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Journal articles on the topic 'Spatial scalability'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Li, Zhengguo G., Susanto Rahardja, and Huifang Sun. "Implicit Bit Allocation for Combined Coarse Granular Scalability and Spatial Scalability." IEEE Transactions on Circuits and Systems for Video Technology 16, no. 12 (December 2006): 1449–59. http://dx.doi.org/10.1109/tcsvt.2006.885176.

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2

Qingwen Hu and S. Panchanathan. "Image/video spatial scalability in compressed domain." IEEE Transactions on Industrial Electronics 45, no. 1 (1998): 23–31. http://dx.doi.org/10.1109/41.661301.

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3

Hu, Qingwen, and Sethuraman Pachanathan. "Image/video spatial scalability in compressed domain." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 403–4. http://dx.doi.org/10.1016/s0920-5489(99)90757-2.

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4

Dugad, R., and N. Ahuja. "A scheme for spatial scalability using nonscalable encoders." IEEE Transactions on Circuits and Systems for Video Technology 13, no. 10 (October 2003): 993–99. http://dx.doi.org/10.1109/tcsvt.2003.816519.

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5

Thang, Truong Cong, Jung Won Kang, Jeong-Ju Yoo, and Yong Man Ro. "Optimal Multilayer Adaptation of SVC Video over Heterogeneous Environments." Advances in Multimedia 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/739192.

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Scalable video coding (SVC) is a new video coding format which provides scalability in three-dimensional (spatio-temporal-SNR) space. In this paper, we focus on the adaptation in SNR dimension. Usually, an SVC bitstream may contain multiple spatial layers, and each spatial layer may be enhanced by several FGS layers. To meet a bitrate constraint, the fine-grained scalability (FGS) data of different spatial layers can be truncated in various manners. However, the contributions of FGS layers to the overall/collective video quality are different. In this work, we propose an optimized framework to control the SNR scalability across multiple spatial layers. Our proposed framework has the flexibility in allocating the resource (i.e., bitrate) among spatial layers, where the overall quality is defined as a function of all spatial layers' qualities and can be modified on the fly.

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6

Kamath, Uday, Carlotta Domeniconi, and Kenneth De Jong. "Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm." Evolutionary Computation 26, no. 1 (March 2018): 43–66. http://dx.doi.org/10.1162/evco_a_00202.

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Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this article we discuss a meta-learning algorithm (PSBML) that combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property. We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin. We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers. We perform extensive experiments to investigate the trade-off achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data. These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.

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7

Wang, Z., and L. J. Lu. "Study on video signal processing for layered spatial scalability." Imaging Science Journal 60, no. 5 (October 2012): 243–47. http://dx.doi.org/10.1179/174313112x13197110618234.

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8

Naghdinezhad, Amir, and Fabrice Labeau. "Reference frame modification techniques for temporal and spatial scalability." Signal Processing: Image Communication 27, no. 10 (November 2012): 1079–95. http://dx.doi.org/10.1016/j.image.2012.09.001.

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9

Van der Auwera, Geert, Prasanth T. David, Martin Reisslein, and Lina J. Karam. "Traffic and Quality Characterization of the H.264/AVC Scalable Video Coding Extension." Advances in Multimedia 2008 (2008): 1–27. http://dx.doi.org/10.1155/2008/164027.

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The recent scalable video coding (SVC) extension to the H.264/AVC video coding standard has unprecedented compression efficiency while supporting a wide range of scalability modes, including temporal, spatial, and quality (SNR) scalability, as well as combined spatiotemporal SNR scalability. The traffic characteristics, especially the bit rate variabilities, of the individual layer streams critically affect their network transport. We study the SVC traffic statistics, including the bit rate distortion and bit rate variability distortion, with long CIF resolution video sequences and compare them with the corresponding MPEG-4 Part 2 traffic statistics. We consider (i) temporal scalability with three temporal layers, (ii) spatial scalability with a QCIF base layer and a CIF enhancement layer, as well as (iii) quality scalability modes FGS and MGS. We find that the significant improvement in RD efficiency of SVC is accompanied by substantially higher traffic variabilities as compared to the equivalent MPEG-4 Part 2 streams. We find that separately analyzing the traffic of temporal-scalability only encodings gives reasonable estimates of the traffic statistics of the temporal layers embedded in combined spatiotemporal encodings and in the base layer of combined FGS-temporal encodings. Overall, we find that SVC achieves significantly higher compression ratios than MPEG-4 Part 2, but produces unprecedented levels of traffic variability, thus presenting new challenges for the network transport of scalable video.

