EECS Projects

This project focuses on developing an autonomous drone system capable of real-time 3D mapping and environmental perception. Traditional inspection and mapping processes are often time-consuming, labor-intensive, and unsafe in complex environments such as construction sites or disaster areas. Our system addresses this problem by integrating onboard sensing and computing to enable automated data collection and mapping. This project is relevant to industries such as infrastructure inspection, search and rescue, and environmental monitoring, where efficient and safe data acquisition is critical.

Cell counting within cell biology labs is a tedious process that is done manually. The 3D-printed microscope using AI/ML Image Recognition provides a cost-effective and efficient solution that streamlines this process. The 3D-printed open-source microscope is affordable and intuitive to use for capturing images of cells. When used in tandem with open-source artificial intelligence cell counting software, biology labs can now effectively count cells at a fraction of the time it takes to count these cells by hand.

3Dance is a tool designed to help users safely learn and practice dance by comparing their movements to instructor videos in real time. Using OpenPose pose estimation, it tracks body key points and provides accuracy feedback, achieving about 3% error compared to human instructors while operating at up to twice the original video speed. The system also delivers real-time feedback at 10–30 fps, making it practical for live practice. Developed as part of a 2025 academic project, it focuses on providing accessible, real-time dance training through motion analysis.

This project addresses the challenge of maintaining healthy greenhouse conditions without constant manual monitoring and intervention. Our team developed an AI-enabled automated greenhouse system that monitors soil moisture, temperature, and humidity, then automatically regulates irrigation and lighting to support plant growth. The project matters because it demonstrates how embedded systems, IoT devices, and machine learning can make plant care more efficient, consistent, and scalable. By reducing unnecessary watering and improving environmental stability, the system supports smarter and more sustainable small-scale agriculture. For more information, please access our Final Project Report: https://drive.google.com/file/d/1TOLH5HtbCEyqZn1P6OdofzXTeMpmVzWa/view?u...

Interactive animatronics used in themed entertainment are often prohibitively expensive, typically costing thousands to tens of thousands of dollars, which limits their accessibility to large organizations and excludes smaller developers and venues. Additionally, many existing systems lack fully autonomous, real-time interaction capabilities and rely on scripted or remotely operated behaviors. This project addresses the need for a low-cost, scalable animatronic platform capable of engaging users through natural, personalized interaction. The work matters because it broadens access to immersive technology, benefiting smaller theme parks, museums, and independent creators while enhancing guest experiences.

Dehydration is a serious yet widely overlooked health condition that occurs when the body loses more fluid than it takes in, potentially leading to complications such as heat injury, kidney problems, seizures, and hypovolemic shock. Despite these risks, millions of people worldwide fail to monitor their hydration levels adequately. AquaDermAI addresses this gap by developing a dehydration-sensing wearable that measures key physiological indicators — skin moisture via galvanic skin response, heart rate via photoplethysmography, and body temperature via thermistor — to detect and predict dehydration in real time. This solution is broadly applicable across diverse populations, from athletes and construction workers to individuals managing chronic illnesses, empowering users to proactively maintain healthy hydration habits.

The AudioVisor project addresses the difficulty individuals with hearing impairments experience in perceiving and localizing sounds within their environment. Many everyday situations depend on auditory awareness, such as recognizing conversations, alarms, or approaching hazards, which can significantly impact safety and communication. This project functions by converting sound signals into visual cues that represent both the direction and intensity of surrounding audio in real time. By providing an alternative method to interpret environmental sounds, the AudioVisor enhances situational awareness and supports greater independence for individuals who are deaf or hard of hearing.

Prolonged sitting is common in modern study and work environments, particularly among students and office workers who spend long hours at a desk. This sedentary behavior can contribute to health issues such as reduced physical activity and musculoskeletal strain. AuraBot addresses this problem by acting as an edge-AI desktop companion that monitors user presence, tracks sitting duration, and provides timely wellness reminders through sensing and voice interaction. By integrating these features into a low-friction desktop system, AuraBot aims to encourage healthier work habits and regular movement during long work sessions.

