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Littérature scientifique sur le sujet « Kernel filtering »

Auteur : Grafiati

Publié le 6 septembre 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Kernel filtering"

Maeda, Yoshihiro, Norishige Fukushima, and Hiroshi Matsuo. "Taxonomy of Vectorization Patterns of Programming for FIR Image Filters Using Kernel Subsampling and New One." Applied Sciences 8, no. 8 (2018): 1235. http://dx.doi.org/10.3390/app8081235.

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This study examines vectorized programming for finite impulse response image filtering. Finite impulse response image filtering occupies a fundamental place in image processing, and has several approximated acceleration algorithms. However, no sophisticated method of acceleration exists for parameter adaptive filters or any other complex filter. For this case, simple subsampling with code optimization is a unique solution. Under the current Moore’s law, increases in central processing unit frequency have stopped. Moreover, the usage of more and more transistors is becoming insuperably complex

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Ramadhan, Helmi Sunjaya, and Erwin Budi Setiawan. "Social Media Based Film Recommender System (Twitter) on Disney+ with Hybrid Filtering Using Support Vector Machine." sinkron 8, no. 4 (2023): 2215–25. http://dx.doi.org/10.33395/sinkron.v8i4.12876.

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In the current era, the culture of watching TV shows and movies has been made easier by the presence of the internet. Now, watching movies on platforms can be done from anywhere, one of which is Disney+. At times, people find it challenging to decide which film to watch given the multitude of genres and film titles available on these platforms. One solution to this issue is a recommendation system that can suggest films based on ratings. The recommendation system to be utilized involves Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering. This is because Collaborative Filter

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Douma, Huub, David Yingst, Ivan Vasconcelos, and Jeroen Tromp. "On the connection between artifact filtering in reverse-time migration and adjoint tomography." GEOPHYSICS 75, no. 6 (2010): S219—S223. http://dx.doi.org/10.1190/1.3505124.

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Finite-frequency sensitivity kernels in seismic tomography define the volumes inside the earth that influence seismic waves as they traverse through it. It has recently been numerically observed that an image obtained using the impedance kernel is much less contaminated by low-frequency artifacts due to the presence of sharp wave-speed contrasts in the background model, than is an image obtained using reverse-time migration. In practical reverse-time migration, these artifacts are routinely heuristically dampened by Laplacian filtering of the image. Here we show analytically that, for an isotr

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Zayed, M. Ramadan. "Effect of kernel size on Wiener and Gaussian image filtering." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 3 (2019): 1455–60. https://doi.org/10.12928/TELKOMNIKA.v17i3.11192.

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In this paper, the effect of the kernel size of Wiener and Gaussian filters on their image restoration qualities has been studied and analyzed. Four sizes of such kernels, namely 3x3, 5x5, 7x7 and 9x9 were simulated. Two different types of noise with zero mean and several variances have been used: Gaussian noise and speckle noise. Several image quality measuring indices have been applied in the computer simulations. In particular, mean absolute error (MAE), mean square error (MSE) and structural similarity (SSIM) index were used. Many images were tested in the simulations; however the results

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Nair, Pravin, and Kunal Narayan Chaudhury. "Fast High-Dimensional Kernel Filtering." IEEE Signal Processing Letters 26, no. 2 (2019): 377–81. http://dx.doi.org/10.1109/lsp.2019.2891879.

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Huang, Di, Xishan Zhang, Rui Zhang, et al. "DWM: A Decomposable Winograd Method for Convolution Acceleration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4174–81. http://dx.doi.org/10.1609/aaai.v34i04.5838.

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Winograd's minimal filtering algorithm has been widely used in Convolutional Neural Networks (CNNs) to reduce the number of multiplications for faster processing. However, it is only effective on convolutions with kernel size as 3x3 and stride as 1, because it suffers from significantly increased FLOPs and numerical accuracy problem for kernel size larger than 3x3 and fails on convolution with stride larger than 1. In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general co

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Cheng, Sheng-Wei, Yi-Ting Lin, and Yan-Tsung Peng. "A Fast Two-Stage Bilateral Filter Using Constant Time O(1) Histogram Generation." Sensors 22, no. 3 (2022): 926. http://dx.doi.org/10.3390/s22030926.

