M.S. Degree - Plan II (Exam)

M.S. Degree - Plan II (Exam)

M.S. Degree - Plan II (Exam)

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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.

Degree Requirements

  • General Requirements 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 II requires thirty-six (36) units of upper-division and graduate coursework (see course requirements below), satisfactory performance on a comprehensive final examination, and a minimum of three-quarters of academic residence. A comprehensive final examination in the major subject is required of each candidate. No thesis is required. The capstone requirement is fulfilled by a capstone written report, and oral examination, on an appropriately comprehensive topic at the end of year two.  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.

    Full-time students must enroll for 12 units per quarter including research, academic and seminar units. Courses may not be taken with the S/U option to fulfill course requirements, unless the course is normally graded as S/U.  A student who elects Plan II can register for 299 research units and should do so while preparing for the Comprehensive Examination. The number of 299 units taken should reflect the amount of time and effort devoted to the preparation.  Once course requirements are completed, students can take additional classes as needed, although the 12 units per quarter are generally fulfilled with a research course (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.
  • Course Requirements
  • Thirty-six (36) units of upper-division and graduate coursework, satisfactory performance on a comprehensive final examination, and a minimum of three quarters of academic residence are required.  At least 24 units must be letter-graded graduate Electrical and Computer Engineering 200 series courses (excluding EEC200, 29X seminar series, and EEC299). Not more than 1 unit of graduate seminar and 3 units of research (299 or equivalent) may be used to satisfy the 36-unit requirement.  The remaining units must either be upper division technical or graduate courses.  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.

    Courses required for the ECE undergraduate degree, or the following courses: EEC100, EEC110A/B, EEC130A/B, EEC140A/B, EEC150, EEC151, EEC161, EEC170, EEC172, and EEC180A/B, may not be used to satisfy the requirements of the ECE M.S. degree.
    The summary requirements of Plan II are
    ♦   Course Requirements: 36 units minimum (100 and 200 series only)
    ♦   24 units of letter-graded ECE graduate courses (200 series)
    ♦   Not more than 1 unit of graduate seminar may be used
    ♦   Not more than 3 units of EEC 299 research units may be used
    ♦   Courses with grades B- or better

    Suggested Courses:
    ♦   Courses in Computer Engineering        
    ♦   Courses in Information Systems
    ♦   Courses in Integrated Circuits and Systems    
    ♦   Courses in Quantum, Photonics, and Electronic Devices
    ♦   Courses in RF-to-Terahertz            
    ♦   Courses in Bio, Ag, and Healthy Technologies
  • Special Requirements
  • All graduate students are required to take EEC290, Seminar in Electrical and Computer Engineering, each Quarter that it is offered. 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.
  • Committees

  • ♦   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, students must file a Program of Study that must be routed through the ECE Graduate Program Coordinator for the ECE Graduate Advisor’s approval.

    ♦   Comprehensive Examination Committee for M.S. Plan II
    When the student advances to candidacy, they will declare an M.S. Comprehensive Examination 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 one other member. 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 give the comprehensive exam and approve the final report.

  • 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.
  • Exam Requirements

  • Fulfillment of the comprehensive examination is the last requirement of the M.S. Plan II. The examination may be taken once the student has completed required courses and advanced to candidacy.

    The purpose of the exam is to demonstrate the student’s ability to initiate and carry to completion a short engineering investigation. The examination has two parts: an oral presentation and a written report.

    The oral presentation consists of a seminar attended by the Comprehensive Examination Committee. After a formal talk on the subject of the investigation, the committee questions the student on that subject and on related topics.

    The topic of the written report is chosen by the major professor in consultation with the student. It can be a literature search, an in-depth study of a particular topic or a brief experimental investigation. The report is concise and follows the style of an IEEE paper.

