El Procesamiento de Datos Discretos 1D y 2D: Fourier, Coseno y Wavelet en Aplicaciones 1.0

Stark, 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.

Rockley, M. G. (1979). Fourier-transformed infrared photoacoustic spectroscopy of polystyrene film. Chemical Physics Letters, 68(2-3), 455-456.

Ozaktas, H. M., & Kutay, M. A. (2001, September). The fractional Fourier transform. In 2001 European Control Conference (ECC)(pp. 1477-1483). IEEE

Ozaktas, H. M., & Kutay, M. A. (2001, September). The fractional Fourier transform. In 2001 European Control Conference (ECC)(pp. 1477-1483). IEEE

Weisstein, E. W. (2015). Fast fourier transform.

Weller, H. (2015). Fourier analysis.

Croft, A. (2017). Engineering mathematics. Pearson education.

Antoniou, A. (2016). Digital signal processing. McGraw-Hill.

Howell, K. B. (2016). Principles of Fourier analysis. CRC Press.

Sogge, C. D. (2017). Fourier integrals in classical analysis (Vol. 210). Cambridge University Press.

Bracewell, R. N., & Bracewell, R. N.(1986). The Fourier transform and its applications (Vol. 31999). New York: McGraw-Hill

Osgood, B. G. (2019). Lectures on the Fourier Transform and Its Applications (Vol. 33). American Mathematical Soc..

Campos, R. G. (2019). The Ordinary Discrete Fourier Transform. In The XFT Quadrature in Discrete Fourier Analysis (pp. 3-37).Birkhäuser, Cham.

Grigoryan, A. M., & Grigoryan, M. M. (2016). Brief notes in advanced DSP: Fourier analysis with MATLAB. CRC Press.

Olson, T. (2017). The Fourier Transform. In Applied Fourier Analysis (pp. 121-148). Birkhäuser, New York, NY.

Wang, 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.

Satsangi, S., & Patvardhan, C. (2016). Application of Genetic Algorithm for Evolution of Quantum Fourier Transform Circuits. In Proceedings of the Second Internationa Conference on Computer and Communication Technologies (pp. 773-782). Springer, New Delhi.

Sundararajan, D. (2018). The Discrete Fourier Transform. In Fourier Analysis—A Signal Processing Approach (pp. 31-55). Springer, Singapore.

Yu, 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.

Turitsyn, 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.

Turitsyn, 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.

Hussein, 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.

Langel, W. (2016). Analysis of perturbed H2O vibrations beyond Fourier transform. arXiv preprint arXiv:1601.05007.

Bülow, H. (2015). Experimental demonstration of optical signal detection using nonlinear Fourier transform. Journal of Lightwave Technology, 33(7), 1433-1439.

Koç, 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.

Ly, 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.

Liu, 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.

Durande, 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.

Yoshimasu, 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.

Wen, D., Yue, F., Ardron, M., & Chen, X. (2016). Multifunctional metasurface lens for imaging and Fourier transform. Scientific reports, 6, 27628.

Zhang, 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.

Durande, 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.

Zhang, 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.

McAndrew, A. (2015). A computational introduction to digital image processing. Chapman and Hall/CRC.

Vyas, A., Yu, S., & Paik, J. (2018). Multiscale Transforms with Application to Image Processing. Springer Singapore.

Singh, A. K., Kumar, B., Singh, G., & Mohan,A. (Eds.). (2017). Medical imagewatermarking: techniques and applications.Springer.

Burger, W., & Burge, M. J. (2016). Digital image processing: an algorithmic introduction using Java. Springer.

Grigoryan, A. M., & Grigoryan, M. M. (2016). Brief notes in advanced DSP: Fourier analysis with MATLAB. CRC Press.

Tripathy, 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.

Bahaz, 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.

Czerwinski, 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

Broughton, S., & Kurt, B. (2018). DiscreteFourier Analysis and Wavelets: Applications to Signal and Image Processing. Hoboken: Wiley.

Demeter, C. (2020). Fourier Restriction, Decoupling, and Applications (Cambridge Studies in Advanced Mathematics. Indiana Cambridge University Press.

Hsu, T. (2020). Fourier Series, Fourier Transforms, and Function Spaces: A Second Course in Analysis. Rhode Island: MAA PRESS American Mathematical Society.

Osgood, B. (2019). Lectures on the Fourierm Transform and Its Applications. Rhode Island: American Mathematical Society.

Radożycki, T. (2020). Solving Problems inMathematical Analysis, Part III: Curves andSurfaces, Conditional Extremes, CurvilinearIntegrals, Complex Functions, ... Fourier Series. USA: Springer.

