普适计算2016

Pervasive Computing

1.Course Info

  • Location：公四4204
  • Time： Wednseday 16:00-17:40
  •  Teacher：Yongcai Wang
  •  TA：
  • Office：Wing building of Science and Technology, 105A
  • Email：ycw@ruc.edu.cn

2.Grading

  • Discussion and Presentation： 50%
  • Project： 50%

3.Topics to Cover (tentative)

1. Data processing algorithms (Low pass filter, high pass filter, RANSAC, Moving average)

2. Optimal filter (Kalman Filter, Extended Kalman Filter, Particle Filter)

3. Classification algorithms (KNN, SVM, RDF, Boosting, HMM, MKL)

4. Activity Recogonition (Personalized, Hierachical)

5. Indoor Navigation and Localization (PDR, WiFi, SDP, ARAP)

6. HCI by depth sensor (Xtion)

7. SLAM (RADAR, Camera)

8. Mobile virtual reality (SLAM + US)

 

4. Course Schedule (tentative)

 
week 1 2016/9/14 Introduction  M. Weiser, "The Computer for the 21st Century". Scientific American, pp. 66-75, September 1991  Pervasive and cognitive computing(普适计算与普适感知绪论).pptx
week2 2016/9/21 贝叶斯滤波 (Bayes Filter)    Chapter2 of Probabilistic Robotics  Bayes Filter(贝叶斯滤波).pptx  Project topic determination  
week3 2016/9/28 卡尔曼滤波 (Kalman Filter) Project proposal  Chapter3 of Probabilistic Robotics Kalman Filter, EKF,(卡尔曼滤波，扩展卡尔曼滤波).pptx matlab code of kaman filter  Hw1 assigned.
week4, no lecture 2016/10/5
week 5 2016/10/12 无迹滤波器、信息滤波器和扩展信息滤波器(UKF, IF and EIF)  Chapter3 of Probabilistic Robotics UKF，IF，EKF.pdf UKF, IF, EIF(无迹卡尔曼滤波，信息滤波，扩展信息滤波).pptx matlab codes of UKF Project proposal due Hw1 due. Essay due. "Computers in 2050".
week 6 2016/10/19 粒子滤波 (Particle Filter, PF) Chapter4 of Probabilistic Robotics Arulampalam 2002，A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking Gandy 2015, The chopthin algorithm for resampling  Particle Filter (粒子滤波).ppt 粒子滤波matlab code
week 7 2016/10/26 优化方法（Optimization）
week 8 2016/11/2 姿态检测与惯性导航的数学基础 (Mathematical Fundation  of pose detection and inertial navigation)  http://www.petercorke.com /RVC/chaps/2/ Mathematical background for attitude estimation (姿态解算与惯性导航的数学基础).pdf  Homework4 homework  
week 9 2016/11/9   姿态解算、惯性导航、与肢体模型 (Algorithms of Inertial navigation， Pose detection and body kinectics) I am a Smartwatch and I can Track my User’s Arm (mobicom16)

Pedestrian Localisation for Indoor Environments, Oliver J. Woodman

Pedestrian tracking with shoe-mounted inertial sensors, Eric Foxlin

Openshoe: http://www.openshoe.org/

  INS, PDR, Activity Recogonition Algorithms (惯性导航，行人航位推算，状态检测).pdf  Home work   IMU Dataset for PDR Filter development and test  
week 10 2016/11/16 Localization 1 (定位算法与系统1)   Indoor Localization without Infrastructure using the Acoustic Background Spectrum (Mobisys2015) EchoTag: Accurate Infrastructure-Free Indoor Location Tagging with Smartphones (Mobicom2015) Last-Mile Navigation Using Smartphones (Mobicom2016) WarpMap: Accurate and Efficient Indoor Location by Dynamic Warping in Sequence-Type Radio-map (Secon2015) Robust Statistical Methods for Securing Wireless Localization in Sensor Networks(IPSN2005)  Lecture 8: Localization I (定位算法：基于测距的多边定位，LSQ, LMS, 基于Fingerprint的定位) WarpMap.pptx  第四次作业提交截止
week 11 2016/11/23 Localization 2 (定位算法与系统2)   Keystroke Recognition Using WiFi Signals (Mobicom 15) SpotFi Decimeter Level Localization Using WiFi (Sigcomm 15) Luxapose, Indoor Positioning with Mobile Phones and Visible Light (Mobicom14) LiTell: Robust Indoor Localization Using Unmodified Light Fixtures (Mobicom2016) EchoTag: Accurate Infrastructure-Free Indoor Location Tagging with Smartphones (Mobicom15) Indoor localization without infrastructure using the acoustic background (Mobisys 11) http://dhalperi.github.io/linux-80211n-csitool/#overview (802.11n CSI tool) Introduction to Music Algorithm Lecture 9: Localization II (acoustic features, lighting features, channel state information, crowd sourcing, unsupervised learning) （声音特征，可见光定位，CSI信道特征定位）  第五次作业提交截止
week 12 2016/11/30 Localization 3 (定位算法与系统3) No need to war drive (Mobisys12) LiFS, locating in fingerprint space (Mobicom 12) Predict Location Semantics (Ubicomp16) Lecture 10: localization III (群智感知，非监督学习)  第六次作业提交截止
week 13 2016/12/7 SLAM1  https://mitpress.mit.edu/books/probabilistic-robotics (Probabilistic Robotics) http://www.petercorke.com/RVC/ (Robot Vision and Control)  Lecture Notes: EKF SLAM (基于EKF的SLAM方法)
week 14 2016/12/14 SLAM2  Notes on Least-Squares and SLAM Cholesky factorization A Tutorial on Graph-Based SLAM    lecture-12-graphslam (基于Graph的SLAM，推导与算法)
week 15 2016/12/21  SLAM3  Course Slam Gradient-based Methods for Optimization. Part II. Lecture 13 Graphslam II (Graph SLAM的细节，包括优化方法的改进，以及流型(manifold)上的优化)
week 16 2016/12/28 Final Report   Reading and project report due
 

5. Resources

  1. how_to_read_an_engineering_research_paper
  2. probabilistic robotics, book by Sebastian Thrun et al., which is widely used in smart robot and pervasive computing lectures.
  3. the-product-and-convolution-of-guassian-distributions
  4. proposal-and-presentation-evaluation-form
  5. a-new-approach-to-forecasting-stock-price-with-ekf
  6. predicting-market-data-using-the-kalman-filter
  7. nonlinear-filters-beyond-the-kalman-filter

6. List of Projects

  1. project 1
  2. project 2
  3. project 3
  4. project 4
  5. project 5
  6. project 6