Signal Processing Commons^™

Power System Transients: Impacts Of Non-Ideal Sensors On Measurement-Based Applications, Aaron Wilson

Doctoral Dissertations

The power system is comprised of thousands of lines, generation sources, transformers, and other equipment responsible for servicing millions of customers. Such a complex apparatus requires constant monitoring and protection schemes capable of keeping the system operational, reliable, and resilient. To achieve these goals, measurement is a critical role in the continued functionality of the power system. However, measurement devices are never completely reliable, and are susceptible to inherent irregularities; imparting potentially misleading distortions on measurements containing high-frequency components. This dissertation analyzes some of these effects, as well as the way they may impact certain applications in the grid that …

Enabling Daily Tracking Of Individual’S Cognitive State With Eyewear, Soha Rostaminia

Doctoral Dissertations

Research studies show that sleep deprivation causes severe fatigue, impairs attention and decision making, and affects our emotional interpretation of events, which makes it a big threat to public safety, and mental and physical well-being. Hence, it would be most desired if we could continuously measure one’s drowsiness and fatigue level, their emotion while making decisions, and assess their sleep quality in order to provide personalized feedback or actionable behavioral suggestions to modulate sleep pattern and alertness levels with the aim of enhancing performance, well-being, and quality of life. While there have been decades of studies on wearable devices, we …

Hyperspectral Unmixing: A Theoretical Aspect And Applications To Crism Data Processing, Yuki Itoh

Doctoral Dissertations

Hyperspectral imaging has been deployed in earth and planetary remote sensing, and has contributed the development of new methods for monitoring the earth environment and new discoveries in planetary science. It has given scientists and engineers a new way to observe the surface of earth and planetary bodies by measuring the spectroscopic spectrum at a pixel scale. Hyperspectal images require complex processing before practical use. One of the important goals of hyperspectral imaging is to obtain the images of reflectance spectrum. A raw image obtained by hyperspectral remote sensing usually undergoes conversion to a physical quantity representing the intensity of …

Remote Human Vital Sign Monitoring Using Multiple-Input Multiple-Output Radar At Millimeter-Wave Frequencies, Toan Khanh Vo Dai

Doctoral Dissertations

Non-contact respiration rate (RR) and heart rate (HR) monitoring using millimeter-wave (mmWave) radars has gained lots of attention for medical, civilian, and military applications. These mmWave radars are small, light, and portable which can be deployed to various places. To increase the accuracy of RR and HR detection, distributed multi-input multi-output (MIMO) radar can be used to acquire non-redundant information of vital sign signals from different perspectives because each MIMO channel has different fields of view with respect to the subject under test (SUT). This dissertation investigates the use of a Frequency Modulated Continuous Wave (FMCW) radar operating at 77-81 …

Efficient Deep Learning And Its Applications, Zi Wang

Doctoral Dissertations

Deep neural networks (DNNs) have achieved huge successes in various tasks such as object classification and detection, image synthesis, game-playing, and biological developmental system simulation. State-or-the-art performance on these tasks is usually achieved by designing deeper and wider DNNs with the cost of huge storage size and high computational complexity. However, the over-parameterization problem of DNNs constrains their deployment in resource-limited devices, such as drones and mobile phones.

With these concerns, many network compression approaches are developed, such as quantization, neural architecture search, network pruning, and knowledge distillation. These approaches reduce the sizes and computational costs of DNNs while maintaining …

Non-Contact Techniques For Human Vital Sign Detection And Gait Analysis, Farnaz Foroughian

Doctoral Dissertations

Human vital signs including respiratory rate, heart rate, oxygen saturation, blood pressure, and body temperature are important physiological parameters that are used to track and monitor human health condition. Another important biological parameter of human health is human gait. Human vital sign detection and gait investigations have been attracted many scientists and practitioners in various fields such as sport medicine, geriatric medicine, bio-mechanic and bio-medical engineering and has many biological and medical applications such as diagnosis of health issues and abnormalities, elderly care and health monitoring, athlete performance analysis, and treatment of joint problems. Thoroughly tracking and understanding the normal …

Towards Secure Deep Neural Networks For Cyber-Physical Systems, Jiangnan Li

Doctoral Dissertations

In recent years, deep neural networks (DNNs) are increasingly investigated in the literature to be employed in cyber-physical systems (CPSs). DNNs own inherent advantages in complex pattern identifying and achieve state-of-the-art performances in many important CPS applications. However, DNN-based systems usually require large datasets for model training, which introduces new data management issues. Meanwhile, research in the computer vision domain demonstrated that the DNNs are highly vulnerable to adversarial examples. Therefore, the security risks of employing DNNs in CPSs applications are of concern.

