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Статті в журналах з теми "Local detection"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Ulyanov, N. A., S. V. Yaskevich, Dergach P. A., and A. V. YablokovAV. "Detection of records of weak local earthquakes using neural networks." Russian Journal of Geophysical Technologies, no. 2 (January 13, 2022): 13–23. http://dx.doi.org/10.18303/2619-1563-2021-2-13.

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Manual processing of large volumes of continuous observations produced by local seismic networks takes a lot of time, therefore, to solve this problem, automatic algorithms for detecting seismic events are used. Deterministic methods for solving the problem of detection, which do an excellent job of detecting intensive earthquakes, face critical problems when detecting weak seismic events (earthquakes). They are based on principles based on the calculation of energy, which causes multiple errors in detection: weak seismic events may not be detected, and high-amplitude noise may be mistakenly detected as an event. In our work, we propose a detection method capable of surpassing deterministic methods in detecting events on seismograms, successfully detecting a similar or more events with fewer false detections.

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2

Fleming, A. D., S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp. "Automated microaneurysm detection using local contrast normalization and local vessel detection." IEEE Transactions on Medical Imaging 25, no. 9 (September 2006): 1223–32. http://dx.doi.org/10.1109/tmi.2006.879953.

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3

Sakr, Mohamed, Walid Atwa, and Arabi Keshk. "Genetic-based Summarization for Local Outlier Detection in Data Stream." International Journal of Intelligent Systems and Applications 13, no. 1 (February 8, 2021): 58–68. http://dx.doi.org/10.5815/ijisa.2021.01.05.

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Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has two phases. First, the summarization phase that tries to summarize the past data arrived. Second, the detection phase detects the outliers from the new arriving data. The summarization phase uses a genetic algorithm to try to find the subset of points that can represent the whole original set. our experiments have been done over real datasets. Our experiments confirming the effectiveness of the proposed approach and the high quality of approximate solutions in a set of real-world streaming data.

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4

Hollocou, Alexandre, Thomas Bonald, and Marc Lelarge. "Multiple Local Community Detection." ACM SIGMETRICS Performance Evaluation Review 45, no. 3 (March 20, 2018): 76–83. http://dx.doi.org/10.1145/3199524.3199537.

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5

Ni, Li, Wenjian Luo, Wenjie Zhu, and Bei Hua. "Local Overlapping Community Detection." ACM Transactions on Knowledge Discovery from Data 14, no. 1 (February 4, 2020): 1–25. http://dx.doi.org/10.1145/3361739.

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6

Wu, Yubao, Ruoming Jin, Jing Li, and Xiang Zhang. "Robust local community detection." Proceedings of the VLDB Endowment 8, no. 7 (February 2015): 798–809. http://dx.doi.org/10.14778/2752939.2752948.

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7

Oliveira, Leonardo D., Fernando Ciriaco, Taufik Abrão, and Paul Jean E. Jeszensky. "Local search multiuser detection." AEU - International Journal of Electronics and Communications 63, no. 4 (April 2009): 259–70. http://dx.doi.org/10.1016/j.aeue.2008.01.009.

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8

Rigolin, G., and C. O. Escobar. "Local detection of entanglement." European Physical Journal D 37, no. 2 (November 16, 2005): 291–96. http://dx.doi.org/10.1140/epjd/e2005-00301-8.

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9

Cidon, I., and J. M. Jaffe. "Local distributed deadlock detection by knot detection." ACM SIGCOMM Computer Communication Review 16, no. 3 (August 1986): 377–84. http://dx.doi.org/10.1145/1013812.18214.

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10

Xiang, Ju, Zhi-Zhong Wang, Hui-Jia Li, Yan Zhang, Shi Chen, Cui-Cui Liu, Jian-Ming Li, and Li-Juan Guo. "Comparing local modularity optimization for detecting communities in networks." International Journal of Modern Physics C 28, no. 06 (May 7, 2017): 1750084. http://dx.doi.org/10.1142/s012918311750084x.

