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Journal articles on the topic 'Multiscale Representation'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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1

Goldstein, Rhys, Azam Khan, Olivier Dalle, and Gabriel Wainer. "Multiscale representation of simulated time." SIMULATION 94, no. 6 (September 28, 2017): 519–58. http://dx.doi.org/10.1177/0037549717726868.

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To better support multiscale modeling and simulation, we present a multiscale time representation consisting of data types, data structures, and algorithms that collectively support the recording of past events and scheduling of future events in a discrete event simulation. Our approach addresses the drawbacks of conventional time representations: limited range in the case of 32- or 64-bit fixed-point time values; problematic rounding errors in the case of floating-point numbers; and the lack of a universally acceptable precision level in the case of brute force approaches. The proposed representation provides both extensive range and fine resolution in the timing of events, yet it stores and manipulates the majority of event times as standard 64-bit numbers. When adopted for simulation purposes, the representation allows a domain expert to choose a precision level for his/her model. This time precision is honored by the simulator even when the model is integrated with other models of vastly different time scales. Making use of C++11 programming language features and the Discrete Event System Specification formalism, we implemented a simulator to test the time representation and inform a discussion on its implications for collaborative multiscale modeling efforts.

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Jiang, Z., M. I. J. van Dijke, K. S. Sorbie, and G. D. Couples. "Representation of multiscale heterogeneity via multiscale pore networks." Water Resources Research 49, no. 9 (September 2013): 5437–49. http://dx.doi.org/10.1002/wrcr.20304.

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Knijnenburg, Theo A., Stephen A. Ramsey, Benjamin P. Berman, Kathleen A. Kennedy, Arian F. A. Smit, Lodewyk F. A. Wessels, Peter W. Laird, Alan Aderem, and Ilya Shmulevich. "Multiscale representation of genomic signals." Nature Methods 11, no. 6 (April 13, 2014): 689–94. http://dx.doi.org/10.1038/nmeth.2924.

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Pauly, Mark, Leif P. Kobbelt, and Markus Gross. "Point-based multiscale surface representation." ACM Transactions on Graphics 25, no. 2 (April 2006): 177–93. http://dx.doi.org/10.1145/1138450.1138451.

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Elizar, Elizar, Mohd Asyraf Zulkifley, Rusdha Muharar, Mohd Hairi Mohd Zaman, and Seri Mastura Mustaza. "A Review on Multiscale-Deep-Learning Applications." Sensors 22, no. 19 (September 28, 2022): 7384. http://dx.doi.org/10.3390/s22197384.

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In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task. Multiscale representation enables the network to fuse low-level and high-level features from a restricted receptive field to enhance the deep-model performance. The main novelty of this review is the comprehensive novel taxonomy of multiscale-deep-learning methods, which includes details of several architectures and their strengths that have been implemented in the existing works. Predominantly, multiscale approaches in deep-learning networks can be classed into two categories: multiscale feature learning and multiscale feature fusion. Multiscale feature learning refers to the method of deriving feature maps by examining kernels over several sizes to collect a larger range of relevant features and predict the input images’ spatial mapping. Multiscale feature fusion uses features with different resolutions to find patterns over short and long distances, without a deep network. Additionally, several examples of the techniques are also discussed according to their applications in satellite imagery, medical imaging, agriculture, and industrial and manufacturing systems.

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Maragos, P. "Pattern spectrum and multiscale shape representation." IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 7 (July 1989): 701–16. http://dx.doi.org/10.1109/34.192465.

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7

Bengtsson, A., and J. O. Eklundh. "Shape representation by multiscale contour approximation." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 1 (1991): 85–93. http://dx.doi.org/10.1109/34.67634.

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8

Jang, Dongik, Donghoh Kim, and Kyungmee O. Kim. "Multiscale representation for irregularly spaced data." Journal of the Korean Statistical Society 46, no. 4 (December 2017): 641–53. http://dx.doi.org/10.1016/j.jkss.2017.09.002.

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Sureau, F., F. Voigtlaender, M. Wust, J. L. Starck, and G. Kutyniok. "Learning sparse representations on the sphere." Astronomy & Astrophysics 621 (January 2019): A73. http://dx.doi.org/10.1051/0004-6361/201834041.