Vyas, A., Yu, S., & Paik, J. (2018). Multiscale Transforms with Application to Image Proc

Joshi, M. A. (2018). Digital image processing: An algorithmic approach. PHI Learning Pvt. Ltd..

López, R. R. (2016). Identificación de la fuente en vídeos de dispositivos móviles.

Aguirre Martín, F. (2017). Desarrollo y análisis de clasificadores de señales de audio.

Peris, P. B., & González, M. S.(2017). Autenticación de Imágenes Digitales Mediante Patrones Locales de Texturas. UNIVERSIDAD COMPLUTENSE DEMADRID.

Huamán, C. Q. (2016). Técnicas AntiForenses para Vídeos de Dispositivos Móviles.

Lezama, J. (2015, October). Image compression by Johnson graphs. In 2015 XVI Workshop on Information Processing and Control (RPIC) (pp. 1-6). IEEE.

Cruz, K. J. A. (2017). Desarrollo de un algoritmo de compresión de datos optimizado para imágenes satelitales (Bachelor's thesis).

Mondragón Contreras, S. (2018). Sistema de inteligencia artificial para el control de androides autónomos.

Torres, 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.).

Lezama, J. (2017). COMPRESIÓN DE IMÁGENES: FORMATO JPEG. Revista de Educación Matemática, 32(2).

Martínez-Aponte, J. M., & Stivenson-Pinto, S.(2015). Design of a communication system between deaf people and hearing people. Iteckne, 12(2), 138-145

Martínez-Aponte, J. M., & Stivenson-Pinto, S. (2015). Diseño de un sistema de comunicación entre personas sordas y personas oyentes. ITECKNE, 12(2), 138-145.

Benítez 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.

Cruz Rodríguez, V. (2012). Diseño de un codificador de imágenes adaptativo multitransformada mediante el uso de la transformada Karhunen-Loève (Bachelor's thesis).

Amer, 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.

Soria 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.

Lloris, A., Fernández, P. G., & Ramírez, J. (2001). Procesamiento de imágenes utilizando la transformada discreta coseno. Revista española de electrónica, (558), 72-75.

Ramos, A. I. C., Riverón, E. M. F., Ramírez,P. M., & Pogrebnyak, O. B. (2016). Filtro derestauración de imágenes basado en latransformada discreta del coseno y el análisisde componentes principales. Research inComputing Science, 120, 169-178.

Portocarrero Rodriguez, M. A. (2018). Diseño de la arquitectura de transformada discreta directa e inversa del coseno para un decodificador HEVC.

Checa, 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.).

Reyes Rodriguez, V. (2015). Study of accuracy and hardware performance in discrete transforms and their fast algorithms (Doctoral dissertation).

Avila-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.

Moya, 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.

Hernández, J. L., Bautista, C. V., Miyatake, M. N., & Meana, H. P. (2015). Algoritmo Esteganografico Robusto a Compresión JPEG Usando DCT. Instituto Politécnico Nacional, 6.

García-Pinzón, J. A., Mendoza, L. E., & Flórez, E. G. (2015). Electronic control arm using electromyographic signals. Facultad de Ingeniería, 24(39), 71-84.

García-Pinzón, J. A., Mendoza, L. E., &Flórez, E. G. (2015). Control de brazo electrónico usando señales electromiográficas. Facultad deIngeniería, 24(39), 71-84.

Gamboa Córdova, R. G. (2017). Diseño e implementación de un sistema MIMO Fast OFDM en módulos NI-USRP (Bachelor's thesis).

Alfonte Zapana, R. (2018). Reducción de la dimensionalidad de series temporales climáticas usando Deep Multi-LayerAutoencoder.

Moreno-Alvarado, R., Pérez-Meana, H.,Nakano-Miyatake, M., & Robles-Camarillo, D.(2019). Método de compresión de electrocardiogramas basado en muestreocompresivo.

Avila-Domenech, E. (2018). Marca de aguadigital basada en DWT-DCT para imágenes de documentos manuscritos: optimización contra ataques de compresión JPEG. Revista Cubana de Ciencias Informáticas, 12(2), 30- 43.

Coy, L., Orjuela, L., & Jiménez, F. (2018). Compresión de video en Streaming usando transformadas Wavelet y DCT. Infometric@- Serie Ingeniería, Básicas y Agrícolas, 1(2).

Alkawaz, 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.

Mahmood, 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.

Siddiqui, 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.

Gong, 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.

