Course Description:
 This course will teach a variety of ideas, concepts, techniques, and algorithms, that are all crucial to understanding and developing mobile systems and        applications. The course will begin from first principles and ramp up to real-world systems and technologies. Keywords related to this course includes: GPS, drones, motion tracking, localization, data analytics, acoustics, IMU sensors, mobile security, privacy, data analytics, pattern matching, etc.

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Course Topics:

0. Foundations:
    - We will start from absolute basics, and cover important modules from linear algebra, probability, and data/signal processing.
    - We will assume that you do not recall anything from prior courses (even if you have taken them).
    - The course is designed with both CS and ECE students in mind, particularly those inclined to software systems and algorithms.

1. GPS and Indoor Localization:
    - Understanding GPS, understanding why indoor positioning still not available ...
    - Location fingerprinting (WiFi, magnetic, BLE), crowd-sourcing, mapping.
    - Unsupervised data-driven learning, clustering, sensor fusion, filtering, simultaneous localization and mapping (SLAM).

2. Activity and Gesture Recognition (Humans and Objects):
    - Understanding IMU (accelerometer, gyroscope, compass)
    - Can a smartwatch track human gestures and activities? Can embedded IMUs track the motion of a fast-moving baseball?
    - Motion models and filtering techniques, introduction to Hidden Markov Models (HMM), Kalman Filters, Particle Filters.

3. Smart Homes and IoT:
    - Ambience sensing (WiFi and Alexa): Can users be tracked from WiFi reflections? Can Alexa learn human activities from everyday sound?
    - Wireless sensing techniques: presence detection, device-free tracking, FMCW, Doppler.
    - Acoustic analytics: Angle of Arrival (AoA), beamforming, blind estimation, mood and hierarchical context classification.

4. Wearable Computing:
    - Next generation of wearable devices: finger rings, ear-buds, smart clothing.
    - Rings: Vibration and ultrasound, receiving vibrations (with IMU and microphone), body-channels.
    - Hearables and earables: noise cancellation, bone conduction, motion to speech recovery, binaural sounds, energy optimization (wake-on-speech)

5. Autonomous Systems (Cars and Drones):
    - Core challenges in autonomous systems: sensing, computing, communications + actuation.
    - Robotic wireless networks, 5G networks, cell-tower on flying drones, ray-tracing, channel optimization.
    - Cars: LIDAR, RADAR, and vision, sensor fusion, relative map creation.

6. Mobile Privacy and Security:
    - Why personal, always-ON devices are a major challenge in security and privacy
    - Side channel attacks, inference algorithms, hardware loopholes, sensor data leaks.
    - Case studies: location privacy, password typing, Alexa attacks, IMU fingerprints, acoustic drone attack, clock leaks, etc.

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 Grading Information:
    - Homework (2):                          15%
    - Paper reviews (10):                   15%
    - 1 mid-term exam:                      25%
    - Final project (part 1 + 2):         15% + 30%

    Final project can be implemented on the the platform of your choice.
    You can focus on building a real system (on smartphones, smartwatches, drones, Echo, etc.) that has a practical and useful application ...
    or focus on developing a challenging algorithm (on MATLAB, Python, C++, etc.) without worrying about utility and practical considerations.