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Many representation systems on the sphere have been proposed in the past, such as spherical harmonics, wavelets, or curvelets. Each of these data representations is designed to extract a specific set of features, and choosing the best fixed representation system for a given scientific application is challenging. In this paper, we show that one can directly learn a representation system from given data on the sphere. We propose two new adaptive approaches: the first is a (potentially multiscale) patch-based dictionary learning approach, and the second consists in selecting a representation from among a parametrized family of representations, the α-shearlets. We investigate their relative performance to represent and denoise complex structures on different astrophysical data sets on the sphere.

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Lam, Ka Chun, Tsz Ching Ng, and Lok Ming Lui. "Multiscale Representation of Deformation via Beltrami Coefficients." Multiscale Modeling & Simulation 15, no. 2 (January 2017): 864–91. http://dx.doi.org/10.1137/16m1056614.

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Cook, Jamie, Vinod Chandran, and Sridha Sridharan. "Multiscale Representation for 3-D Face Recognition." IEEE Transactions on Information Forensics and Security 2, no. 3 (September 2007): 529–36. http://dx.doi.org/10.1109/tifs.2007.902405.

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12

Weinmann, Andreas. "Interpolatory Multiscale Representation for Functions between Manifolds." SIAM Journal on Mathematical Analysis 44, no. 1 (January 2012): 162–91. http://dx.doi.org/10.1137/100803584.

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Altaĭsky, M. V. "Multiscale theory of turbulence in wavelet representation." Doklady Physics 51, no. 9 (September 2006): 481–85. http://dx.doi.org/10.1134/s1028335806090060.

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14

Li, Zhaoping, and Joseph Atick. "Efficient stereo coding in the multiscale representation*." Network: Computation in Neural Systems 5, no. 2 (May 1, 1994): 157–74. http://dx.doi.org/10.1088/0954-898x/5/2/003.

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Li, Zhaoping, and Joseph J. Atick. "Efficient stereo coding in the multiscale representation." Network: Computation in Neural Systems 5, no. 2 (January 1994): 157–74. http://dx.doi.org/10.1088/0954-898x_5_2_003.

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16

Doghmane, Hakim, Hocine Bourouba, Kamel Messaoudi, and Ahmed Bouridane. "Palmprint recognition based on discriminant multiscale representation." Journal of Electronic Imaging 27, no. 05 (October 10, 2018): 1. http://dx.doi.org/10.1117/1.jei.27.5.053032.

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17

Muraki, S. "Multiscale volume representation by a DoG wavelet." IEEE Transactions on Visualization and Computer Graphics 1, no. 2 (June 1995): 109–16. http://dx.doi.org/10.1109/2945.468408.

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18

Dudek, Gregory, and John K. Tsotsos. "Shape Representation and Recognition from Multiscale Curvature." Computer Vision and Image Understanding 68, no. 2 (November 1997): 170–89. http://dx.doi.org/10.1006/cviu.1997.0533.

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19

Tang, Qiling, Yangyang Liu, and Haihua Liu. "Medical image classification via multiscale representation learning." Artificial Intelligence in Medicine 79 (June 2017): 71–78. http://dx.doi.org/10.1016/j.artmed.2017.06.009.

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20

Matei, Basarab, and Sylvain Meignen. "Nonlinear and Nonseparable Bidimensional Multiscale Representation Based on Cell-Average Representation." IEEE Transactions on Image Processing 24, no. 11 (November 2015): 4570–80. http://dx.doi.org/10.1109/tip.2015.2456424.

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21

Chaabane, Marwa, Majdi Mansouri, Kamaleldin Abodayeh, Ahmed Ben Hamida, Hazem Nounou, and Mohamed Nounou. "Effective fault detection in structural health monitoring systems." Advances in Mechanical Engineering 11, no. 9 (September 2019): 168781401987323. http://dx.doi.org/10.1177/1687814019873234.

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A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.

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22

Liang, Dong Tai. "Color Image Denoising Using Gaussian Multiscale Multivariate Image Analysis." Applied Mechanics and Materials 37-38 (November 2010): 248–52. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.248.

