Готові списки джерел за темами / Separable kernels

Добірка наукової літератури з теми "Separable kernels"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Separable kernels"

1

Gutiérrez, José Manuel, Miguel Ángel Hernández-Verón, and Eulalia Martínez. "Improved Iterative Solution of Linear Fredholm Integral Equations of Second Kind via Inverse-Free Iterative Schemes." Mathematics 8, no. 10 (October 11, 2020): 1747. http://dx.doi.org/10.3390/math8101747.

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This work is devoted to Fredholm integral equations of second kind with non-separable kernels. Our strategy is to approximate the non-separable kernel by using an adequate Taylor’s development. Then, we adapt an already known technique used for separable kernels to our case. First, we study the local convergence of the proposed iterative scheme, so we obtain a ball of starting points around the solution. Then, we complete the theoretical study with the semilocal convergence analysis, that allow us to obtain the domain of existence for the solution in terms of the starting point. In this case, the existence of a solution is deduced. Finally, we illustrate this study with some numerical experiments.
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2

Cheng, Xianhang, and Zhenzhong Chen. "Video Frame Interpolation via Deformable Separable Convolution." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10607–14. http://dx.doi.org/10.1609/aaai.v34i07.6634.

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Learning to synthesize non-existing frames from the original consecutive video frames is a challenging task. Recent kernel-based interpolation methods predict pixels with a single convolution process to replace the dependency of optical flow. However, when scene motion is larger than the pre-defined kernel size, these methods yield poor results even though they take thousands of neighboring pixels into account. To solve this problem in this paper, we propose to use deformable separable convolution (DSepConv) to adaptively estimate kernels, offsets and masks to allow the network to obtain information with much fewer but more relevant pixels. In addition, we show that the kernel-based methods and conventional flow-based methods are specific instances of the proposed DSepConv. Experimental results demonstrate that our method significantly outperforms the other kernel-based interpolation methods and shows strong performance on par or even better than the state-of-the-art algorithms both qualitatively and quantitatively.
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3

Bondarenko, Serge, Valery Burov, and Sergey Yurev. "Trinucleon form factors with relativistic multirank separable kernels." Nuclear Physics A 1014 (October 2021): 122251. http://dx.doi.org/10.1016/j.nuclphysa.2021.122251.

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4

Jin, Xing, Ping Tang, Thomas Houet, Thomas Corpetti, Emilien Gence Alvarez-Vanhard, and Zheng Zhang. "Sequence Image Interpolation via Separable Convolution Network." Remote Sensing 13, no. 2 (January 15, 2021): 296. http://dx.doi.org/10.3390/rs13020296.

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Анотація:
Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors, such as cloud noise for optical data. Image interpolation is the method that is often used to deal with this issue. This paper considers the deep learning method to learn the complex mapping of an interpolated intermediate image from predecessor and successor images, called separable convolution network for sequence image interpolation. The separable convolution network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the method is effective to produce high-quality time-series interpolated images, and the data-driven deep model can better simulate complex and diverse nonlinear image data information.
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5

Haley, Stephen B., and Herman J. Fink. "Non-separable pairing interaction kernels applied to superconducting cuprates." Physica C: Superconductivity 500 (May 2014): 44–55. http://dx.doi.org/10.1016/j.physc.2014.03.003.

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6

Gutiérrez, José M., and Miguel Á. Hernández-Verón. "A Picard-Type Iterative Scheme for Fredholm Integral Equations of the Second Kind." Mathematics 9, no. 1 (January 1, 2021): 83. http://dx.doi.org/10.3390/math9010083.