In this dissertation, we study the security of employing DNNs in CPSs from both the data domain …

Machine Learning With Topological Data Analysis, Ephraim Robert Love

Doctoral Dissertations

Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …

Anti-Jam Gps Controlled Reception Pattern Antennas For Man-Portable Applications, Jeffrey A. Maloney

Doctoral Dissertations

Military GPS receivers provide crucial information to soldiers in the field, however, the performance of these devices is degraded by in band RF interference, making GPS susceptible to jamming. Anti-jam techniques for aircraft and vehicular platforms have been developed, but at present there is no system for dismounted soldiers. There is a need for an anti-jam system which meets the demands of a dismounted soldier and conforms to the size, weight, and power requirements of a portable device. A controlled reception pattern antenna, or CRPA, is a potential solution for jammer mitigation. These devices work by steering reception pattern nulls …

Sparsity In Machine Learning: An Information Selecting Perspective, Siwei Feng

Doctoral Dissertations

Today we are living in a world awash with data. Large volumes of data are acquired, analyzed and applied to tasks through machine learning algorithms in nearly every area of science, business, and industry. For example, medical scientists analyze the gene expression data from a single specimen to learn the underlying causes of disease (e.g. cancer) and choose the best treatment; retailers can know more about customers' shopping habits from retail data to adjust their business strategies to better appeal to customers; suppliers can enhance supply chain success through supply chain systems built on knowledge sharing. However, it is also …

A Discrete-Time Technique For Linearity Enhancement Of Wideband Receivers, Mohammad Ghadiri Sadrabadi

Doctoral Dissertations

A new signal processing technique is introduced to enhance the linearity performance of wideband radio frequency (RF) receivers. The proposed technique combines the advancements in mixer first architectures with a library of binary sequences as local oscillator signals to enable wide instantaneous bandwidth and high linearity for the RF receiver. To do so, N-bit pseudo-random-binary-sequences (PRBS) are used as local oscillator signals. The RF input signal is multiplied with the PRBS at the mixer and then averaged over the full sequence. This in effect reduces the amplitude of the signal and improves the overall linearity of the system. In order …

Sequence Design Via Semidefinite Programming Relaxation And Randomized Projection, Dian Mo

Doctoral Dissertations

Wideband is a booming technology in the field of wireless communications. The receivers in wideband communication systems are expected to cover a very wide spectrum and adaptively extract the parts of interest. The literature has focused on mixing the input spectrum to baseband using a pseudorandom sequence modulation and recovering the received signals from linearly independent measurements by parallel branches to mitigate the pressures from required extreme high sampling frequency. However, a pseudorandom sequence provides no rejection for the strong interferers received together with weak signals from distant sources. The interferers cause significant distortion due to the nonlinearity of the …

Analog Signal Processing Solutions And Design Of Memristor-Cmos Analog Co-Processor For Acceleration Of High-Performance Computing Applications, Nihar Athreyas

Doctoral Dissertations

Emerging applications in the field of machine vision, deep learning and scientific simulation require high computational speed and are run on platforms that are size, weight and power constrained. With the transistor scaling coming to an end, existing digital hardware architectures will not be able to meet these ever-increasing demands. Analog computation with its rich set of primitives and inherent parallel architecture can be faster, more efficient and compact for some of these applications. The major contribution of this work is to show that analog processing can be a viable solution to this problem. This is demonstrated in the three …

Learning Multimodal Structures In Computer Vision, Ali Taalimi

Doctoral Dissertations

A phenomenon or event can be received from various kinds of detectors or under different conditions. Each such acquisition framework is a modality of the phenomenon. Due to the relation between the modalities of multimodal phenomena, a single modality cannot fully describe the event of interest. Since several modalities report on the same event introduces new challenges comparing to the case of exploiting each modality separately.

We are interested in designing new algorithmic tools to apply sensor fusion techniques in the particular signal representation of sparse coding which is a favorite methodology in signal processing, machine learning and statistics to …

Computational Imaging Approach To Recovery Of Target Coordinates Using Orbital Sensor Data, Michael D. Vaughan

Doctoral Dissertations

This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data.

Using physical targets and sensors in this scenario would be …

Wide-Area Measurement-Driven Approaches For Power System Modeling And Analytics, Hesen Liu

Doctoral Dissertations

This dissertation presents wide-area measurement-driven approaches for power system modeling and analytics. Accurate power system dynamic models are the very basis of power system analysis, control, and operation. Meanwhile, phasor measurement data provide first-hand knowledge of power system dynamic behaviors. The idea of building out innovative applications with synchrophasor data is promising.

