M.S. Degree - Plan I (Thesis)
This master’s program in electrical and computer engineering gives the student an opportunity to perform in-depth research and thesis writing.
The Department of Electrical and Computer Engineering prepares graduate students to do meaningful research and acquire skills and insights vital to solving some of the world’s most complex technological problems. Many of our graduates go on to leadership and technology management roles in industry.
Graduate program highlights include:
- A challenging and stimulating environment
- Depth of resources
- Highly interdisciplinary culture
- Generous financial support.
- General Reminders and Information
Students should note that ECE program requirements are more stringent than those stated by Graduate Studies. The ECE program requirements, therefore, take precedence. Plan I requires a minimum of 36 units of graduate and upper division courses (the 100 and 200 series only), at least 15 units of which must be graduate engineering courses (200 series), with 12 out of the 15 units of graduate work in the major field, excluding seminar and research units. No more than 3 graduate seminar units and 9 research units may be used to satisfy the 36-unit requirement. In addition, a thesis is required. The thesis serves as the capstone requirement. All courses listed on the Program of Study must be passed with a “B-“ or higher. A course in which a student receives a “C+” or lower cannot be used to satisfy the unit requirement for the M.S. degree, but will count in determining the grade point average.
- Course Requirements
36 units of upper-division and graduate course work, a thesis and a minimum of three quarters of academic residence are required.
At least 15 units must be in graduate engineering courses (excluding 29X seminar series and 299) and of these 15 units, at least 12 units must be in graduate electrical and computer engineering courses (again excluding 29X seminar series and 299). The balance of the 36-unit requirement may be met with a combination of upper division technical elective courses and 29X and 299 in ECE or other approved programs. No more than three seminar units (290-297, excluding 290C) can be counted. A student should register for the number of 299 research and independent study units that reflects the actual effort and time devoted to thesis work, although no more than nine units can be counted.
Courses required for the ECE undergraduate degree, or the following courses: EEC100, EEC110A/B, EEC130A/B, EEC140A/B, EEC150A/B, EEC161, EEC170, EEC172, and EEC180A/B, may not be used to satisfy the requirements of the ECE M.S. degree.
Full-time students must enroll for 12 units per quarter, including research, academic and seminar units. Courses that fulfill any of the program course requirements may not be taken S/U unless the course is normally graded S/U. Once course requirements are completed, students can take additional classes as needed, although the 12 units per quarter are generally fulfilled with a research class (299) and perhaps seminars. Per UC regulations, students cannot enroll in more than 12 units of graduate level courses (200) or more than 16 units of combined undergraduate and graduate level (100, 200, 300) courses per quarter.
- Special Requirements
All graduate students are required to take EEC290, Seminar in Electrical and Computer Engineering, each fall quarter. An S grade in EEC390, the Teaching of Electrical and Computer Engineering, is required to be eligible to hold a teaching assistantship in ECE, but may not be used to satisfy graduate coursework requirements. International students may need to take LIN25, LIN26, LIN391 or a combination thereof, to meet university language proficiency requirements.
♦ Admission Committee
Once the completed application, all supporting materials, and the application fee have been received, the application will be submitted to the admissions committee. The admissions committee consists of the faculty members of ECE’s Graduate Study Committee (GSC) and the GSC admissions chair. Applicants who apply by the space available deadline (but after the general deadline) are not guaranteed to have their application reviewed by the graduate program. Their application will be reviewed only if the graduate program determines that they have additional space available. Based on a review of the entire application, a recommendation is made to accept or decline an applicant’s request for admission. The recommendation to accept or decline an applicant’s request for admission is forwarded to the Dean of Graduate Studies for final approval of admission. Notification of admissions decisions will be sent by Graduate Studies. Applications are accepted from the date the admission system opens (typically in September) through the space available deadline for the next fall-entering class
♦ Course Guidance or Advising Committee
The major professor and the ECE Graduate Advisor will assist the student in developing a Program of Study. See the section below on “Advising and Mentoring.” By the third quarter of enrollment the student must file a Program of Study that must be routed through the ECE Graduate Program Coordinator for the ECE Graduate Advisor’s approval.