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Анотація:
In this work, we present an application of Newton’s method for solving nonlinear equations in Banach spaces to a particular problem: the approximation of the inverse operators that appear in the solution of Fredholm integral equations. Therefore, we construct an iterative method with quadratic convergence that does not use either derivatives or inverse operators. Consequently, this new procedure is especially useful for solving non-homogeneous Fredholm integral equations of the first kind. We combine this method with a technique to find the solution of Fredholm integral equations with separable kernels to obtain a procedure that allows us to approach the solution when the kernel is non-separable.
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7

Peeters, G. "Construction and classification of minimal representations of semi-separable kernels." Journal of Mathematical Analysis and Applications 137, no. 1 (January 1989): 264–87. http://dx.doi.org/10.1016/0022-247x(89)90288-6.

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8

Groenewald, G. J., M. A. Petersen, and A. C. M. Ran. "Characterization of integral operators with semi-separable kernels with symmetries." Journal of Functional Analysis 219, no. 2 (February 2005): 255–84. http://dx.doi.org/10.1016/j.jfa.2004.05.008.

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9

Odibat, Zaid M. "Differential transform method for solving Volterra integral equation with separable kernels." Mathematical and Computer Modelling 48, no. 7-8 (October 2008): 1144–49. http://dx.doi.org/10.1016/j.mcm.2007.12.022.

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10

Becker, Leigh C. "Resolvents and solutions of singular Volterra integral equations with separable kernels." Applied Mathematics and Computation 219, no. 24 (August 2013): 11265–77. http://dx.doi.org/10.1016/j.amc.2013.05.038.

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Дисертації з теми "Separable kernels"

1

Dou, Tianyu. "Multi-Kernel Deformable 3D Convolution for Video Super-Resolution." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42682.

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Video super-resolution (VSR) methods align and fuse consecutive low-resolution frames to generate high-resolution frames. One of the main difficulties for the VSR process is that video contains various motions, and the accuracy of motion estimation dramatically affects the quality of video restoration. However, standard CNNs share the same receptive field in each layer, and it is challenging to estimate diverse motions effectively. Neuroscience research has shown that the receptive fields of biological visual areas will be adjusted according to the input information. Diverse receptive fields in temporal and spatial dimensions have the potential to adapt to various motions, which is rarely paid attention in most known VSR methods. In this thesis, we propose to provide adaptive receptive fields for the VSR model. Firstly, we design a multi-kernel 3D convolution network and integrate it with a multi-kernel deformable convolution network for motion estimation and multiple frames alignment. Secondly, we propose a 2D multi-kernel convolution framework to improve texture restoration quality. Our experimental results show that the proposed framework outperforms the state-of-the-art VSR methods.
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2

Sucic, Victor. "Parameters selection for optimising time-frequency distributions and measurements of time-frequency characteristics of nonstationary signals." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15834/1/Victor_Sucic_Thesis.pdf.

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The quadratic class of time-frequency distributions (TFDs) forms a set of tools which allow to effectively extract important information from a nonstationary signal. To determine which TFD best represents the given signal, it is a common practice to visually compare different TFDs' time-frequency plots, and select as best the TFD with the most appealing plot. This visual comparison is not only subjective, but also difficult and unreliable especially when signal components are closely-spaced in the time-frequency plane. To objectively compare TFDs, a quantitative performance measure should be used. Several measures of concentration/complexity have been proposed in the literature. However, those measures by being derived with certain theoretical assumptions about TFDs are generally not suitable for the TFD selection problem encountered in practical applications. The non-existence of practically-valuable measures for TFDs' resolution comparison, and hence the non-existence of methodologies for the signal optimal TFD selection, has significantly limited the use of time-frequency tools in practice. In this thesis, by extending and complementing the concept of spectral resolution to the case of nonstationary signals, and by redefining the set of TFDs' properties desirable for practical applications, we define an objective measure to quantify the quality of TFDs. This local measure of TFDs' resolution performance combines all important signal time-varying parameters, along with TFDs' characteristics that influence their resolution. Methodologies for automatically selecting a TFD which best suits a given signal, including real-life signals, are also developed. The optimisation of the resolution performances of TFDs, by modifying their kernel filter parameters to enhance the TFDs' resolution capabilities, is an important prerequisite in satisfying any additional application-specific requirements by the TFDs. The resolution performance measure and the accompanying TFDs' comparison criteria allow to improve procedures for designing high-resolution quadratic TFDs for practical time-frequency analysis. The separable kernel TFDs, designed in this way, are shown to best resolve closely-spaced components for various classes of synthetic and real-life signals that we have analysed.
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3

Sucic, Victor. "Parameters Selection for Optimising Time-Frequency Distributions and Measurements of Time-Frequency Characteristics of Nonstationary Signals." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15834/.

