59 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Development of a chipless RFID based aerospace structural health monitoring sensor system
Chipless Radio Frequency Identification (RFID) is modern wireless technology that has been earmarked as being suitable for low-cost item tagging/tracking. These devices do not require integrated circuitry or a battery and thus, are not only are cheap, but also easy to manufacture and potentially very robust. A great deal of attention is also being put on the possibility of giving these tags the ability to sense various environmental stimuli such as temperature and humidity.
This work focusses on the potential use of chipless RFID as a sensor technology for aerospace Structural Health Monitoring. The project is focussed on the sensing of mechanical strain and temperature, with an emphasis placed on fabrication simplicity,
so that the final sensor designs could be potentially fabricated in-situ using existing printing technologies.
Within this project, a variety of novel chipless RFID strain and temperature sensors have been designed, fabricated and tested. A thorough discussion is also presented on the topic of strain sensor cross sensitivity, which places emphasis on issues like, transverse strain, dielectric constant variations and thermal swelling. Additionally, an exploration into other key technological challenges was also performed, with a focus on challenges such as: accurate and reliable stimulus detection, sensor polarization and multi-sensor support.
Several key areas of future research have also been identified and outlined, with aims related to: Enhancing strain sensor fabrication simplicity, enhancing temperature sensor sensitivity and simplicity and developing a fully functional interrogation system
1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface
A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems
Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300
GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including
security sensing, industrial packaging, medical imaging, and non-destructive
testing. Traditional methods for perception and imaging are challenged by novel
data-driven algorithms that offer improved resolution, localization, and
detection rates. Over the past decade, deep learning technology has garnered
substantial popularity, particularly in perception and computer vision
applications. Whereas conventional signal processing techniques are more easily
generalized to various applications, hybrid approaches where signal processing
and learning-based algorithms are interleaved pose a promising compromise
between performance and generalizability. Furthermore, such hybrid algorithms
improve model training by leveraging the known characteristics of radio
frequency (RF) waveforms, thus yielding more efficiently trained deep learning
algorithms and offering higher performance than conventional methods. This
dissertation introduces novel hybrid-learning algorithms for improved mmWave
imaging systems applicable to a host of problems in perception and sensing.
Various problem spaces are explored, including static and dynamic gesture
classification; precise hand localization for human computer interaction;
high-resolution near-field mmWave imaging using forward synthetic aperture
radar (SAR); SAR under irregular scanning geometries; mmWave image
super-resolution using deep neural network (DNN) and Vision Transformer (ViT)
architectures; and data-level multiband radar fusion using a novel
hybrid-learning architecture. Furthermore, we introduce several novel
approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen
Integrated Frequency Combs for Applications in Optical Communications & Microwave Photonics
This dissertation reviews the advancements made in chip-scale optical frequency combs and their applications towards optical communications and optical to RF links. We review different chip-scale comb sources and in particular, chip-scale Kerr microresonator frequency combs. Then, we establish the theoretical background in nonlinear optics which allows the formation and stabilization of Kerr solitons in nonlinear cavities. We also discuss the concept of optical injection locking and in particular, multi-tone injection locking which precedes the idea of regenerative harmonic injection locking. We then go on to show the experimental work involved in soliton generation and characterization. We show efforts towards developing an on-chip massive electronic-photonic optical communications link using Kerr soliton frequency combs as equidistant optical carriers in a DWDM based system using a PAM-4 data modulation format. Potential methods for pushing the limits of communication speeds are also highlighted involving the implementation of other degrees of multiplexing such as space division multiplexing and polarization multiplexing. The second application we explore is based on the synchronization of two pulsed sources via regenerative harmonic injection locking, one with a repetition rate in the microwave regime (10s of GHz) and the other in the mm wave domain (100s of GHz). The two sources we use here are an InP based mode locked laser PIC and the Kerr microresonator. Future goals are discussed which involve techniques for the improvement in long-term stability and chip-scale integrability. This proposal envisions future work to achieve high-capacity optical communication links and optical to RF links utilizing chip-scale Kerr microresonator frequency combs
Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development
Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions.
The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security.
In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB.
The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications
Design, Technologies and Applications of High Power Vacuum Electronic Devices from Microwave to THz Band
The last decade has contributed to the rapid progress in developing high-power microwave sources. This Special Issue aims to bring together information about the most striking theoretical and experimental results, new trends in development, remarkable modern applications, new demands in parameter enhancement, and future goals. Although only a tiny part of the achievements of recent years is included in this Issue, we hope that the presented articles will be useful for experts and students focusing on modern vacuum electronics
Antenna Design for 5G and Beyond
This book is a reprint of the Special Issue Antenna Design for 5G and Beyond that was published in Sensors
Sensors and Systems for Indoor Positioning
This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications
Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication
A new theory of mammalian hearing is presented, which accounts for the
auditory image in the midbrain (inferior colliculus) of objects in the
acoustical environment of the listener. It is shown that the ear is a temporal
imaging system that comprises three transformations of the envelope functions:
cochlear group-delay dispersion, cochlear time lensing, and neural group-delay
dispersion. These elements are analogous to the optical transformations in
vision of diffraction between the object and the eye, spatial lensing by the
lens, and second diffraction between the lens and the retina. Unlike the eye,
it is established that the human auditory system is naturally defocused, so
that coherent stimuli do not react to the defocus, whereas completely
incoherent stimuli are impacted by it and may be blurred by design. It is
argued that the auditory system can use this differential focusing to enhance
or degrade the images of real-world acoustical objects that are partially
coherent. The theory is founded on coherence and temporal imaging theories that
were adopted from optics. In addition to the imaging transformations, the
corresponding inverse-domain modulation transfer functions are derived and
interpreted with consideration to the nonuniform neural sampling operation of
the auditory nerve. These ideas are used to rigorously initiate the concepts of
sharpness and blur in auditory imaging, auditory aberrations, and auditory
depth of field. In parallel, ideas from communication theory are used to show
that the organ of Corti functions as a multichannel phase-locked loop (PLL)
that constitutes the point of entry for auditory phase locking and hence
conserves the signal coherence. It provides an anchor for a dual coherent and
noncoherent auditory detection in the auditory brain that culminates in
auditory accommodation. Implications on hearing impairments are discussed as
well.Comment: 603 pages, 131 figures, 13 tables, 1570 reference
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