♦ Thesis Committee for M.S. Plan I
When the student advances to candidacy, they will declare an M.S. thesis committee. The ECE Graduate Advisor will nominate the committee based on consultations with the student and the major professor. This committee is chaired by the major professor and made up of at least two other members. The majority of this committee must be members of the ECE graduate program. The responsibility of this committee is to assist in the guidance of the student and to read and approve the thesis. The thesis must be prepared in accordance with Graduate Studies guidelines.
- Advising and Mentoring
The major professor is the primary mentor during the student’s career at UC Davis and will assist with developing the student’s Program of Study. The major professor serves as the chair of the Thesis Committee (for Plan I) or Comprehensive Exam Committee (for Plan II). The student must select a major professor from the members of the ECE Graduate Program as soon as possible, but no later than the beginning of the third quarter of enrollment. Changing a major professor, requires the signatures of the previous and new major professor, acknowledging the change. The ECE Vice Chair for Graduate Studies, also referred to as the Graduate Program Chair, will serve as the interim advisor to new students during the process of selecting a major professor.
The Graduate Advisor, who is nominated by the department chair and appointed by the Dean of Graduate Studies, is a resource for information on academic requirements, policies and procedures and registration information until a major professor is selected. The ECE Graduate Advisor is responsible for reviewing programs of study for each student and acting on student petitions.
The Graduate Program Coordinator should be the first person consulted on all actions regarding graduate affairs. The Graduate Program Coordinator may advise the student to contact the ECE Graduate Advisor or the Office of Graduate Studies to address particular issues.
- Advancement to Candidacy
Every student must file an official application for candidacy for the Master of Science degree and pay the candidacy fee after completing half of their course requirements and at least one quarter before completing all degree requirements. This is typically the third quarter. The candidacy for the Master of Science degree form can be found online at: http://www.gradstudies.ucdavis.edu/forms/. A completed form includes a list of courses the student will take to complete degree requirements. If changes must be made to the student’s course plan after they have advanced to candidacy, the Graduate Advisor must recommend these changes to Graduate Studies. Students must have the ECE Graduate Advisor and committee chair, if applicable, sign the candidacy form before it can be submitted to Graduate Studies. If the candidacy is approved, the Office of Graduate Studies will send a copy to the appropriate graduate program coordinator and the student. The thesis committee chair will also receive a copy, if applicable. If the Office of Graduate Studies determines that a student is not eligible for advancement, the program and the student will be told the reasons for the application’s deferral. Some reasons for deferring an application include grade point average below 3.0, outstanding “I” grades in required courses or insufficient units.
- Thesis Requirements
The M.S. thesis must demonstrate the student’s proficiency in research methods and scientific analysis, as well as a thorough knowledge of the state-of-the-art of the student’s chosen field. Original contributions to knowledge are encouraged, but not expected, at the M.S. degree level. Thus, an M.S. thesis may consist of:
♦ An original technical or research contribution of limited scope
♦ A critical evaluation of the state-of-the-art of a current research area
♦ An advanced design project, either analytical or experimental.
Research for the master’s thesis is to be carried out under the supervision of a faculty member of the program. The thesis research must be conducted while the student is enrolled in the program. The thesis is submitted to the thesis committee at least one month before the student plans to make requested revisions. All committee members must approve the thesis and sign the title page before the thesis is submitted to Graduate Studies for final approval. Should the committee determine that the thesis is unacceptable, even with substantial revisions, the program may recommend to the Dean of Graduate Studies that the student be disqualified from the program.
The thesis must be filed in a quarter in which the student is registered or on filing fee. Instructions on preparation of the thesis and a schedule of dates for filing the thesis in final form are available from Graduate Studies; the dates are also printed in the UC Davis General Catalog and in the Class Schedule and Registration Guide issued each quarter. A student must have a GPA of 3.0 for the M.S. degree to be awarded.
