Escudo Universidad de Pamplona

Repositorio Institucional

Universidad de Pamplona

Preservamos, organizamos y difundimos la producción académica, científica, investigativa y cultural de la Universidad de Pamplona, garantizando el acceso abierto al conocimiento generado por nuestra comunidad universitaria.

Explorar colecciones

Por favor, use este identificador para citar o enlazar este ítem: https://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3072
Registro completo de metadatos
Campo DC Valor Lengua/Idioma
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
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2es_CO
dc.type.coarversionhttp://purl.org/coar/resource_type/c_2df8fbb1es_CO
Aparece en las colecciones: Tecnologías e ingenierías

Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
Mendoza_2021_PI.pdf2,47 MBAdobe PDFVisualizar/Abrir


Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.