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Journal articles on the topic 'Graph wavelets'

Author: Grafiati

Published: 18 May 2024

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1

Wu, Jiasong, Fuzhi Wu, Qihan Yang, et al. "Fractional Spectral Graph Wavelets and Their Applications." Mathematical Problems in Engineering 2020 (November 6, 2020): 1–18. http://dx.doi.org/10.1155/2020/2568179.

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One of the key challenges in the area of signal processing on graphs is to design transforms and dictionary methods to identify and exploit structure in signals on weighted graphs. In this paper, we first generalize graph Fourier transform (GFT) to spectral graph fractional Fourier transform (SGFRFT), which is then used to define a novel transform named spectral graph fractional wavelet transform (SGFRWT), which is a generalized and extended version of spectral graph wavelet transform (SGWT). A fast algorithm for SGFRWT is also derived and implemented based on Fourier series approximation. Som

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Hammond, David K., Pierre Vandergheynst, and Rémi Gribonval. "Wavelets on graphs via spectral graph theory." Applied and Computational Harmonic Analysis 30, no. 2 (2011): 129–50. http://dx.doi.org/10.1016/j.acha.2010.04.005.

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Bastos, Anson, Abhishek Nadgeri, Kuldeep Singh, Toyotaro Suzumura, and Manish Singh. "Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6779–87. http://dx.doi.org/10.1609/aaai.v37i6.25831.

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Learning on evolving(dynamic) graphs has caught the attention of researchers as static methods exhibit limited performance in this setting. The existing methods for dynamic graphs learn spatial features by local neighborhood aggregation, which essentially only captures the low pass signals and local interactions. In this work, we go beyond current approaches to incorporate global features for effectively learning representations of a dynamically evolving graph. We propose to do so by capturing the spectrum of the dynamic graph. Since static methods to learn the graph spectrum would not conside

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Paul, Okuwobi Idowu, and Yong Hua Lu. "Facial Prediction and Recognition Using Wavelets Transform Algorithm and Technique." Applied Mechanics and Materials 666 (October 2014): 251–55. http://dx.doi.org/10.4028/www.scientific.net/amm.666.251.

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An efficient facial representation is a crucial step for successful and effective performance of cognitive tasks such as object recognition, fixation, facial recognition system, etc. This paper demonstrates the use of Gabor wavelets transform for efficient facial representation and recognition. Facial recognition is influenced by several factors such as shape, reflectance, pose, occlusion and illumination which make it even more difficult. Gabor wavelet transform is used for facial features vector construction due to its powerful representation of the behavior of receptive fields in human visu

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Xu, Mingxing, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, and Pascal Frossard. "Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets." IEEE Transactions on Signal and Information Processing over Networks 8 (2022): 63–77. http://dx.doi.org/10.1109/tsipn.2022.3140477.

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Tay, D. B. H., and Z. Lin. "Highly localised near orthogonal graph wavelets." Electronics Letters 52, no. 11 (2016): 966–68. http://dx.doi.org/10.1049/el.2016.0482.

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Tremblay, Nicolas, and Pierre Borgnat. "Graph Wavelets for Multiscale Community Mining." IEEE Transactions on Signal Processing 62, no. 20 (2014): 5227–39. http://dx.doi.org/10.1109/tsp.2014.2345355.

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Masoumi, Majid, and A. Ben Hamza. "Shape classification using spectral graph wavelets." Applied Intelligence 47, no. 4 (2017): 1256–69. http://dx.doi.org/10.1007/s10489-017-0955-7.

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Yang, Zhirui, Yulan Hu, Sheng Ouyang, et al. "WaveNet: Tackling Non-stationary Graph Signals via Graph Spectral Wavelets." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9287–95. http://dx.doi.org/10.1609/aaai.v38i8.28781.