- Normative Timeline
Although work for the Master of Science degree can be completed in three quarters of full-time study, generally 18-24 months of full-time study are required to complete the M.S. Plan I. In order to make satisfactory progress, the expectation is that full-time students in the M.S. program will follow the timeline below. The number in each column is the consecutive quarter of enrollment. Students not holding an ECE degree may require additional quarters of study to complete their M.S. degree requirements depending on the number of remedial courses needed.
- Sources of Funding
Please see more information on helpful funding resources.
- PELP, In Absentia and Filing Fee Status
Information about PELP (Planned Educational Leave), In Absentia (reduced fees when researching out of state) and filing fee status can be found in the graduate student guide: https://grad.ucdavis.edu/resources/graduate-student-resources. M.S. students are eligible for filing fee status after completing their coursework (Program of Study) and a working draft of their thesis or comprehensive examination report. In order to be approved for filing fee status, a student must submit the filing fee request along with signatures of all three members of the Thesis Committee or Comprehensive Examination Committee stating they have received an acceptable working draft of the thesis or comprehensive examination report. This application must be routed through the ECE Graduate Program Coordinator for the ECE Graduate Advisor’s approval and then must be filed with Graduate Studies. Filing fee is available for one quarter only, but extensions may be approved on a case-by-case basis. In the event that filing fee status expires, the student must file a readmission application.
- Area of Research: Computer Engineering
Project: GPU Solvers for Flow Computation
Sponsors: Professor John Owens and Postdoc Serban Porumbescu
Description: We are working with the US Army Corps of Engineers to develop a GPU implementation of their "HEC-RAS" river analysis system, the leading system for flow computation. The current implementation of this package is on CPUs and we would like to bring it to GPUs. The core computation is modeling hydraulic flow on unstructured grids, and the research problems are both algorithmic and systems ones. The ideal student will be interested in writing high-quality open-source software in collaboration with fellow graduate students as well as domain experts from government, in the context of a large and active group that is interested in problems across many domains of parallel computing. It is likely that this position will be funded. It is expected that an interested student will pursue a thesis that will result in publication in a high-quality venue. This position should be equally interesting for new PhD students.
Requirements: Desirable: Expertise in numerical computation and/or parallel computing. Highly desirable: C++ expertise, and even more desirable, CUDA experience. Good communicator and collaborator.
Project: "Gunrock" GPU Graph Analytics Framework
Sponsors: Professor John Owens and Postdoc Serban Porumbescu
Description: : John Owens’ research group focuses on GPU computing and has a large open-source project on parallel graph analytics called Gunrock. We have a large number of small projects within Gunrock and believe it would be straightforward to assemble a MS thesis or MS project within Gunrock depending on the interests of the student. We have projects within the core of Gunrock (mostly CUDA/C++-oriented), in writing and improving Gunrock applications (primarily C++), and in interfacing and tuning Gunrock (more likely Python).
Requirements: Gunrock is written in C++ and we have Python-related projects as well. Experience with (in order) CUDA C, C++, and/or Python is highly desirable. Strong (text) writing skills. Experience with parallel computing would be terrific but is not required. We need talented students who can learn quickly, communicate well, and work well both in a group and independently.
Project: Trusted Execution Environments for High-Performance Computing
Sponsors: Professor Venkatesh Akella, Professor Jason Lowe-Power, and Professor Sean Peisert
Description: : In partnership with the Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab), we are developing trusted execution environments (TEEs) for high-performance computing (HPC) systems such as those operated by the U.S. Department of Energy Office of Science’s Advanced Scientific Computing Research (ASCR) program, including the National Energy Research Supercomputing Center (NERSC) at Berkeley Lab. Current commercial TEEs such as Intel SGX and AMD’s SEV are inadequate for HPC a variety of reasons. Our solution involves a RISC-V based approach, along with development and modifications to the security monitor and operating system elements, as well as implementation and experimentation in gem5 simulations or in FPGA clusters. Potential work could be on multiple levels of the stack from programming FPGAs to developing hardware modifications to kernel elements. Research problems include both security and performance elements, as well as tradeoffs between the two. The ideal student will be interested in writing high-quality open-source software in collaboration with fellow graduate students, as well as researchers and HPC operators from the Berkeley Lab. This position may be funded. It is expected that interested students will pursue a thesis that will result in publication in a high-quality venue. This position should be equally interesting for PhD students.
