Anomaly Detection in Large Sets of Climate Data
Student: Joe Hidakatsu
Faculty Mentor: Clayton Scott
Project Description:Classification is a fundamental task in Machine Learning. Nonlinear classifiers are necessary for complex pattern recognition tasks, but state-of-the-art classification algorithms require O(n^2) complexity (space and/or time) where n is the training sample size. This project will examine linear complexity algorithms grounded in theoretical approximations, and connected to the well-known k-center problem in computer science. Duties will include researching and programming of efficient implementations of algorithms, evaluating performance on various pattern recognition tasks, and designing new algorithms as needed. Keywords: kernel methods, sparse approximation, k-center problem, distributed computation, online learning
Fast Modeling of the Static and Dynamic Characteristics of a Switching Semiconductor
Student: Nomar Gonzalez-Santini
Faculty Mentor: Juan Rivas
Project Description:This projects aims to develop circuits and component models to assist in the design of switching power converters switching at VHF frequencies (30MHz to 300MHz). This is between two and three orders of magnitude higher than conventional power electronics design. Among the advantages of designing converters at these frequencies is the reductions in size, the increase in control bandwidth, and the possibility to operate in harsh environments. At these frequencies of operation, the components of the power supply can potentially be directly printed on a passive substrate, improving manufacturability and reliability of high performance power converters.
Near-Ground Wave Propagation Measurements with Miniaturized Antennas at Low VHF Band
Student: Komlan Payne
Faculty Mentor: Kamal Sarabandi
Project Description:Assist in designing, fabricating, and testing new microwave antennas for wireless communications. This will involve using a CAD program to design and then simulate the antenna's operation. Next, the student will assist in fabrication of the antenna. Finally, the antenna will be tested in the radlab's anechoic chamber to determine the antenna pattern as well as its frequency response.
Invisibility Cloaking 101
Student: Dike Zhou
Faculty Mentor: Anthony Grbic
Project Description:This project will delve into the world of transformation electromagnetics (EM), where space is transformed to manipulate and harness electromagnetic fields. The emerging field of transformation EM has lead to numerous new microwave and optical devices since 2006, the most fantastic of which has been the realization of an invisibility cloak. In transformation EM, the path of an electromagnetic wave is controlled through the spatial variation of a material's permittivity and permeability. Specifically, the change from an initial field distribution to a desired one is expressed as a coordinate transformation, which directly translates to a change in the permittivity and permeability of the underlying material.
Shout, MANES, and Distributed Networking
Student: Nathaniel Jones and Jonathan Tiao
Faculty Mentor: Robert Dick
Project Description:Develop Android/GPhone applications and/or user interfaces for secure, difficult-to-interrupt, and censorship-resistant infrastructureless communication among normal people. There is already a team of two Ph.D. students, three undergraduates, and three faculty working on this lively project but there are so many things left to do. Please join us. Example projects follow: (1) Enhance a Twitter-like or instant-messaging client that supports our underlying network protocols (not TCP/IP). (2) Evaluate and design secure ad hoc communication protocols. (3) Modify Android platform devices to use existing public key management tools. (4) Port a particular 802.11b driver to an Android-platform smartphone. (5) Assist in carrying out experiments to evaluate applications and ad hoc communication protocols.
Evaluation and Development of Wireless Receiver
Student: Xuexiu Han
Faculty Mentor: Michael Flynn
Project Description:This research project will evaluate wireless systems, help develop demonstrations of the wireless transceivers and help develop new transceiver circuits.. Michael Flynn?s research group has developed integrated wireless transceivers with record energy efficiency. These devices work with WiFi, Zigbee and other standards. Wireless systems with integrated sensors and processing are also being developed. As an example, a wireless sensor measures magnetic field strength and transmits the measured data to a base station. This SURE project will involve the design of new boards, and the writing test software as well as software to control instruments. Some integrated circuit design will also be included in the project.
Optimizing Transparent Thin Film Transistors
Student: Bradley Frost
Faculty Mentor: Becky Peterson
Project Description:We are building transparent circuitry using liquid inks to print the electronic layers. To test the mechanical properties, they need to be fabricated on flexible and/or transparent substrates, for example, plastic film. The project will include fabrication of electrodes and transistors, and electrical and mechanical testing. The SURE student will work closely with a graduate student mentor throughout the project and will learn design, fabrication and testing of electronic devices.
Using GPUs to Accelerate X-ray CT Image Reconstruction
Student: Shamik Ganguly
Faculty Mentor: Jeff Fessler
Project Description:X-ray CT scanners acquire very large datasets when scanning patients. Our research group is developing sophisticated algorithms for making better X-ray CT images from the raw data acquired by a CT scanner. These improvements should lead to lower X-ray dose to patients. These algorithms require numerous calculations, and they can be accelerated by using graphical processing units (GPUs). The goal of this project is to improve a CT image reconstruction algorithm and implement its key methods on a GPU to investigate how much speedup can be obtained over a CPU.
The project will include the following components. Learn about the existing algorithms developed in the group. Learn the C-language extensions that enable C-programs to run on GPUs. Adapt an image reconstruction algorithm so that it is well suited to high-end GPUs. Investigate methods for optimizing the algorithm for the GPU and measure computational speedup. Compare to an existing implementation that runs on multi-core Intel workstation with POSIX threads. There are many possible ways the algorithms can be adapted to the memory architecture of a GPU and a comparative investigation of more than one approach would be desirable. There is also ample room for improving the algorithms themselves.
The project requires good programming skills (at least EECS 280), an interest in mathematical algorithms for scientific computing and medical imaging, and the ability to learn programming extensions independently. EECS 401 and 451 are also very desirable background. Linear algebra (e.g., Math 417) would also be helpful. These courses are not mandatory, but students who did well in them will be more likely to be selected.
ost likely the project will use the Nvidia "CUDA" C-language extensions or "Open-CL." Prior knowledge of CUDA or Open-CL is not expected, but willingness to learn these by self study of online examples is essential.