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Community detection is one important problem in network theory, and many methods have been proposed for detecting community structures in the networks. Given quality functions for evaluating community structures, community detection can be considered as one kind of optimization problem, such as modularity optimization, therefore, optimization of quality functions has been one of the most popular strategies for community detection. In this paper, we introduced two kinds of local modularity functions for community detection, and the self-consistent method is introduced to optimize the local modularity for detecting communities in the networks. We analyze the behaviors of the modularity optimizations, and compare the performance of them in community detection. The results confirm the superiority of the local modularity in detecting community structures, especially on large-size and heterogeneous networks.

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11

Choumane, Ali, and Abbass Al Akhrass. "Supervised local community detection algorithm." International Journal of Data Science 5, no. 3 (2020): 247. http://dx.doi.org/10.1504/ijds.2020.10035301.

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12

Choumane, Ali, and Abbass Al Akhrass. "Supervised local community detection algorithm." International Journal of Data Science 5, no. 3 (2020): 247. http://dx.doi.org/10.1504/ijds.2020.113061.

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13

GUO, JINHONG KATHERINE, DAVID DOERMANN, and AZRIEL ROSENFELD. "FORGERY DETECTION BY LOCAL CORRESPONDENCE." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 04 (June 2001): 579–641. http://dx.doi.org/10.1142/s0218001401001088.

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Signatures may be stylish or unconventional and have many personal characteristics that are challenging to reproduce by anyone other than the original author. For this reason, signatures are used and accepted as proof of authorship or consent on personal checks, credit purchases and legal documents. Currently signatures are verified only informally in many environments, but the rapid development of computer technology has stimulated great interest in research on automated signature verification and forgery detection. In this paper, we focus on forgery detection of offline signatures. Although a great deal of work has been done on offline signature verification over the past two decades, the field is not as mature as online verification. Temporal information used in online verification is not available offline and the subtle details necessary for offline verification are embedded at the stroke level and are hard to recover robustly. We approach the offline problem by establishing a local correspondence between a model and a questioned signature. The questioned signature is segmented into consecutive stroke segments that are matched to the stroke segments of the model. The cost of the match is determined by comparing a set of geometric properties of the corresponding substrokes and computing a weighted sum of the property value differences. The least invariant features of the least invariant substrokes are given the biggest weights, thus emphasizing features that are highly writer-dependent. Random forgeries are detected when a good correspondence cannot be found, i.e. the process of making the correspondence yields a high cost. Many simple forgeries can also be identified in this way. The threshold for making these decisions is determined by a Gaussian statistical model. Using the local correspondence between the model and a questioned signature, we perform skilled forgery detection by examining the writer-dependent information embedded at the substroke level and try to capture unballistic motion and tremor information in each stroke segment, rather than as global statistics. Experiments on random, simple and skilled forgery detection are presented.

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14

Morrone, M. C., and R. A. Owens. "Feature detection from local energy." Pattern Recognition Letters 6, no. 5 (December 1987): 303–13. http://dx.doi.org/10.1016/0167-8655(87)90013-4.

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15

Melnychuk, Michael C., and Carl J. Walters. "Estimating detection probabilities of tagged fish migrating past fixed receiver stations using only local information." Canadian Journal of Fisheries and Aquatic Sciences 67, no. 4 (April 2010): 641–58. http://dx.doi.org/10.1139/f09-199.

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We developed a method to predict the probability of detecting acoustic tags crossing a receiver station using only detection information at that station. This method is suitable for acoustic or radio telemetry studies in which individually tagged animals migrate past fixed stations (where a station may consist of one or more receivers). It is based on fitting attenuation models to sequences of detections and missed transmissions of individually coded tags in fish migrating past stations of the Pacific Ocean Shelf Tracking Project (POST). We used estimated attenuation model parameters from detected fish at each station to predict the number of fish that crossed the station undetected, which in turn was used to calculate the local detection probability. This estimator was correlated (r = 0.54–0.81 in river and coastal habitats) with mark–recapture estimates of detection probability (pmr) that use nonlocal detection information at stations further along migration routes. This local detection probability estimate can be used as a covariate of pmr in mark–recapture models and can predict approximate values of pmr at final detection stations where pmr is not estimable because of the lack of recaptures further along migration routes.