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10

Bayat, Mozhgan, Ratheesh K. Mungara, and Giuseppe Caire. "Achieving Spatial Scalability for Coded Caching via Coded Multipoint Multicasting." IEEE Transactions on Wireless Communications 18, no. 1 (January 2019): 227–40. http://dx.doi.org/10.1109/twc.2018.2878845.

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11

Lee, Bumshik, and Munchurl Kim. "A Low Complexity Mode Decision Method for Spatial Scalability Coding." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 1 (January 2011): 88–95. http://dx.doi.org/10.1109/tcsvt.2011.2106273.

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12

Mei-Juan Chen, Ming-Chung Chu, and Shen-Yi Lo. "Motion vector composition algorithm for spatial scalability in compressed video." IEEE Transactions on Consumer Electronics 47, no. 3 (2001): 319–25. http://dx.doi.org/10.1109/30.964116.

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13

Lin, Chia-Wen, Chia-Ming Tsai, and Po-Chun Chen. "Low-Overhead Content-Adaptive Spatial Scalability for Scalable Video Coding." IEEE Journal of Selected Topics in Signal Processing 7, no. 6 (December 2013): 1084–95. http://dx.doi.org/10.1109/jstsp.2013.2273659.

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14

Khairy, Mayada, Alaa Hamdy, Amr Elsayed, and Hesham Farouk. "Efficient Enhancement for Spatial Scalable Video Coding Transmission." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8239491.

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Scalable Video Coding (SVC) is an international standard technique for video compression. It is an extension of H.264 Advanced Video Coding (AVC). In the encoding of video streams by SVC, it is suitable to employ the macroblock (MB) mode because it affords superior coding efficiency. However, the exhaustive mode decision technique that is usually used for SVC increases the computational complexity, resulting in a longer encoding time (ET). Many other algorithms were proposed to solve this problem with imperfection of increasing transmission time (TT) across the network. To minimize the ET and TT, this paper introduces four efficient algorithms based on spatial scalability. The algorithms utilize the mode-distribution correlation between the base layer (BL) and enhancement layers (ELs) and interpolation between the EL frames. The proposed algorithms are of two categories. Those of the first category are based on interlayer residual SVC spatial scalability. They employ two methods, namely, interlayer interpolation (ILIP) and the interlayer base mode (ILBM) method, and enable ET and TT savings of up to 69.3% and 83.6%, respectively. The algorithms of the second category are based on full-search SVC spatial scalability. They utilize two methods, namely, full interpolation (FIP) and the full-base mode (FBM) method, and enable ET and TT savings of up to 55.3% and 76.6%, respectively.

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Segall, C. A., and G. J. Sullivan. "Spatial Scalability Within the H.264/AVC Scalable Video Coding Extension." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 9 (September 2007): 1121–35. http://dx.doi.org/10.1109/tcsvt.2007.906824.

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16

Wan, Wade K., Xuemin Chen, and Ajay Luthra. "Video compression for multicast environments using spatial scalability and simulcast coding." International Journal of Imaging Systems and Technology 13, no. 6 (2003): 331–40. http://dx.doi.org/10.1002/ima.10065.

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17

Oliveira, Maxwell Guimarães de, Ana Gabrielle Ramos Falcão, Cláudio De Souza Baptista, Hugo Feitosa de Figueiredo, and Daniel Farias Batista Leite. "Scaling spatial big data in a location-based social network." Revista Brasileira de Administração Científica 5, no. 2 (November 15, 2014): 141–55. http://dx.doi.org/10.6008/spc2179-684x.2014.002.0011.