Auto-Pill Dispenser was developed to address the problem of medication non-adherence among elderly and disabled patients who may forget doses, take the wrong quantity, or rely on caregivers for daily medication management. Traditional pill organizers still require manual sorting and tracking, while many existing automated dispensers are too expensive for widespread use. This project proposes a low-cost smart dispensing system that combines a 3D-printed mechanical design, embedded control, sensors, and a web interface to automate scheduled medication delivery. The project matters because missed or incorrect doses can seriously affect patient health, and the people most affected are seniors, individuals with chronic health conditions, and caregivers who need a more reliable and affordable medication management tool.

Buoyancy Engine CAN Communications (BECAN) is a user-controlled buoyancy engine designed to regulate the depth of a buoyant platform in aquatic environments. Precise depth control remains a significant engineering challenge for applications such as autonomous underwater vehicle docking and environmental monitoring. BECAN addresses this need by enabling controlled adjustment of system buoyancy through regulated water intake and release. This system provides a potential solution for industries and environmental agencies that require reliable underwater positioning and adaptive response to changing conditions.

Wildfires pose a significant threat to ecosystems, infrastructure, and public safety, creating a need for faster and more reliable detection systems. This project develops a collaborative edge–cloud architecture that integrates environmental sensors, UAV imagery, and machine learning models to detect wildfire ignition early. Edge-based models provide fast, low-power detection, while cloud-based models verify events using high-accuracy image analysis. This system improves detection speed, reduces false alarms, and enables monitoring in remote or resource-limited environments.

The CubeSat Avionics and Attitude Control System focuses on developing flight software for a 2U (10x10x20 cm) nanosatellite. We designed algorithms to control the attitude, or orientation, of the satellite using only magnetorquers. A magnetorquer is a coil of wire that generates a localized magnetic field when a current goes through it; this magnetic field produces a torque to turn the satellite. Our software drives the magnetorquer to achieve detumbling, spin stabilization, and inertial pointing of the satellite.

CubeSat ADCS requirements are driven by tight power/mass budgets yet mission success depends on reliable detumbling and pointing. We are building a magnetorquer-only ADCS and avionics stack for a 2U CubeSat that uses multi-mode control (B-dot detumble, spin stabilization, and inertial pointing) and FreeRTOS-based flight software on STM32, validated via hardware-in-the-loop simulation.

The Dance Pose Estimation project addresses the challenge of providing individualized feedback to large groups of dancers by creating an automated 3D pose evaluation web application. Traditional dance education often lacks the resources for instructors to give every student personalized instruction, which can slow down the learning process during private practice. By utilizing computer vision to compare student movements against a reference routine, the system empowers students to refine their technique independently while allowing instructors to focus on high-level group guidance. This project matters because it bridges the gap between digital accessibility and professional dance pedagogy, directly benefiting students and educators at institutions like the UCI Department of Dance.

Wireless communication research requires testing and verification in constantly changing environments. However, existing wireless testing platforms are costly, difficult to operate, or offer limited user configurability. This project presents a practical, low-cost, vehicle-based wireless research platform designed to address these limitations. By using affordable off-the-shelf components, we have developed an adaptable wireless network testing interface that is user friendly and easily configurable, providing a reliable and flexible testbed for experimental research.

Industrial environments often require workers to interact closely with robotic machinery, creating safety risks during operation and maintenance. This project addresses the need for a safer and more responsive control interface by developing EchoSafe, a low-power edge-AI speech keyword detection system. The system allows operators to control machinery using voice commands such as “go” and “stop,” reducing the need for manual interaction near hazardous equipment. By running entirely offline, the system also improves reliability, security, and privacy compared to cloud-based voice systems.

Traditional prosthetics are often prohibitively expensive, ranging from $5,000 to over $100,000, and frequently require invasive medical procedures to function. This leaves many individuals in need of amputee care unable to afford or comfortably access life-changing mobility aids. To address this critical accessibility issue, our project developed a low-cost, electromyography (EMG) controlled prosthetic hand that utilizes a non-invasive dry-electrode placed on the user's wrist. By eliminating the need for invasive procedures and drastically reducing manufacturing expenses, this project demonstrates the viability of highly accessible, neural-network-driven prosthetics for a broader demographic

This project presents the design and implementation of an embedded autonomous navigation system featuring real-time ultrasonic radar visualization. Originally conceptualized as a Smart Digital Closet, the project's scope was strategically redefined to focus on a technically rigorous robotics platform capable of environmental scanning and autonomous decision-making. The system addresses the need for low-latency obstacle detection in mobile robotics by providing a 180 spatial awareness interface. This work is significant for developing accessible, real-time sensing solutions for autonomous indoor vehicles.