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In the existing spectral GNNs, polynomial-based methods occupy the mainstream in designing a filter through the Laplacian matrix. However, polynomial combinations factored by the Laplacian matrix naturally have limitations in message passing (e.g., over-smoothing). Furthermore, most existing spectral GNNs are based on polynomial bases, which struggle to capture the high-frequency parts of the graph spectral signal. Additionally, we also find that even increasing the polynomial order does not change this situation, which means polynomial-based models have a natural deficiency when facing high-f

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Sun, Qingyun, Jianxin Li, Beining Yang, Xingcheng Fu, Hao Peng, and Philip S. Yu. "Self-Organization Preserved Graph Structure Learning with Principle of Relevant Information." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4643–51. http://dx.doi.org/10.1609/aaai.v37i4.25587.

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Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are always incomplete, noisy, or redundant. How to reveal the inherent graph structure in a unified way remains under-explored. We proposed PRI-GSL, a Graph Structure Learning framework guided by the Principle of Relevant Information, providing a simple and unified framework for identifying the self-organization and revealing the hidden structure. PRI-GSL learns a struct

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Chen, Lianggangxu, Youqi Song, Shaohui Lin, Changbo Wang, and Gaoqi He. "Kumaraswamy Wavelet for Heterophilic Scene Graph Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (2024): 1138–46. http://dx.doi.org/10.1609/aaai.v38i2.27875.

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Graph neural networks (GNNs) has demonstrated its capabilities in the field of scene graph generation (SGG) by updating node representations from neighboring nodes. Actually it can be viewed as a form of low-pass filter in the spatial domain, which smooths node feature representation and retains commonalities among nodes. However, spatial GNNs does not work well in the case of heterophilic SGG in which fine-grained predicates are always connected to a large number of coarse-grained predicates. Blind smoothing undermines the discriminative information of the fine-grained predicates, resulting i

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12

Gong, Bo, Benjamin Schullcke, Sabine Krueger-Ziolek, et al. "EIT Imaging Regularization Based on Spectral Graph Wavelets." IEEE Transactions on Medical Imaging 36, no. 9 (2017): 1832–44. http://dx.doi.org/10.1109/tmi.2017.2716825.

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13

Tay, David B. H., Yuichi Tanaka, and Akie Sakiyama. "Almost Tight Spectral Graph Wavelets With Polynomial Filters." IEEE Journal of Selected Topics in Signal Processing 11, no. 6 (2017): 812–24. http://dx.doi.org/10.1109/jstsp.2017.2726972.

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14

Manoharan, Samuel. "STUDY ON HERMITIAN GRAPH WAVELETS IN FEATURE DETECTION." Journal of Soft Computing Paradigm 2019, no. 1 (2019): 24–32. http://dx.doi.org/10.36548/jscp.2019.1.003.

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The enormous information flow in our day today life, initiates the necessitates of the identifying the valuable data that are to be concentrated. In case of image segmentation and signal processing, the feature detection takes up the role of fixating to the data that are to be focused. Thus directing to the pixels or information that are to be concentrated eliminating the time and the energy wastage in examining the pixels or the information’s that are of least important. The paper is the study, focusing on the advantages of utilizing the Hermitian wavelet transform incorporated with the graph

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Wang, Qingzheng, Huixin Wu, Hua Yang, Yue Liu, Chenming Zhang, and Bin Li. "Data-Specific Anisotropic Mexican Hat Wavelets for Structure-Preserving Image Processing." Scientific Programming 2022 (April 13, 2022): 1–13. http://dx.doi.org/10.1155/2022/4455871.

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This paper proposes a novel approach for structure-sensitive image processing based on the rigorous mathematical derivation of data-specific anisotropic Mexican hat wavelets (DAM). Our DAM is derived from the negative first-order derivative of the fundamental solution of heat diffusion equation with respect to time, which not only shares similar properties with Mexican hat wavelet but also intrinsically embeds the image-specific properties. Through the scale-aware DAM transform and its inverse transform, we are capable of conducting structure-sensitive image processing. Our key idea is to repr

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Prakash, Anurag, and Subrat Kar. "Graph wavelets for fault localization in optical mesh networks." Optical Fiber Technology 72 (September 2022): 103006. http://dx.doi.org/10.1016/j.yofte.2022.103006.

