Welcome to the UCR Time Series Classification/Clustering Page

This data resource was funded by an NSF Career Award 0237918, from 2003 to 2008, and continues to be funded through NSF awards 0803410 and 0808770. Partial funding was also made available by a gift from ISCA technologies

This webpage has been created as a public service to the data mining/machine learning community, to encourage reproducible research for time series classification and clustering. 

Note that the data here is useful for testing classification / clustering, and the accuracy of indexing techniques. However the datasets are too small to make claims about the efficiency of indexing. For this, email Dr. Keogh requesting a free CD-rom of larger datasets.  If you want datasets to test anomaly detection algorithms, many such datasets are here. A comparison of the results below with classic machine learning algorithms is here, thanks to Tony Bagnall and to Weka for this.
 

Name First paper or data creator Number of classes Size of training set Size of testing set Time series Length  1-NN Euclidean Distance

1-NN Best Warping Window DTW (r)

Note that r is the percentage of time series length

 1-NN DTW, no Warping Window
Synthetic Control

 Pham 

6 300 train 300 test 60

0.12

 0.017 (6)  0.007
Gun-Point  Ratanamahatana  2  50  150  150  0.087  0.087 (0)  0.093
CBF    3  30  900  128  0.148  0.004 (11)  0.003
Face (all) Xi  14  560  1,690  131  0.286  0.192 (3)  0.192
OSU Leaf Gandhi   6  200  242  427  0.483  0.384 (7)  0.409
Swedish Leaf Soderkvist  15  500  625  128  0.213  0.157 (2) 0.210
50Words  Rath  50  450  455  270  0.369  0.242 (6)  0.310
Trace Roverso   4  100  100  275  0.24  0.01 (3)  0.0
Two Patterns  Geurts  4  1,000  4,000  128  0.09  0.0015 (4)  0.0
Wafer  Olszewski  2  1,000  6,174  152  0.005  0.005 (1)  0.020
Face (four)  Ratanamahatana  4  24  88  350  0.216  0.114 (2)  0.170
Lightning-2

 Eads

 2  60  61  637  0.246  0.131 (6)  0.131
Lightning-7  Eads  7  70  73  319  0.425  0.288 (5)  0.274
ECG Olszewski 2 100 100 96 0.12 0.12 (0) 0.23
Adiac

Jalba

37 390 391 176 0.389 0.391 (3) 0.396
Yoga Xi 2 300 3000 426 0.170 0.155 (2) 0.164
Fish (readme) Lee 7 175 175 463 0.217 0.160(4) 0.167
Plane readme 7 105 105 144 0.038 0.0(5) 0
Car readme 4 60 60 577 0.267 0.233(1) 0.267
Beef
Tony Bagnall  30 30  470  0.467
0.467(0)
0.5
Coffee
Tony Bagnall 2
28
28
286
0.25
0.179(3)
0.179
OliveOil
Tony Bagnall 4
30
30
570
0.133
0.167(1)
0.133
CinC_ECG_torso   4 40 1380 1639 0.103 0.07(1) 0.349
ChlorineConcentration Lei Li & C. Faloutsos 3 467 3840 166 0.35 0.35(0) 0.352
DiatomSizeReduction rbg-web2.rbge.org.uk/ADIAC/ 4 16 306 345 0.065 0.065(0) 0.033
ECGFiveDays physionet.org & E. Keogh 2 23 861 136 0.203 0.203(0) 0.232
FacesUCR E. Keogh 14 200 2050 131 0.231 0.088(12) 0.0951
Haptics   5 155 308 1092 0.63 0.588(2) 0.623
InlineSkate Fabian Morchen 7 100 550 1882 0.658 0.613(14) 0.616
ItalyPowerDemand JJ van Wijk 2 67 1029 24 0.045 0.045(0) 0.05
MALLAT   8 55 2345 1024 0.086 0.086(0) 0.066
MedicalImages   10 381 760 99 0.316 0.253(20) 0.263
MoteStrain   2 20 1252 84 0.121 0.134(1) 0.165
SonyAIBORobot SurfaceII (D. Vail & M. Veloso) & E. Keogh 2 27 953 65 0.141 0.141(0) 0.169
SonyAIBORobot Surface (D. Vail & M. Veloso) & E. Keogh 2 20 601 70 0.305 0.305(0) 0.275
StarLightCurves Pavlos Protopapas 3 1000 8236 1024 0.151 0.095(16) 0.093
Symbols E. Keogh & J. Brady 6 25 995 398 0.1 0.062(8) 0.05
TwoLeadECG physionet.org & E. Keogh 2 23 1139 82 0.253 0.132(5) 0.096
WordsSynonyms E. Keogh 25 267 638 270 0.382 0.252(8) 0.351
Cricket_X M. H. Ko, G. West, S. Venkatesh, and M. Kuma. 12 390 390 300 0.426 0.236(7) 0.223
Cricket_Y 12 390 390 300 0.356 0.197(17) 0.208
Cricket_Z 12 390 390 300 0.38 0.18(7) 0.208
uWaveGestureLibrary_X Rice Efficient Computing Group, Rice University 8 896 3582 315 0.261 0.227(4) 0.273
uWaveGestureLibrary_Y 8
896 3582 315 0.338 0.301(4) 0.366
uWaveGestureLibrary_Z 8 896 3582 315 0.35 0.322(6) 0.342
Non-Invasive Fetal ECG Thorax1 Bing Hu and Eamonn Keogh 42 1800 1965 750 0.171 0.185(1) 0.209
Non-Invasive Fetal ECG Thorax2 Bing Hu and Eamonn Keogh 42 1800 1965 750 0.120 0.129(1) 0.135