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The widespread of the World Wide Web has resulted in a high volume of volunteered generated information using different formats including text, photography and video. The technological advances of recent years enabled the emergence and the popularization of various mobile devices equipped with GPS and connectivity to the Internet. This scenario contributed to the advent of several location-based applications and aroused the interest of many users in the geographical context of the information. An example of such applications are the Location-Based Social Networks (LBSN), in which the users interact with information classified by geographic region, as in the context of Smart Cities, in which citizens can interact pinning their criticisms, opinions and comments on various topics related to their city or neighborhood. The LBSNs have increasingly attracted the interest of the population and have consequently registered an increase in both the number of users interacting and the volume of shared information. This popularity brings up concerns about scalability, since it is essential to provide an environment that maintains the users active and motivated for contributing. Thus, the LBSNs must ensure acceptable response times, especially in spatial queries performed by their users, otherwise such applications may collapse due to the abandonment of their faithful users. Among several proposals of LBSNs in the community, it is still difficult to find out approaches concerned in scalability. In this context, this paper proposes an approach based on Big Data technologies to provide scalability in LBSNs and thus handle large volumes of spatial data. Our approach exploits NoSQL databases, the Map/Reduce technique and the development of extensions for indexing and querying Spatial Big Data.

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18

Wang, Ren-Jie, Jiunn-Tsair Fang, Yan-Ting Jiang, and Pao-Chi Chang. "Quantization-Distortion Models for Interlayer Predictions in H.264/SVC Spatial Scalability." IEEE Transactions on Broadcasting 60, no. 2 (June 2014): 413–19. http://dx.doi.org/10.1109/tbc.2014.2307486.

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19

Wang, Zhang, and Lijun Lu. "An efficient scalable intra coding algorithm for spatial scalability in enhancement layer." International Journal of Electronics 98, no. 5 (May 2011): 583–98. http://dx.doi.org/10.1080/00207217.2011.552841.

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20

Shin, Sungyoon, and Sungwook Yu. "An Efficient Mode Decision Method for All Intra Spatial Scalability in SHVC." Transactions of The Korean Institute of Electrical Engineers 69, no. 4 (April 30, 2020): 602–9. http://dx.doi.org/10.5370/kiee.2020.69.4.602.

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21

Enigo, V. S. F., and V. Ramachandran. "Enhancing the Scalability of Non-Spatial Concurrent Queries in Wireless Sensor Networks." Computer Journal 57, no. 12 (November 22, 2013): 1912–24. http://dx.doi.org/10.1093/comjnl/bxt139.

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22

Jadhav, Parul, and Shirish Kshirsagar. "Efficient rate control scheme using modified inter-layer dependency for spatial scalability." Sādhanā 41, no. 12 (November 17, 2016): 1415–24. http://dx.doi.org/10.1007/s12046-016-0568-6.

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23

Kim, Hyung-Il, Jae Young Choi, Seung Ho Lee, and Yong Man Ro. "Feature scalability for a low complexity face recognition with unconstrained spatial resolution." Multimedia Tools and Applications 75, no. 12 (April 30, 2015): 6887–908. http://dx.doi.org/10.1007/s11042-015-2616-3.

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24

Ruoss, Anian, Maximilian Baader, Mislav Balunović, and Martin Vechev. "Efficient Certification of Spatial Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2504–13. http://dx.doi.org/10.1609/aaai.v35i3.16352.

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Recent work has exposed the vulnerability of computer vision models to vector field attacks. Due to the widespread usage of such models in safety-critical applications, it is crucial to quantify their robustness against such spatial transformations. However, existing work only provides empirical robustness quantification against vector field deformations via adversarial attacks, which lack provable guarantees. In this work, we propose novel convex relaxations, enabling us, for the first time, to provide a certificate of robustness against vector field transformations. Our relaxations are model-agnostic and can be leveraged by a wide range of neural network verifiers. Experiments on various network architectures and different datasets demonstrate the effectiveness and scalability of our method.

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25

Pashchanka, Mikhail. "Multilevel self-organization on anodized aluminium: discovering hierarchical honeycomb structures from nanometre to sub-millimetre scale." Physical Chemistry Chemical Physics 22, no. 28 (2020): 15867–75. http://dx.doi.org/10.1039/d0cp01717e.

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Novel hierarchical honeycomb-like structures on anodized aluminium with unprecedented scalability of pores from nanometre to sub-millimetre range open up new frontiers of multiscale spatial self-organization in electrochemistry.

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26

Tesfa, T. K., H. Y. Li, L. R. Leung, M. Huang, Y. Ke, Y. Sun, and Y. Liu. "A subbasin-based framework to represent land surface processes in an Earth system model." Geoscientific Model Development 7, no. 3 (May 20, 2014): 947–63. http://dx.doi.org/10.5194/gmd-7-947-2014.