Li, 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.

Alotaibi, R. A., & Elrefaei, L. A. (2018). Textimage watermarking based on integer wavelet transform (IWT) and discrete cosine transform (DCT). Applied Computing and Informatics.

Miri, 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.

Smith, 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.

Prabukumar, 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.

Jain, 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.

Tan, E. L., & Gan, W. S. (2015). Perceptual Image Coding with Discrete Cosine Transform. New York: Springer-Verlag Singapur.

Rao, K. R., & Ochoa-Dominguez, H. (2019). Discrete Cosine Transform. CRC Press.

Chui, C. K. (2016). An introduction to wavelets. Elsevier.

Navarro J. F, Martinez D. El (2010).Introducción a la transformada wavelet continua. Ahmad, K. (2018).

Applications in Image Processing. In Wavelet Packets and Their Statistical Applications (pp. 203-224). Springer, Singapore.

Addison, P. S. (2017). The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicineand finance. CRC press.

Ahmad, K. (2018). Applications in Image Processing. In Wavelet Packets and Their Statistical Applications (pp. 203-224). Springer, Singapore.

Zhang, D. (2019). Wavelet transform. In Fundamentals of Image Data Mining (pp. 35-44). Springer, Cham.

Chatterjee, P. (2015). Wavelet analysis in civil engineering. CRC Press.

Baleanu, D. (Ed.). (2015). Wavelet Transform and Some of Its Real-World Applications. BoD–Books on Demand.

Radhakrishnan, S. (Ed.). (2018). Wavelet Theory and Its Applications. BoD–Books on Demand.

Wang, S. H., Zhang, Y. D., Dong, Z., & Phillips, P. (2018). Wavelet Families and Variants. In Pathological Brain Detection(pp. 85-104). Springer, Singapore.

Kolekar, 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.

Subasi, 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.

Krivoshein, A., Protasov, V., & Skopina, M. A.(2016). Multivariate wavelet frames (p. 182).Singapore: Springer.

Du, R. (2019). Engineering monitoring anddiagnosis using wavelet transforms.In Computer-Aided Design, Engineering, andManufacturing (pp. 312-341). CRC Press.

Haldorai, A., & Ramu, A. (2018). Anintelligent-based wavelet classifier foraccurate prediction of breast cancer.In Intelligent Multidimensional Data andImage Processing (pp. 306-319). IGI Global.

Meng, 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.

Zhang, 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 entropyand generalized eigenvalue proximal supportvector machine (GEPSVM). Entropy, 17(4),1795-1813.

Wang, 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.

Bai, Y., Li, Y., Wang, X., Xie, J., & Li, C.(2016). Air pollutants concentrationsforecasting using back propagation neuralnetwork based on wavelet decomposition withmeteorological conditions. Atmosphericpollution research, 7(3), 557-566.

Le 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 forestclassification. In 2017 39th AnnualInternational Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 475-478). IEEE.

Wozniak, 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

Alickovic, 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.

Ghassemian, H. (2016). A review of remote sensing image fusion methods. Information Fusion, 32, 75-89.

Zheng, Y., Blasch, E., & Liu, Z. (2018). Multispectral Image Fusion and Colorization. SPIE Press.

Singh, 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.

Sudarshan, V. K., Mookiah, M. R. K., Acharya, U. R., Chandran, V., Molinari, F., Fujita, H., & Ng, K. H. (2016). Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review. Computers in biology and medicine, 69, 97-111.

Lai, 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.

Nayak, 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.

Li, C., Huang, Y., & Zhu, L. (2017). Color texture image retrieval based on Gaussian copula models of Gabor wavelets. Pattern Recognition, 64, 118-129.

Mughal, B., Muhammad, N., Sharif, M., Saba, T., & Rehman, A. (2017). Extraction of breast border and removal of pectoral muscle inwavelet domain. BiomedicalResearch, 28(11), 5041-5043.

Das, 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.

Bascoy, 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.

Li Vigni, M., Prats-Montalban, J. M., Ferrer, A.,& Cocchi, M. (2018). Coupling 2D-waveletdecomposition and multivariate image analysis (2D WT-MIA). Journal of Chemometrics, 32(1), e2970.

Ahuja, S., & Mehan, A. (2018, April). Designof Orthogonal Wavelet for Human Palmprint Recognition. In 2018 InternationalConference on Intelligent Circuits and Systems (ICICS) (pp. 265-270). IEEE.

Sivakumar, R., & Mohan, E. (2018). HighResolution Satellite Image EnhancementUsing Discrete Wavelet Transform. International Journal of Applied Engineering Research, 13(11), 9811-9815.