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16

Cidon, I., J. M. Jaffe, and M. Sidi. "Local Distributed Deadlock Detection by Cycle Detection and Clusterng." IEEE Transactions on Software Engineering SE-13, no. 1 (January 1987): 3–14. http://dx.doi.org/10.1109/tse.1987.232560.

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17

Bauer, H. U., and T. Geisel. "MOTION DETECTION AND DIRECTION DETECTION IN LOCAL NEURAL NETS." International Journal of Neural Systems 01, no. 02 (January 1989): 187–92. http://dx.doi.org/10.1142/s0129065789000098.

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We present a model for motion and direction detection of moving pulses whose performance is independent of pulse velocity, size and shape. The input signal activates one row of instantaneous nodes and one row of time integrating input nodes acting as short-term memories. Motion detection is achieved locally by subnetworks which are trained with a synthetic training set using the backpropagation algorithm. The global network is constructed from these subnetworks, one for each position. We test its performance with different pulse shapes and sizes and find the response to be invariant in a window of pulse velocities an order of magnitude wide. The window can be shifted by adjusting the memory time of the input nodes.

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18

Wang, Xin, Chunyan Zhang, Chen Ning, Yuzhen Zhang, and Guofang Lv. "Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2483169.

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For infrared images, it is a formidable challenge to highlight salient regions completely and suppress the background noise effectively at the same time. To handle this problem, a novel saliency detection method based on multiscale local sparse representation and local contrast measure is proposed in this paper. The saliency detection problem is implemented in three stages. First, a multiscale local sparse representation based approach is designed for detecting saliency in infrared images. Using it, multiple saliency maps with various scales are obtained for an infrared image. These maps are then fused to generate a combined saliency map, which can highlight the salient region fully. Second, we adopt a local contrast measure based technique to process the infrared image. It divides the image into a number of image blocks. Then these blocks are utilized to calculate the local contrast to generate a local contrast measure based saliency map. In this map, the background noise can be suppressed effectually. Last, to make full use of the advantages of the above two saliency maps, we propose combining them together using an adaptive fusion scheme. Experimental results show that our method achieves better performance than several state-of-the-art algorithms for saliency detection in infrared images.

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19

Liu, Xiaochun, Yang Shang, Zhihui Lei, and Qifeng Yu. "Change detection by local illumination compensation using local binary pattern." Optical Engineering 51, no. 9 (September 13, 2012): 097202–1. http://dx.doi.org/10.1117/1.oe.51.9.097202.

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20

Yasmin, Sabina, and Md Masud Rana. "Performance Study of Soft Local Binary Pattern over Local Binary Pattern under Noisy Images." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 1161. http://dx.doi.org/10.11591/ijece.v6i3.8383.

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In this paper, the performance of soft local binary pattern (SLBP) method has been investigated with edge detection techniques for face recognition in case of noisy condition. Various edge detection techniques such as Canny, Robert and Log methods have been used with SLBP for recognizing faces. The results obtained using SLBP with various edge detection for noisy condition based on image quality measurement shows better recognition rate compared to the results obtained using local binary pattern (LBP). Simplified edge detection methods simplify the images as a result SLBP with edge detection requires less computation time compared with edge detection of LBP.

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21

Yasmin, Sabina, and Md Masud Rana. "Performance Study of Soft Local Binary Pattern over Local Binary Pattern under Noisy Images." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 1161. http://dx.doi.org/10.11591/ijece.v6i3.pp1161-1167.

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In this paper, the performance of soft local binary pattern (SLBP) method has been investigated with edge detection techniques for face recognition in case of noisy condition. Various edge detection techniques such as Canny, Robert and Log methods have been used with SLBP for recognizing faces. The results obtained using SLBP with various edge detection for noisy condition based on image quality measurement shows better recognition rate compared to the results obtained using local binary pattern (LBP). Simplified edge detection methods simplify the images as a result SLBP with edge detection requires less computation time compared with edge detection of LBP.