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Abstract. Realistically representing spatial heterogeneity and lateral land surface processes within and between modeling units in Earth system models is important because of their implications to surface energy and water exchanges. The traditional approach of using regular grids as computational units in land surface models may lead to inadequate representation of subgrid heterogeneity and lateral movements of water, energy and carbon fluxes. Here a subbasin-based framework is introduced in the Community Land Model (CLM), which is the land component of the Community Earth System Model (CESM). Local processes are represented in each subbasin on a pseudo-grid matrix with no significant modifications to the existing CLM modeling structure. Lateral routing of water within and between subbasins is simulated with the subbasin version of a recently developed physically based routing model, Model for Scale Adaptive River Transport (MOSART). The framework is implemented in two topographically and climatically contrasting regions of the US: the Pacific Northwest and the Midwest. The relative merits of this modeling framework, with greater emphasis on scalability (i.e., ability to perform consistently across spatial resolutions) in streamflow simulation compared to the grid-based modeling framework are investigated by performing simulations at 0.125°, 0.25°, 0.5°, and 1° spatial resolutions. Comparison of the two frameworks at the finest spatial resolution showed that a small difference between the averaged forcing could lead to a larger difference in the simulated runoff and streamflow because of nonlinear processes. More systematic comparisons conducted using statistical metrics calculated between each coarse resolution and the corresponding 0.125°-resolution simulations showed superior scalability in simulating both peak and mean streamflow for the subbasin based over the grid-based modeling framework. Scalability advantages are driven by a combination of improved consistency in runoff generation and the routing processes across spatial resolutions.

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27

Ahmed, K. Nafees, and T. Abdul Razak. "Scalable density based spatial clustering with integrated one-class SVM for noise reduction." International Journal of Engineering & Technology 7, no. 2.22 (April 29, 2018): 28. http://dx.doi.org/10.14419/ijet.v7i2.9.10093.

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Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class SVM, a machine learning technique for noise reduction, a modified variant of DBSCAN called NRDBSCAN. Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. Further, the proposed technique is parallelized using Spark architecture, thereby increasing its scalability and its ability to handle large amounts of data. Experiments and comparisons with similar techniques indicate high scalability levels and high homogeneity levels in the clustering process.

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28

Burns, Christopher, Barbara Bollard, and Ajit Narayanan. "Machine-Learning for Mapping and Monitoring Shallow Coral Reef Habitats." Remote Sensing 14, no. 11 (June 2, 2022): 2666. http://dx.doi.org/10.3390/rs14112666.

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Mapping and monitoring coral reef benthic composition using remotely sensed imagery provides a large-scale inference of spatial and temporal dynamics. These maps have become essential components in marine science and management, with their utility being dependent upon accuracy, scale, and repeatability. One of the primary factors that affects the utility of a coral reef benthic composition map is the choice of the machine-learning algorithm used to classify the coral reef benthic classes. Current machine-learning algorithms used to map coral reef benthic composition and detect changes over time achieve moderate to high overall accuracies yet have not demonstrated spatio-temporal generalisation. The inability to generalise limits their scalability to only those reefs where in situ reference data samples are present. This limitation is becoming more pronounced given the rapid increase in the availability of high temporal (daily) and high spatial resolution (<5 m) multispectral satellite imagery. Therefore, there is presently a need to identify algorithms capable of spatio-temporal generalisation in order to increase the scalability of coral reef benthic composition mapping and change detection. This review focuses on the most commonly used machine-learning algorithms applied to map coral reef benthic composition and detect benthic changes over time using multispectral satellite imagery. The review then introduces convolutional neural networks that have recently demonstrated an ability to spatially and temporally generalise in relation to coral reef benthic mapping; and recurrent neural networks that have demonstrated spatio-temporal generalisation in the field of land cover change detection. A clear conclusion of this review is that existing convolutional neural network and recurrent neural network frameworks hold the most potential in relation to increasing the spatio-temporal scalability of coral reef benthic composition mapping and change detection due to their ability to spatially and temporally generalise.

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Qiao, Baiyou, Ling Ma, Linlin Chen, and Bing Hu. "A PID-Based kNN Query Processing Algorithm for Spatial Data." Sensors 22, no. 19 (October 9, 2022): 7651. http://dx.doi.org/10.3390/s22197651.