Requirements: Expertise in OS/kernel function, computer architecture, and modification, and/or FGPA programming. Expertise in programming C/C++ and Python, and software-engineering methodologies. Excellent written and verbal English communication skills. Looking for motivated and pro-active students who are great collaborators.
Project: Optimizing Compiler Instruction Scheduling Using GPU-Accelerated Intelligent Search
Sponsors: Prof. John Owens (UC Davis), Ghassan Shobaki (California State University)
Financial support provided by : National Science Foundation (NSF)
Description: Master’s students are needed to work as Research Assistants (RAs) on an NSF-funded project at California State University, Sacramento (CSUS). Selected Master’s students will not have to transfer to CSUS to work on this project. They can work on the project as UC Davis students, and their theses will be based on their work on this project. Master’s students will be co-advised by UC Davis Professor John Owens. All the work for this project may be done remotely whether the campus is closed or open.
In this project, we use a combination of intelligent search techniques (specifically, Branch-and-Bound and Ant Colony Optimization) to solve a long-standing problem in compiler optimization, and thus generate more efficient code for a wide range of programs running on CPUs and GPUs. The official project abstract may be found at: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911235
A Research Assistant on this project will develop parallel versions of existing intelligent-search algorithms and/or enhance the sequential versions. The algorithms will be first implemented in the LLVM compiler and later in the GCC compiler. The project will involve collaboration with open-source compiler engineers from Apple, IBM, Google, Redhat, as well as the GPU compiler team at AMD. We are looking for students who can understand complex compiler optimization algorithms and successfully implement them in a production compiler.
Requirements: The ideal candidate for this position is a junior, senior or Master’s student who is interested in conducting serious research in this area and producing quality publications that will help him/her build a strong career either in academia or in the industry. Undergraduate students who are interested in pursuing a Master’s degree right after graduation are also encouraged to apply. They can do their Master’s at UC Davis, and their theses will be based on their work on this project. Productive students may continue to work on the project after their graduation if they are interested.
Requirements include: Strong analytical and problem solving skills. Strong background in algorithms, especially graph algorithms (see the list of topics below). Strong programming skills in C/C++. Self-motivated and able to work independently with minimum supervision.
Having some background in one or more of the following areas is desirable but not required: Code generation and optimization. GPU computing. Artificial intelligence, with emphasis on Branch-and-Bound search and Ant Colony Optimization.
Project: A hypothetical RISC-V based game console
Sponsors: Professor Christopher Nitta
Description: : Christopher Nitta has developed a simulator for a hypothetical RISC-V based game console (available at https://github.com/UCDClassNitta/riscv-console/). The simulator is designed as an educational tool to be used in courses such as Operating Systems or Machine Dependent Programming. We are looking to expand the simulator to support auto grading, add new hardware components, and to improve the portability of the project.
Requirements: You should have taken EEC 270 or equivalent (graduate course in computer architecture), and have strong C/C++ programming skills. Ideally, we are looking for a single student to continue the project.
- Area of Research: Photonic and Electronic Devices
Project: MS projects in the Integrated Nanodevices & Nanosystems Laboratory
Sponsor: Professor Saif Islam
Description: Project opportunities include:
1.- Silicon photodiodes for 100gigabit/sec and beyond data communication.
2.- Computational imaging with photon-trapping photdiodes.
3.- Photon detectors for quantum internet.
4.- Transparent solar cells for window based on UV and IR light absorption.
5.- High resolution time-of-flight (TOF) sensing with ultra-fast photodiodes.
6.- LIDAR: technological challenges and recent developments.
7.- Ionizing air and gases to trap COVID-19 virus and prevent airborne transmission. Financial support available for strong candidates!