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Inspired by the human vision system, a new image representation and analysis model based on Gaussian multiscale multivariate image analysis (MIA) is proposed. The multiscale color texture representations for the original image are used to constitute the multivariate image, each channel of which represents a perceptual observation from different scales. Then the MIA decomposes this multivariate image into multiscale color texture perceptual features (the principal component score images). These score images could be interpreted as 1) the output of three color opponent channels: black versus white, red versus green and blue versus yellow, and 2) the edge information, and 3) higher-order Gaussian derivatives. Finally the color image denoising approach based on the models is presented. Experiments show that this denoising method against Gaussian filters significantly improves the denoising effect by preserving more edge information.

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23

EL RUBÉ, IBRAHIM, NAIF ALAJLAN, MOHAMED S. KAMEL, MAHER AHMED, and GEORGE H. FREEMAN. "MTAR: A ROBUST 2D SHAPE REPRESENTATION." International Journal of Image and Graphics 06, no. 03 (July 2006): 421–43. http://dx.doi.org/10.1142/s021946780600232x.

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In this paper, a new 2D shape Multiscale Triangle-Area Representation (MTAR) method is proposed. This representation utilizes a simple geometric principle, that is, the area of the triangles formed by the shape boundary points. The wavelet transform is used for smoothing and decomposing the shape boundaries into multiscale levels. At each scale level, a TAR image and the corresponding Maxima-Minima lines are obtained. The resulting MTAR is more robust to noise, less complex, and more selective than similar methods such as the curvature scale-space (CSS). Furthermore, the MTAR is invariant to the general affine transformations. The proposed MTAR is tested and compared to the CSS method using MPEG-7 CE-shape-1 part B and Columbia Object Image Library (COIL-20) datasets. The results show that the proposed MTAR outperforms the CSS method for the conducted tests.

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24

Mahadevan, Sridhar. "Representation Discovery in Sequential Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1718–21. http://dx.doi.org/10.1609/aaai.v24i1.7766.

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Automatically constructing novel representations of tasks from analysis of state spaces is a longstanding fundamental challenge in AI. I review recent progress on this problem for sequential decision making tasks modeled as Markov decision processes. Specifically, I discuss three classes of representation discovery problems: finding functional, state, and temporal abstractions. I describe solution techniques varying along several dimensions: diagonalization or dilation methods using approximate or exact transition models; reward-specific vs reward-invariant methods; global vs. local representation construction methods; multiscale vs. flat discovery methods; and finally, orthogonal vs. redundant representa- tion discovery methods. I conclude by describing a number of open problems for future work.

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Ma, Xianghua, Zhenkun Yang, and Shining Chen. "Multiscale Feature Filtering Network for Image Recognition System in Unmanned Aerial Vehicle." Complexity 2021 (February 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/6663851.

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For unmanned aerial vehicle (UAV), object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks (CNNs) have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network (MFFNet) is proposed in this paper for image recognition system in the UAV. A novel building block, namely, multiscale feature filtering (MFF) module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks.

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26

Chabot, Vincent, Maëlle Nodet, and Arthur Vidard. "Multiscale Representation of Observation Error Statistics in Data Assimilation." Sensors 20, no. 5 (March 6, 2020): 1460. http://dx.doi.org/10.3390/s20051460.

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Accounting for realistic observation errors is a known bottleneck in data assimilation, because dealing with error correlations is complex. Following a previous study on this subject, we propose to use multiscale modelling, more precisely wavelet transform, to address this question. This study aims to investigate the problem further by addressing two issues arising in real-life data assimilation: how to deal with partially missing data (e.g., concealed by an obstacle between the sensor and the observed system), and how to solve convergence issues associated with complex observation error covariance matrices? Two adjustments relying on wavelets modelling are proposed to deal with those, and offer significant improvements. The first one consists of adjusting the variance coefficients in the frequency domain to account for masked information. The second one consists of a gradual assimilation of frequencies. Both of these fully rely on the multiscale properties associated with wavelet covariance modelling. Numerical results on twin experiments show that multiscale modelling is a promising tool to account for correlations in observation errors in realistic applications.