    With the advance endorsement of the student’s major professor and the ECE Graduate Advisor, a student in the Ph,D, program may request permission from their Ph,D, Qualifying Exam Committee to combine the comprehensive exam for the M.S. Plan II degree with the Ph.D. qualifying exam. The Ph.D. Qualifying Exam Committee will only approve such requests provided that the M.S. requirements are met as a subset of a successful Ph.D. Qualifying Examination, as determined by the Ph.D. Qualifying Exam Committee. With the permission of the Ph.D. Qualifying Exam Committee, a student may submit a version of the Ph.D. research proposal in fulfillment of the M.S. Plan II written report requirement. The M.S. Plan II report must be considered separately from the student’s Ph.D. research proposal; however, the Ph.D. research proposal may contain text from the M.S. written report.

    The exam committee’s unanimous vote is required to pass a student on the exam. If a student does not pass the exam, the committee may recommend that the student be reexamined a second time, but only if the Graduate Advisor concurs with the committee. The second exam must take place within one quarter of the first exam. The format of the second exam is the same as that of the first and may include the submission of an amended version of the report. The examination may not be repeated more than once. A student who does not pass on the second attempt will be recommended for disqualification from further graduate work in the program to the Dean of Graduate Studies.

    After passing the examination a copy of the M.S. Plan II report must be submitted to the ECE Graduate Program Coordinator. The master’s report form is signed by the program graduate advisor and then forwarded to the Office of Graduate Studies. The deadlines for completing this requirement are listed each quarter in the campus General Catalog (available online at the website of the Office of the Registrar). A candidate must be a registered student or in filing fee status at the time the program submits the form, with the exception of the summer period between the end of the spring quarter and the beginning of fall quarter. The program must file the report with Graduate Studies within one week of the end of the quarter in which the student’s degree will be conferred.

  • Normative Timeline
  • Although work for the Master of Science degree can be completed in three quarters of full-time study, generally at least 12 months of full-time study are required to complete the M.S. Plan II. 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.
    ECE MS Timeline
  • 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.

Capstone Projects

  • Project: Detection and measurement of cauliflower heads in foliage, using mm-wave radio

  • Sponsor: Professors Omeed Momeni (ECE) and Stavros Vougioukas (BAE)

    Description: A major obstacle in the path to robotic harvesting of cauliflower is detecting the cauliflower head inside the plant leaves, and if a head is present, the estimation of its size in real-time, in the field. If the head is smaller than a threshold size, the plant should not be cut; cutting plants with no head inside or small head inside results in economic losses. The head is surrounded by thick and tall leaves (see pictures of the plant and head cross-sections below), and therefore, traditional visible-spectrum imaging is ineffective. We plan to use mm-wave spectrum of 70-220 GHz to perform experiments and evaluate the possibility for detecting the cauliflower head and its size.

    Requirements: Experience and interest in RF technologies. One (1) student will work on this project.

  • Project: CeDP:  Electrophysiological Recording System Fabrication

  • Sponsor: Professor Erkin Şeker

    Description: Electrical activity of neural cells are commonly used in neuroscience to study behavior of neural circuits and individual neurons. In these applications, neural cells are grown on photolithographically patterned gold electrode traces over glass substrate (called microelectrode arrays, MEAs), where small voltage fluctuations of the cells are amplified, digitized, and recorded for subsequent analysis. This project involves the construction of the board on which MEAs can be reversibly mounted and interfaced with Intan amplifier/DAQ electronics (https://intantech.com/). The student(s) will collaborate with other graduate students working on the neuroengineering aspects of this project.

    Requirements: PCB board lay-out, soldering, electrical testing, and interfacing with data acquisition instrumentation are required.

  • Project: CeDP:  Computational Efficiency of Deep Learning in Digital Pathology

  • Sponsor: Professor Chen-Nee Chuah

    Description: While supervised learning (SL) techniques such as convolutional neural networks achieve promising results in pathology images, the computational complexity is still significantly heavy due to the gigapixel resolution of pathology images. To make deep learning more practical in digital pathology, it is necessary to comprehensively study the tradeoff between performance and complexity. In this project, we will study how to deploy efficient deep learning models on edge devices for pathology image analysis and how to remove unnecessary computation in the recent state-of-the-art deep learning networks. We will also benchmark the complexity of different models on our pathology datasets.

