cerebro Computadora (ICC) basado en Potenciales Evocados Visuales de Estado Estacionario (PEVEE),» Bioengineering Solutions for Latin American Health , vol. 18, pp. 87-90, 2007.es_CO
    dc.relation.referencesA. Quintero Rincon , M. Risk y S. Liberezuk, «Procesamiento de EEG con Filtros Hampel,» Argencon , vol. 2012, nº 89, 2012.es_CO
    dc.relation.referencesÁ. de la Torre Vega, Procesamiento de voz, Universidad de Granada, 2007.es_CO
    dc.relation.referencesOlson, T. (2017). The Fourier Transform. In Applied Fourier Analysis (pp. 121-148). Birkhäuser, New York, NY.es_CO
    dc.relation.referencesD. M. Ballesteros Larrotta, «Aplicación de la transformada wavelet discreta en el filtrado de señales bioeléctricas,» Umbral Cientifico, nº 5, pp. 92-98, 2004.es_CO
    dc.relation.referencesTripathy, R. K., Mendez, A. Z., de la O, S., Arrieta Paternina, M. R., Arrieta, J. G., & Naik, G. R. (2018). Detection of life threatening ventricular arrhythmia using digital taylor fourier transform. Frontiers in physiology, 9, 722.es_CO
    dc.relation.referencesJ. López Hernández, C. Velasco Bautista , M. Nakano Miyatake y H. Pérez Meana, «Algoritmo Esteganografico Robusto a Compresión JPEG Usando DCT,» San Francisco Culhuacan, Mexico D.F.es_CO
    dc.relation.referencesVan der Walt, S., Schönberger, J. L., Nunez- Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., ... & Yu, T. (2014). scikit-image: image processing in Python. PeerJ, 2, e453.es_CO
    dc.relation.referencesCanty, M. J. (2014). Image analysis, classification and change detection in remote sensing: with algorithms for ENVI/IDL and Python. Crc Press.es_CO
    dc.relation.referencesVan Rossum, G., & Drake, F. L. (2011). The python language reference manual. Network Theory Ltd..es_CO
    dc.relation.referencesLynch, S. (2018). Image Processing with Python. In Dynamical Systems with Applications using Python (pp. 471-489). Birkhäuser, Cham.es_CO
    dc.relation.referencesLoredo, T., & Scargle, J. (2019, March). Time series exploration in Python and MATLAB: Unevenly sampled data, parametric modeling, and periodograms. In AAS/High Energy Astrophysics Division (Vol. 17).es_CO
    dc.relation.referencesTuck, J. (2018). Estimating the Discrete Fourier Transform using Deep Learning.es_CO
    dc.relation.referencesPine, D. J. (2019). Introduction to Python for Science and Engineering. CRC Press.es_CO
    dc.relation.referencesArias Páez, A. S., & Rubiano Venegas, D. A. (2018). Método automático de reconocimiento de voz para la clasificación de vocales al lenguaje de señas colombiano.es_CO
    dc.relation.referencesWang, S., Yang, M., Zhang, Y., Li, J., Zou, L., Lu, S., ... & Zhang, Y. (2016). Detection of leftsided and right-sided hearing loss via fractional Fourier transform. Entropy, 18(5), 194.es_CO
    dc.relation.referencesBahaz, M., & Benzid, R. (2018). Efficient algorithm for baseline wander and powerline noise removal from ECG signals based on discrete Fourier series. Australasian physical & engineering sciences in medicine, 41(1), 143-160.es_CO
    dc.relation.referencesAgustí Melchor, M. (2019). DFT vs DCT: un ejemplo visual de uso mediante OpenCV.es_CO
    dc.relation.referencesCONGO PASTRANA, J. W. (2018). APLICACIONES DEL SOFTWARE LIBRE PYTHON PARA PRÁCTICAS DE LABORATORIO APLICADO A LA ASIGNATURA DE TRATAMIENTO DIGITAL DE SEÑALES DE LA UNIVERSIDAD TECNOLÓGICA ISRAEL (Bachelor's thesis, Quito).es_CO
    dc.relation.referencesGrinberg, M. (2018). Flask web development: developing web applications with python. " O'Reilly Media, Inc.".es_CO
    dc.relation.referencesSatsangi, S., & Patvardhan, C. (2016). Application of Genetic Algorithm for Evolution of Quantum Fourier Transform Circuits. In Proceedings of the Second InternationalConference on Computer and Communication Technologies (pp. 773-782). Springer, New Delhi.es_CO
    dc.relation.referencesConference on Computer and Communication Technologies (pp. 773-782). Springer, New Delhi.es_CO