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Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often preserving both noise and edges in denoising. This letter proposes a novel Dual-Histogram BF (DHBF) method that exploits an edge-preserving noise-reduced guidance image to compute the range kernel, removing isolated noisy pixels for better denoising results. Furthermore, we approximate the spatial kernel using mean filtering based on column his

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Liu, Ning, and Thomas Schumacher. "Improved Denoising of Structural Vibration Data Employing Bilateral Filtering." Sensors 20, no. 5 (2020): 1423. http://dx.doi.org/10.3390/s20051423.

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With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based o

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Yijie Tang, Yijie Tang, Guobing Qian Yijie Tang, Wenqi Wu Guobing Qian, and Ying-Ren Chien Wenqi Wu. "An Efficient Filtering Algorithm against Impulse Noise in Communication Systems." 網際網路技術學刊 24, no. 2 (2023): 357–62. http://dx.doi.org/10.53106/160792642023032402014.

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The kernel adaptive filter (KAF), which processes data in the reproducing kernel Hilbert space (RKHS), can improve the performance of conventional adaptive filters in nonlinear systems. However, the presence of impulse noise can seriously degrade the performance of KAF. In this paper, we propose a kernel modified-sign least-mean-square algorithm (KMSLMS) to mitigate the impact of impulse noise in communication systems. Moreover, we apply the nearest-instance-centroid estimation (NICE) algorithm to reduce the computational complexity of our KMSLMS algorithm, called the NICE-KMSLMS algo

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Hu, Yongbing, Chenchong Bi, and Yong Chen. "Kernel Adaptive Filtering Algorithm Based on Hyperbolic Tangent Mixed Error Function." Symmetry 16, no. 12 (2024): 1624. https://doi.org/10.3390/sym16121624.

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This paper proposes an adaptive filtering algorithm based on the symmetry Kernel Hyperbolic Tangent Mixed Error Criterion (KHTMC), aimed at addressing the identification of nonlinear systems under non-Gaussian noise environments. The algorithm optimizes signal processing by constructing a mixed cost function that combines the symmetry logarithmic square error and the hyperbolic tangent function and integrates it with the kernel adaptive filtering method. Simulation results show that, compared to existing kernel adaptive filtering algorithms, the KHTMC algorithm exhibits significant advantages

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Thèses sur le sujet "Kernel filtering"

Sun, Xinyuan. "Kernel Methods for Collaborative Filtering." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/135.

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The goal of the thesis is to extend the kernel methods to matrix factorization(MF) for collaborative ltering(CF). In current literature, MF methods usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. The best known member of kernel methods is support vector machine (SVM) on linearly non-separable data. In this thesis, we apply kernel methods on MF, embedding the data into a possibly higher dimensional space and conduct factorization in that space. To improve kernelized matrix factorization, we apply multi-kernel learning methods to selec

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Kabbara, Jad. "Kernel adaptive filtering algorithms with improved tracking ability." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123272.

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In recent years, there has been an increasing interest in kernel methods in areas such as machine learning and signal processing as these methods show strong performance in classification and regression problems. Interesting "kernelized" extensions of many well-known algorithms in artificial intelligence and signal processing have been presented, particularly, kernel versions of the popular online recursive least squares (RLS) adaptive algorithm, namely kernel RLS (KRLS). These algorithms have been receiving significant attention over the past decade in statistical estimation problems, among w

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Bilal, Tahir. "Content Based Packet Filtering In Linux Kernel Using Deterministic Finite Automata." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613710/index.pdf.

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In this thesis, we present a content based packet filtering Architecture in Linux using Deterministic Finite Automata and iptables framework. New generation firewalls and intrusion detection systems not only filter or inspect network packets according to their header fields but also take into account the content of payload. These systems use a set of signatures in the form of regular expressions or plain strings to scan network packets. This scanning phase is a CPU intensive task which may degrade network performance. Currently, the Linux kernel firewall scans network packets separately for ea

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Polato, Mirko. "Definition and learning of logic-based kernels for categorical data, and application to collaborative filtering." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3427260.

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The continuous pursuit of better prediction quality has gradually led to the development of increasingly complex machine learning models, e.g., deep neural networks. Despite the great success in many domains, the black-box nature of these models makes them not suitable for applications in which the model understanding is at least as important as the prediction accuracy, such as medical applications. On the other hand, more interpretable models, as decision trees, are in general much less accurate. In this thesis, we try to merge the positive aspects of these two realities, by injecting int

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Fischer, Manfred M., and Peter Stumpner. "Income Distribution Dynamics and Cross-Region Convergence in Europe. Spatial filtering and novel stochastic kernel representations." WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/3969/1/SSRN%2Did981148.pdf.