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Zhong, Ming, and Hong Qin. "Sparse approximation of 3D shapes via spectral graph wavelets." Visual Computer 30, no. 6-8 (2014): 751–61. http://dx.doi.org/10.1007/s00371-014-0971-0.

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Cui, Beibei, and Jean-Charles Créput. "NCC Based Correspondence Problem for First- and Second-Order Graph Matching." Sensors 20, no. 18 (2020): 5117. http://dx.doi.org/10.3390/s20185117.

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Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, identification, registration, and many derived tasks. In this paper, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute. We proposed two optimized algorithms: first-order and second-order for graph matching. On the one hand, a first-order normalized cross-correlation (NCC) based graph matching algorithm using entropy and response through Marr wavelets within the

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Bala, B. Kiran, and S. Audithan. "Identification of Spectral Graph Wavelets for Microcalcifications in Mammogram Images." Indian Journal of Public Health Research & Development 9, no. 5 (2018): 251. http://dx.doi.org/10.5958/0976-5506.2018.00448.5.

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20

Farouk, R. M. "Iris recognition based on elastic graph matching and Gabor wavelets." Computer Vision and Image Understanding 115, no. 8 (2011): 1239–44. http://dx.doi.org/10.1016/j.cviu.2011.04.002.

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Sakiyama, Akie, Kana Watanabe, and Yuichi Tanaka. "Spectral Graph Wavelets and Filter Banks With Low Approximation Error." IEEE Transactions on Signal and Information Processing over Networks 2, no. 3 (2016): 230–45. http://dx.doi.org/10.1109/tsipn.2016.2581303.

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BOURBAKIS, N., P. YUAN, and P. KAKUMANU. "A GRAPH BASED OBJECT DESCRIPTION AND RECOGNITION METHODOLOGY." International Journal on Artificial Intelligence Tools 17, no. 06 (2008): 1161–94. http://dx.doi.org/10.1142/s0218213008004345.

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This paper presents a methodology for recognizing 3D objects using synthesis of 2D views. In particular, the methodology uses wavelets for rearranging the shape of the perceived 2D view of an object for attaining a desirable size, local-global (LG) graphs for representing the shape, color and location of each image object's region obtained by an image segmentation method and the synthesis of these regions that compose that particular object. The synthesis of the regions is obtained by composing their local graph representations under certain neighborhood criteria. The LG graph representation o

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Irfan, Muhammad Abeer, and Enrico Magli. "Joint Geometry and Color Point Cloud Denoising Based on Graph Wavelets." IEEE Access 9 (2021): 21149–66. http://dx.doi.org/10.1109/access.2021.3054171.

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24

Li, Nannan, Shengfa Wang, Ming Zhong, Zhixun Su, and Hong Qin. "Generalized Local-to-Global Shape Feature Detection Based on Graph Wavelets." IEEE Transactions on Visualization and Computer Graphics 22, no. 9 (2016): 2094–106. http://dx.doi.org/10.1109/tvcg.2015.2498557.

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25

Behjat, Hamid, Nora Leonardi, Leif Sörnmo, and Dimitri Van De Ville. "Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping." NeuroImage 123 (December 2015): 185–99. http://dx.doi.org/10.1016/j.neuroimage.2015.06.010.

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26

Petrovic, Miljan, Thomas A. W. Bolton, Maria Giulia Preti, Raphaël Liégeois, and Dimitri Van De Ville. "Guided graph spectral embedding: Application to the C. elegans connectome." Network Neuroscience 3, no. 3 (2019): 807–26. http://dx.doi.org/10.1162/netn_a_00084.

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Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions—for example, based on wavelets and Slepians—that can be applied to filter signals defined on the graph. In this work, we take inspiration from these constructions to define a new guided spectral embedding that combines maximizing energy concentration with minimizing modified embedded distance for a given importance weighting of the nodes. We show that these optimizatio

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Shigeta, Hironori, Tomohiro Mashita, Junichi Kikuta, et al. "Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature." Journal of Bioinformatics and Computational Biology 15, no. 05 (2017): 1740004. http://dx.doi.org/10.1142/s0219720017400042.