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As a popular spatial operation, the k-Nearest Neighbors (kNN) query is widely used in various spatial application systems. How to efficiently process a kNN query on spatial big data has always been an important research topic in the field of spatial data management. The centralized solutions are not suitable for spatial big data due to their poor scalability, while the existing distributed solutions are not efficient enough to meet the high real-time requirements of some spatial applications. Therefore, we introduce the Proportional Integral Derivative (PID) control technology into kNN query processing and propose a PID-based kNN query processing algorithm (PIDKNN) for spatial big data based on Spark. In this algorithm, the whole data space is divided into grid cells of the same size using the grid partition method, and the grid-based index is constructed. On this basis, the grid-based density peak clustering algorithm is used to cluster spatial data, and the corresponding PID parameters are set for each cluster. When performing kNN queries, the PID algorithm is used to estimate the radius growth step size of kNN queries, thereby realizing kNN query processing with a variable query radius growth step based on a feedback mechanism, which greatly improves the efficiency of kNN query processing. A series of experimental results show that the PIDKNN algorithm has good performance and scalability and is superior to the existing parallel kNN query processing methods.

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30

IlHong Shin and Hyun Wook Park. "Adaptive Up-Sampling Method Using DCT for Spatial Scalability of Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 19, no. 2 (February 2009): 206–14. http://dx.doi.org/10.1109/tcsvt.2008.2009256.

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31

An-Chao Tsai, K. Bharanitharan, Jhing-Fa Wang, and Jar-Ferr Yang. "Classified Multifilter Up-Sampling Algorithm in Spatial Scalability for H.264/SVC Encoder." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 6 (June 2010): 861–69. http://dx.doi.org/10.1109/tcsvt.2010.2046052.

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32

Shanableh, T. "Hybrid M-JPEG∕MPEG-2 video streams using MPEG-2 compliant spatial scalability." Electronics Letters 39, no. 23 (2003): 1644. http://dx.doi.org/10.1049/el:20031086.

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33

Schreiber, Martin, Pedro S. Peixoto, Terry Haut, and Beth Wingate. "Beyond spatial scalability limitations with a massively parallel method for linear oscillatory problems." International Journal of High Performance Computing Applications 32, no. 6 (February 3, 2017): 913–33. http://dx.doi.org/10.1177/1094342016687625.

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This paper presents, discusses and analyses a massively parallel-in-time solver for linear oscillatory partial differential equations, which is a key numerical component for evolving weather, ocean, climate and seismic models. The time parallelization in this solver allows us to significantly exceed the computing resources used by parallelization-in-space methods and results in a correspondingly significantly reduced wall-clock time. One of the major difficulties of achieving Exascale performance for weather prediction is that the strong scaling limit – the parallel performance for a fixed problem size with an increasing number of processors – saturates. A main avenue to circumvent this problem is to introduce new numerical techniques that take advantage of time parallelism. In this paper, we use a time-parallel approximation that retains the frequency information of oscillatory problems. This approximation is based on (a) reformulating the original problem into a large set of independent terms and (b) solving each of these terms independently of each other which can now be accomplished on a large number of high-performance computing resources. Our results are conducted on up to 3586 cores for problem sizes with the parallelization-in-space scalability limited already on a single node. We gain significant reductions in the time-to-solution of 118.3× for spectral methods and 1503.0× for finite-difference methods with the parallelization-in-time approach. A developed and calibrated performance model gives the scalability limitations a priori for this new approach and allows us to extrapolate the performance of the method towards large-scale systems. This work has the potential to contribute as a basic building block of parallelization-in-time approaches, with possible major implications in applied areas modelling oscillatory dominated problems.

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Nath, Sumit K., and Eric Dubois. "An improved, wavelet-based, stereoscopic image sequence codec with SNR and spatial scalability." Signal Processing: Image Communication 21, no. 3 (March 2006): 181–99. http://dx.doi.org/10.1016/j.image.2005.09.004.

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35

Cho, Hyung-Ju. "A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks." Sensors 21, no. 16 (August 4, 2021): 5258. http://dx.doi.org/10.3390/s21165258.

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Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.

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Abdelaal, Tamim, Soufiane Mourragui, Ahmed Mahfouz, and Marcel J. T. Reinders. "SpaGE: Spatial Gene Enhancement using scRNA-seq." Nucleic Acids Research 48, no. 18 (September 21, 2020): e107-e107. http://dx.doi.org/10.1093/nar/gkaa740.

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Abstract Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas.

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Eken, S., E. Aydın, and A. Sayar. "DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 209–13. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-209-2017.