The Facial-Orientation and Concentration Understanding System (FOCUS) addresses the growing challenge of maintaining attention among young students, particularly those with ADHD or focus-related difficulties, in learning environments. Many students struggle with sustained concentration during independent work, and there is a lack of accessible, real-time tools that provide immediate, personalized feedback on attention levels. This project develops a webcam-based application that analyzes facial orientation, gaze direction, and behavioral indicators to detect inattention and deliver timely reminders. By supporting students, educators, and caregivers with actionable insights, FOCUS aims to improve engagement, learning outcomes, and overall academic performance.

Low visibility caused by fog presents a significant safety challenge for drivers on the road. This project presents FogHacks, an AI powered hazard detection system designed to detect road hazards in foggy environments using image detection ML models and defogging algorithms. The system integrates a Raspberry Pi 5 with a Pi Camera to capture real world images then utilizes defogging techniques to improve visibility before performing object detection. A customized YOLOv10 model was trained on road sign and foggy driving datasets to identify key road hazards such as cars, buses, bicycles, pedestrians, and traffic signs. To improve detection performance under foggy conditions, two defogging techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Dark Channel Prior (DCP), were implemented in the preprocessing pipeline. Testing on this custom model found improvements in accuracy as compared to the base YOLOv10 model, with some variation in results due to factors such as glare and...

Disaster situations require a swift and agile response to identify all victims in hazardous environments, but human efforts may be limited due to environmental severity. HERMES is an autonomous robotic system composed of a compact robot and a centralized server that will aid rescue operations. Rather than relying on a static map, the server utilizes behavior trees to drive autonomous decision-making. This framework enables the robot to systematically explore unknown spaces by dynamically balancing thorough area coverage with victim identification. At deployment, the controller synthesizes sensor data to localize the robot, map victim positions, and ensure autonomous, collision-free navigation.

The global financial markets process trillions of dollars daily, making microsecond-level order execution critical for high-frequency trading (HFT) profitability. Standard CPU-based trading engines often struggle with performance bottlenecks due to sequential execution and memory access limitations. This project addresses the need for ultra-low-latency, high-throughput computing to execute large volumes of trades in real time. By overcoming software limitations, the system provides a scalable solution that benefits modern financial institutions and traders relying on speed and reliability. The system utilizes a hybrid hardware-software architecture, employing an FPGA-based Tensor Processing Unit (TPU) to run a lightweight neural network for intelligent order placement. A novel FPGA-based MultiQueue BRAM architecture is used for deterministic, parallel order matching. Meanwhile, the host computer manages over 100,000 orders using lock-free Red-Black Trees for cache-efficient, large-scale storage. The machine learning component leverages a Proximal Policy Optimization (PPO) reinforcement learning agent to dynamically rank trades for caching in the...

HoloPhase is a projection system designed to render clear images through dynamically changing turbid media such as fog, where Mie scattering would otherwise blur and distort the projected content. Our approach combines computational optics, a Digital Micromirror Device (DMD)– based wavefront shaper, and a camera-in-the-loop optimization pipeline that iteratively updates the projected wavefront to compensate for scattering. By jointly designing the optical setup, fog chamber, and control software, we can recover higher-contrast, sharper images through fog than conventional, uncorrected projection.

Our project address the need for reliable, low-cost communication in areas where traditional networks (cellular or internet) are unavailable, unreliable, or compromised (such as during natural disasters, remote outdoor activities, or infrastructure outages). The project's goal is to create a decentralized, off-grid messaging networks using low-power LoRa radios, enabling users to communicate over long distances without centralized infrastructure. This project can be impactful for a wide range of individuals who depend on consistent access to communication in challenging or unpredictable environments, which can include outdoor enthusiasts, emergency responders, rural communities, and anyone requiring secure, infrastructure-independent communication.