Taking advantage of the real-time wide-area measurements, one of phasor measurements’ novel applications is to develop a synchrophasor-based auto-regressive with exogenous inputs (ARX) model that can be updated online to estimate or predict system dynamic responses.

Furthermore, since auto-regressive models are in a big family, the ARX model …

Covert Wireless Communications In A Dynamic Environment, Tamara V. Sobers

Doctoral Dissertations

This dissertation investigates covert communication in dynamic wireless communication environments. A key goal is to provide insight about the capabilities of a transmitter desiring to remain covert and analogously, the capabilities of the party attempting to detect covert communications. The first chapter provides background on covert communications prior to this work. The second chapter studies the theoretical limits of covert communication and proves that positive rate is achievable when a jammer is added to the classical Alice/Bob/Warden Willie model. The third chapter expands on the second chapter by considering more generally the impact of the dynamics of the environment on …

Image Processing And Understanding Based On Graph Similarity Testing: Algorithm Design And Software Development, Jieqi Kang

Doctoral Dissertations

Image processing and understanding is a key task in the human visual system. Among all related topics, content based image retrieval and classification is the most typical and important problem. Successful image retrieval/classification models require an effective fundamental step of image representation and feature extraction. While traditional methods are not capable of capturing all structural information on the image, using graph to represent the image is not only biologically plausible but also has certain advantages. Graphs have been widely used in image related applications. Traditional graph-based image analysis models include pixel-based graph-cut techniques for image segmentation, low-level and high-level image …

Topography Measurements Using An Airborne Ka-Band Fmcw Interferometric Synthetic Aperture Radar, Kan Fu

Doctoral Dissertations

Radar interferometry at millimeter-wave frequencies has the ability of topography measurement of different types of terrain, such as water surfaces and tree canopies. A Ka-band interferometric radar was mounted on an airborne platform, and flown over the Connecticut river region in western Massachusetts near Amherst on June 11, 2012. More than 20 Gigabytes of raw data was recorded. This dissertation outline presents the results of the data processing, which includes (1) the estimation and removal of the embedded high frequency phase error in the raw data; (2) the synthetic aperture processing; (3) the interferometric processing. The digital elevation model (DEM) …

A Quantitative Measure Of Mono-Componentness For Time-Frequency Analysis, Austin P. Albright

Doctoral Dissertations

Joint time-frequency (TF) analysis is an ideal method for analyzing non-stationary signals, but is challenging to use leading to it often being neglected. The exceptions being the short-time Fourier transform (STFT) and spectrogram. Even then, the inability to have simultaneously high time and frequency resolution is a frustrating issue with the STFT and spectrogram. However, there is a family of joint TF analysis techniques that do have simultaneously high time and frequency resolution – the quadratic TF distribution (QTFD) family. Unfortunately, QTFDs are often more troublesome than beneficial. The issue is interference/cross-terms that causes these methods to become so difficult …

Multi-Classifier Fusion Strategy For Activity And Intent Recognition Of Torso Movements, Abhijit Kadrolkar

Doctoral Dissertations

As assistive, wearable robotic devices are being developed to physically assist their users, it has become crucial to develop safe, reliable methods to coordinate the device with the intentions and motions of the wearer. This dissertation investigates the recognition of user intent during flexion and extension of the human torso in the sagittal plane to be used for control of an assistive exoskeleton for the human torso. A multi-sensor intent recognition approach is developed that combines information from surface electromyogram (sEMG) signals from the user’s muscles and inertial sensors mounted on the user’s body. Intent recognition is implemented by following …

An Innovative Approach To Johnson Noise Thermometry By Means Of Spectral Estimation, Nora Dianne Bull

Doctoral Dissertations

Instrumentation in a nuclear power plant is critical in monitoring the stability and safety levels of a reactor. Temperature is a key measurement performed on the core of a reactor to control the power output and sustain a safe thermal margin. If there is a dramatic change in temperature, failure is likely to follow if action is not taken to cool the system. Traditionally, to measure the temperature of a reactor, several resistance temperature detectors are placed in predefined locations on the system. Resistance temperature detectors (RTD) are typically platinum coiled wire wrapped around a ceramic cylinder and encased in …

Face Centered Image Analysis Using Saliency And Deep Learning Based Techniques, Rui Guo

