CS/ECE 434: Mobile Computing and Applications

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 Location:
                   ECE 2015
 Instructor URL:       Romit Roy Choudhury
 Email:                        croy@illinois.edu
 Office hours:             After class

 Course TA:               Zhijian Yang
                                    zhijian7@illinois.edu    
                                   
 TA hours:                  Thu, 10:30am, CSL 241

 Prerequisites:            Linear Algebra, Probability,
                                     Programming (Py or Matlab)

Course Topics:
 
Math Foundations (from scratch)

      Linear Algebra, Data/Signal Processing, Probability and Learning

GPS, WiFi, Sensor Fusion

      Algorithms: Triangulation, Trilateration, Clustering, SLAM, Kalman filter

       Applications: Outdoor and indoor localization, mapping, IoT

IMU Sensors, Motion Tracking

      Algorithms: Dead reckoning, PCA, Hidden Markov Models (HMM), Kalman Filters

      Applications: Activity tracking, Gesture recognition, Sports analytics, wearable computing

Microphone, Speakers

      Algorithms: SVD, Noise Cancellation, Dynamic Time Warping, 3D Binaural Sounds

      Applications: Acoustic Sensing, Voice Assistants, Earphone computing

Camera, Light

      Algorithms: SIFT/SURF, Wavelets, 3D Point clouds, Structure from motion (SfM)

      Applications: Augmented reality, Visual Communication, Shadow sensing

Wireless Radios (WiFi, BLE, 5G)

      Algorithms: Beamforming, Time of Flight (ToF), Clock synchronization, FMCW, Doppler

      Applications: Presence detection, Liquid identification, Bio-monitoring, Digital agriculture

Mobile Sensing, Spatial Diversity

      Algorithms: Synthetic aperture, RF imaging, Particle filter

      Applications: Drones, Autonomous vehicles

Security and Privacy

      Algorithms: Classification, Non-linearity, Viterbi decoding, MLE, Stochastic gradient descent

      Applications: Fingerprinting, side channel, inference

Emerging areas: Edge computing, battery-free devices, Earable computing,

   Grading Information:
    - Homework, Assignmeents:       20%
    - Paper reviews (~10):                 15%
    - 1 mid-term exam (Apr 15th):     25%
    - Final project:                             40% (you can use platform of your choice, e.g., MATLAB, Python, Java, Android, etc.)
    - Project report (5+ pgs):             Only for 4 credit students

Course Calendar
(subject to change)
Lecture Content:
Material:
Deadlines and ToDo Items:
Introduction:
- Overview slides

Foundations:
- Linear algebra refresher
- Introduction to signal processing


- NOTES: Linear Algebra Foundations
- NOTES:
FFT Foundations

HW1 (math basics)
Mobile sensors:
- Overview of sensors on mobile devices


IMU Sensors Slides
MP1 (due Mon, Mar 16, 11:59pm)
GPS and WiFi based Localization (outdoor and indoor):
- GPS basics, trilateration, acquisition, correlation, time offsets
  (read Section 1 and 2 of CO-GPS, skim SafetyNet)

- WiFi trilateration (RADAR), fingerprinting
- Sensor fusion: ambience sensing (SS), Unsupervised loc. (UnLoc)


- Direction finding, beamforming, and angle of arrival (AoA)


- RF triangulation (ArrayTrack)
- In-body localization (ReMix)


- GPS Linearize video
- SafetyNet slides

- NOTES: Wireless Channel Basics
- RADAR+SS slides, and UnLoc slides

- NOTES: Beamforming Foundations
- REFER: Delay and Sum algorithm
- REFER: Classical AoA algo. (Sec. 3)

- ArrayTrack slides
- ReMix slides

Reviews: CO-GPS and UnLoc
Review guideline Review template

ONLINE CLASSES DUE TO COVID-19
Motion Tracking:
- Foundations of IMU based motion tracking
  (read MUSE paper)


- Ball and player tracking for sports analytics (read iBall paper)

- Foundations of Bayesian inference (Bayesian Filters, HMM)











- Motion tracking application: Arm posture using watch IMU
  (Read ArmTrak paper)


- LECTURE VIDEO: Mar. 23: IMU Tracking
- SLIDES: IMU Tracking foundation
- REFER: Google Tech Talk on IMU

- SLIDES: iBall

- LECTURE VIDEO: Mar. 30: Probability Recap
- LECTURE VIDEO: Mar. 30: Bayesian Filter (1/3)
- LECTURE NOTES: Mar. 30: Recap + Bayesian

- LECTURE VIDEO: Apr. 1: Bayesian Filter (2/3)

- LECTURE VIDEO: Apr. 6: HMM + apps (3/3)
- LECTURE NOTES: Apr 6: Full HMM

- CLEAN NOTES: Bayesian Filtering, HMM
- REFER: Magazine article (Bayesian)

- LECTURE VIDEO: Apr. 8: Arm Tracking App.
- SLIDES: ArmTrak slides






Review: iBall (due Wed, Apr 1, 11:59pm)

HW2 (Loc, AoA, IMU) (due Fri, Apr 10, 11:59pm)
Review: ArmTrak (due Wed, Apr 15, 11:59pm)


MP2 (3D Orientation) (due Wed, Apr 29, 11:59pm)
Midterm Exam: Apr 15 on Zoom

- LECTURE VIDEO: Apr. 8 Part 2 Midterm Review
- SLIDES: Midterm Review


Motion Tracking (continued) ...
- Continuous time filtering, Non-linear measurement and transition
- Application in localization: (read map matching (Zee))


- LECTURE VIDEO: Apr. 13: Kalman/Particle Filter
- SLIDES: Zee slides,

Microphone and Speakers:
- Active noise cancellation
  (read MUTE)


- Vibratory communications and sensing (Ripple 1 and 2)
- Vibratory side channels (Read Vibraphone)


- Time of Flight (ToF) estimation under clock errors
- Frequency Modulated Continuous Wave (FMCW)
  (Read BeepBeep and CAT)


- Acoustic side channels and hardware non-linearity
  (read BackDoor, LipRead)


- Dynamic Time Warping (DTW), Doppler
  (read AAMouse, FingerIO)

 
- LECTURE VIDEO: Apr. 20: MUTE
- SLIDES: MUTE slides


- LECTURE VIDEO:Apr. 22: Ripple
- SLIDES: Ripple


- LECTURE VIDEO:Apr. 27: ToF ranging and FMCW
- NOTES:
- SLIDES: CAT Slides


- LECTURE VIDEO: Apr. 29: Backdoor
- SLIDES: Backdoor slides


- LECTURE VIDEO:May. 4: DTW
- SLIDES: DTW slides
- SLIDES: AAMouse Slides



Review: MUTE (due Sun, Apr 26, 11:59pm)











Review: Backdoor (due Fri, May 8, 11:59pm)



Rapid Fire Survey of Assorted Ideas and Applications

- Head Related Transfer Functions (HRTF) --> Acoustic AR
- Visual fingerprinting --> Location privacy (Insight)
- Motion leakage --> Stealing passwords (MoLe)
- UWB timing resolution --> Liquid Sensing (LiquID)
- Sound pockets --> ICASSP paper
- Device free tracking --> WiSee



- LECTURE VIDEO:May. 6: Assorted ideas & final exam instructions

- SLIDES:









FINAL EXAM: Online project submission



Final Project Description
Final Project Data