    Requirements: Expertise in machine learning concepts, Docker, and Python programming inclusive of scikit-learn, Pandas, PyTorch/Tensorflow.

  • Project: SSL-Pathology: Semi-supervised Learning in Pathology Detection of Alzheimer's Disease

  • Sponsor: Professor Chen-Nee Chuah

    Description: While supervised learning (SL) techniques such as convolutional neural networks achieve promising results in medical images, procuring a sufficiently large dataset with annotations is labor-intensive, especially in gigapixel pathology images. To circumvent the need for large labeled datasets, semi-supervised learning (SSL) can be a potential approach. Amyloid-beta plaques are hallmarks of Alzheimer's disease. A supervised detection model has been established to classify three types of plaques. However, it relies on more than 50,000 annotated images for training the model. In this project, we will adopt SSL to this problem and explore the upper bound of SSL to relieve the reliance on a large labeled dataset.

    Requirements: Expertise in machine learning concepts, Docker, and Python programming inclusive of scikit-learn, Pandas, PyTorch/Tensorflow.

  • Project: Computer-Vision Assisted Autism Disorder Spectrum (ADS) Behavior Detection using Videos

  • Sponsor: Professors Chen-Nee Chuah and Samson Cheung

    Description: Early intensive intervention has been shown to be highly promising for young children with autism spectrum disorder (ASD) and hence a measure that could identify ASD risk during this period of onset offers the opportunity for intervention before the full set of symptoms is present. In collaboration with the MIND Institute, our team have developed computer vision (CV) and deep learning (DL) based video-based screening tool that utilizes a large library of video clips. The videos are collected under the Video-referenced Infant Rating System for Autism (VIRSA) project and depict a wide range of social-communication ability and relies solely on video in the ratings, with no written descriptions of behavior. We hypothesized that the semantic clarity afforded by video would provide improved early discrimination of infants at highest risk for ASD. In this project, we will expand on our previous efforts to explore (a) optimized models for mobile screening platform, (b) mitigation for bias in AI models, and (c) security and privacy issues associated our CV/DL-based pipeline.

    Requirements: Expertise in machine learning and computer vision concepts, Python programming inclusive of scikit-learn, Pandas, PyTorch/Tensorflow.

  • Project: Characterization of Silicon Photodiodes for Precision Sensing Applications

  • Sponsor: Professor Soheil Ghiasi

    Description: A large number of light-based precision sensing systems rely on silicon photodiodes (PD) as their sensors for which, accurate circuit model characterization is not available via commercial vendors. The goal of this project is to measure the output of a specific silicon PD under a number of known input conditions, and use the data to infer the parameters of the PD’s circuit model, such as dark current, shunt resistance, shunt capacitance, etc.

    Requirements: Solid background in electrical circuit, and interest in data-driven analytics

  • Project: Implementation of All Digital Phase-Locked Loops (PLL) on FPGAs

  • Sponsor: Professor Soheil Ghiasi

    Description: Phase-Locked Loops (PLL) is a control system that generates an output signal whose phase is related to the phase of an input signal. PLLs are a key component of networking (and some precision sensing) applications. The goal of this project is explore FPGA IP blocks for implementation of All digital PLLs, and explore the tradeoffs involved in their implementation on FPGAs.

    Requirements: Solid background in digital design with FPGAs; understanding of PLL operation will be helpful.

  • Project: Model-Based Design of Signal Processing Blocks Targeting FPGAs and Microcontrollers

  • Sponsor: Professor Soheil Ghiasi

    Description: The objective of this project is to explore model-based design of several signal processing blocks, such as standard filters and signal transformation modules, and their automated synthesis from Simulink and/or Matlab models targeting FPGAs and/or microcontrollers.