How to get the datasets:

The Synthetic Control datasets are available above, and the code to reproduce the 1-NN Euclidean Distance result is available below. For the rest of the data, read on.

In order to get the password to the data1 and  data2 please carefully read the points below.

 

After reading the above, cut out the text to either A or B below, sign it with your full name, and email it to Dr. Keogh. If you are a grad student/post-doc, you must discuss this with your adviser first and CC him/her when requesting the password. 

 A) I have read the points above, and agree to all of them. I have CCed my academic advisor. Please send me the password.

 B) I have read the points above, but I do not agree to all of them. In particular, I do NOT agree with... (please enumerate). Nevertheless, I want the data.  Please send me the password.

 

 

 

Code:

If you want to compare a new distance measure with the results above, all you need to do is to change one line of code in the Classification_Algorithm function! p;

Note that this code is optimized for simplicity, not speed! Please do not report timing results using this code. Euclidean Distance can be speeded up using branch and boundLB_Keogh.

function UCR_time_series_test %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% (C) Eamonn Keogh %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
TRAIN = load('synthetic_control_TRAIN'); % Only these two lines need to be changed to test a different dataset. %
TEST  = load('synthetic_control_TEST' ); % Only these two lines need to be changed to test a different dataset. %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


TRAIN_class_labels = TRAIN(:,1);     % Pull out the class labels.
TRAIN(:,1) = [];                     % Remove class labels from training set.
TEST_class_labels = TEST(:,1);       % Pull out the class labels.
TEST(:,1) = [];                      % Remove class labels from testing set.

correct = 0; % Initialize the number we got correct

for i = 1 : length(TEST_class_labels) % Loop over every instance in the test set
      classify_this_object = TEST(i,:);
   this_objects_actual_class = TEST_class_labels(i);
   predicted_class = Classification_Algorithm(TRAIN,TRAIN_class_labels, classify_this_object);
   if predicted_class == this_objects_actual_class
       correct = correct + 1;
   end;
   disp([int2str(i), ' out of ', int2str(length(TEST_class_labels)), ' done']) % Report progress
end;

