CSCE 400/800: Computational Wireless Sensing
Course Meeting Location and Time:
Instructor Name(s): Hongzhi Guo
Day, Time, Location: MWF, 11:30 AM-12:20 PM, Avery 119
Course Prerequisites:
This course is open to advanced undergraduate, master's, and doctoral students. CSCE 230 or CSCE 231; SOFT 260, CSCE 310, CSCE 310H, CSCE 311 or equivalent; senior or graduate standing or instructor permission.
Course Description:
This course focuses on the latest wireless sensing systems empowered by artificial intelligence. The course will cover emerging applications requiring wireless sensing capabilities, such as digital twins, extended re-ality (XR), and the metaverse. This course comprises three sections, namely, wireless, computation, and sensing. In the first section, basic wireless communication and wireless sensing concepts, models, algo-rithms, and system designs will be introduced and reviewed. Particularly, this course will use Radio Fre-quency Identification (RFID) and Near Field Communication (NFC) as tools to introduce emerging battery-free wireless sensing technologies. Recent research progress and industrial standards will be reviewed. In the second section, we will introduce basic signal processing algorithms and deep learning-based advanced architectures for wireless sensing. Students will practice the implementation of these algorithms using programming languages. In the third section, wireless sensing applications using RFID and NFC tags, includ-ing localization, motion sensing, and liquid material and level sensing will be introduced. We will explore these areas by reading and discussing recently published papers. An integrated wireless sensing system de-sign considering security, identification, power management, and networking will be discussed in the end. Research projects will be assigned to students to explore related research topics in this area.
Learning Outcomes:
1. Identify applications of computational wireless sensing.
2. Describe the models and key modules of wireless communication and wireless sensing systems.
3. Identify RFID and NFC systems and protocols.
4. Implement basic signal processing algorithms and deep learning models to process wireless sensing signals.
5. Contrast traditional basic signal processing algorithms with recent deep learning models.
6. Interpret existing computational wireless sensing systems for localization, motion sensing, and liq-uid sensing.
7. Develop and evaluate computational wireless sensing models or systems for novel applications.
Course Required Materials:
No required textbooks.
Course Expectations and Rules Specific to Course
Students are expected to attend class and submit assignments on time. Discussions are allowed, but as-signments should be completed individually. Exams are closed-book.
Course Grading Policy:
1. 400
a. Homework (4) 30%
b. Exam (1) 30%
c. Paper review (4) 20%
d. Project (1) 20%
2. 800
a. Homework (4) 30%
b. Exam (1) 20%
c. Paper review (4) 20%
d. Project (1) 30%
Course Schedule:
Module 1: Concepts and applications of computational wireless sensing
Module 2: Wireless I: Basics of wireless communications and wireless sensing
Module 3: Wireless II: RFID and NFC systems
Module 4: Wireless III: Advanced RFID and NFC systems
Module 5: Computation I: Basic signal processing algorithms
Module 6: Computation II: Machine learning and deep learning architectures
Module 7: Sensing I: Localization
Module 8: Sensing II: Motion
Module 9: Sensing III: Liquid
Module 10: Wireless sensing systems: security, identification, power, and networking
Module 11: Research projects
Space provided for instructor to add additional information if needed. Otherwise, leave blank.
UNL Course Policies and Resources:
Students are responsible for knowing the university policies and resources found here:
• University-wide Attendance Policy
• Academic Honesty Policy
• Services for Students with Disabilities
• Mental Health and Well-Being Resources
• Final Exam Schedule
• Fifteenth Week Policy
• Emergency Procedures
• Diversity & Inclusiveness
• Title IX Policy
• Other Relevant University-Wide Policies