From linear algebra and probability ... to sensing techniques ... to applications, such as localization, gesture recognition, augmented reality, drones, etc.
Course
Description: This course will teach a variety of techniques
and algorithms crucial to understanding and developing mobile systems
and applications. Topics of
interest
include:
Who should take this course?


Time and
Location: M/W 3 to 4:20pm @ 2015 ECE Building
Instructor: Romit Roy Choudhury (croy@illinois.edu) Office hours: M/W 4:20 to 5:00pm 

Some more details:


Grading
Information:
Homework and reviews:
10%
Programming assignment: 25% 1 midterm exam: 25% Final Project: 40% 
Topics 
Material 
Requirements 

Introduction [ intro.ppt ] 
Lecture 0.1: Overview of the course Lecture 0.2: Introduction and motivation ... best practices during the course ... thoughts on projects, etc. 
0.1 Submit: none 0.2 Submit: none 

Wireless Radio [ wireless.ppt ] [ rate_antenna.ppt ] 
Lecture 1.1: Wireless basics and WiFi case
study (MACAW). (Wireless channel, CSMA/CA, TDMA, hidden and exposed terminals, SINR, carrier sensing, backoff ...) Lecture 1.2: WiFi rate selection (RBAR) ... Overview of Bluetooth (BLE), RFID, and IoT beacons (SNR, BER, symbols, pathloss, multipath, fairness, ...) Optional readings: Directional antennas (DMAC), WiFi Energy (SleepWell), Bluetooth low energy (BLE) 
1.1 Submit: none 1.2 Submit: reviews for MACAW and RBAR 

Sep
1, 6:30pm in class 
Android
Tutorial by TA (Ashutosh Dhekne) 

Mini project 1: Released. [pdf]  Sensor data collection from smartphones  Step count estimation and basic activity recognition 
Released Mon Aug 29 Due: Sun Sep 11, 11:59pm 

Outdoor GPS Navigation [ gps.ppt ] 
Lecture 2.1: Basics of GPS positioning (Linear Systems, solving Ax=b, vector spaces, rank, independence, basis, dimension, least squares, trilateration, triangulation, TDoA) Lecture 2.2: Differential GPS and emerging problems in drones (SafetyNet) (Carrier phase, integer ambiguity, rotation matrices, glimpse of randomness, glimpse of filtering ...) 
2.1 Submit: none ...
but read the Linear_Algebra_primer 2.2 Submit: review for SafetyNet (skip section 4 and 5) 

Mini project 2:  Walking direction estimation  Trajectory tracking 

Indoor Localization [ radar.ppt ] [ surroundSense.ppt ] [ maloc.ppt] [ unloc.ppt ] 
Lecture 3.1: WiFi Signal Strength based
Indoor Localization (RADAR) (Visualize high dimension data, KNN classification, fading models, radio propagation model) Lecture 3.2: Sensing the Ambiance for Localization (SurroundSense and MaLoc) (Random variables, distributions, expectation, conditional probability, Bayes' rule, introduction to graphical models, Kalman & particle filters) Lecture 3.3: Localization without Relying on any Infrastructure (UnLoc and Zee) (Law of large numbers, Clustering, Kalman & particle filtering) Lecture 3.4: Review techniques and brainstorm project ideas (UWB, indoor GPS, sound localization, CSI based, visual, etc.) Optional readings: Indoor localization from GPS (CoinGPS), localization using lights (LuxaPose) ... 
3.1 Submit: review
for the RADAR 3.2 Submit: none ... but read MaLoc 3.3 Submit: reviews for UnLoc and Zee 3.4 Submit: none 

Mini project 3:  Topic TBD based on class interest 

Location Privacy [ privacy.ppt ] 
Lecture 3.5: Location privacy of motion
traces (CacheCloak) (Kanonymity, spacetime intersections, Entropy) 
3.5 Submit: review for CacheCloak  
Activity and Gesture Recognition [ phonePen.ppt ] [ warping.ppt] [ armTrack.ppt ] [ mole.ppt ] 
Lecture 4.1: Tracking arm motion with
smartphones (uWave
and PhonePen) (Time series, Time Warping, DTW) Lecture 4.2: Tracking arm motion with smart watches (ArmTrack) (3D orientation, introduction to Hidden Markov Models, Viterbi decoding) Lecture 4.3: Decoding finger motion from smart watches and Dictionary (MoLe) (Random variables, joint distributions, marginalization, priors, maximum likelihood estimation) 
4.1 Submit: none 4.2 Submit: review for ArmTrack 4.3 Submit: review for MoLe 

Final project:  Proposal submission deadline 

[
witrack.ppt
] [ swordFight.ppt ] 
Lecture 4.4: Gesture recognition through
WiFi signals (WiTrack) (Doppler shift, DFT, FFT, classification) Lecture 4.5: Phone to phone 3D ranging, walk estimation, and brainstorming (SwordFight) (PCA, Correlation, time of flight) 
4.4 Submit: none 4.5 Submit: review for WiTrack 

[
midterm.ppt
] 
Midterm (around Nov 2nd week ... date to be
finalized later) 

Smart Objects and IoT [ Buzz.ppt ] [ ripple.ppt ] 
Lecture 5.1: Smart toys and vehicular sensing (Buzz) (DTW, Decision Trees) Lecture 5.2: Vibration based communication and sensing (Ripple and VibraPhone) (Modulation, demodulation, QAM, synchronization, intersymbol, PN sequence, phase lock, MIMO, RPCA, harmonics, filter design) 
5.1 Submit none ...
but read Decision_Trees 5.2 Submit: Review for Ripple 

Camera
Sensor
Fusion
for Augmented Reality [ Overlay.ppt ] [ insight.ppt ] 
Lecture 6.1: Augmented reality through
sensor fusion (OverLay) (Image as vectors, features, SIFT, SURF, image compression, Eigen vectors, PCA, Linear programming) Lecture 6.2: Continuous Vision and InSight ... and other new ideas (InSight) (Image sensors, energy proportionality, spatiograms, Kalman filter, string matching) 
6.1 Submit: review
for OverLay 6.2 Submit none 

Assorted/Student choice [ sports.ppt ] [ robotic.ppt ] [ sleepwell.ppt ] 
Lecture 7.1: Sport analytics and project
updates (AoA, Gyroscope+magnetometer fusion, clock drift, Kalman filter) Lecture 7.2: Robotic wireless networks (WiFi, multipath, fading, shadowing, optimal stopping theory) Lecture 7.3: SleepWell (CAM, PSM, distributed fair sharing, clock tricks) 
7.1 Submit none 7.2 Submit none 7.3 Submit none 

Final project demo and presentation
(811am, Friday, Dec 9) 

Course Organization: The course will begin with various futurefacing topics in mobile sensing, sampled from those listed above. Once we have the bigger picture, we will select one application at a time, and zoom into each of the technical pieces of the puzzle. We will discuss various firstcut solutions to each problem and highlight its limitations ... with the goal of fully appreciating the difficulty of the problem. Then, we will dive into learning various mathematical techniques, starting from the first principles of linear algebra and probability. For instance, while discussing "GPS and indoor localization", we will cover techniques such as:
In the second half of semester, when you have pickedup a number of techniques and are starting to use them effectively, you would start conceiving your own application as a final project for the course. You would carefully motivate the project, argue that it is technically nontrivial, and apply some of the learnt techniques to achieve your goal. The semester will end with each group demonstrating their project to the entire class. 