%%%%%%%%%%%%%%%%% Create Report %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp(['The dataset you tested has ', int2str(length(unique(TRAIN_class_labels))), ' classes'])
disp(['The training set is of size ', int2str(size(TRAIN,1)),', and the test set is of size ',int2str(size(TEST,1)),'.'])
disp(['The time series are of length ', int2str(size(TRAIN,2))])
disp(['The error rate was ',num2str((length(TEST_class_labels)-correct )/length(TEST_class_labels))])
%%%%%%%%%%%%%%%%% End Report %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Here is a sample classification algorithm, it is the simple (yet very competitive) one-nearest
% neighbor using the Euclidean distance.
% If you are advocating a new distance measure you just need to change the line marked "Euclidean distance"
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function predicted_class = Classification_Algorithm(TRAIN,TRAIN_class_labels,unknown_object)
best_so_far = inf;
 for i = 1 : length(TRAIN_class_labels)
     compare_to_this_object = TRAIN(i,:);
     distance = sqrt(sum((compare_to_this_object - unknown_object).^2)); % Euclidean distance

        if distance < best_so_far
          predicted_class = TRAIN_class_labels(i);
     best_so_far = distance;
    end
end;

>> UCR_time_series_test

1 out of 300 done
2 out of 300 done
...
298 out of 300 done
299 out of 300 done
300 out of 300 done
The dataset you tested has 6 classes
The training set is of size 300, and the test set is of size 300.
The time series are of length 60
The error rate was 0.12

 


Notes:

  1. This approach, where we find the best width of the Sakoe-Chiba Band by a search over the training set is a special case of Ratanamahatana-Keogh Band classification where the threshold is the length of the time series. See Ratanamahatana, C. A. and Keogh. E. (2004). Making Time-series Classification More Accurate Using Learned Constraints and see Ratanamahatana, C. A. andnd Keogh. E. (2004). Everything you know about Dynamic Time Warping is Wrong.
  2. D.T. Pham and< A.B. Chan. (1998). Control Chart Pattern Recognition using a New Type of Self Organizing Neural Network.
  3. A.C. Jalba, M.H.F. Wilkinson, J.B.T.M. Roerdink, M.M. Bayer andnd S. Juggins. Automatic Diatom Identification using Contour Analysis by Morphological Curvature Scale Spaces.
  4. D. Eads, D. Hill, S. Davis, S. Perkins, J. Ma, R. Porter, and J. Theiler
  5. Robert T. Olszewski. Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, 2001.
  6. Geurts, P. (2002). Contributions to decision tree induction: bias/variance tradeoff and time series classification. PhD thesis, Department of Electrical Engineering, University of Liege, Belgium.
  7. Oskar J. O. Soderkvist. Computer Vision Classification of Leaves from Swedish Trees, Master thesis, Linkoping University, Sweden, 2001.
  8. Ashit Gandhi. Content-Based Image Retrieval: Plant Species Identification. Master thesis, Oregon State University, September, 2002.
  9. Rath, T. & Manmatha, R. (2003). Word Image matching using dynamic time warping. CVPR, Vol. II, pp.521-527.
  10. Roverso, D. (2000). Multivariate temporal classification by windowed wavelet decomposition and recurrent neural networks. In 3rd ANS International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface, 2000.
  11. Li Wei, Eamonn J. Keogh: Semi-supervised time series classification. KDD 2006: 748-753
  12. Cherry picking, literally meaning harvesting cherries, is used metaphorically to accuse someone of pointing at individual cases which seem to confirm his or her position, while ignoring a significant portion of related cases that may contradict it.
  13. Salzberg S. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1997; 1:317--327
  14. Keogh, E. &  Kasetty, S. (2002). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada. pp 102-111.
  15. D.J. Lee, R. Schoenberger, D. Shiozawa, X. Xu, and P. Zhan, Contour Matching for a Fish Recognition and Migration Monitoring System. SPIE Optics East, Two and Three-Dimensional Vision Systems for Inspection, Control, and Metrology II, vol. 5606-05, p. 37-48, Philadelphia, PA, USA, October 25-28, 2004.
  16. The fish in question are mostly close to horizontal, facing left as they swam passed the camera, however a a rotation invariantLB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures. VLDB 2006.
  17. N. Thakoor and J. Gao, Occlusion resistant shape classifier based on warped optimal path matching, International Transactions on Communication and Signal Processing, vol. 9, no. 1, 2006.
  18. http://timemachine.iic.harvard.edu/

 

People that have Downloaded the Data:

  • Jiawei Han & Manish Gupta

  • Shiyuan Liu, Li Lv.