The United Nations estimates that 400 million tons of plastic are produced annually leading to 4.83 trillion microplastic (MP) particles floating in the ocean.  When ingested by humans, MPs cause inflammation, endocrine disruption, and DNA damage. MicroMola is a semi-autonomous floating near-surface robot designed to reduce microplastic density and support ocean health monitoring efforts by agencies like the EPA and Orange County Water Board. The main objective of the robot is to reduce the microplastic density in a body of water with minimal maintenance. The key impact is filtration of MPs as MicroMola possesses the potential to help reduce the risk of MPs to human health and ocean ecosystems. During fall quarter, the initial design of the MicroMola was completed by determining a bill of materials, calculating the necessary power budget, and discussing the protocols for communication. In the following Winter Quarter, the objectives achieved were: completing a working control...

“Omni-Pedal” is a digital multi-effect guitar pedal, that is accessible and affordable for guitarists or music enthusiasts of any skill level. The guitar pedal is built from the ground-up off of a Raspberry Pi to perform digital signal processing (DSP). Through DSP, seven guitar effects can be applied to the input signal from the guitar to change the sound of the guitar to the user's choice. The interactive GUI of the pedal also allows for accessible switching between the different guitar effects and also adjusting the volume, mix, and other parameters of the effect over the guitar's sound.

The FAA now requires all drones to be compliant with Remote ID, requiring an on-drone device that transmits information about itself over Blutetooth or WiFi. However, these signals can be weak and may not be picked up by user devices alone. Traditional methods of increasing reception range involve high cost and complex fully-coherent sampling digital beamformers. By leveraging a hybrid digital-analog beamforming strategy, this project achieves a similar result a remarkably lower cost, complexity, and footprint.

The goal of this project is to increase the reception range of 2.4GHz Bluetooth/WiFi signals by using an antenna array fed into a passive RF network allowing multiple different directional beams to be monitored at the same time using low cost microcontrollers. The collected data is sent over Bluetooth to a user device where the information is displayed on a custom React Native app.

The Photon Flight (Autonomous UAV) project focuses on creating a high-performance tethered drone capable of sending two real-time video streams and complete flight telemetry through a single fiber optic cable. Using a lightweight fiber tether instead of conventional RF connections, the platform demonstrates how advanced networking, optical communications, and embedded systems can be merged within an aerial vehicle. Throughout the process, the team gains experience with IP video streaming, low-latency video encoding, network design, Embedded Linux integration, and connecting entire system with a flight controller (MATEK F405). The main objectives are to build a reliable, high-bandwidth communication system, show consistent fiber-based command and control, and assess the advantages of optical tethering for secure, interference-resistant drone operations. In the end, this drone acts as a test platform for new communication technologies in robotics, monitoring, and environments where electromagnetic interference is an issue.

The main goal of this project is to...

PoseMotive is a wearable posture-monitoring system designed to help users improve their posture and body language through continuous feedback and data analysis. The system consists of 5 inertial measurement unit (IMU) sensors embedded in a wearable garment that measure body orientation and motion. A microcontroller gathering all the data from these sensors and aggregating the information and forwards it to a web application via bluetooth. The application communicates to a backend system through wireless communication which processes the sensor data and classifies the user's posture into interpretable categories such as slouched, straight, leaning left, leaning right, and arm positioning such as open or closed. The overall goal of the project is to promote better ergonomic habits by helping users become aware of posture issues and correct them through consistent monitoring and feedback.

This project addresses the need for fast and reliable defect detection in pharmaceutical manufacturing, where traditional inspection methods often rely on human oversight or cloud based processing that introduces latency and inconsistency. Defects such as micro cracks, improper sealing, or temperature anomalies in vials can compromise drug safety, leading to costly recalls and potential risks to patient health. By leveraging edge computing, the system performs real time, on device analysis that reduces latency while also lowering data transmission requirements and overall carbon footprint compared to cloud dependent approaches. This work directly impacts pharmaceutical manufacturers, quality assurance engineers, and ultimately patients who depend on safe and properly handled medications.

