This course is taught in-person. Every single class in this course is an experience. This course requires image license and release form to be signed by all students. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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: wireless sensing, localization, GPS, drones, sensors, motion tracking, acoustics, location privacy, etc.
Course Topics
We will start from absolute basics, and cover important modules from linear algebra and data/signal processing.
We will assume that you do not recall anything from prior courses (even if you have taken them).
We will discuss the crossover of ideas from these modules to wireless communication and mobile computing.
The course is designed with CS students in mind, particularly those inclined towards systems and networking.
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).
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
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)
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.
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.
Course Format
The underlying concepts discussed in this course are taught in class. Students are expected to read research papers about the topic, answer questions about the research paper before class, and engage in classroom discussions. We will be discussing those papers, and students are expected to participate in the class with comments and thoughts about the work. The class is interactive and we make the classroom a fun place. In-person attendance is expected. We will go on field trips in and around campus and talk about mobile computing and IoT technology is different contexts.
This course has homeworks, programming assignments, a midterm, and a final project (no final exam). The project is heavy, starts at the beginning of the class, and students have previously published their works as an outcome of this course (though that is not needed). Examples: Intrusense, Pettrack, Notification Control Demo, uFindMe. See papers or slides on: https://faculty.cc.gatech.edu/~dhekne/Links to an external site. This course is NOT EASY. In previous years students have worked very hard to score well. We expect to be done by the last day of class (Dec 2). We will not use the exam time unless there are specific exceptions. If you want to book tickets for the winter break, assume you are occupied until end of business December 2. Lecture Recordings, Live-stream, Image Release
This is a course with a lot of interaction, demos in class, tangible props in class, etc. Overall, students benefit significantly by attending class in person. However, personal circumstances may require students to miss a class or two as an exception. I will therefore make good faith effort to record the lectures and may live-stream lectures as well. Link will be only provided via Canvas so that only registered students can attend the live lecture. Please remember this is an accommodation and not an expectation.
I am also planning to bring in innovative new technology to class which might video-record not just the instructor but also the entire classroom. For this reason, I will need all those who are attending in person to sign the Image Release form (https://legal.gatech.edu/sites/default/files/images/group_image_license_release_for_gt_activity_internet.pdf) LocationThe class will be held at the Kendeda Building Room 210 every Monday and Wednesday from 2:00pm to 3:15pm https://goo.gl/maps/esoFEbxhDrRH9KH8ALinks to an external site.About InstructorName: Ashutosh Dhekne. Please call me: Ashutosh or Prof. Dhekne Pronouns: He/Him/His Grading (Tentative)
Homework (2): 5 + 10
Short Programming Assignments: 5 + 10 Paper Reading: 10 (any 10 out of 11 papers listed) Midterm: 15 Project: 45 Course Calendar (Subject to Change)
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