  • Thiago Santos Quirino, Mei-Ling Shyu University of Miami.

  • Pierre-Franqois Marteau,

  • Cosmin Bocaniala, Lancaster University.

  • Yueguo Chen,Anthony K.H. Tung, Beng Chin Ooi, National University of Singapore.

  • Vernon Rego, Vernon Rego. Purdue University.

  • Mislav Malenica, Tomislav Smuc

  • Man Hon WONG, ZHOU Mi, The Chinese University of Hong Kong.

  • Guillaume Bouchard Xerox Research Centre Europe.

  • Hoa Vo and David Joslin Seattle University.

  • Carlotta Orsenigo , University degli Studi di Milano.

  • Dr. Paolo Ciaccia

  • Xiaoqing Weng, Jiaotong University.

  • Longin Jan Latecki and Qiang Wang, Temple University.

  • Tony Bagnall

  • Michail Vlachos, IBM.

  • Bernard Hugueney

  • Sourav Mukherjee

  • Marcos M. Campos (Oracle)

  • Ludmila I. Kuncheva

  • Edward Omiecinski and Jun Li

  • Victor Eruhimov, Intel

  • Rob Jasper

  • Andre Coelho

  • Gernot Herbst

  • Vit Niennattrakul

  • Nozomi Matsuda

  • Flavio Miguel Varejao and Idilio Drago

  • HAORIANTO COKROWIJOYO TJIOE

  • Molnar Miklos

  • Steinn Gudmundsson and Thomas Runarsson

  • Niall Adams and Sai Wing Man

  • Isabel Maria Marques da Silva, Maria Eduarda da Rocha Pinto Augusto da Silva and Joaquim Fernando Pinto da Costa

  • Panagiotis Papapetrou and George Kollios

  • Huang Tan

  • Sergio Guadarrama

  • Alicia Troncoso Lora

  • Pyry Avist

  • Peng-Yi Lai

  • Yong Fu

  • Soheil Bahrampour

  • Long Yao and Meng Bo

  • Robert Moskovitch and Yuval Shahar

  • Abdellali Kelil

  • Puspadevi Kuppusamy

  • Qun Dai and Songcan Chen

  • Lisa Gralewski

  • Maria Teresinha Arns Steiner and Rosangela Villwock

  • Amir Ahmad and Galvin Brown

  • Abhijit Jayant Kulkarni

  • Xingquan (Hill) Zhu

  • Amol Deshpande and Qiang Qiu

  • Vercellis Carlo and Gianni Alberti

  • Pamela Nerina Llop

  • Tobias Scheffer

  • Jochen Fischer

  • Mao Ye and Yingying Zhu

  • Cintia Lera

  • George Runger and Rohit Das

  • Omar U. Florez and Seungjin Lim

  • Ruy Luiz Milidiu and Pedro Teixeira

  • Mykola Galushka and Dave Patterson

  • Rahul Sinha

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  • Eric Eaton

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  • Rayner Alfred and Samry Mohd Shamrie Sainin

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  • Hanjing, Su

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  • Susan Cheng and Min Ding

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  • Benjamin Bustos and Victor Sepulveda

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  • Shen Wang and Haimonti Dutta

  • Victor Garcia-Portilla

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  • Meizhu Liu and Baba C. Vemuri

  • Jose Principe and Sohan Seth

  • Ramanuja Simha and Rahul Tripath

  • Rodolphe JENATTON and Francis Bach

  • Siladitya Dey and Ambuj Singh

  • Mahsa Orang and Nematollaah SHIRI V.

  • Saket Saurabh

  • Wu Wush

  • Deepti Dohare

  • Subhajit Dutta.