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27

Huang, Lian, Shaosheng Dai, Tao Huang, Xiangkang Huang, and Haining Wang. "Infrared small target segmentation with multiscale feature representation." Infrared Physics & Technology 116 (August 2021): 103755. http://dx.doi.org/10.1016/j.infrared.2021.103755.

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28

Aristizábal Q, Luz Angela, and Nicolás Toro G. "Multilayer Representation and Multiscale Analysis on Data Networks." International journal of Computer Networks & Communications 13, no. 3 (May 31, 2021): 41–55. http://dx.doi.org/10.5121/ijcnc.2021.13303.

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The constant increase in the complexity of data networks motivates the search for strategies that make it possible to reduce current monitoring times. This paper shows the way in which multilayer network representation and the application of multiscale analysis techniques, as applied to software-defined networks, allows for the visualization of anomalies from "coarse views of the network topology". This implies the analysis of fewer data, and consequently the reduction of the time that a process takes to monitor the network. The fact that software-defined networks allow for the obtention of a global view of network behavior facilitates detail recovery from affected zones detected in monitoring processes. The method is evaluated by calculating the reduction factor of nodes, checked during anomaly detection, with respect to the total number of nodes in the network.

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29

Xiaohui Xue and Xiaolin Wu. "Directly operable image representation of multiscale primal sketch." IEEE Transactions on Multimedia 7, no. 5 (October 2005): 805–16. http://dx.doi.org/10.1109/tmm.2005.854471.

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30

Miaohong Shi and G. Healey. "Hyperspectral texture recognition using a multiscale opponent representation." IEEE Transactions on Geoscience and Remote Sensing 41, no. 5 (May 2003): 1090–95. http://dx.doi.org/10.1109/tgrs.2003.811076.

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31

Lam, W. M., and G. W. Wornell. "Multiscale representation and estimation of fractal point processes." IEEE Transactions on Signal Processing 43, no. 11 (1995): 2606–17. http://dx.doi.org/10.1109/78.482111.

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32

Bouchemha, Amel, Noureddine Doghmane, Mohamed Cherif Nait-Hamoud, and Amine Nait-Ali. "Multispectral palmprint recognition methodology based on multiscale representation." Journal of Electronic Imaging 24, no. 4 (July 29, 2015): 043005. http://dx.doi.org/10.1117/1.jei.24.4.043005.

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33

Tadmor, Eitan, and Prashant Athavale. "Multiscale image representation using novel integro-differential equations." Inverse Problems & Imaging 3, no. 4 (2009): 693–710. http://dx.doi.org/10.3934/ipi.2009.3.693.

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34

Hong, Wei, John Wright, Kun Huang, and Yi Ma. "Multiscale Hybrid Linear Models for Lossy Image Representation." IEEE Transactions on Image Processing 15, no. 12 (December 2006): 3655–71. http://dx.doi.org/10.1109/tip.2006.882016.

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35

Lisowska, A. "Smoothlets—Multiscale Functions for Adaptive Representation of Images." IEEE Transactions on Image Processing 20, no. 7 (July 2011): 1777–87. http://dx.doi.org/10.1109/tip.2011.2108662.

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36

Doghmane, Hakim, Abdelhani Boukrouche, and Larbi Boubchir. "A novel discriminant multiscale representation for ear recognition." International Journal of Biometrics 11, no. 1 (2019): 50. http://dx.doi.org/10.1504/ijbm.2019.096568.

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Doghmane, Hakim, Larbi Boubchir, and Abdelhani Boukrouche. "A novel discriminant multiscale representation for ear recognition." International Journal of Biometrics 11, no. 1 (2019): 50. http://dx.doi.org/10.1504/ijbm.2019.10016808.

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38

Feng, Xin, Kelin Xia, Zhan Chen, Yiying Tong, and Guo-Wei Wei. "Multiscale geometric modeling of macromolecules II: Lagrangian representation." Journal of Computational Chemistry 34, no. 24 (June 29, 2013): 2100–2120. http://dx.doi.org/10.1002/jcc.23364.

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Xia, Kelin, Xin Feng, Zhan Chen, Yiying Tong, and Guo-Wei Wei. "Multiscale geometric modeling of macromolecules I: Cartesian representation." Journal of Computational Physics 257 (January 2014): 912–36. http://dx.doi.org/10.1016/j.jcp.2013.09.034.

