The BIEN (Biosystems Internships for Engineers) projects that will be offered during the Summer 2010 session are listed below. Project names are linked to their respective descriptions. Faculty members and project directors are linked to their home pages where available.
Two new projects have been added. See items (1) "Biosensors for Cell Monitoring" and (9) "Micro-Robot Control and Coordination."
1. Biosensors for Cell Monitoring
Prof. Pamela Abshire
Interfacing electronics to biological systems leads to the possibility of creating devices that directly monitor the responses of living biological cells. Potential applications include cell-based sensing, medical diagnosis, drug screening, pathogen detection, and scientific research into cellular mechanisms. Students will contribute to ongoing research to develop such bioelectronic and biophotonic interfaces to single cells. Bioelectronic sensors include bioamplifiers and capacitance sensors, detecting weak electrical signals from electrically active cells and cell-substrate interactions which correlate with cell health, respectively. Biophotonic sensors include contact imagers and fluorescence sensors, detecting objects in close physical proximity to an imaging surface and the results of fluorescence assays, respectively. These sensors form the technical basis for applications such as nose-on-a-chip, rapid throughput screening and microscale fluorescence detection and lab-on-a-chip. BIEN interns will explore aspects of the technology and applications including sensor characterization and validation in experimental platforms such as the nose-on-a-chip, development of new component technologies such as spectral filters or packaging methods, and development of new functional capabilities such as cell steering or screening.
2. Measurement of Energy Absorption from Antennas Placed Near Models of the Human Body
Prof. Chris Davis
Non-ionizing radiation from wireless devices, including cell phones, mobile and wireless handsets, cellular base stations, and other radiofrequency transmitters, is absorbed by the human body to varying degrees depending on proximity to the antenna, the frequency involved and geometric factors. To avoid excessive exposure the Federal Communications Communication sets advisory exposure limits that are influenced by internationally recognized exposure standards such as those promulgated by the IEEE and the International Council for Non-Ionizing Radiation Protection ( ICNIRP). Excessive exposure to non-ionizing radiation can have health consequences if the exposure reaches thermal limits. This project involves research on a new method for measuring energy absorption in models of the human body. Transparent models of the human body (phantoms) are placed near an appropriate antenna and the energy absorption is measured by measuring the spatial distribution of the rate of temperature rise that occurs in the phantom. These rates of temperature increase are typically are very small (less than 1 mK/s). Several laser beams pass through the phantom and are deflected by the temperature gradients produced by energy absorption. The deflections are measured with very sensitive position sensitive photodetectors, which can detect beam movement of a few nanometers. This project involves learning how to operate the system, working on sensitivity improvements, and writing computer algorithms to operate the system and automatically analyze the data.
3. Algorithms on Noisy Speech for Cochlear Implant Users
Prof. Carol Espy-Wilson
The Speech Communication Lab led by Prof. Espy-Wilson is developing a landmark-based approach to speech recognition which involves the explicit extraction Landmark-based Speech Recognition Study and Evaluation of Enhancement of specific linguistic information from the speech signal. A spectro-temporal profile of a speech signal reveals the periodic components that result from quasiperiodic oscillation of the vocal cords and aperiodic components that result from a noisy excitation of the vocal tract. One such noise known as aspiration is generated at the glottis where the vocal folds are fully are partially separated, but close enough to generate enough of a constriction to cause turbulence at the glottis and slightly above as the airflow hits the epiglottis. Another type of noise is generated well above the glottis (in the front half of the vocal tract in American English) by the close proximity of an articulator to the palate or teeth resulting in a narrow constriction that generates noise. For some sounds, this noise is also directed against the teeth that further strengthens the noise. The first part of this speech processing project will study the characteristics of these different types of noises produced by speakers so that we can automatically classify the noise as gstridenth or not, and as gturbulenceh or gaspirationh in order to facilitate robust speech recognition. The speech communication lab has developed an algorithm to increase the signal-to-noise ratio (SNR) of noisy speech signals that does not require an estimate of the noise. Objective measures show that this algorithm does a better job than many other speech enhancement schemes, especially when the noise is fluctuating. The second part of the project aims at determining the effectiveness of speech enhancement algorithms to improve the intelligibility of speech for cochlear implant users for many different noise types including car noise, subway noise, and babble noise.