8.-Ultra-fast silicon photodiodes for real-time visualization of tumor boundaries during surgery enabled by fluorescent lifetime imaging (FLIm).
9.-Semiconductor transistors/memory for extreme temperature and harsh environment.
10.- Memory and logic based on memristors: Simulations and design.
Project: MS projects in the Woodall Research Laboratory
Sponsor: Professor Jerry Woodall
Description: Project opportunities include:
1.- Compound semiconductor materials and epilayer projects
a. Materials: AlGaAs, GaP, ZnSe/GaAs digital alloys
b. Epi tools - LPE for GaP, AlGaAs; MBE for III-V and II-VIxIII-V1-x
2.- LPE Devices: GaP-solar cells, AlGaAs "true red" 610nm for high efficiency LEDs for pixelated displays.
3.- MBE Devices based on ZnSe/GaAs and InAs interfaces
a. ZnSe/GaAs digital superlattices; RGB LEDs/lasers, BG 1.4-2.7 eV.
b. ZnSe/GaAS HJ solar cells and THz HBTs.
c. ZnSe/pseudomorphic InGaAs MOS-C and CMOSFETs.
d. InAs/ZnSe Schotty diodes for high power electronics.
4.- Latent heat storage of intermittent solar and wind power
a. Convert intermittent solar/wind power to 24/7 power via latent heat energy storage: 577 C Al-Si eutectit and Si phase change batteries.
5.- Hydrogen Generation via Stored Energy in Aluminum and Water
a. Split water using Al-Ga alloys to make UHP H2 and UHP Al2O3.
Project: Tailored NMEMS-plasmonic platform for gas/cancer detection
Sponsors: Professor J. Sebastian Gomez-Diaz and Texas Instruments
Description: This project deals with the development of a platform that combines NMEMS at RF with tailored metasurfaces at IR to detect specific spectral fingerprints of gases and cancer cells. The project will include (i) development of plasmonic metasurface and characterization with a Fourier Transform Infrared Spectrometer with microscope; (ii) update an existing RF and laser testing set-up; and (iii) development of a testing chamber printed in 3D. The project requires knowledge of electromagnetics and the use of numerical software (Matlab and CST/COMSOL). Once the system is ready, it will be applied to the analysis of gases and biological samples. The project will be developed in coordination with Texas Instruments.
Requirements: Knowledge of electromagnetic waves, RF, and optics. Experience with instrumentation software (Labview/Matlab), metasurfaces and MEMS design would be useful but it is not required.
Project: Reconfigurable Computing with Photonic Interconnects and AI
Sponsors: Professor S. J. Ben Yoo.
Description: This project seeks innovations in scalable high-performance cloud computing systems through a combination of new generation of optical interconnect technologies as well as existing electronic switching architectures. The current project team is planning to conduct computing and networking experiments through a combination of off-the-shelf computing and networking equipment and research-grade optical interconnect and switching devices. The MS student is expected to assist the NGNS researchers with FPGA programming, Ethernet network switches configurations and automation, Linux servers’ configuration, and software-defined networking programming. This project can accommodate two students.
Requirements: Proficiency in one or more script languages (e.g. Python, Matlab), C/C++, etc. Good knowledge of Linux operating system (e.g. Ubuntu). Familiar with distributed programming and MPI protocol. Familiar with HDL language and FPGA programming platforms (e.g. Xilinx Vivado). Familiar with Ethernet and TCP/IP networks, LAN configuration, and Ethernet switches configuration and routing protocols.
Project: AI-Assisted Self-Driving Autonomic Optical Networking
Sponsors: Professor S. J. Ben Yoo.
Description: This position seeks innovations in next-generation autonomous and self-driving optical networking systems leveraging existing and emerging machine learning and AI tools. The current project team is planning to build novel prototype network control plane algorithms and experiments. The MS student will assist the NGNS researchers with conducting computing and networking systems integration, and software-defined networking programming to implement novel and scalable control and management plane architectures and algorithms. In particular, we are looking for someone helping implementing AI-driven resource calculation modules, application interfaces, communication protocol extensions, and network telemetry functions. This project can accommodate two students.
