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  • Undergraduate Poster Abstracts
  • Electrical/Electronics/Communications Engineering

    THU-736 DETECTING MOTION ARTIFACTS, ADDRESSING SIGNAL QUALITY, AND DETERMINING HEART RATE ACCURACY IN AN ECG

    • Laura Gonzalez ;
    • Edgar Lobaton ;

    THU-736

    DETECTING MOTION ARTIFACTS, ADDRESSING SIGNAL QUALITY, AND DETERMINING HEART RATE ACCURACY IN AN ECG

    Laura Gonzalez, Edgar Lobaton.

    North Carolina State University, Raleigh, NC.

    Wearable technology has the potential to drive down increasing healthcare costs by allowing physicians to monitor their patients and detect problems outside of a clinical office. One diagnostic tool currently performed by wearable technology is an electrocardiogram (ECG), which is paramount in diagnosing heart problems. However, motion artifacts, signals caused by a wearer’s movements, often distort the biosignals produced. This research will determine the quality of ECG signals and heart-rate estimation and develop algorithms for motion artifact detection based on ECG, kinematic, and inertial data. ECG data will be collected from multiple platforms which include an off-the-shelf SHIMMER and a prototype wearable sensing platform for research. All signals will be compared against a Holter monitor, which is commonly used by doctors to monitor and record their patient’s ECGs. An algorithm to detect these motion artifacts will be developed which solely uses ECG and machine-learning techniques. This project has the potential for advancing wearable technology by determining if the platforms used have the potential to produce ECG and heart rate data usable by medical staff and by developing methods to detect motion artifacts.

    FRI-736 MICROWAVE BREAST CANCER DETECTION: BREAST TISSUE COMPOSITION SIMULATION MODEL

    • Charlene Cuellar ;
    • Joel Harley ;

    FRI-736

    MICROWAVE BREAST CANCER DETECTION: BREAST TISSUE COMPOSITION SIMULATION MODEL

    Charlene Cuellar1, Joel Harley2.

    1San Jose State University, San Jose, CA, 2The University of Utah, Salt Lake City, UT.

    Today, X-ray mammography is the primary means of breast cancer tumor detection. Although X-ray imaging provides excellent resolution, it exhibits several drawbacks. X-ray radiation can gradually cause harm to the tissue with each successive screening. Also, X-rays are poor at detecting early stage tumors. Specifically, mammography exhibits low statistical sensitivity and specificity because benign tissues and early stage malignant tissues possess relatively similar dielectric properties at X-ray frequencies. As an alternative to mammography, microwave imaging has been found to provide significant advantages. Microwave imaging employs inexpensive, low-power, and safe microwave radiation. Microwaves also exhibit much higher statistical sensitivity and selectivity to tumors in their early stages. Yet, microwaves are also highly sensitive to benign inhomogeneous tissue characteristics, which can distort the waves and significantly reduce imaging performance. The work presented serves to help alleviate this challenge by creating a fast, reliable simulation model of inhomogeneous breast tissue. This model will be used to study the effects on inhomogeneity and to create new algorithms to account for these characteristics. The model simulates wave properties by averaging the electric permittivity of a tissue composition between any 2 points in space. Both 2D and 3D models were created within a MATLAB environment and successfully tested with various samples of permittivity data measured from magnetic resonance (MR) imaging. We will demonstrate how this simulation model calculates microwave propagation behavior and will ultimately lead to an improved tumor detection system.

    FRI-738 ULTRAFAST MAGNETIZATION SWITCHING OF FERROMAGNETIC ALLOYS THROUGH OPTICALLY GENERATED SPIN CURRENT

    • Divyashish Kumar ;
    • Richard Wilson ;
    • Jeffrey Bokor ;

    FRI-738

    ULTRAFAST MAGNETIZATION SWITCHING OF FERROMAGNETIC ALLOYS THROUGH OPTICALLY GENERATED SPIN CURRENT

    Divyashish Kumar1, Richard Wilson2, Jeffrey Bokor2.

    1College of San Mateo, San Mateo, CA, 2University of California, Berkeley, Berkeley, CA.

    Current methods of controlling the magnetization dynamics of a ferromagnetic metal for storin information have substantial limitations, especially with regards to processing speed and data storage density. Magnetization switching by spin-transfer-torque is a promising phenomenon for the operation of ultrafast nanoscale devices. In our study, we investigated the possibility of using optically generated spin current to manipulate the magnetization of a 0.5 nm ferromagnetic layer. The spin current is generated by the rapid demagnetization of a ferromagnetic metal with the use of an ultrashort laser pulse. To understand the experimental limitations, we developed numerical simulations that compare the measured temperature dependence of the remnant magnetization with the optically generated temperature rises in order to calculate the spin currents in 4 different ferromagnetic metals: Fe, Ni, Co50Fe50, and Ni43Fe57. From this model, we predict that Ni43Fe57 excited with a 100 fs laser pulse with a fluence of 25 Jm-2 can generate a net spin current of approximately 1010 A/s for approximately 0.5 ps. The model also suggests that 1010 A/s is the spin current threshold for switching the spin of a 0.5 nm layer of Co50Fe50B. This investigation allows us to explore new ways of generating spin currents to flip the magnetization of a ferromagnetic film on the timescale of a few picoseconds.

