The ferromagnetic resonance (FMR) phenomenon serves as a sensitive probe of the effective internal fields in a magnetic material. FMR spectroscopy has consequently become a well-established technique, extensively employed in assessing material properties such as magnetic anisotropy, Landé g-factor, damping parameter, and the magnetoelastic constants of magnetic materials. Determining these...
A multidisciplinary perspective is necessitated for the analysis of wave energy conversion systems, spanning hydrodynamics, mechanics, electric power, and control systems. The complexity inherent in these scientific domains poses challenges for unified analysis. This paper addresses these challenges by connecting various domains through the application of circuit theory, characterizing the...
Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
There has been tremendous growth in using data analytic and machine learning algorithms to make critical decisions, such as in the national power grid, healthcare operations, and autonomous vehicles. Employing data analytic for decision-making allows cyber attackers to manipulate the decisions of these algorithms through data falsification. Hence, the trustworthiness...
High-potential molecules derived from biomass sources may suitably replace or supplement traditional nonrenewable hydrocarbon fuels to reduce pollution and fuel processing costs. Due to expensive and time-consuming property testing, models that predict key properties from optical data would initially vet potential additives before investment and bench-scale testing. Attenuated Total Reflection...
Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
We present a theoretical free-space optical (FSO) transmitter that utilizes a dynamically steered and shaped laser beam to communicate with a randomly moving receiver adorned with a retroreflector. The transmitter tracks the receiver's position by repeatedly scanning the field of view (FOV), measuring reflections from the retroreflector, and estimating the...
Sensor system plays an important role in connecting everything including human body to the electrical information systems that we have built and that we are going to build, to make the world more intelligent and efficient. One of the key propulsive forces behind these emerging techniques is CMOS scaling that...
High-performance mechatronic systems are widely used in precision manufacturing equipment such as CNC machine tools, 3D-Printers, photolithography systems, industrial robots, and Coordinate Measuring Machines (CMMs). These equipment are utilized in producing parts and components for aviation, semiconductor, optics, and many other emerging industries, with geometric features and surface properties within...
Significance: Movement intent decoding algorithms can interpret human bioelectrical signals to control prosthetic limbs with many degrees of freedom (DOFs). This work involves decoding volitional movement intent from surface electromyogram (sEMG) signals to control prosthetic arms. To train these algorithms, patients flex their muscles to “follow” a movement prompt, and...
The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
We study joint nonlinear state estimation with multi-period measurement vectors that are potentially corrupted by sparse gross errors. The identifiability-aware approach is proposed to leverage common characteristics of fundamentally identifiable gross errors to enhance error correction performance. First, we derive a necessary rank condition that the sparsity pattern of any...
Accurate positioning has become an active research area in recent years. It has a wide range of applications in many fields such as navigation, asset tracking, health care, proximity marketing/location-based advertising, and sport analytics. Transmitter positioning via radio frequency (RF) signals is the most widely encountered scenario, and it uses...
Accurate information of power network parameters is essential for performing various power system monitoring and control tasks including state estimation, economic dispatch, and contingency analysis. In this paper, we present a novel approach of power network parameter correction wherein we exploit the sparse nature of parameter errors. Parameter error correction...
Crowdsourcing is a popular paradigm to address the high demands for labeled data in big data deluge. It aims to produce accurate labels by effectively integrating noisy, non-expert labeling from crowdsourced workers (annotators). The machine learning community has been studying effective crowdsourcing methods for many years, and many models and...
Ring amplifiers (ringamps) have shown excellent power efficiency in the latest state-of-the-art analog to digital converters (ADCs). This thesis describes circuit techniques to ensure robust operation of ringamps using standard analog techniques and proportional-integral-derivative(PID) controller analogy. Large-signal and small-signal analysis of a ringamp are performed using simple RC settling and...
Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
Structural health monitoring (SHM) systems perform automated non-destructive damage detection and characterization for a variety of large structures including civil structures such as bridges and aerospace structures such as aircrafts and space vehicles. The goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing maintenance costs, and increasing the...
We consider multiple Compressive Sensing (CS) problems wherein the supports of signal vectors of CS problems are restricted to satisfy a collection of joint logical constraints, which we refer to as coupling constraints. We consider a case where the coupling constraints are encoded in a graph and present a sequential...
This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
This thesis concerns the development of a direct torque control strategy using a sliding mode control approach to optimize the power output of an oscillating water column (OWC) wave energy converter (WEC). The OWC WEC is a device that has a submerged vertical tail tube open at both ends, which...
Magnetic particle imaging (MPI) is a biomedical imaging technique which detects the presence of magnetic nanoparticles which have been introduced into the specimen prior to imaging. MPI has shown promise for real-time imaging with spatial resolution comparable to magnetic resonance imaging (MRI). MPI research has focused predominantly on the development...
Cold air pools are spatiotemporal phenomena that occur when cold air from higher elevations roll down the slope to accumulate in lower elevations. Behaviors like this lead to microclimate anomalies such as the city of Corvallis (Oregon) experiencing persistent cold weather even on a sunny day. We analyze multivariate temperature...
Triangulateration uses geometric distance to estimate the location of user by employing techniques like received signal strength (RSS), time-of-arrival (TOA), time-difference-of-arrival (TDOA) and image processing.
Radio frequency (RF) signal positioning using TOA or TDOA techniques generally requires timing synchronization of the anchors and/or the anchors and targets. If the desired...
In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
In bioacoustics, automatic animal voice detection and recognition from audio recordings is an emerging topic for animal preservation. Our research focuses on bird bioacoustics, where the goal is to segment bird syllables from the recording and predict the bird species for the syllables. Traditional methods for this task addresses the...
Incremental ADCs (IADCs) have found wide applications in sensor interface circuitry since, compared to ∆Σ ADCs, they provide low-latency high-accuracy conversion and easy multiplexing among multiple channels. On the other hand, continuous-time ∆Σ ADCs (CTDSM) have been receiving more and more attention as a power-efficient solution in targeting medium to...
Magnetic thin films have potential to improve devices such as on-chip inductors, and enable new technologies such as magnetic random access memory (MRAM).
The use of magnetic cores in on-chip inductors is typically limited to applications well under 1 GHz. At higher frequencies, the performance of the magnetic core is...
Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
In this work, we study network coding technique, its relation to random matrices, and their applications to communication systems. The dissertation consists of three main contributions. First, we propose efficient algorithms for data synchronization via a broadcast channel using random network coding. Second, we study the resiliency of network coding...
Impulse-radio ultra-wide-band (IR-UWB) signaling is a promising technique
for high-speed, short-range relay communications networks. Depending on how
the relay node retransmits the signal, there are two main relay schemes: conventional
one-directional (one-way) relay model, and bi-directional (two-way) relay
model. In bi-directional relay communications, wireless network coding (WNC),
also called physical-layer...
Many devices and methods for radiological searches are currently being developed, including scanning using simple detectors, mapping using large-volume detectors, and Compton imaging using 3-D position sensitive detectors. However, these devices are typically expensive and the methods used require long periods of time to generate a direction or location. The...
Bioacoustics analysis can be used to conduct environmental monitoring by detecting the presence of birds species. This analysis usually involves identifying the species from their calls. In most frameworks, bird song syllables are extracted from audio recordings and individual syllables are input to a classifier to identify the species. Extraction...
We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from sites to sites to collect data from the sensors periodically. There are different costs associated with the...
Data converters are essential interface circuits between the analog world that people live in and the digital processors that people live with. Linearity, which often is a tradeoff against other performance criteria, is one of the major performance demands from applications for both analog-to-digital converts (ADC) and digital-to-analog converters (DAC)....