Doctoral Dissertations

Image analysis starts with the purpose of configuring vision machines that can perceive like human to intelligently infer general principles and sense the surrounding situations from imagery. This dissertation studies the face centered image analysis as the core problem in high level computer vision research and addresses the problem by tackling three challenging subjects: Are there anything interesting in the image? If there is, what is/are that/they? If there is a person presenting, who is he/she? What kind of expression he/she is performing? Can we know his/her age? Answering these problems results in the saliency-based object detection, deep learning structured …

Electromagnetic Scattering Models For Insar Correlation Measurements Of Vegetation And Snow, Yang Lei

Doctoral Dissertations

Interferometric Synthetic Aperture Radar (InSAR) has proved successful and efficient in measuring the vertical structure of the distributed targets such as vegetation and snow, which are dominated by volume scattering. In particular, the InSAR correlation measurement has been utilized to retrieve the target vertical structural information. One existing and well-known electromagnetic scattering model of the InSAR correlation was first brought forward focusing on the single-pass InSAR observation of a sparse random medium like vegetation. However, the lack of the adaption of this InSAR scattering model for repeat-pass InSAR observation of vegetation as well as for single-pass InSAR observation of snow …

Magnetic Local Positioning System With Supplemental Magnetometer-Accelerometer Data Fusion, Benjamin Scott Prothro

Doctoral Dissertations

Geo-location and tracking technology, once confined to the industrial and military sectors, have been widely proliferated to the consumer world since early in the twenty-first century. The commoditization of Global Positioning System (GPS) and inertial measurement integrated circuits has made this possible, with devices small enough to fit in a cellular phone. However, GPS technology is not without its drawbacks: Its power use is high, and it can fail in smaller, obstructed spaces. Magnetic positioning, which exploits the magnetic field coupling between a set of transmitter beacon coils and a set of receiver coils, is an often overlooked, complementary technology …

Applications In Low-Power Phased Array Weather Radars, Robert A. Palumbo Jr

Doctoral Dissertations

Low-cost X-band radars are an emerging technology that offer significant advantages over traditional systems for weather remote sensing applications. X-band radars provide enhanced angular resolution at a fraction of the aperture size compared to larger, lower frequency systems. Because of their low cost and small form factor, these radars can now be integrated into more research and commercial applications. This work presents research and development activities using a low-cost, X-band (9410 MHz) Phase-Tilt Radar. The phase-tilt design is a novel phased array architecture that allows for rapid electronic scanning in azimuth and mechanical tilting in elevation, as a compromise between …

Exploiting Cross Domain Relationships For Target Recognition, Wei Wang

Doctoral Dissertations

Cross domain recognition extracts knowledge from one domain to recognize samples from another domain of interest. The key to solving problems under this umbrella is to find out the latent connections between different domains. In this dissertation, three different cross domain recognition problems are studied by exploiting the relationships between different domains explicitly according to the specific real problems.

First, the problem of cross view action recognition is studied. The same action might seem quite different when observed from different viewpoints. Thus, how to use the training samples from a given camera view and perform recognition in another new view …

Compressed Sensing In Resource-Constrained Environments: From Sensing Mechanism Design To Recovery Algorithms, Shuangjiang Li

Doctoral Dissertations

Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study of …

X-Band Dual Polarization Phased-Array Radar For Meteorological Applications, Krzysztof Orzel

Doctoral Dissertations

This dissertation details the development and operation of a novel dual-polarized Phase-Tilt Weather Radar (PTWR) designed for meteorological applications. The use of radar has a well-documented history in detection and classification of weather phenomena, but due to the limited mechanical scanning speed, its usage for severe weather observations remains far from ideal. The PTWR utilizes phased-array technology and provides unique capabilities such as smart scanning, fast scan update, and tracking. This technology is considered a candidate for a replacement and consolidation of the current US weather and surveillance radar networks. The dissertation can be divided into three parts. First, the …

Signal Processing In Wireless Communications: Device Fingerprinting And Wide-Band Interference Rejection, Adam C. Polak

Doctoral Dissertations

The rapid progress of wireless communication technologies that has taken place in recent years has significantly improved the quality of everyday life. However with this expansion of wireless communication systems come significant security threats and significant technological challenges, both of which are due to the fact that the communication medium is shared. The ubiquity of open wireless Internet access networks creates a new avenue for cyber-criminals to impersonate and act in an unauthorized way. The increasing number of deployed wide-band wireless communication systems entails technological challenges for effective utilization of the shared medium, which implies the need for advanced interference …