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22

Ovi, J. A., M. E. Haque, A. Kalam, S. A. Jarin, M. S. Ali, and M. Hasan. "Malaria Detection Using Local Composition Pattern." Journal of Physics: Conference Series 1803, no. 1 (February 1, 2021): 012014. http://dx.doi.org/10.1088/1742-6596/1803/1/012014.

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23

Liavåg, Ivar, and M. D. Aker. "Detection and Treathent of Local Recurrence." Scandinavian Journal of Gastroenterology 23, sup149 (January 1988): 163–65. http://dx.doi.org/10.3109/00365528809096976.

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24

Ibtissam, Zaaj, Chaouki Brahim, and Masmoudi Lhoussaine. "Building Detection using Local Gabor Feature." International Journal of Computer Applications 181, no. 33 (December 17, 2018): 17–20. http://dx.doi.org/10.5120/ijca2018918216.

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25

LI, Hong-you, Tong-qing WANG, Qing LIU, Chun-hua GUO, and Jian-chun JIANG. "Moving targets detection using local OGHM." Journal of Computer Applications 29, no. 3 (May 6, 2009): 736–38. http://dx.doi.org/10.3724/sp.j.1087.2009.00736.

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26

Xiang, Xiaoyu, Renee Jessome, Eric Maggard, Yousun Bang, Minki Cho, and Jan Allebach. "Blockwise Based Detection of Local Defects." Electronic Imaging 2019, no. 10 (January 13, 2019): 303–1. http://dx.doi.org/10.2352/issn.2470-1173.2019.10.iqsp-303.

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27

Zhang, Ying-Ying, Shuo Zhang, Ping Zhang, Hai-Zhen Song, and Xin-Gang Zhang. "Local Regression Ranking for Saliency Detection." IEEE Transactions on Image Processing 29 (2020): 1536–47. http://dx.doi.org/10.1109/tip.2019.2942796.

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28

Shin, Seong-Yoon, Chan-Yong Jin, and Yang-Won Rhee. "Scene Change Detection Using Local Information." Journal of the Korean Institute of Information and Communication Engineering 16, no. 6 (June 30, 2012): 1199–203. http://dx.doi.org/10.6109/jkiice.2012.16.6.1199.

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29

Donohue, K. D., and N. M. Bilgutay. "OS characterization for local CFAR detection." IEEE Transactions on Systems, Man, and Cybernetics 21, no. 5 (1991): 1212–16. http://dx.doi.org/10.1109/21.120072.

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30

Bacon, William F., and Howard E. Egeth. "Local processes in preattentive feature detection." Journal of Experimental Psychology: Human Perception and Performance 17, no. 1 (1991): 77–90. http://dx.doi.org/10.1037/0096-1523.17.1.77.

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31

Ciatto, S. "Detection of breast cancer local recurrences." Annals of Oncology 6 (1995): S23—S26. http://dx.doi.org/10.1093/annonc/6.suppl_2.s23.

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32

van Daatselaar, AN, DA Tyndall, and PF van der Stelt. "Detection of caries with local CT." Dentomaxillofacial Radiology 32, no. 4 (July 2003): 235–41. http://dx.doi.org/10.1259/dmfr/86813332.

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33

Ronhovde, Peter, and Zohar Nussinov. "Local multiresolution order in community detection." Journal of Statistical Mechanics: Theory and Experiment 2015, no. 1 (January 6, 2015): P01001. http://dx.doi.org/10.1088/1742-5468/2015/01/p01001.

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34

Robbins, Ben, and Robyn Owens. "2D feature detection via local energy." Image and Vision Computing 15, no. 5 (May 1997): 353–68. http://dx.doi.org/10.1016/s0262-8856(96)01137-7.