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Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone rem

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Venkat, Aarthi, Martina Damo, Nikhil S. Joshi, and Smita Krishnaswamy. "Mapping the gene space at single-cell resolution with gene signal pattern analysis." Journal of Immunology 210, no. 1_Supplement (2023): 251.03. http://dx.doi.org/10.4049/jimmunol.210.supp.251.03.

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Abstract In single-cell RNA sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to map the gene space. A mapping that preserves gene-gene relationships within the dataset is particularly useful for characterizing cellular heterogeneity within cell types, where boundaries between cell subpopulations are often unclear or even arbitrary. Here, we present gene signal pattern analysis, a new paradigm for analyzing single cells. We build a cell-cell graph and design a dictionary of diffusion wavelets, capturing a multiscale

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CHENG, Hao, Hao YAN, Li-jun BAI, and Bao-guo WANG. "Exploration of whole brain networks modulated by acupuncture at analgesia acupoint ST36 using scale-specific wavelet correlation analysis." Chinese Medical Journal 126, no. 13 (2013): 2459–64. http://dx.doi.org/10.3760/cma.j.issn.0366-6999.20122681.

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Background Previous studies have demonstrated that acupuncture could modulate various brain systems in the resting brain networks. Graph theoretical analysis offers a novel way to investigate the functional organization of the large-scale cortical networks modulated by acupuncture at whole brain level. In this study, we used wavelets correlation analysis to estimate the pairwise correlations between 90 cortical and subcortical human brain regions in normal human volunteers scanned during the post-stimulus resting state. Methods Thirty-two college students, all right-handed and acupuncture naïv

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Lakshmi, K. Sai Prasanna. "HAAR Wavelets and Graph based Model for Content Based Image Retrieval in MNIST." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 90–98. http://dx.doi.org/10.22214/ijraset.2018.4020.

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Watson, James R., Zach Gelbaum, Mathew Titus, Grant Zoch, and David Wrathall. "Identifying multiscale spatio-temporal patterns in human mobility using manifold learning." PeerJ Computer Science 6 (June 15, 2020): e276. http://dx.doi.org/10.7717/peerj-cs.276.

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When, where and how people move is a fundamental part of how human societies organize around every-day needs as well as how people adapt to risks, such as economic scarcity or instability, and natural disasters. Our ability to characterize and predict the diversity of human mobility patterns has been greatly expanded by the availability of Call Detail Records (CDR) from mobile phone cellular networks. The size and richness of these datasets is at the same time a blessing and a curse: while there is great opportunity to extract useful information from these datasets, it remains a challenge to d

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Li, Yong Mei, Bing Zhou, Guo Fu Sun, and Bo Yan Yang. "Study on Damage Location of Spatial Structures Based on Wavelet Analysis of Model Strain Energy." Advanced Materials Research 639-640 (January 2013): 1033–37. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.1033.

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The research to identify and locate the damage to the engineering structure mainly aimed at some simple structure forms before, such as beam and framework. Damage shows changes of local characteristics of the signal, while wavelet analysis can reflect local damage traits of the signal in time domain and frequency domain. For confirming the validity and applicability of structural damage identification methods, wavelet analysis is used to spatial structural damage detection. The wavelet analysis technique provides new ideas and methods of spatial steel structural damage detection. Based on the

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Larsen, Nadia S., and Iain Raeburn. "Projective multi-resolution analyses arising from direct limits of Hilbert modules." MATHEMATICA SCANDINAVICA 100, no. 2 (2007): 317. http://dx.doi.org/10.7146/math.scand.a-15026.