Stroke survivors frequently experience upper-limb impairment, with 55–75% losing fine motor control, and recovery often plateaus within six months, highlighting the need for accessible, high-repetition rehabilitation tools. Because proprioception—the body’s sense of limb position and movement—is commonly impaired after stroke, improving it is critical for restoring hand function. This project addresses that need by developing REX0, a dual-glove wearable rehabilitation system that enables motion mimicry, allowing movements from a patient’s healthy hand to be replicated on the impaired hand for proprioceptive training. The system aims to improve long-term rehabilitation outcomes for stroke survivors who require effective and engaging therapy for hand motor recovery.

Additive manufacturing has greatly improved rapid prototyping and small-scale production, but post-processing steps such as support removal, surface finishing, and print-bed cleaning remain largely manual and labor-intensive. These tasks introduce inefficiencies, inconsistencies in product quality, and potential safety risks due to exposure to chemicals like acetone used in vapor smoothing. Scrappy addresses this problem by developing a robotic system capable of automating these post-processing operations. By reducing manual labor and human exposure to hazardous materials, the project aims to improve safety, repeatability, and overall efficiency within additive manufacturing workflows.

SmartCan is a smart sorting bin designed to identify and collect recyclable materials using computer vision. Improper waste disposal and low recycling rates contribute to environmental issues, while individuals with mobility limitations may face challenges in properly disposing of waste. This project addresses both concerns by creating an autonomous system that can detect recyclable objects and move toward them. By combining accessibility with sustainability, SmartCan aims to promote cleaner environments and make recycling more convenient for a wider range of users.

The signal strength necessary to identify small radar cross sections of miniature drones has resulted in most modern radars designed for this task being highly expensive and energy-intensive, rendering them impractical for use in an instructional setting and pushing university curricula to lean toward simulation as opposed to real-world testing. We are designing a cost-effective, energy-efficient phased array radar module using the ADALM-PLUTO software-defined radio (SDR), capable of detecting unmanned aerial vehicles (UAVs) at short to medium ranges. The proposed model may act as a replicable instructional platform for radar experimentation and a foundation for small-scale research in UAV detection and target classification. 

The project addresses the challenge of making analog signal processing and synthesizer operation accessible to beginners, since conventional synthesizers are often costly, highly complex, and difficult to interpret without prior technical knowledge. To address this, a modular educational system called Synthesizer Blocks was developed, in which individual signal-processing functions such as waveform generation, filtering, mixing, and amplitude modulation are implemented as physically interchangeable blocks. This architecture enables users to assemble signal chains while directly observing waveform changes through built-in test points, supporting a clearer understanding of electrical engineering fundamentals. The scope of the project included the design, fabrication, and validation of PCB-based functional modules capable of producing audible output while serving as a hands-on instructional platform for signal processing concepts.

Water scarcity and inefficient irrigation practices continue to pose challenges for agriculture and small-scale plant management, as traditional watering methods often rely on fixed schedules that fail to adapt to real environmental conditions. This project addresses the need for a smarter irrigation solution by developing TeraFlow, an autonomous irrigation system that integrates environmental sensors, IoT communication, and AI-based image analysis to make informed watering decisions. By collecting real-time data on soil moisture, temperature, humidity, and light conditions, the system helps users monitor plant environments and automatically control irrigation when necessary. The project matters because it promotes more efficient water usage and provides accessible tools for gardeners, small-scale growers, and plant caretakers who need reliable and data-driven irrigation management.

Home security remains a important concern for residents in the United States, with millions of residential burglaries reported each year, yet many existing security systems are expensive, complicated, or difficult to customize. Because of this, many households do not have access to reliable protection for their homes. The Guardian Home Security System addresses this problem by creating an affordable system that combines motion detection, camera monitoring, door locking, and fingerprint authentication into one easy-to-use platform. The Guardian shows how modern technology can make home security more accessible to everyday homeowners and renters who want a simple and effective way to monitor and protect their property.

Phase locked loops are a critical component in modern communication, sensing, and imaging systems. While computer automated design tools allow for accurate simulation, they often take weeks of design and computing time. Our project's research intends to model a specific non-ideal performance of analog phase locked loops, previously not studied, as closed form equations. This allows designers to observe their system's performance in seconds, rather than weeks. 