Requirements: Proficiency in one or more script languages (e.g. Python, Matlab), Java, C/C++, etc. Familiar with machine learning algorithms (e.g. deep reinforcement learning) and tools (e.g. Tensor Flow or PyTorch). Familiar with Ethernet and TCP/IP networks, LAN configuration, Ethernet switches configuration and routing protocols, network monitoring tools (e.g., Wireshark). Familiar with software defined networking (SDN) and Open Network Operating System (ONOS®).
Project: 3D Ultrafast Laser Inscription
Sponsors: Professor S. J. Ben Yoo.
Description: This project seeks to design, inscribe, and test arbitrary 3D waveguides for future computing, networking, and imaging applications. Utilizing the unique ultrafast laser inscription facility, the project team has realized 3D waveguides of arbitrary shapes and forms. More descriptions are available in this publication:
S. J. Ben Yoo, Binbin Guan and Ryan P. Scott, “Heterogeneous 2D/3D photonic integrated microsystems“, Microsyst Nanoeng 2, 16030 (2016).
Requirements: Proficiency in one or more script languages (e.g. Python, Matlab), Java, C/C++, etc. Good knowledge of optics and waves. Overall good skills in laboratory experiments. Familiarity with computer controlled instrumentation is desired but not necessary. Familiarity with computer aided design is desired but not necessary.
- Area of Research: Information Systems
Project: Deep Camera Calibration – Deep Learning for Accurate Camera Calibration in Assembly Automation
Sponsors: Professor Iman Soltani.
Description: This project is going to be conducted at LARA (Laboratory for Artificial Intelligence, Robotics and Automation). The overall goal of the project is to develop a deep learning scheme for accurate and streamlined camera calibration that is suitable for precision assembly automation.
Camera calibration is the first and foremost step in any robotics application involving vision. Currently the models used for this purpose are simplified and the calibration process is cumbersome. These simplifications lead to rather inaccurate calibration results that are acceptable for only a subset of applications relying on vision such as mobile robotics in which obstacle avoidance is the main objective. However, applications requiring high precision positioning such as assembly automation cannot rely on vision alone solely due to low accuracy of the vision-based object positioning methods.
This project aims to rely on deep learning to form more complex models of camera 3D to 2D mapping and develop streamlined calibration schemes that can be easily implemented.
Project: Learning from Simulation in Assembly Automation and Quality Control
Sponsors: Professor Iman Soltani.
Description: This project will be conducted at LARA (Laboratory for Artificial Intelligence, Robotics and Automation). The focus of this project is on generalization performance of deep networks trained on simulated training data. The main application under consideration is quality control and assembly automation. As part of this project we aim to train deep networks to detect certain keypoints on an image of a given mechanical component or assembly. The detection of these keypoints will help us estimate the absolute or relative position of the parts in 3D space. This information can be used for assembly quality control or for assembly automation.
However, training deep networks for keypoint detection requires large volumes of training data. Such training data include thousands of images of mechanical parts in which the keypoints of interest are annotated manually by human operators. This process is cumbersome, requiring capture of thousands of images from various perspectives and annotating the corresponding keypoints. This has to be repeated upon product design updates or sometimes after a significant change in the assembly environment e.g. lighting.
To avoid the complications and cost associated with training data generation, we plan to develop a training scheme solely reliant on synthetic training data generation. In this approach component CAD information is used to synthesize realistic images. In this form the keypoints can be annotated automatically. As such, thousands of training images can be generated very quickly.
However, the deep learning schemes develop should benefit from a robust generalization performance such that their ability do not deteriorate when test samples come from real images of same components.
The ideal outcome of this project is a deep learning architecture that performs reliably on real images of parts of interest. This network will be trained fully on simulated (synthetic) images of the same parts e.g. generated through a CAD software.