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Rosin, P. L. "Multiscale Representation and Matching of Curves Using Codons." CVGIP: Graphical Models and Image Processing 55, no. 4 (July 1993): 286–310. http://dx.doi.org/10.1006/cgip.1993.1020.

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41

Pak, Tannaz, Ian B. Butler, Sebastian Geiger, Marinus I. J. van Dijke, Zeyun Jiang, and Rodrigo Surmas. "Multiscale pore-network representation of heterogeneous carbonate rocks." Water Resources Research 52, no. 7 (July 2016): 5433–41. http://dx.doi.org/10.1002/2016wr018719.

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Nounou, Mohamed N., and Hazem N. Nounou. "Multiscale Latent Variable Regression." International Journal of Chemical Engineering 2010 (2010): 1–8. http://dx.doi.org/10.1155/2010/935315.

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Multiscale wavelet-based representation of data has been shown to be a powerful tool in feature extraction from practical process data. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of some of the latent variable regression models, such as Principal Component Regression (PCR) and Partial Least Squares (PLS), by developing a multiscale latent variable regression (MSLVR) modeling algorithm. The idea is to decompose the input-output data at multiple scales using wavelet and scaling functions, construct multiple latent variable regression models at multiple scales using the scaled signal approximations of the data and then using cross-validation, and select among all MSLVR models the model which best describes the process. The main advantage of the MSLVR modeling algorithm is that it inherently accounts for the presence of measurement noise in the data by the application of the low-pass filters used in multiscale decomposition, which in turn improves the model robustness to measurement noise and enhances its prediction accuracy. The advantages of the developed MSLVR modeling algorithm are demonstrated using a simulated inferential model which predicts the distillate composition from measurements of some of the trays' temperatures.

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Choi, Young-Seok. "Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation." BioMed Research International 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/830926.

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This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from ratsn=9experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool.Corrigendum to “Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation”

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Nounou, Mohamed N., and Hazem N. Nounou. "Reduced Noise Effect in Nonlinear Model Estimation Using Multiscale Representation." Modelling and Simulation in Engineering 2010 (2010): 1–8. http://dx.doi.org/10.1155/2010/217305.

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Nonlinear process models are widely used in various applications. In the absence of fundamental models, it is usually relied on empirical models, which are estimated from measurements of the process variables. Unfortunately, measured data are usually corrupted with measurement noise that degrades the accuracy of the estimated models. Multiscale wavelet-based representation of data has been shown to be a powerful data analysis and feature extraction tool. In this paper, these characteristics of multiscale representation are utilized to improve the estimation accuracy of the linear-in-the-parameters nonlinear model by developing a multiscale nonlinear (MSNL) modeling algorithm. The main idea in this MSNL modeling algorithm is to decompose the data at multiple scales, construct multiple nonlinear models at multiple scales, and then select among all scales the model which best describes the process. The main advantage of the developed algorithm is that it integrates modeling and feature extraction to improve the robustness of the estimated model to the presence of measurement noise in the data. This advantage of MSNL modeling is demonstrated using a nonlinear reactor model.

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Orbay, Günay, Mehmet Ersın Yümer, and Levent Burak Kara. "Sketch-based shape exploration using multiscale free-form surface editing." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26, no. 3 (August 2012): 337–50. http://dx.doi.org/10.1017/s0890060412000182.

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AbstractThe hierarchical construction of solid models with current computer-aided design systems provide little support in creating and editing free-form surfaces commonly encountered in industrial design. In this work, we propose a new design exploration method that enables sketch-based editing of free-form surface geometries where specific modifications can be applied at different levels of detail. This multilevel detail approach allows the designer to work from existing models and make alterations at coarse and fine representations of the geometry, thereby providing increased conceptual flexibility during modeling. At the heart of our approach lies a multiscale representation of the geometry obtained through a spectral analysis on the discrete free-form surface. This representation is accompanied by a sketch-based surface editing algorithm that enables edits to be made at different levels. The seamless transfer of modifications across different levels of detail facilitates a fluid exploration of the geometry by eliminating the need for a manual specification of the shape hierarchy. We demonstrate our method with several design examples.