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35

Varytimidis, Christos, Konstantinos Rapantzikos, Yannis Avrithis та Stefanos Kollias. "α-shapes for local feature detection". Pattern Recognition 50 (лютий 2016): 56–73. http://dx.doi.org/10.1016/j.patcog.2015.08.016.

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36

Ren, Haoyu, and Ze-Nian Li. "Object detection using boosted local binaries." Pattern Recognition 60 (December 2016): 793–801. http://dx.doi.org/10.1016/j.patcog.2016.07.010.

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37

Zhang, Ming, Yu Pang, Yunhe Wu, Yue Du, Hui Sun, and Ke Zhang. "Saliency detection via local structure propagation." Journal of Visual Communication and Image Representation 52 (April 2018): 131–42. http://dx.doi.org/10.1016/j.jvcir.2018.01.004.

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38

CHANG, CHENG-SHANG, CHIH-JUNG CHANG, WEN-TING HSIEH, DUAN-SHIN LEE, LI-HENG LIOU, and WANJIUN LIAO. "Relative centrality and local community detection." Network Science 3, no. 4 (August 17, 2015): 445–79. http://dx.doi.org/10.1017/nws.2015.23.

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AbstractIn this paper, we develop a formal framework for what a good community should look like and how strong a community is (community strength). One of the key innovations is to incorporate the concept of relative centrality into structural analysis of networks. In our framework, relative centrality is a measure that measures how important a set of nodes in a network is with respect to another set of nodes, and it is a generalization of centrality. Building on top of relative centrality, the community strength for a set of nodes is measured by the difference between its relative centrality with respect to itself and its centrality. A community is then a set of nodes with a nonnegative community strength. We show that our community strength is related to conductance that is commonly used for measuring the strength of a small community. We define the modularity for a partition of a network as the average community strength for a randomly selected node. Such a definition generalizes the original Newman's modularity and recovers the stability in as special cases. For the local community detection problem, we also develop efficient agglomerative algorithms that guarantee the community strength of the detected local community.

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39

Biswas, Sovan, and R. Venkatesh Babu. "Anomaly detection via short local trajectories." Neurocomputing 242 (June 2017): 63–72. http://dx.doi.org/10.1016/j.neucom.2017.02.058.

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40

Interdonato, Roberto, Andrea Tagarelli, Dino Ienco, Arnaud Sallaberry, and Pascal Poncelet. "Local community detection in multilayer networks." Data Mining and Knowledge Discovery 31, no. 5 (July 6, 2017): 1444–79. http://dx.doi.org/10.1007/s10618-017-0525-y.

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41

Cham, Tat-Jen, and Roberto Cipolla. "Symmetry detection through local skewed symmetries." Image and Vision Computing 13, no. 5 (June 1995): 439–50. http://dx.doi.org/10.1016/0262-8856(95)99731-f.

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42

Digarse, Deepika, and Krishna Chauhan. "Shadow Detection by Local Color Constancy." International Journal of Computer Applications 124, no. 14 (August 18, 2015): 36–41. http://dx.doi.org/10.5120/ijca2015905816.

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43

Elmabrouk, A., and A. Aggoun. "Edge detection using local histogram analysis." Electronics Letters 34, no. 12 (1998): 1216. http://dx.doi.org/10.1049/el:19980851.

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44

Wu, Ying-Jun, Han Huang, Zhi-Feng Hao, and Feng Chen. "Local Community Detection Using Link Similarity." Journal of Computer Science and Technology 27, no. 6 (November 2012): 1261–68. http://dx.doi.org/10.1007/s11390-012-1302-4.

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45

Papei, Hadi, and Yang Li. "Stochastic Local Community Detection in Networks." Algorithms 16, no. 1 (January 1, 2023): 22. http://dx.doi.org/10.3390/a16010022.

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Анотація:

We propose a stochastic agglomerative algorithm to detect the local community of some given seed vertex/vertices in a network. Instead of giving a deterministic binary local community in the output, our method assigns every vertex a value that is the probability that this particular vertex would be in the local community of the seed. The proposed procedure has several advantages over the existing deterministic algorithms, including avoiding random tie-breaking, evaluating uncertainties, detecting hierarchical community structure, etc. Synthetic and real data examples are included for illustration.