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The authors have recently shown how direct limits of Hilbert spaces can be used to construct multi-resolution analyses and wavelets in $L^2(\mathsf R)$. Here they investigate similar constructions in the context of Hilbert modules over $C^*$-algebras. For modules over $C(\mathsf T^n)$, the results shed light on work of Packer and Rieffel on projective multi-resolution analyses for specific Hilbert $C(\mathsf T^n)$-modules of functions on $\mathsf R^n$. There are also new applications to modules over $C(C)$ when $C$ is the infinite path space of a directed graph.

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Gersztenkorn, Adam. "A new approach for detecting topographic and geologic information in seismic data." GEOPHYSICS 77, no. 2 (2012): V81—V90. http://dx.doi.org/10.1190/geo2011-0216.1.

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Interpreting 3D seismic volumes can be an intensive and time-consuming endeavor. Algorithms that provide additional information and expedite this process can therefore be useful tools for the interpreter. To further this goal, an algorithm that gives a topographic perspective of seismic data is described. After applying the continuous wavelet transform to the data, templates having a directional orientation are constructed locally in the complex wavelet domain for a number of scales. For each scale, a complex matrix is formed having real and imaginary parts, which are independently designed fo

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Khushi, Bhoj, Choksi Kuldeep, Kitawat Rishi, and Rana Manish. "Review on various face recognition databases." i-manager’s Journal on Pattern Recognition 9, no. 2 (2022): 17. http://dx.doi.org/10.26634/jpr.9.2.19189.

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Face recognition is one of the multimedia items that has seen a remarkable increase in popularity in recent years. Face continues to be the most difficult study topic for experts in the field of computer vision and image processing since it is an item with different properties for detection. We have attempted to handle the most challenging facial aspects in this survey work, including posture invariance, aging, illuminations, and partial occlusion. When applied to facial photographs, they are regarded as essential components of face recognition systems. The most recent face detection methods a

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SMALTER, AARON, JUN HUAN, and GERALD LUSHINGTON. "GRAPH WAVELET ALIGNMENT KERNELS FOR DRUG VIRTUAL SCREENING." Journal of Bioinformatics and Computational Biology 07, no. 03 (2009): 473–97. http://dx.doi.org/10.1142/s0219720009004187.

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In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of

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Deb, Swakshar, Sejuti Rahman, and Shafin Rahman. "SEA-GWNN: Simple and Effective Adaptive Graph Wavelet Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11740–48. http://dx.doi.org/10.1609/aaai.v38i10.29058.

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The utilization of wavelet-based techniques in graph neural networks (GNNs) has gained considerable attention, particularly in the context of node classification. Although existing wavelet-based approaches have shown promise, they are constrained by their reliance on pre-defined wavelet filters, rendering them incapable of effectively adapting to signals that reside on graphs based on tasks at hand. Recent research endeavors address this issue through the introduction of a wavelet lifting transform. However, this technique necessitates the use of bipartite graphs, causing a transformation of t

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Cui, Lihong, Qiaoyun Wu, Jiale Liu, and Jianjun Sun. "Dual Wavelet Frame Transforms on Manifolds and Graphs." Journal of Mathematics 2019 (May 28, 2019): 1–12. http://dx.doi.org/10.1155/2019/1637623.

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In this paper, we consider the dual wavelet frames in both continuum setting, i.e., on manifolds, and discrete setting, i.e., on graphs. Firstly, we give sufficient conditions for the existence of dual wavelet frames on manifolds by their corresponding masks. Then, we present the formula of the decomposition and reconstruction for the dual wavelet frame transforms on graphs. Finally, we give a numerical example to illustrate the validity of the dual wavelet frame transformation applied to the graph data.

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Zhou, Jie, and Zeze Zhang. "A Brief Survey of the Graph Wavelet Frame." Complexity 2022 (October 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/8153249.