Indoor positioning systems can be used for many applications such as indoor navigation,emergence response, asset monitoring, and shopper assistance. Due to the weak received signal and multipath reflection, the global positioning system (GPS) generally does not work in indoor environments. There are a variety of radio frequency (RF) signals and...
Nowadays, needs for wideband and high accuracy analog-to-digital converter are increasing rapidly in manifold applications such as wireless communication, digital video and other consumer electronics. Besides, low power consumption is required to have longer battery life in portable systems. CMOS technology scaling and innovative modulator topology make the implementation much...
Gibbs sampling method is an important tool used in parameter estimation for many probabilistic models. Specifically, for many scenarios, it is difficult to generate high-dimensional data samples from its joint distribution. The Gibbs sampling provides a way to draw high-dimensional data via the conditional distributions which are typically easier to...
Continues-Time (CT) Delta-Sigma (ΔΣ) Analog-to-Digital Converters (ADCs) have one important constrain, namely the excess loop delay. Most previous excess lop delay compensation methods need to know the exact value of the excess loop delay in advance. However, the value of the excess loop delay is a uniformly distribution random variable....
Macrosomia is a medical term describing a new baby born with an excessive birth weight (greater than 4000g). Fetal macrosomia may lead to both pregnancy complications, and increased risk of mother's and baby's health problems after birth. But the potential complications may be mitigated by a cesarean delivery. As such,...
The potential for electric energy generation from ocean waves is substantial and much research is being conducted on the conversion process as a renewable, grid-connected, power source. Some of the same attributes that make wave energy harvesting attractive as a grid-connected source also make it attractive as a remote, or...
Auctions are used to solve resource allocation problem between many agents and many items in real-world settings. Unfortunately, in most cases, it is possible for selfish agents to manipulate the system for their own interest at the expense of the social welfare. Such manipulation can be prevented using the Vickrey-Clarke-Groves...
We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their...
Incremental ADCs (IADCs) have many advantages for low-frequency high-accuracy data conversion—they are easy to multiplex between channels, need simpler digital decimation filter, and allow extended counting with a Nyquist-rate ADC. A single-loop incremental ADC was designed and fabricated in 90 nm for a biosensor interface circuit. It incorporates one integrator,...
There is a significant role in emergency response and personal radiation safety that can be played by a compact radiation detector that is capable of identifying radionuclides. Herein is described the design, construction, and characterization of a small, low-cost, low-power gamma ray spectrometer prototype intended to fill this role, conducted...
Data converters are ubiquitous building blocks of a signal chain. The rapid increase in
communication and connectivity devices presents new avenues for pushing the state of
the art analog to digital converters. Techniques for improving resolution, bandwidth,
linearity and bit-error rate, while reducing the power, energy and area is the...
This dissertation presents two high-speed pipeline successive approximation analog-to-digital converters (SAR ADCs). Capacitive DACs and resistive DACs are utilized in these two pipeline SAR ADCs, respectively.
The pipeline SAR ADC with capacitive DACs can save 50% switching power compared with other time-interleaved SAR ADCs since the total capacitance of the...
Markov Decision Process (MDP) is a well-known framework for devising the optimal decision making strategies under uncertainty. Typically, the decision maker assumes a stationary environment which is characterized by a time-invariant transition probability matrix. However, in many real-world scenarios, this assumption is not justified, thus the optimal strategy might not...
Traditionally, networking protocol designs have placed much emphasis on point-to-point reliability and efficiency. With the recent rise of mobile and multimedia applications, other considerations such as power consumption and/or Quality of Service (QoS) are becoming increasingly important factors in designing network protocols. As such, we present a new flexible framework...
Ocean testing of Wave Energy Converter (WEC) prototypes is necessary to facilitate commercial WEC development. The Ocean Sentinel Instrumentation Buoy, completed in August 2012, provides a stand-alone load for WEC prototypes during ocean testing. The first part of this work was to develop the power conversion and data acquisition equipment...