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46

Aminu, Ali Ahmad, and Nwojo Nnanna Agwu. "General Purpose Image Tampering Detection using Convolutional Neural Network and Local Optimal Oriented Pattern (LOOP)." Signal & Image Processing : An International Journal 12, no. 2 (April 30, 2021): 13–32. http://dx.doi.org/10.5121/sipij.2021.12202.

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Digital image tampering detection has been an active area of research in recent times due to the ease with which digital image can be modified to convey false or misleading information. To address this problem, several studies have proposed forensics algorithms for digital image tampering detection. While these approaches have shown remarkable improvement, most of them only focused on detecting a specific type of image tampering. The limitation of these approaches is that new forensic method must be designed for each new manipulation approach that is developed. Consequently, there is a need to develop methods capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep learning techniques which used constrained pre-processing layers to suppress the effect of image content in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish among different types of image tampering. Through a number of detailed experiments, our results demonstrate that the proposed general purpose image tampering method can achieve high detection accuracies in individual and multiclass image tampering detections respectively and a comparative analysis of our results with the existing state of the arts reveals that the proposed model is more robust than most of the exiting methods.

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47

Ito, So, Yusuke Shima, Daichi Kato, Kimihisa Matsumoto, and Kazuhide Kamiya. "Development of a Microprobing System for Side Wall Detection Based on Local Surface Interaction Force Detection." International Journal of Automation Technology 14, no. 1 (January 5, 2020): 91–98. http://dx.doi.org/10.20965/ijat.2020.p0091.

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Анотація:

This study proposes a novel microprobing system for the surface detection of the side wall of micrometric scale workpieces based on the detection of the local surface interaction force. A spherical tip-shaped glass capillary tube with a micrometric scale diameter was employed as a micro-stylus. To obtain a low measuring force, the local attractive interaction force on the surface of the workpieces was detected by the vibrating micro-stylus and used as the probing trigger signal. The vibration in the main axis direction of the stylus allowed detection of the local surface interaction force in all directions around the stylus shaft. In this paper, the principle and configuration of the developed microprobe are mentioned. Probing detections around the stylus shaft were verified by the surface detection of a pin gauge. Repeatability of the probing by the developed microprobing system was evaluated.

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48

Hamad, Y. A., F. P. Kapsargin, and K. V. Simonov. "Algorithms for detection and recognition of local regions on images." Informatization and communication, no. 2 (April 30, 2020): 25–34. http://dx.doi.org/10.34219/2078-8320-2020-11-2-25-34.

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The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description is also given of modern edge detection and classification algorithms suitable for isolating and characterizing local objects (for example, a brain tumor, etc.).

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Al-Chalaby, Aamer Yehya Hamid. "Detection of Escherichia coli from Imported and Local Beef Meat in Mosul City." Journal of Pure and Applied Microbiology 14, no. 1 (March 31, 2020): 383–88. http://dx.doi.org/10.22207/jpam.14.1.39.

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50

Su, Wei. "Magnetic Anomaly Data Detection of Local Marine Geomagnetic Field Model considering Robust Trend Surface Scientific Calculation Algorithm." Scientific Programming 2022 (May 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/4055976.

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Анотація:

As a momentous part of marine data detection, modeling the marine geomagnetic field and detecting its magnetic data have momentous theoretical and practical value for obtaining its geomagnetic field parameters and characteristic distribution and then carrying out marine environment research. The detection of magnetic anomaly data of local marine geomagnetic field model has become a momentous prerequisite for obtaining key parameter info of local ocean and subsequent development and utilization. On account of this, this paper first analyzes the detection of local marine geomagnetic anomaly data, then studies the theoretical basis of local marine geomagnetic field model, as well as the measurement and modeling of local marine geomagnetic field data, and finally gives the analysis of the detection results of local marine geomagnetic anomaly data considering the robust trend surface.

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