  • Evgeny Pyatkov

  • Ion George TODORAN

  • Qin Zou

  • K.S.SUBHASHINI

  • Feng Gu, Julie Greensmith

  • Pedro Felzenszwalb

  • Onur Seref

  • Alessia Albanese

  • Seniha Esen Yuksel and Paul Gader

  • Alexandros Iosifidis

  • Rana Hussein and Neamat Farouk El-Gayar

  • Matteo Danieletto

  • Ying Zhang

  • Saeid Rashidi

  • Xiaobo Wu and S Chen

  • Sun Lei and Yu-Jiu Yang

  • Belen Martin Barragan

  • Mustafa Ahmed and Hua-Liang Wei

  • KHALIL BRAHMIA

  • Maria Luisa Sapino and Rosaria Rossini

  • Peiman Barnaghi and Azuraliza Abu Bakar

  • Hammoud Aljoumaa and Dirk Soeffker

  • Haris Gavranovic

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  • Mohammad Reza Daliri

  • FABIO STELLA

  • Rohit J. Kate

  • Ruoqian Liu, Yi Lu Murphey

  • jennifer bavani krishnan kehoe and Rahul Singh

  • George Runger and Mustafa Baydogan

  • Mohamed Ghalwash and ZORAN OBRADOVIC

  • Jieyue Li

  • Josif Grabocka

  • Marco Cristani

  • Tomas Olsson

  • Tianwei Liu

  • Cexus Jean-Christophe

  • Antonello Rizzi and Antonello Rizzi

  • Zhai Ting Ting

  • Ming Zhang

  • Shanghai Jiao Tong and Zhang Zhongneng

  • Zhang Zhang and Dacheng Tao

  • Trevor Tian

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  • Nikita Mishra and Somesh Kumar

  • Mike Jones

  • Deák Szilárd  Nicola Rebagliati

  • Ira Assent and Søren Chrestensen

  • Marcela Svarc

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  • Liu, Yueming and Su, Jianzhong

  • hongxiong ye and QIAN-LI MA

  • Huseyin Kaya

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  • OSman Gunay

  • Lu Min and Xiaoru Yuan

  • Hiba Bhamroukhi

  • Laurence Chu

  • Puneet Singh

  • Joaquim Vinicius C Assuncao

  • B Kalyan Kumar and debarun kar

  • Jim Austin and Alexander Fargus

  • Jundong Li and Osmar R. Zaiane

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  • Oya Celiktutan, bulent sankur and Cem Sübakan

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  • Hamdan Ana Arribas Gil

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  • Alexio Kotsifakos and Vassilis Athitsos

  • Vincent S. Tseng

  • Ryan Kleck and Gu Quanquan

  • Gavin Smith

  • Usue Mori, Jose A. Lozano, Alexander Mendiburu

  • Mohammed Hasan Al-Weshah

  • Sarah Brockhaus and Sonja Greven

  • Fenghuan Li

  • Wang Yu

  • Mayank Mohta and Adit Madan

  • Srinivasulu Reddy and Naga sundaram

  • Chiatung Mao and Jia-Dong MAO

  • Maciej Luczak

  • Zhiguang Wang and Tim Oates

  • Lukas Pfahler and marco stolpe

  • Rickey Agarwal

  • Yada Zhu and Jingrui He

  • Thanhvinh Vo and Duong Tuan Anh

  • Chris Carbone

  • Xiaohui Huang

  • Nicolas Ragot

  • Tanmoy Mondal

  • Guillem Rigaill

  • Fatemeh Kaveh-Yazdy and mohammad reza zare

  • Azer Kerimov

  • Hanan Shteingart

  • Xin Qi

  • S. Mohanavalli

  • Emanuele Ruffaldi and Leonard Johard

  • Tomasz Pander
    Pierre Allain and Thomas Corpetti
    Changcheng Xiang
    Wojciech Mioduszewski and Jurek Blaszczynski
    Zhang Daokun
    Xiangzeng Kong
    Amparo Alonso Betanzos
    Olivia Maoy and Yixin Chen
    Carlo GAETAN and Paolo Girardi
    Farzad Noorian
    Jing Yang
    Komang Sidhi Artha
     

