The BIEN (Biosystems Internships for Engineers) projects that were offered during the Summer 2012 session are listed below. Faculty members and project directors are linked to their home pages where available.
1. Project TBA in April 2012
Prof. Pamela Abshire
- See 2011 MERIT Projects.
2. Speech Recognition Project 1
Prof. Carol Espy-Wilson
In this project, we are looking at deriving articulatory-based features from the acoustic signal and using them to do multilingual speech recognition (that is, recognition of speech regardless of the language being spoken).
3. Speech Recognition Project 2
Prof. Carol Espy-Wilson
In this project, we have developed a landmark-based speech recognition tool (base on a PhD thesis conducted several years ago in our lab) and we want to analyze its performance using traditional features vs. features we have developed in our lab.
4. Integrated Bio-Micro-Systems for Detecting and Studying Bacterial Biofilms
Prof. Reza Ghodssi
Bacterial infections are the leading cause of disease worldwide, and the development of antimicrobial drugs has been one of the highest priorities of biomedical research. It has been shown that certain types of bacteria communicate with each other through small signaling molecules. This capability, called quorum sensing, allows bacteria to perform population-coordinated actions and to overcome the host's immune system. Bacteria aggregate and form a pathogenic matrix known as a biofilm, which is impenetrable to conventional antibiotics. A promising new approach for combating bacteria is to develop drugs that disable their communication, making them less pathogenic and more susceptible to antibiotics. Researchers at the MEMS Sensors and Actuators Lab (MSAL) in the Electrical and Computer Engineering (ECE) department are developing microfluidic devices and implantable MEMS for studying in vitro and in vivo bacterial quorum sensing. Growth of clinically relevant bacterial biofilms is optically monitored in microfluidic devices, and biofilm growth is correlated with environmental factors. The use of microfluidic technology will ultimately allow large numbers of potential drugs to be tested rapidly with small sample volumes. In parallel, we are developing implantable MEMS sensors to detect in vivo bacterial infections by using surface acoustic waves (SAW). The implantable device will be able to monitor bacterial quorum sensing continuously at sites susceptible to bacterial infection, such as artificial organ or joint implants. The sensor will thus provide an early alert system and prevent severe infection and invasive surgery.
This project is a collaborative effort involving groups in the Bioengineering, Electrical and Computer Engineering, and Materials Science and Engineering departments as well as the Institute for Systems Research at the University of Maryland. It offers interdisciplinary learning opportunities to MERIT students. The student will use advanced bioengineering equipment, a microfluidic test station and an acoustic wave sensor measurement setup. The goal of the project will be to perform measurements of biofilm growth using standard laboratory tools. The results of this study will be used to validate the experiments and to further refine the micro/nano devices.
5. Nano-Biomaterials Integrated Microfluidic Electrochemical Biosensor for Label-Free Biomolecules Detection
Prof. Reza Ghodssi
In the last decades, nanostructured materials have received much attention due the unique properties of nanomaterials that offer excellent platforms as electronic and optical signal transduction to design a new generation of biosensing devices. By the integration with electrochemical sensors fabrication, these nanomaterials present interesting properties such as increased surface area leading to higher signal-to-noise ratio, increased sensitivity and dynamic range, short distances for mass transport and charge transfer as well as their ability to create complex nano-bioarchitectures that allow volume change and unique selective biological functionality.
BIEN participants will develop a nano-biomaterials integrated electrochemical sensor for label-free DNA hybridization detection. The participants will gain a solid understanding of a variety of different engineering fields, and will learn about the area of MEMS (micro-electro-mechanical systems) technology and how it applies to the creation of bio-hybrid nanostructures. The participants will work hands-on with all of the biological and chemical materials necessary to fabricate the metallic nano-bio-template sensor including time spent in the lab’s own clean room. Then, the participant will use the fabricated sensor for the electrochemical analysis of DNA hybridization events.
 K. Gerasopoulos, et al. 16th International Solid-State Sensors, Actuators and Microsystems Conference (TRANSDUCERS). 2011.
6. Neurally-Inspired Search Behavior in a Robot Bat
Prof. Timothy Horiuchi
This project seeks to explore neurobiologically-inspired computations that can support intelligent search behavior using a bat-inspired echolocation system. Starting from models of (bottom-up) sensory-guided attention, we will explore how to construct networks of neurons to guide exploratory behavior. The ultimate goal is to design networks that can embody knowledge about rooms and common objects to guide the sonar head and robot to actively look for objects based on expectation and to solve simple navigation problems.
7. Synthetic Flocking and Bioinspiration
Prof. P. S. Krishnaprasad
There is a tremendous level of interest and excitement in the investigation of collective behavior in nature, found across many length scales, as in large graceful, dynamic flocks of European starlings evading peregrine falcons, small highly synchronized groups of marine mammals such as spinner dolphins foraging for food, mobile swarms of midges, and further down in length scale, in rafts of bacterial assemblies. Gathering reliable 3D data of such collective behavior to obtain insights into the underlying structure and quantitative characteristics is a challenge that is slowly being addressed in a variety of university centers. Building and analyzing mathematical models based on such understanding would be a foundation for designing control laws to synthesize flocks, and implementing them in software applications to achieve versatile collective behavior in robotics. In the summer of 2010, the Intelligent Servosystems Laboratory will organize a small team to explore such modeling and analysis studies of synthetic flocking, inspired by biological data, and using novel mathematical formulations. Applications to robotics will also be possible using a small number of physical platforms interacting with a collective of virtual platforms in a distributed processing environment. Students with enthusiasm for mathematics, biology, physics and software are likely to find the project rewarding. The project will be supervised by Professor P. S. Krishnaprasad and his Ph.D. students.
8. Signal Processing in the Human Brain
Prof. Jonathan Z. Simon
Human brain activity is observable via a variety of tools. Most fall into the broad category of brain-imaging (e.g. fMRI) and are too slow to measure real-time neural computations. Magnetoencephalography (MEG) is an alternative that is sensitive to neural processes changing as fast as every millisecond. MEG is related to the more commonly used clinical tool electroencephalography (EEG), but it has key advantages due to its use of neural magnetic fields: the brain is magnetically, but not electrically, transparent. Since the entire brain is active simultaneously, however, the neurally generated magnetic fields become a mix of signals generated in many cortical areas, and they require a variety of signal processing techniques to determine the underlying neural processes performed in individual areas. Summer students will be given the opportunity to apply both traditional and cutting-edge signal processing techniques to neural data acquired via MEG, as well as being able to take part in conducting the experiments in which the MEG data is itself acquired. The goal in conducting these auditory experiments, and their data analysis, is to characterize, understand, and quantify the neural computations performed by the brain.