A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Reservoir Computing with Boolean Logic Network Circuits(2021) Komkov, Heidi; Lathrop, Daniel P; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)To push the frontiers of machine learning, completely new computing architectures must be explored which efficiently use hardware resources. We test an unconventional use of digital logic gate circuits for reservoir computing, a machine learning algorithm that is used for rapid time series processing. In our approach, logic gates are configured into networks that can exhibit complex dynamics. Rather than the gates explicitly computing pre-programmed instructions, they are used collectively as a dynamical system that transforms input data into a higher dimensional representation. We probe the dynamics of such circuits using discrete components on a circuit board as well as an FPGA implementation. We show favorable machine learning performance, including radiofrequency classification accuracy comparableto a state of the art convolutional neural network with a fraction of the trainable parameters. Finally, we discuss the design and fabrication of a reservoir computing ASIC for high-speed time series processing.Item ENABLING HARDWARE TECHNOLOGIES FOR AUTONOMY IN TINY ROBOTS: CONTROL, INTEGRATION, ACTUATION(2016) Lee, Tsung-Hsueh; Abshire, Pamela A; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The last two decades have seen many exciting examples of tiny robots from a few cm3 to less than one cm3. Although individually limited, a large group of these robots has the potential to work cooperatively and accomplish complex tasks. Two examples from nature that exhibit this type of cooperation are ant and bee colonies. They have the potential to assist in applications like search and rescue, military scouting, infrastructure and equipment monitoring, nano-manufacture, and possibly medicine. Most of these applications require the high level of autonomy that has been demonstrated by large robotic platforms, such as the iRobot and Honda ASIMO. However, when robot size shrinks down, current approaches to achieve the necessary functions are no longer valid. This work focused on challenges associated with the electronics and fabrication. We addressed three major technical hurdles inherent to current approaches: 1) difficulty of compact integration; 2) need for real-time and power-efficient computations; 3) unavailability of commercial tiny actuators and motion mechanisms. The aim of this work was to provide enabling hardware technologies to achieve autonomy in tiny robots. We proposed a decentralized application-specific integrated circuit (ASIC) where each component is responsible for its own operation and autonomy to the greatest extent possible. The ASIC consists of electronics modules for the fundamental functions required to fulfill the desired autonomy: actuation, control, power supply, and sensing. The actuators and mechanisms could potentially be post-fabricated on the ASIC directly. This design makes for a modular architecture. The following components were shown to work in physical implementations or simulations: 1) a tunable motion controller for ultralow frequency actuation; 2) a nonvolatile memory and programming circuit to achieve automatic and one-time programming; 3) a high-voltage circuit with the highest reported breakdown voltage in standard 0.5 μm CMOS; 4) thermal actuators fabricated using CMOS compatible process; 5) a low-power mixed-signal computational architecture for robotic dynamics simulator; 6) a frequency-boost technique to achieve low jitter in ring oscillators. These contributions will be generally enabling for other systems with strict size and power constraints such as wireless sensor nodes.Item Development and Testing of a Multiplexed Temperature Sensor(2008-08-08) Anderson, Greg; di Marzo, Marino; Kim, Jungho; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Researchers studying phenomena associated with steep surface temperature gradients, such as boiling, need to be able to obtain a detailed surface temperature distribution. Such a distribution can be obtained by taking measurements at a number of discrete locations on the surface using multiple individual temperature sensors. Because each sensor requires at least two electrical connections, this approach has historically been limited to relatively few temperature measurements; the most extensive measurements made this way are still limited to a 10 × 10 array. A new temperature sensor has been developed to address this measurement problem. The new sensor consists of a 32 × 32 array of diode temperature sensors in a 10.24 mm2 area, with each component diode measuring 100 × 100 micron^2. Unlike previous array-type sensors, the new sensor uses a multiplexing scheme to reduce the number of external leads required; only 64 leads are required to obtain measurements from over 1000 individual temperature sensors. The new sensor also incorporates eight resistive heater elements to provide the heat flux to initiate and sustain boiling. The heaters are capable of delivering up to 100 W/cm^2. This dissertation describes the design and testing of the new temperature sensor and the supporting hardware and software. The system is demonstrated by determining the local heat transfer coefficients for a jet of FC-72 from a 0.241 mm diameter nozzle. The surface temperature distribution is measured for various combinations of applied heat flux, jet velocity, and nozzle standoff distance; these measurements are then used to determine the local heat transfer coefficient distribution. These measured values compare favorably to those predicted using several correlations available in the literature.