Chaddad, 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.

Huang, 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.

Phinyomark, A., Nuidod, A., Phukpattaranont,P., & Limsakul, C. (2012). Feature extraction and reduction of wavelet transform coefficients for EMG patternclassification. Elektronika ir Elektrotechnika, 122(6), 27-32.

de A Berger, P., Francisco, A. D. O., do Carmo, J. C., & da Rocha, A. F. (2006). Compression of EMG signals with wavelettransform and artificial neural networks. Physiological measurement, 27(6),457.

Subasi, A., Yaman, E., Somaily, Y.,Alynabawi, H. A., Alobaidi, F., & Altheibani, S.(2018). Automated EMG Signal Classificationfor Diagnosis of Neuromuscular Disorders Using DWT and Bagging. Procedia Computer Science, 140, 230-237.

Ryan, Ø. (2019). Linear Algebra, Signal Processing and Wavelets – a unified Approach. Python Version. Oslo, Norway: Springer.

Hariharan. (2019). Wavelet Solutions for Reaction–Diffusion Problems in Science and Engineering. Tamil Nadu, India: Springer International Publishing.

Abood, S. (2020). Digital Signal Processing: A Primer with MATLAB. CRC Press.

Panja, M. M., & Mandal, B. N. (2020). Wavelet Based Approximation Schemes for Singular Integral Equations. Kolkata, India: CRC Press.

Krantz, S. (2020). Differential Equations: A Modern Approach with Wavelets. New York: Chapman and Hall/CRC.

S. Bird, E. Klein y E. Loper, Natural Language Processing with Python, O'Reilly Media, 2009.

Q. Nafiul Islam, Mastering PyCharm, Packt Publishing, 2015.

«Python Software Foundation,» 2001-2019. [En línea]. Available: https://www.python.org/downloads/. [Último acceso: 05 Abril 2019].

JetBrains 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].

Travis E., Guide to Numpy, Oliphant , 2006.

S. Tosi, Matplotlib for Python Developers, Packt Publishing, 2009.

G. Bradski y A. Kaebler, Learning OpenCV Compiter Vision with the OpenCV Library, M. Loakides, Ed., O'Reilly, 2008.

A. 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].

G. Gonzales, «Series de Fourier, Transformadas de Fourier y Aplicaciones,» Divulgaciones Matematicas , vol. 5, nº 1, pp. 43-67, 1997.

P. Athanasios, Sistemas Digitales y Analogicos Transformadas de Fourier, Estimación Espectral, Barcelona- Mexico: Marcombo Boixareu Editores, 1986.

A. Fournié y G. Boog, «Estudio del Ritmo Cardíaco fetal,» El Sevier, vol. 40, pp. 1-21, 2004.

F. 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.

A. Quintero Rincon , M. Risk y S. Liberezuk, «Procesamiento de EEG con Filtros Hampel,» Argencon , vol. 2012, nº 89, 2012.

Á. de la Torre Vega, Procesamiento de voz, Universidad de Granada, 2007.

D. M. Ballesteros Larrotta, «Aplicación de la transformada wavelet discreta en el filtrado de señales bioeléctricas,» Umbral Cientifico, nº 5, pp. 92-98, 2004.

J. 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.

Van der Walt, S., Schönberger, J. L., NunezIglesias, J., Boulogne, F., Warner, J. D., Yager, N., ... & Yu, T. (2014). scikit-image: image processing in Python. PeerJ, 2, e453.

Canty, M. J. (2014). Image analysis, classification and change detection in remote sensing: with algorithms for ENVI/IDL and Python. Crc Press.

Van Rossum, G., & Drake, F. L. (2011). The python language reference manual. Network Theory Ltd..

Lynch, S. (2018). Image Processing with Python. In Dynamical Systems with Applications using Python (pp. 471-489). Birkhäuser, Cham.

Loredo, 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).

Tuck, J. (2018). Estimating the Discrete Fourier Transform using Deep Learning.

Pine, D. J. (2019). Introduction to Python for Science and Engineering. CRC Press.

Arias Páez, A. S., & Rubiano Venegas, D. A. (2018). Método automático de reconocimiento de voz para la clasificación de vocales al lenguaje de señas colombiano.

Agustí Melchor, M. (2019). DFT vs DCT: un ejemplo visual de uso mediante OpenCV.

CONGO 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).

Grinberg, M. (2018). Flask web development: developing web applications with python. " O'Reilly Media, Inc.".