    Requirements: Solid background in digital design with FPGAs, and digital signal processing

  • Project: Complete Diode Characterization

  • Sponsor: Professor Anh-Vu Pham

    Description: In this project, a student will fully characterize and model aspects of a diode.  The student will measure the diode’s behavior when changing conditions such as bias voltage and frequency. The student will then analyze this data in order to extract the intrinsic and parasitic characteristics of the diode. Lastly, the student will validate these characteristics by modeling the diode in a circuit simulation software and comparing the model’s simulated results with the measured results.

    Requirements: Basic circuit knowledge; basic programming skills; basic circuit simulation software skills

  • Project: Voltage-Controlled Oscillator Design
  • Sponsor: Professor Anh-Vu Pham
     
    Description: A voltage-controlled oscillator (VCO) is a critical component in communications circuits. Often, a VCO’s performance is limited by its phase noise. For this project, a student will use computer-aided-design tool to design a VCO at microwave frequencies to achieve low phase noise. The student will design and analyze a variety of circuits and study trade-off in phase noise.

    Requirements: Basic circuit knowledge; knowledge in circuit design software
  • Project: Educational Computer Game Development
  • Sponsor: Professor Erkin Şeker

    Description: There is a need for educational tools that can reach to a growing number of students and the general public with minimal resources to access laboratory courses that require expensive research infrastructure, such as a microfabrication facility. In order to address this need, we have developed an educational computer game that simulates a microfabrication laboratory and teaches the essential techniques such as wet chemistry and photolithography, in an interactive manner. The goal of this project is to continue improving this game and/or start other educational games for teaching different laboratory techniques (e.g., analytical chemistry, biology, etc.).

    Requirements: Programming with Unity3D or willingness to learn how to program in Unity3D
  • Project: Protecting Deep Neural Networks with Numerical Codes
  • Sponsor: Professor Robert Redinbo
     
    Description: Deep neural networks have interconnected stages, each of which has thousands of linear weightings and summations that pass through outputs to individual neuron activations functions. Processing errors can dramatically affect the final classification results of the network. Numerical-based error detecting codes can be used to determine the occurrence of such errors. However, most known numerical codes are linear that can complicate detection in the neuron activation processing which involve nonlinear functions. One protecting method employs a technique called algorithm-based fault tolerance, which, while linear, can be modified for application to the neuron activation steps by approximating this function by a Taylor series expansion. A project can explore the possibilities of these protection techniques and their effectiveness by MatLab simulations.
  • Project: On Chip Leaky-Wave Antennas
  • Sponsors: Professors J. Sebastian Gomez-Diaz and Omeed Momeni
     
    Description: This project deals with the analysis, design, fabrication and characterization of broadband composite right/left-handed leaky-wave antennas operating from 50 GHz to 100 GHz. The first step will be to analyze and design a unit-cell of this type of antennas using numerical simulations. Then, the response of an entire antenna composed of dozens of unit-cells will be investigated in terms of efficiency and tunable radiation patterns. After fabrication, the device will be measured and characterized. Finally, the leaky-wave antenna will be combined with non-Foster circuits to significantly enhance its operational bandwidth and enable its use in radar systems and future communication networks.
     
    Requirements: Basic knowledge of electromagnetic waves and electronic circuits. Experience with full-wave simulation software (such as HFSS/CST) would be great but it is not required. It is a project for 1~2 students.
  • Project: Robotic Packaging of Strawberries