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In recent years, the research of wavelet frames on the graph has become a hot topic in harmonic analysis. In this paper, we mainly introduce the relevant knowledge of the wavelet frames on the graph, including relevant concepts, construction methods, and related theory. Meanwhile, because the construction of graph tight framelets is closely related to the classical wavelet framelets on ℝ , we give a new construction of tight frames on ℝ . Based on the pseudosplines of type II, we derive an MRA tight wavelet frame with three generators ψ 1 , ψ 2 , and ψ 3 using the oblique extension principle (

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Babić, Ranko, and Lidija Babić. "A New Type of Bipartite Random Graph as a Transform of Seismogram and Its Potential for Organizing Seismic Databases." Applied Sciences 13, no. 18 (2023): 10303. http://dx.doi.org/10.3390/app131810303.

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This paper proposes a method to reduce seismogram variability as a determining factor in its interpretation, processing, and clustering. By introducing the concept of single fluctuations (SFs), the seismogram can be parsed into a sequence of random impulses with subsequent ordering. This rearrangement of SFs, if they are assigned by positive integers, represents the formal mapping of a regular string of integers into a random one, which can be represented with a bipartite random graph (bigraph). Due to its specific randomness, such a bigraph is considered a new type of random balanced bigraph.

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Ouda, Eman Hassan. "Direct Method for Variational Problems Using Boubaker Wavelets." Ibn AL-Haitham Journal For Pure and Applied Sciences 36, no. 3 (2023): 427–36. http://dx.doi.org/10.30526/36.3.3048.

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The wavelets have many applications in engineering and the sciences, especially mathematics. Recently, in 2021, the wavelet Boubaker (WB) polynomials were used for the first time to study their properties and applications in detail. They were also utilized for solving the Lane-Emden equation. The aim of this paper is to show the truncated Wavelet Boubaker polynomials for solving variation problems. In this research, the direct method using wavelets Boubaker was presented for solving variational problems. The method reduces the problem into a set of linear algebraic equations. The fundamental i

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Gong, Bo, Benjamin Schullcke, Sabine Krueger-Ziolek, and Knut Moeller. "Regularization of EIT reconstruction based on multi-scales wavelet transforms." Current Directions in Biomedical Engineering 2, no. 1 (2016): 423–26. http://dx.doi.org/10.1515/cdbme-2016-0094.

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AbstractElectrical Impedance Tomography (EIT) intends to obtain the conductivity distribution of a domain from the electrical boundary conditions. This is an ill-posed inverse problem usually solved on finite element meshes. Wavelet transforms are widely used for medical image reconstruction. However, because of the irregular form of the finite element meshes, the canonical wavelet transforms is impossible to perform on meshes. In this article, we present a framework that combines multi-scales wavelet transforms and finite element meshes by viewing meshes as undirected graphs and applying spec

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Arkhipova, O. V., N. N. Dolgikh, S. Yu Dolinger, V. Z. Kovalev, and D. S. Osipov. "Wavelet transform algorithm of daily load graphs for choosing parameters of hybrid energy storage." Omsk Scientific Bulletin, no. 174 (2020): 57–62. http://dx.doi.org/10.25206/1813-8225-2020-174-57-62.

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The paper presents an algorithm for frequency decomposition of daily load graphs based on a discrete wavelet transform. This algorithm makes it possible to choose the optimal type of wavelet function, optimal level and wavelet decomposition tree. The inverse wavelet transform (recovery) along a single branch of the approximating coefficient allows obtaining the lowfrequency component of the power graph for selecting the optimal mode of the hybrid energy storage battery. The detailing branch of the wavelet coefficients determines the operating mode of the supercapacitor. A numerical experiment

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Schab, Esteban, Carla Casanova, and Fabiana Piccoli. "Graph Representations for Reinforcement Learning." Journal of Computer Science and Technology 24, no. 1 (2024): e03. http://dx.doi.org/10.24215/16666038.24.e03.