- Area of Research: RF-to-THz Electronics and Waves
Project: Nonreciprocal phased array antennas
Sponsors: Professor J. Sebastian Gomez-Diaz
Description: This project deals with the analysis, design, fabrication and characterization of nonreciprocal phased-array antennas able to transmit and receive RF signals with different patterns at the same operation frequency with polarization control. The project entails the design of antenna in simulation software (HFSS or CST), the use of nonlinear circuit analysis (ADS), fabrication, and measurement in an anechoic chamber.
Requirements: Knowledge of electromagnetic waves and electronic circuits. Experience with full-wave simulation software (such as HFSS/CST and ADS) would be great but it is not required. It is a project for 1~2 students.
Project: THz imaging
Sponsors: Professor J. Sebastian Gomez-Diaz
Description: This project deals with the development of a imaging system based on time-domain terahertz spectroscopy. The goal is to automatize the system with a 2D positioner, aiming to implement imaging of biological samples from 0.1 to 4.5 THz. The project requires the analysis of THz waves, the implementation of signal processing algorithms, and the development of instrumentation code. Once the system is ready, it will be applied to the analysis of biological healthy/cancer biological samples.
Requirements: Knowledge of electromagnetic waves and Matlab. Experience with instrumentation software (Labview/Matlab) would be useful but it is not required.
Project: UC Davis Dark E-field Radio experiment
Sponsor: Professor Tony Taylor
Description: The UC Davis Dark E-field Radio experiment is a search for the electromagnetic signature from a low mass dark matter candidate called a dark photon. It involves massively averaging the EM noise inside an RF shielded environment to look for high Q candidate signals 80 dB below the Johnson noise threshold. For the first phase of this project, we are building a 64-million channel real-time FFT over the 30-300 MHz region. However, this will produce terabytes of data that need to be efficiently packaged, compressed, stored, and analyzed on a remote data server. We are looking for someone to design this data analysis tool chain and implement it on experimental data.
Requirements: Proficiency with common programming languages such as C++ and Python. Courses in Signals and Systems.
- Area of Research: Integrated Circuits and Systems
Project: CMOS Analog IC design
Sponsor: Professor Stephen Lewis
Description: Continue the class project in EEC 210 or do another project related to analog CMOS integrated-circuit design.
Requirements: Receiving a B or higher in EEC 210.
- Area of Research: Bio Ag and Health Technologies
Project: Time-resolved near-infrared spectroscopy for blood oxygenation measurement
Sponsor: Professors Weijian Yang and Soheil Ghiasi
Description: Blood oxygenation is the fraction of oxygen-saturated hemoglobin relative to total hemoglobin (unsaturated + saturated) in the blood. A healthy individual regulates a very precise and specific balance of oxygen in the blood and there is medical significance to monitor oxygen saturation in patients. Near-infrared (NIR) spectroscopy provides a noninvasive approach to conveniently measure the blood oxygenation. In this project, we will study the various approaches of NIR spectroscopy for such measurements. In particularly, we will investigate and develop a time-resolved NIR spectroscopy system, which could not only provide the measurement results from the typical continuous-wave (CW) systems, but also rich information of the tissues under the measurement probe. We will develop the model, perform simulation, explore the components, build and characterize the prototype and perform in-vitro (and in-vivo) measurements.
Requirements: Electronic circuits, Basic optics, Matlab. It is a project for 2 students.
Project: Target brain stimulation using surface electrodes
Sponsor: Professor Weijian Yang
Description: Delivering electrical field into the brain for stimulation has been shown to be effective to treat depression, stroke, dementia and several other medical conditions. The existing brain electrical stimulation paradigms either rely on electrodes implanted deep into the brain or surface electrodes on the skull. The former approach is highly invasive whereas the latter one lacks a spatial specificity. Recently, a new technology utilizes temporal interference of fields from multiple surface electrode pairs to noninvasively stimulate specific brain regions. In this project, we will optimize the design parameters of such temporal interference system to further increase the spatial specificity of the stimulation region, through finite element method simulation. We will also build a prototype of this electrical stimulation system and test it on rodents.
Requirements: Electronic circuits, Electromagnetic waves, Matlab. It is a project for 1~2 students.