Background

UAV Forge constitutes a multidisciplinary engineering design team with a specific focus on the comprehensive development cycle of autonomous aerial vehicles, encompassing design, manufacturing, programming, and rigorous testing. The paramount objective of this design endeavor is to adhere to the stipulated constraints, thereby enabling active participation in the SUAS 2025-2026 competition season.

The SUAS competition mandates that the UAV system possesses autonomous flight capabilities, proficient object avoidance capabilities pertaining to both stationary and dynamic entities, and adeptness in object detection, localization, and classification. Furthermore, the system is required to execute an airdrop delivery mechanism, ensuring the precise delivery of a payload object to a designated GPS location without incurring any damage.

Goal and Objectives 

While the immediate focus of this year’s team centers on achieving commendable performance within the competitive arena, the overarching goal is to provide undergraduate participants with a practical application of their engineering...

Background

UAV Forge constitutes a multidisciplinary engineering design team with a specific focus on the comprehensive development cycle of autonomous aerial vehicles, encompassing design, manufacturing, programming, and rigorous testing. The paramount objective of this design endeavor is to adhere to the stipulated constraints, thereby enabling active participation in the SUAS 2025-2026 competition season.

The SUAS competition mandates that the UAV system possesses autonomous flight capabilities, proficient object avoidance capabilities pertaining to both stationary and dynamic entities, and adeptness in object detection, localization, and classification. Furthermore, the system is required to execute an airdrop delivery mechanism, ensuring the precise delivery of a payload object to a designated GPS location without incurring any damage.

Goal and Objectives 

While the immediate focus of this year’s team centers on achieving commendable performance within the competitive arena, the overarching goal is to provide undergraduate participants with a practical application of their engineering...

The CubeSat team at UCI is a student-led undergraduate interdisciplinary research and design project with the goal of launching a 2U nanosatellite, AntSat 01, into orbit to test a UCI research payload. The satellite operates with five main engineering subsystems: Avionics, Communications, Structures, Power, and Systems. They all work to house STMS's (Spacecraft Thermal Management Systems) research payload within the 2U nanosatellite.

The research payload is a variable emissivity device (VED) that is developed by Spacecraft Thermal Management Systems (STMS). The payload will be tested as a thermal regulator, and our task is to evaluate its performance under varying levels of solar exposure and at different adjustable emissivity settings. We aim to determine if materials similar to the sample can serve as an inexpensive method for thermal management on future spacecraft.

BACKGROUND:
In recent years, the space sector has undergone a significant transformation with the emergence of privatization. This shift...

The CubeSat team at UCI is a student-led undergraduate interdisciplinary research and design project with the goal of launching a 2U nanosatellite, AntSat 01, into orbit to test a UCI research payload. The satellite operates with five main engineering subsystems: Avionics, Communications, Structures, Power, and Systems. They all work to house STMS's (Spacecraft Thermal Management Systems) research payload within the 2U nanosatellite.

The research payload is a variable emissivity device (VED) that is developed by Spacecraft Thermal Management Systems (STMS). The payload will be tested as a thermal regulator, and our task is to evaluate its performance under varying levels of solar exposure and at different adjustable emissivity settings. We aim to determine if materials similar to the sample can serve as an inexpensive method for thermal management on future spacecraft.

BACKGROUND:
In recent years, the space sector has undergone a significant transformation with the emergence of privatization. This shift...

Litter and debris accumulation in outdoor pedestrian areas poses significant environmental, aesthetic, and economic challenges, driving up municipal maintenance costs while degrading public spaces used by communities daily. Zpotless addresses this problem by designing a fully autonomous, solar-powered cleaning robot capable of detecting, retrieving, and storing trash in outdoor public spaces with minimal human intervention. The system is built to be entirely self-sustaining by leveraging photovoltaic (solar) energy, eliminating dependence on external power infrastructure and enabling extended operation. This project matters because it targets a recurring, labor-intensive problem that affects residents, pedestrians, and facility managers in both residential and urban commercial environments.