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This paper suggests an empirical framework for analysing income distribution dynamics and cross-region convergence in the European Union of 27 member states, 1995- 2003. The framework lies in the research tradition that allows the state income space to be continuous, puts emphasis on both shape and intra-distribution dynamics and uses stochastic kernels for studying transition dynamics and implied long-run behaviour. In this paper stochastic kernels are described by conditional density functions, estimated by a product kernel estimator of conditional density and represented by means of n

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Mahfouz, Sandy. "Kernel-based machine learning for tracking and environmental monitoring in wireless sensor networkds." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0025/document.

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Cette thèse porte sur les problèmes de localisation et de surveillance de champ de gaz à l'aide de réseaux de capteurs sans fil. Nous nous intéressons d'abord à la géolocalisation des capteurs et au suivi de cibles. Nous proposons ainsi une approche exploitant la puissance des signaux échangés entre les capteurs et appliquant les méthodes à noyaux avec la technique de fingerprinting. Nous élaborons ensuite une méthode de suivi de cibles, en se basant sur l'approche de localisation proposée. Cette méthode permet d'améliorer la position estimée de la cible en tenant compte de ses accélérations,

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Vaerenbergh, Steven Van. "Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals." Doctoral thesis, Universidad de Cantabria, 2010. http://hdl.handle.net/10803/10673.

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En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no li

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Suutala, J. (Jaakko). "Learning discriminative models from structured multi-sensor data for human context recognition." Doctoral thesis, Oulun yliopisto, 2012. http://urn.fi/urn:isbn:9789514298493.

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Abstract In this work, statistical machine learning and pattern recognition methods were developed and applied to sensor-based human context recognition. More precisely, we concentrated on an effective discriminative learning framework, where input-output mapping is learned directly from a labeled dataset. Non-parametric discriminative classification and regression models based on kernel methods were applied. They include support vector machines (SVM) and Gaussian processes (GP), which play a central role in modern statistical machine learning. Based on these established models, we propose var

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Verzotto, Davide. "Advanced Computational Methods for Massive Biological Sequence Analysis." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3426282.

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With the advent of modern sequencing technologies massive amounts of biological data, from protein sequences to entire genomes, are becoming increasingly available. This poses the need for the automatic analysis and classification of such a huge collection of data, in order to enhance knowledge in the Life Sciences. Although many research efforts have been made to mathematically model this information, for example finding patterns and similarities among protein or genome sequences, these approaches often lack structures that address specific biological issues. In this thesis, we present n

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Hsiao, Ming-Yuen, and 蕭閔元. "Indoor Positioning With Distributed Kernel-Based Bayesian Filtering." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/3328rw.

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碩士 國立中興大學 電機工程學系所 101 In the wireless sensor network, several localization algorithms have been proposed for indoor positioning systems. However, the computational complexity of these schemes is high, which may not be suitable to be implemented in sensor nodes. For example, the limited sensor capabilities lead to performing the particle filtering with a very small set of samples, which results in high positioning errors. Hence, a novel sampling scheme may be required to improve estimation accuracy for the particle filter method. In this thesis, the concept of support vector regress

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Livres sur le sujet "Kernel filtering"

Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. Wiley, 2010.

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Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. John Wiley, 2010.

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Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. Wiley, 2010.

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Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. Wiley, 2010.

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Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. Wiley, 2010.

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Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. John Wiley, 2010.

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Principe, Jos, Simon Haykin, and Weifeng Liu. Kernel Adaptive Filtering. Wiley & Sons, Incorporated, John, 2010.

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Haykin, Simon, José C. Principe, and Weifeng Liu. Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley & Sons, Incorporated, John, 2010.

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Haykin, Simon, José C. Principe, and Weifeng Liu. Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley & Sons, Incorporated, John, 2011.

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Haykin, Simon, José C. Principe, and Weifeng Liu. Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley & Sons, Incorporated, John, 2011.

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Chapitres de livres sur le sujet "Kernel filtering"

Ozeki, Kazuhiko. "Kernel Affine Projection Algorithm." In Theory of Affine Projection Algorithms for Adaptive Filtering. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55738-8_7.