    dc.relation.referencesSundararajan, D. (2018). The Discrete Fourier Transform. In Fourier Analysis—A Signal Processing Approach (pp. 31-55). Springer, Singapore.es_CO
    dc.relation.referencesYu, H., Lu, R., Han, S., Xie, H., Du, G., Xiao, T., & Zhu, D. (2016). Fourier-transform ghost imaging with hard X rays. Physical review letters, 117(11), 113901.es_CO
    dc.relation.referencesTuritsyn, S. K., Prilepsky, J. E., Le, S. T., Wahls, S., Frumin, L. L., Kamalian, M., & Derevyanko, S. A. (2017). Nonlinear Fourier transform for optical data processing and transmission: advances and perspectives. Optica, 4(3), 307-322.es_CO
    dc.relation.referencesHussein, H. J., Hadi, M. Y., & Hameed, I. H. (2016). Study of chemical composition of Foeniculum vulgare using Fourier transform infrared spectrophotometer and gas chromatography-mass spectrometry. Journal of Pharmacognosy and Phytotherapy, 8(3), 60-89.es_CO
    dc.relation.referencesLangel, W. (2016). Analysis of perturbed H2O vibrations beyond Fourier transform. arXiv preprint arXiv:1601.05007.es_CO
    dc.relation.referencesCzerwinski, D., & Powroznik, P. (2018, November). Human Emotions Recognition with the Use of Speech Signal of Polish Language. In 2018 Conference on Electrotechnology: Processes, Models, Control and Computer Science (EPMCCS)(pp. 1-6). IEEE.es_CO
    dc.relation.referencesBülow, H. (2015). Experimental demonstration of optical signal detection using nonlinear Fourier transform. Journal of Lightwave Technology, 33(7), 1433-1439.es_CO
    dc.relation.referencesKoç, A., Bartan, B., Gundogdu, E., Çukur, T., & Ozaktas, H. M. (2017). Sparse representation of two-and three-dimensional images with fractional Fourier, Hartley, linear canonical, and Haar wavelet transforms. Expert Systems with Applications, 77, 247-255.es_CO
    dc.relation.referencesLy, H. B., Monchiet, V., & Grande, D. (2016). Computation of permeability with Fast Fourier Transform from 3-D digital images of porous microstructures. International Journal of Numerical Methods for Heat & Fluid Flow, 26(5), 1328-1345.es_CO
    dc.relation.referencesLiu, J., Bai, T., Shen, X., Dou, S., Lin, C., & Cai, J. (2017). Parallel encryption for multichannel images based on an optical joint transform correlator. Optics Communications, 396, 174-184.es_CO
    dc.relation.referencesDurande, M., Tlili, S., Homan, T., Guirao, B., Graner, F., & Delanoë-Ayari, H. (2019). Fast determination of coarse-grained cell anisotropy and size in epithelial tissue images using Fourier transform. Physical Review E, 99(6), 062401.es_CO
    dc.relation.referencesYoshimasu, T., Kawago, M., Hirai, Y., Ohashi, T., Yata, Y., Fusamoto, A., ... & Nishimura, Y. (2017). P3. 13-012 Fast Fourier Transform Analysis for the Outline of Pulmonary Nodules on Computed Tomography Images. Journal of Thoracic Oncology, 12(11), S2320.es_CO
    dc.relation.referencesWen, D., Yue, F., Ardron, M., & Chen, X. (2016). Multifunctional metasurface lens for imaging and Fourier transform. Scientific reports, 6, 27628.es_CO
    dc.relation.referencesZhang, Y. D., Wang, S. H., Liu, G., & Yang, J. (2016). Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform. Advances in Mechanical Engineering, 8(2), 1687814016634243.es_CO
    dc.relation.referencesDurande, M., Tlili, S., Homan, T., Guirao, B., Graner, F., & Delanoë-Ayari, H. (2019). Fast determination of coarse-grained cell anisotropy and size in epithelial tissue images using Fourier transform. Physical Review E, 99(6), 062401.es_CO
    dc.relation.referencesZhang, Y., Hu, Q., Guo, Z., Xu, J., & Xiong, K. (2018, June). Multi-Class Brain Images Classification Based on Reality-Preserving Fractional Fourier Transform and Adaboost. In 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) (pp. 444-447). IEEE.es_CO
    dc.relation.referencesBroughton, S., & Kurt, B. (2018). Discrete Fourier Analysis and Wavelets: Applications to Signal and Image Processing. Hoboken: Wiley.es_CO