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In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

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Tong, Xing, Yang Liu, Zhao Shi, Peng Zeng, and Hai Bin Yu. "SR-Tree: An Index Structure of Sensor Management System for Spatial Approximate Query." Advanced Materials Research 756-759 (September 2013): 885–89. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.885.

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Sensor management plays an important role in the field of Internet of Things. Therefore, the requests of spatial approximate query increase dramatically. Indexing is no doubt a feasible way for efficient spatial approximate search. However, there is a lack of an effective index structure for spatial approximate query. In this paper, we propose a new type of index structure called SR-tree for providing more intelligent retrieval, which is based on R-tree and inverted table. Our index can support for spatial approximate search and work freely either in memory or external memory. The experimental results show that the structure proposed can provide high scalability and fast response time.

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Ruiqin Xiong, Jizheng Xu, Feng Wu, Shipeng Li, and Ya-Qin Zhang. "Subband Coupling Aware Rate Allocation for Spatial Scalability in 3-D Wavelet Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 10 (October 2007): 1311–24. http://dx.doi.org/10.1109/tcsvt.2007.903550.

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40

Motupalle, Haritha, and Syed Jahangir Badashah. "A Novel VLSI Architecture for SPHIT Encoder." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 4 (August 15, 2013): 1522–30. http://dx.doi.org/10.24297/ijct.v10i4.3252.

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In this Paper we propose a highly scalable image compression scheme based on the set partitioning in hierarchical trees (SPIHT) algorithm. Our algorithm called highly scalable SPIHT (HS-SPIHT), supports spatial and SNR scalability and provides a bit stream that can be easily adapted (reordered) to given bandwidth and resolution requirements by a simple transcoder (parser). The HS-SPIHT algorithm adds the spatial scalability feature without sacrificing the SNR embeddedness property as found in the original SPIHT bit stream. HS-SPIHT finds applications in progressive Web browsing, flexible image storage and retrieval, and image transmission over heterogeneous networks. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.

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Li, Sibo, Roberto Paoli, and Michael D’Mello. "Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme." Scientific Programming 2020 (March 11, 2020): 1–11. http://dx.doi.org/10.1155/2020/9083620.

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Compressible density-based solvers are widely used in OpenFOAM, and the parallel scalability of these solvers is crucial for large-scale simulations. In this paper, we report our experiences with the scalability of OpenFOAM’s native rhoCentralFoam solver, and by making a small number of modifications to it, we show the degree to which the scalability of the solver can be improved. The main modification made is to replace the first-order accurate Euler scheme in rhoCentralFoam with a third-order accurate, four-stage Runge-Kutta or RK4 scheme for the time integration. The scaling test we used is the transonic flow over the ONERA M6 wing. This is a common validation test for compressible flows solvers in aerospace and other engineering applications. Numerical experiments show that our modified solver, referred to as rhoCentralRK4Foam, for the same spatial discretization, achieves as much as a 123.2% improvement in scalability over the rhoCentralFoam solver. As expected, the better time resolution of the Runge–Kutta scheme makes it more suitable for unsteady problems such as the Taylor–Green vortex decay where the new solver showed a 50% decrease in the overall time-to-solution compared to rhoCentralFoam to get to the final solution with the same numerical accuracy. Finally, the improved scalability can be traced to the improvement of the computation to communication ratio obtained by substituting the RK4 scheme in place of the Euler scheme. All numerical tests were conducted on a Cray XC40 parallel system, Theta, at Argonne National Laboratory.

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Preskill, John. "Sufficient condition on noise correlations for scalable quantum computing." Quantum Information and Computation 13, no. 3&4 (March 2013): 181–94. http://dx.doi.org/10.26421/qic13.3-4-1.

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I study the effectiveness of fault-tolerant quantum computation against correlated Hamiltonian noise, and derive a sufficient condition for scalability. Arbitrarily long quantum computations can be executed reliably provided that noise terms acting collectively on $k$ system qubits are sufficiently weak, and decay sufficiently rapidly with increasing $k$ and with increasing spatial separation of the qubits.

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Chen, Shuo, Weihang Liu, Puyu Feng, Tao Ye, Yuchi Ma, and Zhou Zhang. "Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield." Remote Sensing 14, no. 10 (May 12, 2022): 2340. http://dx.doi.org/10.3390/rs14102340.

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Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.

