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USF College of Engineering> IGERT> Research> Electronics and Information Processing

Integrative Graduate Education and Research Training


Electronics and Information Processing
(Faculty: S. Bhansali, D. Hilbelink, K. Muffly, N. Ranganathan, R. Sankar, R. Schlaf, T. Weller, P. Wiley)
Our vision is to produce inexpensive sensing devices by means of sensor integration and electronic miniaturization using MEMS. These devices will monitor a wide variety of physiological parameters and will be equipped with significant processing and memory (i.e., sensor integration with DSP). They also will provide wireless communication capabilities where the sensors themselves become nodes in an ad-hoc network (i.e., sensor integration with RF communication).
Characterization of ultrasonic probe discrimination ability in the investigation of skin biomechanical and anatomical properties
Development of skin tissue models: Several models will be developed to assist in the experimental investigations. This will include natural tissues such as human cadaver skin and pigskin and other materials such as gelatin-based “spiked” samples. All tissues will be fully characterized anatomically using appropriate optical microscope and other techniques. We also intend to incorporate skin with known diseases and other conditions of interest, including various forms of skin cancer. In addition, age, gender, and other compositional influences will be addressed. Longer term, informed human subjects will be included as part of the clinical validation process, again including normal skin and other medical conditions of interest (cancer, traumatized tissue, wound healing, aging, etc.). All human subject testing will be approved by USF’s Institutional Review Board (IRB).
Characterization of ultrasound devices (low and high frequency): Using the skin models, the performance of 5, 10, 20, and 50 MHz devices will be explored, with an emphasis on comparing the relative abilities of the various frequencies, using both A-mode and B-mode. In addition to the pure frequency effects, consideration of operator factors such as skin surface pressure will also be addressed. In human subject testing, Doppler ultrasound will also be included since skin microcirculation will be functional.
Understanding ultrasound information for tissues under mechanical loading: The model tissues will be subjected to various mechanical loading (compressive, shear, torsional) and the ultrasound responses analyzed for extractable information. In parallel, the biomechanical properties of the specimens will be characterized via stress-strain curves (except as related to human subject testing). Real-time ultrasound response during dynamic loading (for example, cyclic loading) will also be studied.
VLSI and information processing for bio-sensor systems
Image reconstruction and processing algorithms: We will develop efficient algorithms for 3-D image reconstruction from the impedance measurements done by the microsensors. We will investigate adapting existing approaches as well as new algorithms for obtaining a 3-D image of the cell distribution under the skin surface. Further, we will develop algorithms for processing the images for tissue characterization.
Image and data compression: We plan to develop efficient lossless and medically lossless compression and decompression of biomedical images. We have extensive expertise in the design of data compression algorithms.Most existing lossy image compression algorithms cannot be applied for biomedical images since we do not want to loose “medically valuable” information and the lossless ones designed for general purpose images will not exploit specific properties in biomedical images. We intend to specifically investigate skin and other biomedical images for lossy and the lossless compression.
Application Specific Integrated Circuits: We will develop hardware algorithms and architectures for control of sensors as well as decision making systems for applications such as alarm triggering/monitoring and drug delivery. The ASICs will be realized as either field programmable gate arrays or custom VLSI circuitry. These circuits will be required to be area and power efficient while yielding high speed computations.
Synthesis and low power design techniques: We have developed a new method for low power VLSI design called dynamic frequency clocking which when combined with voltage scaling allows a circuit to operate at different modes in terms of performance and power consumption by allowing the chip to vary in speed dynamically based on the operation being performed at a given time. We have also developed new synthesis techniques for implementing basic datapath components which will aid in design of low power circuits for integration with the skin sensors.
Intelligent decision making system: In the recent past, we have developed an intelligent decision making system which integrates a neural network for learning the dynamically changing environment and an expert system which has rules coded based on apriori knowledge about the system. Such a system results in a more accurate and less expensive system compared to independent neural network-based or expert system-based approaches. We have successfully demonstrated several real-time applications such as traffic control, autonomous vehicle navigation and failure control where such an integrated system performs much better. For drug delivery systems, precision decision making and failure control will be critical and will beinvestigated in the context of skin sensor control and response monitoring.