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Chen, Badong, Lin Li, Weifeng Liu, and José C. Príncipe. "Nonlinear Adaptive Filtering in Kernel Spaces." In Springer Handbook of Bio-/Neuroinformatics. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-30574-0_41.

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García-Vega, S., A. M. Álvarez-Meza, and Germán Castellanos-Domínguez. "Estimation of Cyclostationary Codebooks for Kernel Adaptive Filtering." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_43.

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Zhang, Tao, and Wu Huang. "Kernel Relative-prototype Spectral Filtering for Few-Shot Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20044-1_31.

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Kang, Dong-Ho, and Rhee Man Kil. "Nonlinear Filtering Based on a Network with Gaussian Kernel Functions." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26555-1_7.

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Scardapane, Simone, Danilo Comminiello, Michele Scarpiniti, Raffaele Parisi, and Aurelio Uncini. "PM10 Forecasting Using Kernel Adaptive Filtering: An Italian Case Study." In Neural Nets and Surroundings. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35467-0_10.

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Blandon, J. S., C. K. Valencia, A. Alvarez, J. Echeverry, M. A. Alvarez, and A. Orozco. "Shape Classification Using Hilbert Space Embeddings and Kernel Adaptive Filtering." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_28.

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Liu, Jiaming, Ji Zhao, Qiang Li, Lingli Tang, and Hongbin Zhang. "Single Feedback Based Kernel Generalized Maximum Correntropy Adaptive Filtering Algorithm." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8079-6_1.

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Zhang, Jing, Dong Hu, Biqiu Zhang, and Yuwei Pang. "Hierarchical Convolution Feature for Target Tracking with Kernel-Correlation Filtering." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34120-6_24.

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Barash, Danny, and Dorin Comaniciu. "A Common Viewpoint on Broad Kernel Filtering and Nonlinear Diffusion." In Scale Space Methods in Computer Vision. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_48.

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Actes de conférences sur le sujet "Kernel filtering"

Bi, Chenchong, Yingsong Li, Liping Li, and Nikola Zlatanov. "Kernel Adaptive Filtering with Weibull M-transform Maximum Versoria Criterion." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10671059.

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Yildirim, Samed, and Cihan Topal. "High-throughput median filtering for large kernel sizes on CUDA." In Seventeenth International Conference on Machine Vision (ICMV 2024), edited by Wolfgang Osten. SPIE, 2025. https://doi.org/10.1117/12.3055187.

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Wada, Tomoya, and Toshihisa Tanaka. "Doubly adaptive kernel filtering." In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282173.

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De Luca, Patrick Medeiros, and Wemerson Delcio Parreira. "Simulação do comportamento estocástico do algoritmo KLMS com diferentes kernels." In Computer on the Beach. Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p004-006.

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The kernel least-mean-square (KLMS) algorithm is a popular algorithmin nonlinear adaptive filtering due to its simplicity androbustness. In kernel adaptive filtering, the statistics of the inputto the linear filter depends on the kernel and its parameters. Moreover,practical implementations on systems estimation require afinite non-linearity model order. In order to obtain finite ordermodels, many kernelized adaptive filters use a dictionary of kernelfunctions. Dictionary size also depends on the kernel and itsparameters. Therefore, KLMS may have different performanceson the estimation of a no

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Chen, Badong, Nanning Zheng, and Jose C. Principe. "Survival kernel with application to kernel adaptive filtering." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6706866.

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Liu, Jin, Hua Qu, Badong Chen, and Wentao Ma. "Kernel robust mixed-norm adaptive filtering." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889889.

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Xia, Zhonghang, Wenke Zhang, Manghui Tu, and I.-Ling Yen. "Kernel-based Approaches for Collaborative Filtering." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.41.

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Li, Kan, Badong Chen, and Jose C. Principe. "Kernel adaptive filtering with confidence intervals." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6707045.

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Rawat, Paresh, and Manish D. Sawale. "Gaussian kernel filtering for video stabilization." In 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE). IEEE, 2017. http://dx.doi.org/10.1109/rise.2017.8378142.

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Fernandez-Berni, J., R. Carmona-Galan, and A. Rodriguez-Vazquez. "Image filtering by reduced kernels exploiting kernel structure and focal-plane averaging." In 2011 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2011. http://dx.doi.org/10.1109/ecctd.2011.6043324.

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