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Dongdong Zhang, Wenjun Zhang, Jizheng Xu, Feng Wu, and Hongkai Xiong. "A Cross-Resolution Leaky Prediction Scheme for In-Band Wavelet Video Coding With Spatial Scalability." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 4 (April 2008): 516–21. http://dx.doi.org/10.1109/tcsvt.2008.918536.

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Jiaying Liu, Yongjin Cho, Zongming Guo, and Jay Kuo. "Bit Allocation for Spatial Scalability Coding of H.264/SVC With Dependent Rate-Distortion Analysis." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 7 (July 2010): 967–81. http://dx.doi.org/10.1109/tcsvt.2010.2045924.

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Mehrseresht, N., and D. Taubman. "An efficient content-adaptive motion-compensated 3-D DWT with enhanced spatial and temporal scalability." IEEE Transactions on Image Processing 15, no. 6 (June 2006): 1397–412. http://dx.doi.org/10.1109/tip.2005.864236.

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Joslin, Ronald D., Ulf R. Hanebutte, and Mohammad Zubair. "Scalability of parallel spatial direct numerical simulations on intel hypercube and IBM SP1 and SP2." Journal of Scientific Computing 10, no. 2 (June 1995): 233–69. http://dx.doi.org/10.1007/bf02089951.

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Zhao, J. H., X. Z. Wang, F. Y. Wang, Z. H. Shen, Y. C. Zhou, and Y. L. Wang. "A NOVEL APPROACH OF INDEXING AND RETRIEVING SPATIAL POLYGONS FOR EFFICIENT SPATIAL REGION QUERIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 131–38. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-131-2017.

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Spatial region queries are more and more widely used in web-based applications. Mechanisms to provide efficient query processing over geospatial data are essential. However, due to the massive geospatial data volume, heavy geometric computation, and high access concurrency, it is difficult to get response in real time. Spatial indexes are usually used in this situation. In this paper, based on k-d tree, we introduce a distributed KD-Tree (DKD-Tree) suitbable for polygon data, and a two-step query algorithm. The spatial index construction is recursive and iterative, and the query is an in memory process. Both the index and query methods can be processed in parallel, and are implemented based on HDFS, Spark and Redis. Experiments on a large volume of Remote Sensing images metadata have been carried out, and the advantages of our method are investigated by comparing with spatial region queries executed on PostgreSQL and PostGIS. Results show that our approach not only greatly improves the efficiency of spatial region query, but also has good scalability, Moreover, the two-step spatial range query algorithm can also save cluster resources to support a large number of concurrent queries. Therefore, this method is very useful when building large geographic information systems.

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Meng, Fanrong, Junjie Zhou, Dengkui Kong, Min Yao, Kongyi Wu, Xuchun Liu, Xuefei Wang, and Yike Guo. "The Design and Implementation of Geospatial Information Verification Middle Platform for Natural Resources Government Affairs." ISPRS International Journal of Geo-Information 11, no. 10 (September 21, 2022): 496. http://dx.doi.org/10.3390/ijgi11100496.

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Geospatial Information Verification Mid-End Platform for Natural Resource Administration is designed in response to issues such as repeated development, low scalability, and inconsistent verification rules in existing approval and supervision application systems. We first discussed the architecture of the middle platform and micro-services and also examined the business requirements. Secondly, we presented the architecture of the spatial information verification platform. Finally, the application method in the construction land approval business is introduced. Practical applications proved that the spatial information verification platform is highly scalable and maintainable, with reusable business components and data services for a variety of government affairs application systems.

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Cao, Weilin, Jie Qian, Jie Li, Qi Xu, Yizhen Lin, and Xiang Wang. "Space Layout Method for Carton Conveying System Based on SOSG Matrix." Journal of Physics: Conference Series 2095, no. 1 (November 1, 2021): 012044. http://dx.doi.org/10.1088/1742-6596/2095/1/012044.

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Abstract The traditional cigarette conveying system of cigarette industry adopts a one-to-one hard connection method, which has problems such as poor spatial scalability and high equipment coupling. A spatial layout method for carton conveying system based on the SOSG (Solid Orthogonal Structure Graph) matrix is proposed. Through this method, a matrix cigarette conveying system is constructed. Compared with the traditional mode, its space utilization rate is increased by 67%, equipment coupling is reduced, and the downtime rate of carton conveying line due to the shutdown of sealing machine can be reduced from 100% to 0%.

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