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Анотація:
The quadratic class of time-frequency distributions (TFDs) forms a set of tools which allow to effectively extract important information from a nonstationary signal. To determine which TFD best represents the given signal, it is a common practice to visually compare different TFDs' time-frequency plots, and select as best the TFD with the most appealing plot. This visual comparison is not only subjective, but also difficult and unreliable especially when signal components are closely-spaced in the time-frequency plane. To objectively compare TFDs, a quantitative performance measure should be used. Several measures of concentration/complexity have been proposed in the literature. However, those measures by being derived with certain theoretical assumptions about TFDs are generally not suitable for the TFD selection problem encountered in practical applications. The non-existence of practically-valuable measures for TFDs' resolution comparison, and hence the non-existence of methodologies for the signal optimal TFD selection, has significantly limited the use of time-frequency tools in practice. In this thesis, by extending and complementing the concept of spectral resolution to the case of nonstationary signals, and by redefining the set of TFDs' properties desirable for practical applications, we define an objective measure to quantify the quality of TFDs. This local measure of TFDs' resolution performance combines all important signal time-varying parameters, along with TFDs' characteristics that influence their resolution. Methodologies for automatically selecting a TFD which best suits a given signal, including real-life signals, are also developed. The optimisation of the resolution performances of TFDs, by modifying their kernel filter parameters to enhance the TFDs' resolution capabilities, is an important prerequisite in satisfying any additional application-specific requirements by the TFDs. The resolution performance measure and the accompanying TFDs' comparison criteria allow to improve procedures for designing high-resolution quadratic TFDs for practical time-frequency analysis. The separable kernel TFDs, designed in this way, are shown to best resolve closely-spaced components for various classes of synthetic and real-life signals that we have analysed.
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4

Samarasinghe, Devanarayanage Pradeepa. "Efficient methodologies for real-time image restoration." Phd thesis, 2011. http://hdl.handle.net/1885/9859.

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Анотація:
In this thesis we investigate the problem of image restoration. The main focus of our research is to come up with novel algorithms and enhance existing techniques in order to deliver efficient and effective methodologies, applicable in real-time image restoration scenarios. Our research starts with a literature review, which identifies the gaps in existing techniques and helps us to come up with a novel classification on image restoration, which integrates and discusses more recent developments in the area of image restoration. With this novel classification, we identified three major areas which need our attention. The first developments relate to non-blind image restoration. The two mostly used techniques, namely deterministic linear algorithms and stochastic nonlinear algorithms are compared and contrasted. Under deterministic linear algorithms, we develop a class of more effective novel quadratic linear regularization models, which outperform the existing linear regularization models. In addition, by looking in a new perspective, we evaluate and compare the performance of deterministic and stochastic restoration algorithms and explore the validity of the performance claims made so far on those algorithms. Further, we critically challenge the ne- cessity of some complex mechanisms in Maximum A Posteriori (MAP) technique under stochastic image deconvolution algorithms. The next developments are focussed in blind image restoration, which is claimed to be more challenging. Constant Modulus Algorithm (CMA) is one of the most popular, computationally simple, tested and best performing blind equalization algorithms in the signal processing domain. In our research, we extend the use of CMA in image restoration and develop a broad class of blind image deconvolution algorithms, in particular algorithms for blurring kernels with a separable property. These algorithms show significantly faster convergence than conventional algorithms. Although CMA method has a proven record in signal processing applications related to data communications systems, no research has been carried out to the investigation of the applicability of CMA for image restoration in practice. In filling this gap and taking into account the differences of signal processing in im- age processing and data communications contexts, we extend our research on the applicability of CMA deconvolution under the assumptions on the ground truth image properties. Through analyzing the main assumptions of ground truth image properties being zero-mean, independent and uniformly distributed, which char- acterize the convergence of CMA deconvolution, we develop a novel technique to overcome the effects of image source correlation based on segmentation and higher order moments of the source. Multichannel image restoration techniques recently gained much attention over the single channel image restoration due to the benefits of diversity and redundancy of the information between the channels. Exploiting these benefits in real time applications is often restricted due to the unavailability of multiple copies of the same image. In order to overcome this limitation, as the last area of our research, we develop a novel multichannel blind restoration model with a single image, which eliminates the constraint of the necessity of multiple copies of the blurred image. We consider this as a major contribution which could be extended to wider areas of research integrated with multiple disciplines such as demosaicing.
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Книги з теми "Separable kernels"