  • ndre Rodrigo Sanches and Nina Sumiko Tomita Hirata

  • Claudio Piciarelli and Gian Luca Foresti

  • Wei T. Yue 

  • Michael Botsch and Josef A. Nossek

  • Bingyu Sun

  • Sun, Fu-Shing

  • Babak Amiri

  • Xing ChunXiao and Du Xutao, Tsinghua University

  • Elloumi Samir , Sondess Bentekaya

  • Li Shijin

  • Erik Learned-Miller, Marwan A. Mattar

  • Chiranjib Bhattacharya,Karthik K

  • Nicandro Cruz Ramirez

  • Jiankui Guo Fudan University

  • Bin Z Zhang IBM

  • Yi-Dong Shen and Zhiyong Shen

  • Georgios Evangelidis, Leonidas Karamitopoulos

  • Hendrik Purwins

  • Jignesh M. Patel and Michael Morse

  • Gert Van Dijck and Marc Van Hulle

  • Chao Hui Lee and Vincent Tseng

  • Linh Tran (Boeing)

  • Hugo Alonso Vilares Monteiro and Joaquim Fernando Pinto da Costa

  • David Minnen

  • Tsuyoshi Mikami

  • Qiang Yang and Sinno Pan

  • Paolo Tormene

  • Hui Ding and Peter Scheuermann

  • Ronaldo Cristiano Prati

  • Christian Gruber and Bernhard Sick

  • Silvia Chiappa

  • Ankur Jain

  • Maria Cristina Ferreira de Oliveira and Aretha Barbosa Alencar

  • Febri Andriani

  • Myeong-Seon Gi

  • Pengtao jia

  • Farid Seifi.

  • Clodoaldo Aparecido de Moraes Lima

  • Konstantinos Blekas

  • Inderjit Dhillon and HYUK CHO

  • Aida Valls

  • Narayanan Chatapuram Krishnan and Sethuraman Panchanathan

  • Stephan Gunnemann and Thomas Seidl

  • Thirumaran Ekambaram and M. Narasimha Murty

  • Christine Preisach and Lars Schmidt-Thieme

  • Dino Isa and Rajprasad Kumar

  • Feibao Zhuo

  • Frans van den Bergh

  • Kfir Glik

  • Xiao Yu

  • Yingying Zhu

  • Rosanna Verde and Antonio Balzanella

  • Paul Baggenstoss

  • Koichi ASAKURA and Wei Fan

  • Lucia Sacchi and Iyad Batal

  • Morne Neser

  • Luca Chiaravalloti

  • vikram deshmukh

  • Harri M.T. Saarikoski

  • Caio Nogara Andreatta and Neusa Grando

  • Ying Xie

  • Ulf Grobekathofer

  • Christophe Genolini

  • Celine Robardet

  • Musa Chemisto

  • Wangmeng Zuo

  • Paolo Remagnino

  • Soumi Ray and Tim Oates

  • Pierre Ganarski and Francois Petitjean

  • Joydeep Ghosh and John Tourish

  • Alireza-Xaker

  • Wilfgang nejdl and ERNESTO DIAZ-AVILES

  • Romain Tavenard and Laurent Amsaleg

  • Hahn-Ming Lee, Christos Faloutsos,Hsing-Kuoh Pao, Ching-Hao (Eric) Mao

  • Woong-Kee Loh

  • bikesh singh

  • Marco Grimaldi, Cesare Furlanello and Giuseppe Jurman

  • Chonghui Guo

  • Saeid Rashidi

  • Yanchang Zhang

  • S R Kannan

  • FRANCISCO JAVIER CUBEROS GARCIA-BAQUERO

  • Dr. SOTIRIOS P. CHATZIS

  • Ming Luo and Igor G. Zurbenko

  • Yuya Sakamoto

  • Yosi Keller

  • David Corney

  • Alexander Gray and Nishant Mehta

  • Tomoyuki Hiroyasu and Takuma Nishii

  • Blaz Strle and Martin Mozina

  • Yasin Bakis

  • Mrinal Mandal and Cheng Lu

  • Hendrik Blockeel and Kurt Driessens

  • Jonas Haustad and Yongluan Zhou

  • Julien Rabin

  • Oscar Gerardo Sanchez Siordia and Isaac Martin de Diego

  • Scott Deeann Chen

  • Lixia Wu

  • Tewari, Ashutosh

  • RANGEENA T V radhakrishnan

  • Zhou Zhou

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