Communications and information/signal processing for biosensors
Development of methods and algorithms for signal processing, detection and classification using Statistical Hidden Markov Models, Wavelet transforms, and Neural Network Approaches: Biomedical signal processing is complex since (i) involves nonlinear processing methods for better analysis of biomedical signals and representation of signals as non-stationary random processes for better accuracy. (ii) bulk volume of data need to be measured and analyzed to extract any useful information (iii) exhibit low signal to noise ratio or high degree of artifacts. Three well established methods – that is – statistical hidden Markov models and discriminant analysis, wavelet transforms, and neural networks will be investigated for feature extraction, detection and classification. We have extensive expertise in the development of signal processing algorithms, which will aid in adapting exiting techniques and as well as developing new methods suitable for this application.
Communications and Telemetry - Wireless Sensors Networking: We propose to use low powered RF communication using Bluetooth technology as a possible solution for communications and telemetry among sensor devices. The sensors will permit remote monitoring and tracking of physiological parameters from each of the signal profiles such as bio-impedance, temperature, heart rate, blood pressure, etc. by placing the sensors or electrodes on the skin surface or in its proximity. Bluetooth provides low power (1 mW), communications capability (peak data transfer rate of 1 Mbps) using the unlicensed ISM band at 2.4 GHz. The sensor devices will be networked to form piconets (small networks of master and slave devices). The proposed work will involve tailoring the Bluetooth protocols for the RF communications and telemetry from the sensors to the external devices for processing and analysis. The cable replacement protocol, RFCOMM will be implemented to emulate the serial communications interface between the devices. In the next phase, we plan on extending the piconets to form ad-hoc networks where the sensor network will have distributed control for sharing information among them and as well as to any external unit. We have successfully developed intelligent techniques for mobility management and protocols for wireless networks that will aid in the design of wireless sensor network.
RF electronics for integrated bio-sensors
Low Voltage RF MEM Switches with Thermal & Electrostatic Actuation: One of the challenges in MEM switch design, which is especially important in the SKINS micro-sensor project, is minimizing the required actuation force. Electro-static actuation is most commonly used, and while it requires essentially no power for operation the required voltages can be quite high (typically 10 Vdc or greater). As actuation voltages are reduced, the switch can be susceptible to vibration and RF performance can be affected. In this project, a combination of electro-static and thermal actuation will be utilized. The objectives will be to optimize the balance between thermal and electro-static actuation to minimize power dissipation and actuation voltage. The goal is to achieve RF MEM switches with 1.5V actuation voltage that remain stable in the vibration environment expected to exist with the SKINS micro-sensors.
RF MEM Switch Modulators for Low Data Rate Communications: In communications applications requiring only low data rates (a few KHz at the most) and close proximity (e.g., within a room or laboratory, or less), application specific telemetry systems can be designed that require little to no power to operate. In this project, integrated architectures, built around the RF MEM switches described above, will be investigated. In operation, a continuous wave (CW) signal delivered by a source is received at the sensor by a small, planar antenna, modulated using on-off keying by the MEM switch, and subsequently re-transmitted in an orthogonal polarization back to the source. Prototype systems that implement the technique (passive modulating reflectenna, or PMR) are currently being studied at USF. The focus of the IGERT-related project will be to expand this work toward the development of higher frequency, retrodirective PMR arrays. The retrodirective capability will allow semi-arbitrary orientation between the sensor and the transmitter/receiver.
Multi-Layer Integration of High Performance RF and Analog CMOS Circuitry: The goal of co-fabrication and co-location of RF circuitry with the sensor-related structures will be addressed through the IGERT project. Conventional and emerging design methodologies for microwave passives (beyond a few GHz) are almost exclusively based on the assumption of available thick, low-loss substrate material. The IGERT project will address basic research in design and fabrication of passive microwave circuitry that is integrated on semi-conducting silicon substrates. Results of this work will advance a capability to imbed microwave/wireless connectivity on essentially any (potentially lossy) surface such as silicon integrated circuits, solar cells and plastics.
Design of Optimum Integrated Micro-Sensor Antennas using Genetic Algorithm Techniques: A proposed IGERT project is to develop efficient design and optimization techniques for integrated antennas embedded in complex electromagnetic (EM) environments. The antennas will be developed for the SKINS multi-layer sensor architecture. The objective is to represent the entire sensor architecture and surrounding medium within the numerical EM simulation, and be able to efficiently identify semi-arbitrary, planar antenna geometries that meet performance specifications. GA-MOM is an elegant formulation that requires the computationally expensive impedance matrix to be determined only once, with the majority of the optimization process falling to efficient GA routines.
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