    dc.relation.referencesMcAndrew, A. (2015). A computational introduction to digital image processing. Chapman and Hall/CRC.es_CO
    dc.relation.referencesVyas, A., Yu, S., & Paik, J. (2018). Multiscale Transforms with Application to Image Processing. Springer Singapore.es_CO
    dc.relation.referencesSingh, A. K., Kumar, B., Singh, G., & Mohan, A. (Eds.). (2017). Medical image watermarking: techniques and applications. Springer.es_CO
    dc.relation.referencesDemeter, C. (2020). Fourier Restriction, Decoupling, and Applications (Cambridge Studies in Advanced Mathematics. Indiana: Cambridge University Press.es_CO
    dc.relation.referencesHsu, T. (2020). Fourier Series, Fourier Transforms, and Function Spaces: A Second Course in Analysis. Rhode Island: MAA PRESS American Mathematical Society.es_CO
    dc.relation.referencesOsgood, B. (2019). Lectures on the Fourier Transform and Its Applications. Rhode Island: American Mathematical Society.es_CO
    dc.relation.referencesRadożycki, T. (2020). Solving Problems in Mathematical Analysis, Part III: Curves and Surfaces, Conditional Extremes, Curvilinear Integrals, Complex Functions, ... Fourier Series. USA: Springer.es_CO
    dc.relation.referencesWang, S. H., Zhang, Y. D., Dong, Z., & Phillips, P. (2018). Wavelet Families and Variants. In Pathological Brain Detection(pp. 85-104). Springer, Singapore.es_CO
    dc.relation.referencesVyas, A., Yu, S., & Paik, J. (2018). Multiscale Transforms with Application to Image Proces_CO
    dc.relation.referencesJoshi, M. A. (2018). Digital image processing: An algorithmic approach. PHI Learning Pvt. Ltd..es_CO
    dc.relation.referencesLópez, R. R. (2016). Identificación de la fuente en vídeos de dispositivos móviles.es_CO
    dc.relation.referencesAguirre Martín, F. (2017). Desarrollo y análisis de clasificadores de señales de audio.es_CO
    dc.relation.referencesPeris, P. B., & González, M. S. (2017). Autenticación de Imágenes Digitales Mediante Patrones Locales de Texturas. UNIVERSIDAD COMPLUTENSE DE MADRID.es_CO
    dc.relation.referencesHuamán, C. Q. (2016). Técnicas Anti- Forenses para Vídeos de Dispositivos Móviles.es_CO
    dc.relation.referencesLezama, J. (2015, October). Image compression by Johnson graphs. In 2015 XVI Workshop on Information Processing and Control (RPIC) (pp. 1-6). IEEE.es_CO
    dc.relation.referencesCruz, K. J. A. (2017). Desarrollo de un algoritmo de compresión de datos optimizado para imágenes satelitales (Bachelor's thesis).es_CO
    dc.relation.referencesMondragón Contreras, S. (2018). Sistema de inteligencia artificial para el control de androides autónomos.es_CO
    dc.relation.referencesTorres, D. F. M. (2016). Pronóstico de vida útil restante en rodamientos, con base en datos de vibraciones y sistemas de inferencia estocástica c on degradación no lineal (Doctoral dissertation, Universidad Tecnológica de Pereira. Facultad de Ingenierías Eléctrica, Electrónica, Física y Ciencias de la Computación. Maestría en Ingeniería Eléctrica.).es_CO
    dc.relation.referencesKolekar, M. K. H., Raja, G. L., & Sengupta, S. (2018). An Introduction to Wavelet-Based Image Processing and Its Applications. In Computer Vision: Concepts, Methodologies, Tools, and Applications (pp. 110-128). IGI Global.es_CO
    dc.relation.referencesLezama, J. (2017). COMPRESIÓN DE IMÁGENES: FORMATO JPEG. Revista de Educación Matemática, 32(2).es_CO
    dc.relation.referencesMartínez-Aponte, J. M., & Stivenson-Pinto, S. (2015). Design of a communication system between deaf people and hearing people. Iteckne, 12(2), 138-145.es_CO
    dc.relation.referencesMartínez-Aponte, J. M., & Stivenson-Pinto, S. (2015). Diseño de un sistema de comunicación entre personas sordas y personas oyentes. ITECKNE, 12(2), 138-145.es_CO
    dc.relation.referencesBenítez López, J. (2016). El sistema de compresión JPEG. Un pequeno paseo por la transformada discreta de Fourier y la coseno. Gaceta de la Real Sociedad Matemática Española, 19(1), 25-45.es_CO
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