4. Microfluidic and Implantable MEMS Devices for Studying Bacterial Films
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 communications, making them less pathogenic and more susceptible to antibiotics. Researchers at the MEMS Sensors and Actuators Lab (MSAL) in the Department of Electrical and Computer Engineering (ECE) and the Institute for Systems Research (ISR) are developing microfluidic devices and implantable MEMS for studying in vitro and in vivo bacterial quorum sensing. Growth of clinically relevant bacterial biofilms within the microfluidic devices is monitored optically, and its growth is correlated with factors in the environment. The use of microfluidic technology will ultimately allow large numbers of potential drugs to be tested rapidly with small sample volumes. In addition, we are developing implantable MEMS sensors to detect in vivo bacterial infections by using surface acoustic waves (SAW) methodology. 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 to prevent severe infection and invasive surgery.
This project is a collaborative effort involving groups in the Bioengineering and Materials Engineering departments and the University of Maryland Biotechnology Institute (UMBI). 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 devices.
5. Pattern Memory and Analysis in Bat-Inspired Echolocation Systems
Prof. Timothy Horiuchi
In a project that will support the development of a neuromorphic VLSI model of sonar-based, spatial memory (e.g., orientation of local objects), our student team will adaptthe design of an existing sonar system to be controlled by an FPGA (field-programmable gate-array) board. This sonar will include a rotating head (servo-based) that allows the sonar to search for objects by rotating. Following successful completion of the sonar control, a rudimentary object recognition system will be developed to analyze and recognize objects
based on experience. The project will be tightly coordinated with the on-going development of a biologically-inspired spatial memory system that will eventually use the recognition outputs for a higher-level navigation and mapping behavior.
We envision three phases to the project:
- Phase 1: to integrate a vestibular sensor mounted on the sonar board to detect when the board is passively rotated and demonstrate the ability of the FPGA to counter that rotation with the servo motors (vestibulo-sonar reflex).
- Phase 2: the FPGA will be programmed to perform object recognition tasks. It will use the sonar to obtain sonar signatures from the configuration of compound objects (3 or 4 objects each) and to distinguish between them.
- Phase 3: To use the system to explore an environment with an unknown number of targets, creating a rapid snapshot-style of object memory.
Ideal (although not required) skill set: MATLAB programming, FPGA design/programming experience, Electronics Design and/or Test Skills, and Experience with sonar systems
6. 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.
7. Signal Processing in the Human Brain
Prof. Jonathan Z. Simon
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. An alternative is magnetoencephalo-graphy (MEG), which 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.
8. Security and Privacy Protection of Biometric Data
Prof. Min Wu
Imagine that you no longer need to bring any identification cards to prove who you are, and no longer need to remember different passwords to perform transactions or log-in to your personal accounts online. Biometric technology has become increasingly popular.
By associating people with their unique and hard-to-reproduce physiological or behavioral characteristics (such as fingerprint, iris, face, and voice), biometrics enables person identity recognition and authentication based on who you really are rather than what you have (e.g. ID cards) and what you know (e.g. passwords). In order for biometrics to be broadly adopted, one of the critical issues that must be addressed is security and privacy protection of biometric data.
TThrough this REU project, students will be involved in the research on security and privacy protection of biometric data. Some of the work includes robust, distinctive, and efficient feature extraction from biometric data; secure storage of biometric data over distributed network clouds and potentially adversarial environments; and secure and privacypreserving matching of biometric data. During this interdisciplinary study, students will be exposed to and integrate state-of-the-art techniques from relevant research areas of image processing, multimedia retrieval, information coding, and applied cryptography. Students will also learn to identify new technical/research issues related to secure biometrics and develop algorithms and software prototypes to showcase their work.
9. Micro-Robot Control and Coordination
Prof. Pamela Abshire
Most previous work in the field of robotic controls has assumed that the robots possess advanced sensing and movement capabilities. However, this will not be true in the case of extremely resource-constrained robots, for example sub-cm3 robots or "antbots". The focus of this project is on small robots with limited sensing and movement capabilities. These robots can be used for a number of applications including surveillance and infrastructure inspection and monitoring. Resources available to the micro-robots will be extremely limited, including power, sensors, communication bandwidth, and computation. Thus, in order to realize controllable micro-robots it will be necessary to implement custom designs for many of the system components including communication, computation, and actuator drivers. One promising method to address this problem is to create an end-to-end asynchronous system, with "smart" sensors that detect when there is relevant data to communicate and de-centralized controllers that coordinate swarm activities based on locally available, noisy information. BIEN interns will engage in hands-on construction and testing of prototypes and algorithms as well as design of system components such as low power pulse-based communication and asynchronous processors.