  • Sponsor: Professor Stavros G. Vougioukas 

    Description: Fresh-market strawberries are perishable and 100% hand-harvested. Strawberry growers face increasingly tighter labor supplies, driving up harvest costs and increasing the risk of labor shortages to complete a full harvest. Several companies are pursuing robotic strawberry harvesting robots and focus on strawberry detection and plucking. After picking, robot arms typically deposit strawberries on a conveyance system. The fruits have irregular shapes and they become available one at a time, on a moving conveyor belt. However, packing strawberries robotically in clamshells (Fig. 1 top left) has not being addressed. In this project, a 3D camera will be used to represent the strawberry that must be placed inside the clamshell as a set of voxels Vs (Fig. 1 top center) and the strawberries already inside the clamshell as a set of voxels Vc (Fig. 1 top right). The system will compute the best location for place the next strawberry given Vs and Vc (Fig. 1 bottom left). An existing vacuum gripper will be used on a two-arm robot (Fig. 1 bottom right) that will be programmed to perform this pick-and-place operation.
    Clamshell
    Fig. 2. Packed clamshell (top left); strawberry to be placed in clamshell – Vs (top center); 3D camera view of strawberries already inside clamshell -Vc (top right; best location to place strawberry (bottom left); robotic testbed (bottom right).

    Requirements: C++, Linux, open source development tools and platforms including GitHub. Two (2) students would ideally work on this project.

  • Project: High-Impact Materials and Devices

  • Sponsor: Professor Jerry Woodall

    a) Photonic and Electronic Devices based on ZnSe Heterojunctions.
    Qualifications: An intense interest in theory, analysis and experimental work on new or undeveloped compound semiconductor materials and devices.  Work will require being trained to work in the clean room of the Center for Nano and Micro-scale Manufacturing (CNM2).
    Contact:
    Prof. Jerry Woodall, Kemper 2001 (jwoodall@ucdavis.edu).
    Group Leader: Zongjian Fan

    b) Phase change materials systems for storing heat generated by solar and wind power captured by selective absorbing materials.
    Qualifications: An intense interest in theory, analysis and experiential work on new or undeveloped materials for capturing solar and wind power, storing it as heat and converting it to electrical power.
    Contact:
    Prof. Jerry Woodall, Kemper 2001 (jwoodall@ucdavis.edu).
    Group Leader: Richard Dering.

    c) Photonic and PV Devices based on Liquid Phase Epitaxy.
    Qualifications: An intense interest in PV devices made of  AlGaAs and GaP. Work will require being trained to work in the clean room of the Center for Nano and Micro-scale Manufacturing (CNM2).
    Contact:
    Prof. Jerry Woodall, Kemper 2001 (jwoodall@ucdavis.edu).
    Group Leader: Hui-Ying Siao, and Zongjian Fan

    d) Splitting water with Al-Ga alloys
    Qualifications: An intense interest in theory, analysis and experiential work on new Al-Ga materials for splitting water into hydrogen gas, heat and alumina.
    Contact:
    Prof. Jerry Woodall, Kemper 2001 (jwoodall@ucdavis.edu).
    Group Leader for Material R&D: Joel Schmierer and Miheer Shah.
    Group Leader for Demo Engineering:  Ricky Obregon

    e) Ga2O3 based Exploratory Materials and Device Engineering. 
    Qualifications: An intense interest in theory, analysis and experiential investigation of a relatively new and unexplored wide gap semiconductor, Ga2O3 for UV-Solar Blind Detector and Thermo-electric Generator (TEG) applications.
    Contact:
    Prof. Jerry Woodall, Kemper 2001 (jwoodall@ucdavis.edu).
    Group Leader: Ryan Bunk

  • Project: Numerical Data Protection Using Numerical-Based Codes

  • Sponsor: Professor Robert Redinbo

    Description:

    Numerical data are represented by digits (usually bits) in a computer word, which is stored or transmitted by using a finite-field (e.g., binary ) error- correcting code. The digits are encoded by expanding to a larger group of digits, transmitted or stored individually. However, there exist numerical-based error- correcting codes that accept the numerical data as a group of numerical symbols, expanding them to a larger group of numerical symbols; numbers into numbers. These codeword symbols are ultimately described by a digital format where the defining digits are stored or transmitted in the usual way, but with no additional coding introduced.

    When using numerical codes the numbers processed can encounter roundoff noise. Since the data are normally processed after retrieval from coding, there is roundoff noise introduced anyway.