1

Separate and unequal: The Kerner Commission and the unraveling of American liberalism. Basic Books, 2018.

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2

Gillon, Steven M. Separate and Unequal: The Kerner Commission and the Unraveling of American Liberalism. Basic Books, 2018.

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3

van Moerbeke, Pierre. Determinantal point processes. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.11.

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This article presents a list of algebraic, combinatorial, and analytic mechanisms that give rise to determinantal point processes. Determinantal point processes have been used in random matrix theory (RMT) since the early 1960s. As a separate class, determinantal processes were first used to model fermions in thermal equilibrium and the term ‘fermion’ point processes were adopted. The article first provides an overview of the generalities associated with determinantal point processes before discussing loop-free Markov chains, that is, the trajectories of the Markov chain do not pass through the same point twice almost surely. It then considers the measures given by products of determinants, namely, biorthogonal ensembles. An especially important subclass of biorthogonal ensembles consists of orthogonal polynomial ensembles. The article also describes L-ensembles, a general construction of determinantal point processes via the Fock space formalism, dimer models, uniform spanning trees, Hermitian correlation kernels, and Pfaffian point processes.
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Частини книг з теми "Separable kernels"

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Kanwal, Ram P. "Integral Equations With Separable Kernels." In Linear Integral Equations, 7–24. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-0765-8_2.

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2

Kanwal, Ram P. "Integral Equations With Separable Kernels." In Linear Integral Equations, 7–24. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6012-1_2.

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3

Gohberg, Israel, Seymour Goldberg, and Marinus A. Kaashoek. "Integral Operators with Semi-Separable Kernels." In Classes of Linear Operators Vol. I, 148–62. Basel: Birkhäuser Basel, 1990. http://dx.doi.org/10.1007/978-3-0348-7509-7_10.

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4

Eidelman, Yuli, Israel Gohberg, and Iulian Haimovici. "Kernels of Quasiseparable of Order One Matrices." In Separable Type Representations of Matrices and Fast Algorithms, 33–49. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0612-1_3.

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5

Gohberg, Israel, Seymour Goldberg, and Nahum Krupnik. "Determinants of Integral Operators with Semi-separable Kernels." In Traces and Determinants of Linear Operators, 213–42. Basel: Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8401-3_14.

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6

Vittal, P. R., S. Jayasankar, and V. Muralidhar. "Storage Models for a Class of Master Equations with Separable Kernels." In The Legacy of Alladi Ramakrishnan in the Mathematical Sciences, 401–15. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6263-8_25.

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7

Providas, E. "Approximate Solution of Fredholm Integral and Integro-Differential Equations with Non-Separable Kernels." In Approximation and Computation in Science and Engineering, 693–708. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-84122-5_38.

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8

Zemyan, Stephen M. "Fredholm Integral Equations of the Second Kind (Separable Kernel)." In The Classical Theory of Integral Equations, 1–30. Boston, MA: Birkhäuser Boston, 2012. http://dx.doi.org/10.1007/978-0-8176-8349-8_1.