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Graph analysis is becoming increasingly important due to the expressive power of graph models and the efficient algorithms available for processing them. Reinforcement Learning is one domain that could benefit from advancements in graph analysis, given that a learning agent may be integrated into an environment that can be represented as a graph. Nevertheless, the structural irregularity of graphs and the lack of prior labels make it difficult to integrate such a model into modern Reinforcement Learning frameworks that rely on artificial neural networks. Graph embedding enables the learning of

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Kazantsev, S., A. Pavlov, and O. Chekha. "Wavelet transforms of the time series of small wholesale prices in the agricultural sector." IOP Conference Series: Earth and Environmental Science 937, no. 3 (2021): 032075. http://dx.doi.org/10.1088/1755-1315/937/3/032075.

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Abstract The article provides a wavelet analysis of small wholesale prices for white cabbage in Rostov-on-Don from 2017 to 2020 year. Approximation coefficients show a steady trend, the detailing coefficients reflect seasonal and insignificant temporary price fluctuations. The constituent scaling approximation coefficients and the detailing components are highlighted in the form of separate graphs. The series was decomposed up to the 6th level using the Haar and Daubechies wavelets.

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Srinivasa, Kumbinarasaiah, Haci Mehmet Baskonus, and Yolanda Guerrero Sánchez. "Numerical Solutions of the Mathematical Models on the Digestive System and COVID-19 Pandemic by Hermite Wavelet Technique." Symmetry 13, no. 12 (2021): 2428. http://dx.doi.org/10.3390/sym13122428.

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This article developed a functional integration matrix via the Hermite wavelets and proposed a novel technique called the Hermite wavelet collocation method (HWM). Here, we studied two models: the coupled system of an ordinary differential equation (ODE) is modeled on the digestive system by considering different parameters such as sleep factor, tension, food rate, death rate, and medicine. Here, we discussed how these parameters influence the digestive system and showed them through figures and tables. Another fractional model is used on the COVID-19 pandemic. This model is defined by a syste

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Liu, Xinlin, Viktor Krylov, Su Jun, et al. "Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology." Mathematical Biosciences and Engineering 19, no. 7 (2022): 6923–39. http://dx.doi.org/10.3934/mbe.2022326.

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<abstract> <p>An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved v

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Leontiev, N. A. "Using of Beylkin Wavelet for Speech Recognition." Journal of Physics: Conference Series 2096, no. 1 (2021): 012080. http://dx.doi.org/10.1088/1742-6596/2096/1/012080.

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Abstract This paper describes the application of the Beylkin wavelet for speech segmentation. The problem of speech segmentation in the Yakut language is that there are segmentation difficulties due to the peculiarities of the language. The use of long vowels and double consonants in the Yakut language complicates the correct segmentation of oral speech. For the analysis, the window method of analyzing the energy of the wavelet signal is used. The experience of using different wavelet functions has shown that it is not always possible to accurately find the segment boundaries in some cases. Th

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LOURENS, TINO, and ROLF P. WÜRTZ. "EXTRACTION AND MATCHING OF SYMBOLIC CONTOUR GRAPHS." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 07 (2003): 1279–302. http://dx.doi.org/10.1142/s0218001403002848.

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We describe an object recognition system based on symbolic contour graphs. The image to be analyzed is transformed into a graph with object corners as vertices and connecting contours as edges. Image corners are determined using a robust multiscale corner detector. Edges are constructed by line-following between corners based on evidence from the multiscale Gabor wavelet transform. Model matching is done by finding subgraph isomorphisms in the image graph. The complexity of the algorithm is reduced by labeling vertices and edges, whereby the choice of labels also makes the recognition system i

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Yang, Weike, and Zheng Tao. "Wavelet Analysis of Bitcoin Price and Twitter-Based Economic Uncertainty Index." Proceedings of Business and Economic Studies 5, no. 5 (2022): 96–101. http://dx.doi.org/10.26689/pbes.v5i5.4414.

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In this paper, we analyze the time-series graphs of Bitcoin price and Twitter-based economic uncertainty index over the past two years and use a wavelet coherence graph to determine their relationship. We found a causal relationship between Bitcoin (BTC) and Twitter-based economic uncertainty (TEU) index in different frequency bands, which would help predict Bitcoin price movements in the future. Our study provides reference to academics and investors.

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