    Most previous work on numerical-based coding has concerned rows of a discrete Fourier transform (DFT) matrix. A large class of DFT codes is defined through a matrix structure that dictates the parity-check equations (over numbers). These checks are actually DFT coefficients. These equations provide codewords with separation properties permitting error correction methods to be effective.

    Errors are considered as large numerical excursions added to the codewords. The large errors occur only on a few codeword symbols. Error correction requires the erroneous codeword symbols be located followed by determining the actual additive error values (possibly complex numbers).

    The only known technique for locating and evaluating error excursions uses a Berlekamp-Massey Algorithm {first discovered for binary error-correcting codes, also applicable to numerical fields}. The algorithm examines the parity-checking symbols associated with a codeword and locates errors by iteratively determining the shortest recursive formula for modeling these observed parity values. At the end of the locating phase, the known parity coefficients are extended followed by an inverse DFT that produces the errors’ numerical values at the proper locations in a codeword. These aspects of numerical-based coding can be a project in itself.

    The design of a numerical data protection system and evaluation simulations of its performance can be a complete project. MatLab tools are appropriate for such a project. This would be a “software” project.
    An alternate project in a similar vein could be the implementation of a data protection scheme on a stand-alone processor. The numerical data, e.g., floating- point format, is processed within a hardware system (simulation tools are available in the department). This could involve a hardware description language, e.g., Verilog, with the necessary “test bench” verifying overall operations.

    Requirements: Proficiency in Matlab OR Verilog.  EEC 269A/B is desirable.

  • Project: Implementation of tunable coherent sources for fast multi-spectral time resolved fluorescence spectroscopy and imaging

  • Sponsor: Professor Diego R. Yankelevich 

    Description: Fluorescence spectroscopy is a powerful technique for in vivo tissue diagnosis. Of particular interest for clinical applications, time and wavelength-resolved fluorescence spectroscopy has proven to be a reliable technique for tissue characterization since it is capable of determining multiple parameters including fluorescence intensity, spectrum, and lifetime. We propose the development of compact sources of tunable radiation between 400 and 450 nm (violet-blue), to be used in fluorescent spectroscopy, that are based on cascaded nonlinear wavelength conversion using a periodically poled lithium tantalite pumped by microchip Q-switch lasers. The microchip laser monolithic construction, where the laser crystal is directly contacted with the end mirrors, makes them very compact and alignment-free. Such lasers will be pumped with laser diodes either directly or via an optical fiber and will emit pulses of between 5 and 1000 mJ pulse energy, pulse widths in the nanosecond to sub-nanosecond range and repetition rates from single-shot to hundreds of kHz. Their simple and compact construction, as well the use of widely available inexpensive pump laser diodes, makes them reliable at a fraction of the cost of amplified fiber lasers. The infra-red pulses generated by the micro-chip lasers will be converted to the blue spectral range by using cascaded nonlinear interactions using a single a periodic nonlinear crystal to obtain optical parametric oscillation and sum-frequency generation.

    Requirements: Undergraduate courses which examine plane wave electromagnetic wave propagation, electrical and electronic circuits.  Interfacing with scientific instruments using LabView or similar platform.  ECE236 and ECE237A concurrently.

  • 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: Weijian Yang (ECE)

    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.

  • Project: Semi-Autonomous Wall-Climbing Robot for Marking on Vertical Walls

  • Sponsor: Professor Saif Islam

    External Sponsor (potential): Professor Aykutlu Dana

    Description: The objective of this project is to create a vehicle that is capable of following a trajectory on a flat surface programmed by the students. The system would be equipped with robust and reliable navigation system and will host a device to draw preprogrammed linear and curvilinear lines using a marker on the surface at precise positions. Additional devices will be used to sense and apply pressure on the drawn lines at varying speed with a goal of erasing the lines. The vehicle is expected to operate both on horizontal as well as vertical flat surfaces. The positioning of the system will be based on remote sensing of the position and orientation of the vehicle using real-time image processing and 3D scene generation. A stereoscopic 3D capture camera (Kinect) along with OpenCV or Matlab code will be used to extract scene and vehicle information. Luminescent markers on the vehicle will be utilized using mathematical framework borrowed from super-resolution localization microscopy to accurately estimate position and trajectory of the vehicle. The project will involve robot design, sensing, motion planning and control, mapping and localization functionality.  The students will build model using a 3D printer, servos and microcontrollers with wireless connectivity. The students will familiarize with Python libraries, microcontroller programming and sensor interfacing, image processing and feature extraction, real-time feedback and feedforward control and 3D scene generation.