    THU-737 FPGA PROTOTYPING AND SOFTWARE CONSTRUCTION FOR ASIC AND USB INTERFACES FOR A STEREO VISION HARDWARE ACCELERATOR

    • Sungil Kim ;
    • Ziyun Li ;
    • Hun-Seok Kim ;
    • David Blaauw ;

    THU-737

    FPGA PROTOTYPING AND SOFTWARE CONSTRUCTION FOR ASIC AND USB INTERFACES FOR A STEREO VISION HARDWARE ACCELERATOR

    Sungil Kim1, Ziyun Li, Hun-Seok Kim, David Blaauw.

    University of Michigan, Ann Arbor, MI.

    ASIC (application-specific integrated circuits) development cost is increasing due to emerging design and computational complexity while time to market is decreasing due to fast-growing technology. To reduce potential failures and cost, ASIC designs that use FPGA (field-programmable gate array) prototyping to verify functionality of the circuit are both effective and reliable, especially for real-time systems like stereo cameras. We develop a faster stereo vision hardware accelerator that consumes less power than a GPU-based stereo camera by addressing the data communication between the ASIC chip, FX3, and host PC. Streaming from the chip and into the chip also needs verification. This study determines the extent to which functionality of ASICs and the interface between the chip and USB peripheral controller (FX3) can be verified. Coupled with software verification, FPGA adjusts the logic behavior of the chip to run at near real time with data transfer rate of 2 gigabits per second. As the interface between ASIC and FX3 involves 12 signals: clock, flags, read/write strobe, address, and 32-bit data, debugging of the behavior requires designers to observe real-time signals and transitions. Using Xilinx ChipScope Pro, we verify the handshaking and timing. From the host side, we prototype with compatible software libraries for USB interfaces. After processing left and right images, the developed software verifies the bidirectional data transfer. These results suggest FPGA prototyping is particularly effective for handling real-time signals. The verifying step via FPGA prototyping can significantly reduce chip failure and manufacturing cost and increase design pace.

    THU-735 PEDOT:PSS MICROECOG ARRAY FOR SIMULTANEOUS RECORDING OF SPECIFIC SUBCORTICAL REGIONS

    • Valeria Gonzalez ;
    • Oscar Guerrero ;
    • Shadi Dayeh ;

    THU-735

    PEDOT:PSS MICROECOG ARRAY FOR SIMULTANEOUS RECORDING OF SPECIFIC SUBCORTICAL REGIONS

    Valeria Gonzalez, Oscar Guerrero, Shadi Dayeh.

    University of California, San Diego, La Jolla, CA.

    MicroECoG electrode arrays offer a high temporal and spatial resolution interface with large areas of the cortex. Their high resolution allows for real-time activity mapping that supports a wide range of medical applications, including diagnosing and treating patients with neurological diseases. Current electrode arrays experience mechanical-mismatch between the device and brain tissue. Furthermore, they are incapable of acquiring signals from specific locations across the cortex from their conventional rectangular array structures. Here, we present a highly flexible 30-channel microECoG array with specific electrode placement across the right hemisphere of a rat’s brain, enabling simultaneous activity mapping of particular regions. The electrodes are built on highly durable and flexible polyimide layers (HD 4100 and PI 2610) approved as biocompatible substrates. The electrode sensors are coated with PEDOT:PSS, providing a superior electrochemical interface with the motor, visual, and somatosensory regions. The device is fabricated through photolithography and a lift-off process. To cost-effectively test a great number of design variations, the photomasks were commercially printed on a 12 in x 18 in transparency sheet that fits 18 photomasks at an overall cost of $75. Mask variations varied in combinations of distinct wiring paths, wire thickness, and angle styles. We expect that devices with longer and thinner wire paths will provide greater flexibility and lower impedance, resulting in appropriate conformity to the brain and higher acquisition of signals. Identifying the superior wiring design will provide a guide for quick prototyping of different array structures focused on specific regions along large areas of the cortex.

    FRI-737 HUMAN ACTIVITY RECOGNITION USING ANGULAR DIFFERENCE FROM INERTIAL SENSORS

    • Angel Ramirez ;
    • Edgar Lobaton ;

    FRI-737

    HUMAN ACTIVITY RECOGNITION USING ANGULAR DIFFERENCE FROM INERTIAL SENSORS

    Angel Ramirez1, Edgar Lobaton2.

    1Polytechnic University of Puerto Rico, San Juan, PR, 2North Carolina State University, Raleigh, NC.

    Human activity recognition with IMU sensors can be used in a wide range of applications such as activity tracking for wellness monitoring or medical rehabilitation and behavioral tracking for mental disease management. In the literature, many comparative studies on activity recognition techniques and their performance have been reported. The objective for this project was the implementation and comparison of state-of-the-art techniques based on motion capture and inertial sensing data in order to allow for future integration with other multimodal environmental and physiological sensing. As part of this project, the accelerometer, gyroscope, and magnetometer sensors were calibrated to share a common coordinate frame in order to properly fuse their data. An algorithm for recovering orientation from the inertial data was tested and integrated with a machine learning pipeline in order to perform activity recognition. Trials of users performing different activities were recorded in order to test classification accuracy. Afterwards, machine learning techniques will be able to determine important parameters that will determine the effectiveness of the activity recognition and classification. These results will then offer a better perspective of the work that must be done in order to continue improving wearable sensor technologies.