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Eliav, Tamir, Shir R. Maimon, Johnatan Aljadeff, Misha Tsodyks, Gily Ginosar, Liora Las, and Nachum Ulanovsky. "Multiscale representation of very large environments in the hippocampus of flying bats." Science 372, no. 6545 (May 27, 2021): eabg4020. http://dx.doi.org/10.1126/science.abg4020.

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Hippocampal place cells encode the animal’s location. Place cells were traditionally studied in small environments, and nothing is known about large ethologically relevant spatial scales. We wirelessly recorded from hippocampal dorsal CA1 neurons of wild-born bats flying in a long tunnel (200 meters). The size of place fields ranged from 0.6 to 32 meters. Individual place cells exhibited multiple fields and a multiscale representation: Place fields of the same neuron differed up to 20-fold in size. This multiscale coding was observed from the first day of exposure to the environment, and also in laboratory-born bats that never experienced large environments. Theoretical decoding analysis showed that the multiscale code allows representation of very large environments with much higher precision than that of other codes. Together, by increasing the spatial scale, we discovered a neural code that is radically different from classical place codes.

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Jiang, Liu, Jianyong Shi, Zeyu Pan, Chaoyu Wang, and Nazhaer Mulatibieke. "A Multiscale Modelling Approach to Support Knowledge Representation of Building Codes." Buildings 12, no. 10 (October 9, 2022): 1638. http://dx.doi.org/10.3390/buildings12101638.

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Knowledge representations of building codes are essential and critical resources for the organization, retrieval, sharing, and reuse of implicit knowledge in the AEC industry. Against this background, traditional code compliance checking is time-consuming and error-prone. This research aimed to utilize various knowledge representation techniques to establish a knowledge model of building codes to facilitate the automated code compliance checking. The proposed knowledge model consists of three levels to achieve conceptual, logical, and correlational representations of building codes. The concept-level model provides the basic knowledge elements. The clause-level model was developed based on a unified top schema and provides the conceptual graph, mapping logics, and checking logics of each clause. The code-level model is constructed based on the explicit cross-references and semantic connections between clauses. The investigations on the model applications indicate two aspects. On the one hand, the proposed knowledge model shows high potential for semantic searching and knowledge recommendation. On the other hand, the automated code-compliance-checking processes based on the proposed multiscale knowledge model can achieve three main advantages: guiding designers to create a building model with completely necessary information, mitigating the differences between building information and regulatory information, and making the checking procedures more friendly and relatively transparent to users.

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Khafagy, Khaled H., Siddhant Datta, and Aditi Chattopadhyay. "Multiscale characterization and representation of variability in ceramic matrix composites." Journal of Composite Materials 55, no. 18 (January 28, 2021): 2431–41. http://dx.doi.org/10.1177/0021998320978445.

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Low density, high strength, and high creep and oxidation resistance properties of ceramic matrix composites (CMCs) make them an ideal choice for use in extreme environments in space and military applications. This paper presents a detailed characterization study of structural and manufacturing flaws in Carbon fiber Silicon-Carbide-Nitride matrix (C/SiNC) CMCs at different length-scales. Energy-dispersive spectroscopy (EDS) is used for the chemical characterization of the material’s elemental constituents. High-resolution multiscale graphs obtained from scanning electron microscope (SEM) and confocal laser scanning microscope (LSM) are used to characterize the distribution and morphology of defects at different length scales. This is followed by the classification and quantification of the common manufacturing defects. An image processing algorithm based on the image segmentation process is developed to quantify the variability of various scale-dependent architectural parameters. Finally, a three-dimensional stochastic representative volume element (SRVE) generation algorithm is developed to provide precise representations of material textures at multiple length scales. The developed algorithm accurately accounts for material features and flaws based on a range of multiscale structural and defects characterization results.

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Zhang, Shuzhen, Shutao Li, Wei Fu, and Leiyuan Fang. "Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification." Remote Sensing 9, no. 2 (February 7, 2017): 139. http://dx.doi.org/10.3390/rs9020139.

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Lu, Jian. "Contrast enhancement of medical images using multiscale edge representation." Optical Engineering 33, no. 7 (July 1, 1994): 2151. http://dx.doi.org/10.1117/12.172254.

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