    Requirements: Programming in Python, Matlab, OpenCV. Register for EEC 289K in Winter or Spring 2019.

  • Project: Implementing Domain Specific Accelerators for RISC-V

  • Sponsor: Professor Venkatesh Akella 

    Description: The discipline of computer architecture is witnessing two dramatic, potentially game changing trends, just as the demand for computation is growing exponentially with machine learning and data-driven-everything. First is the end of Moore’s law and the second is the emergence of an open source computer architecture movement (analogous to Linux in the OS community) based on RISC-V. The future of computer architecture will involve creating domain specific architectures centered around a processing core to improve the performance of a set of closely related applications. In this project, we will explore designing and programming domain specific accelerators for emerging applications in machine learning and computer security based on RISC-V and prototyping them on a FPGA or the Amazon Cloud.

    Requirements: You should have taken EEC 270 or equivalent (graduate course in computer architecture) and also EEC 298 in Spring 2019. Ideally, we are looking for a group of 2 or 3 students.

  • Project: Non-intrusive Detection of Human Motions or Vital Data using WiFi/Bluetooth

  • Sponsor: Professor Zhi Ding

    Description: Wi-Fi networks are widely available for communication purposes. Interestingly, the ability to detect motion and extract vital animal information has also been recently demonstrated. In this project, we shall build a testbed and experiment with the use of WiFI/Bluetooth for motion detection and subject monitoring with the intrusive video camera. It is important to see whether such technologies can detect simple gestures and detect potentially risky behavior or symptoms of illness. This project contains both software and hardware aspects.

    Requirements: Familiarity with common programming languages such as C, C++, Python. Courses in Signals, Systems, Communications, and Networking. Candidates will be interviewed by the Faculty mentor. Ideally, we are looking for a group of 2-3 students.

  • Project: Design and Implementation of an FMCW Radar System

  • Sponsor: Professor Xiaoguang Leo Liu

    Description: The project focuses on the implementation of a Frequency Modulated Continuous Wave (FMCW) radar system that can perform range, Doppler and Synthetic Aperture Radar (SAR) measurements. In the first quarter, the students build an FMCW radar system using off-the-shelf components. In the second and the third quarter, the students focus on either improving the system by implementing their own design or utilizing the system for a specific application, such as speed limit enforcement or remote vital sign detection.

    Requirements: Register for EEC 289K in Winter or Spring 2019.

  • Project: Optimizing Application Parameters in the “Gunrock” GPU Graph Analytics Framework

  • Sponsor: Professor John Owens and Postdoc Serban Porumbescu

    Description: John Owens’ research group focuses on GPU computing and has a large project on parallel graph analytics called Gunrock.  Gunrock applications have a significant number of user- specified parameters. We have historically set those parameters in an ad hoc way. It is challenging for Gunrock developers to find the right parameters. We wish to automate this process, and make it easy for a developer to find the best settings for dataset X run on application Y. At first, we hope that a student would automate the search process (this would be more of a project-sized piece of work), but what we really hope is that a student will be able to gain some insight into why we want to use particular settings and by so doing, be able to set better defaults (this would be more of a thesis-sized piece of work). We would hope to train you in GPU computing and in using our framework. Funding may be available.

    Requirements: Python is likely the language of choice; C/C++ skills are also desirable. Strong (text) writing skills. Experience with parallel computing would be terrific but is not required. We need talented students who can learn quickly and work well both in a group and independently.