Sensor | Yao Zheng@UHM

Introduction to Computer Engineering with R-Pi Pico W

Wed, 29 Oct 2025 04:14:54 -0800

Executive Summary

This project course introduces students to hands-on Computer Engineering (CENG) projects through the Pico platform. Students learn fundamentals of embedded systems, sensors, and wireless communication while designing small-scale prototypes that integrate hardware and software. The course emphasizes practical experimentation, teamwork, and iterative design—preparing students for advanced VIP and capstone projects in automation, robotics, and intelligent sensing systems.

Logistics

CRN

Semester ECE 196 ECE 296

Spring 2026 86916 86945
Personnel

Advisor Office Hours

Yao Zheng

Email with ‘’[x96 R-Pi Pico W]’’ in the subject line.

Galen Sasaki N/A
Meeting:

Time Location

R 12:00pm - 13:00pm HH389

Semester	ECE 196	ECE 296
Spring 2026	86916	86945

Advisor	Office Hours
Yao Zheng Email with ‘’[x96 R-Pi Pico W]’’ in the subject line.
Galen Sasaki	N/A

Time	Location
R 12:00pm - 13:00pm	HH389

Learning Objectives

Enhance their Python programming skills through practical applications.
Explore computer hardware concepts, including microcontrollers, with a focus on the Raspberry Pi Pico (RP2040).
Understand the interaction between hardware and software.
Design and implement engaging and practical engineering projects.
Build technical expertise and confidence to:
- Learn new skills and technologies independently.
- Develop personal projects.
- Seek internships, on-campus jobs, and professional opportunities.
- Participate in hackathons and engineering competitions.
Cultivate creativity and innovation in engineering design.

Course Structure

Labs: Nine hands-on laboratories.
Homework: Assignments for the labs.
Final Project: Students will design, develop, and demonstrate a personal project applying the concepts learned throughout the course.

Lab Schedule

Lab	Time Range	Focus and Core Tasks
Lab 1: Embedded Computing Foundations	Week 1	Raspberry Pi Pico W architecture and I/O Toolchain setup and debugging First embedded program
Lab 2: Circuits and Prototyping Basics	Week 2	Breadboard prototyping Passive components (resistors, LEDs) Power and grounding basics
Lab 3: Digital I/O and Sensor Interfaces	Week 3	Digital inputs and outputs Button debouncing and state machines Temperature sensor interfacing
Lab 4: Human–Device Interfaces	Week 4 → Week 5 (first half)	I²C/SPI communication OLED display pipelines Joystick input and interactive application
Lab 5: Networked Embedded Systems	Week 5 (second half) → Week 6	Embedded web servers Socket-based client–server communication Remote control of LEDs and displays
Lab 6: Internet Services and Performance	Week 7 → Week 8 (first half)	REST APIs and cloud data ingestion Latency and throughput considerations Sensorless “weather station”
Lab 7: Concurrent and Multicore Systems	Week 8 (second half) → Week 9	Multicore programming on Pico W Concurrent sensing and display tasks Synchronization primitives
Lab 8: Collaborative Software Development	Week 10	Git and GitHub workflows Branching, pull requests, and code review Team-based development
Lab 9: AI-Assisted Development	Week 11	AI-assisted coding and refactoring Debugging with AI tools Validation and responsible use
Final Project: Integrated IoT System	Weeks 12–15	End-to-end embedded and networked system

Required Lab Components

Components will be provided.

Time Commitment: Average of 5-6 hours per week.

Radar-Based Non-Contact Continuous Identity Authentication

Wed, 01 Jul 2020 00:00:00 +0000

CRII: NeTS: Power Efficient Millimeter Wave Data Delivery for Remote Invasive Species Monitoring

Fri, 01 May 2020 00:00:00 -1000

Executive Summary

The growing data resolution in remote sensing spurs the adoption of millimeter-wave (mmWave) communication modules on energy-harvesting devices to increase the data delivery bandwidth. The energy conditions on these devices are not always satisfiable to initiate or maintain the mmWave links and require systems to be capable of anticipating communication failures and take preemptive actions to minimize energy expenditures. Fortunately, the environmental information provided by remote sensors contains sufficient knowledge to enable the design of such systems. The goal of this project is to develop algorithms and tools to exploit this information and augment the solar-harvesting remote invasive species monitoring system in the State of Hawaii with mmWave data delivery capability. The work in this project will enable researchers, industry, and students to realize high bandwidth real-time remote sensing with power-constrained devices in real-world applications. The results of this research will impact fields across scientific, industrial, and military interests, including agriculture, ecology, meteorology, infrastructure, and public utility monitoring, etc., where timely communication of high-resolution sensory data is essential.

The fundamental intuition of the proposed approach is that environmental factors, such as weather conditions, signal blockages, can be recognized via the inherent capability or interactions between the remote sensors. Knowledge of these factors can be utilized to optimize device awakening, beam scanning, and signal amplification, etc., at the physical layer. Three complimentary research thrusts are pursued: 1) extracting the correlation between solar harvesting conditions and mmWave signal attenuations; developing models and circuits to estimate the mmWave signal attenuations at specific solar conditions; 2) designing a distributed sensing architecture to detect mmWave beam blockage and accelerate beam alignment, by exploiting the low-power decimeter band communication implemented by the existing system; 3) formulating and solving a constrained route placement problem for an autonomous aerial data collector to optimize its mmWave signal reception as it maneuvers between sensor clusters and flight restricted regions. All products of this work will be made freely available to the research community, along with documentation and tutorials. The in-lab testbed to be established during the project will be made available online for remote testing. The hardware schematic of the sensor platform, deployment profiles, data traces, and important meta-data will be posted online to spur further use, test, and research to advance the field.

FaIR: Federated Incumbent Detection in CBRS Band

Fri, 01 Nov 2019 00:00:00 +0000