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9

Miao, Guoying, Matthew M. Peet, and Keqin Gu. "Inversion of Separable Kernel Operator and Its Application in Control Synthesis." In Delays and Interconnections: Methodology, Algorithms and Applications, 265–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11554-8_17.

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10

Marwedel, Peter. "System Software." In Embedded Systems, 203–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60910-8_4.

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AbstractIn order to cope with the complexity of applications of embedded systems, reuse of components is a key technique. As pointed out by Sangiovanni-Vincentelli (The context for platform-based design. IEEE Design and Test of Computers, 2002), software and hardware components must be reused in the platform-based design methosdology (see p. 296). These components comprise knowledge from earlier design efforts and constitute intellectual property (IP). Standard software components that can be reused include system software components such as embedded operating systems (OSs) and middleware. The last term denotes software that provides an intermediate layer between the OS and application software. This chapter starts with a description of general requirements for embedded operating systems. This includes real-time capabilities as well as adaptation techniques to provide just the required functionality. Mutually exclusive access to resources can result in priority inversion, which is a serious problem for real-time systems. Priority inversion can be circumvented with resource access protocols. We will present three such protocols: the priority inheritance, priority ceiling, and stack resource protocols. A separate section covers the ERIKA real-time system kernel. Furthermore, we will explain how Linux can be adapted to systems with tight resource constraints. Finally, we will provide pointers for additional reusable software components, like hardware abstraction layers (HALs), communication software, and real-time data bases. Our description of embedded operating systems and of middleware in this chapter is consistent with the overall design flow.
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Тези доповідей конференцій з теми "Separable kernels"

1

Huusari, Riikka, and Hachem Kadri. "Entangled Kernels." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/358.

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We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels. Borrowing tools and concepts from the field of quantum computing, such as partial trace and entanglement, we propose a new view on operator-valued kernels and define a general family of kernels that encompasses previously known operator-valued kernels, including separable and transformable kernels. Within this framework, we introduce another novel class of operator-valued kernels called entangled kernels that are not separable. We propose an efficient two-step algorithm for this framework, where the entangled kernel is learned based on a novel extension of kernel alignment to operator-valued kernels. The utility of the algorithm is illustrated on both artificial and real data.
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2

Shy and Perona. "X-Y separable pyramid steerable scalable kernels." In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/cvpr.1994.323835.

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3

Fukushima, Norishige, Shu Fujita, and Yutaka Ishibashi. "Switching dual kernels for separable edge-preserving filtering." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178238.

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4

Tuncel, Tolga, and Orhan Akbulut. "Integral Image based Fast Convolution Operations via Separable Kernels." In 2021 29th Signal Processing and Communications Applications Conference (SIU). IEEE, 2021. http://dx.doi.org/10.1109/siu53274.2021.9478001.

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5

Anwar, Sajid, Kyuyeon Hwang, and Wonyong Sung. "Learning separable fixed-point kernels for deep convolutional neural networks." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471839.

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6

Tsubakino, Daisuke, Federico Bribiesca Argomedo, and Miroslav Krstic. "Backstepping-forwarding control of parabolic PDEs with partially separable kernels." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7040207.

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7

Bondarenko, Serge. "Separable kernels of the NN interaction within Bethe-Salpeter approach." In XXII International Baldin Seminar on High Energy Physics Problems. Trieste, Italy: Sissa Medialab, 2015. http://dx.doi.org/10.22323/1.225.0026.

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8

Peet, Matthew M., and Antonis Papachristodoulou. "Using polynomial semi-separable kernels to construct infinite-dimensional Lyapunov functions." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739245.

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9

Gahlawat, Aditya, and Matthew M. Peet. "Output feedback control of inhomogeneous parabolic PDEs with point actuation and point measurement using SOS and semi-separable kernels." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402377.

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10

Fang, Lu, Haifeng Liu, Feng Wu, Xiaoyan Sun, and Houqiang Li. "Separable Kernel for Image Deblurring." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.369.

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