2,037 research outputs found
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
Stereoscopic Depth Perception Through Foliage
Both humans and computational methods struggle to discriminate the depths of
objects hidden beneath foliage. However, such discrimination becomes feasible
when we combine computational optical synthetic aperture sensing with the human
ability to fuse stereoscopic images. For object identification tasks, as
required in search and rescue, wildlife observation, surveillance, and early
wildfire detection, depth assists in differentiating true from false findings,
such as people, animals, or vehicles vs. sun-heated patches at the ground level
or in the tree crowns, or ground fires vs. tree trunks. We used video captured
by a drone above dense woodland to test users' ability to discriminate depth.
We found that this is impossible when viewing monoscopic video and relying on
motion parallax. The same was true with stereoscopic video because of the
occlusions caused by foliage. However, when synthetic aperture sensing was used
to reduce occlusions and disparity-scaled stereoscopic video was presented,
whereas computational (stereoscopic matching) methods were unsuccessful, human
observers successfully discriminated depth. This shows the potential of systems
which exploit the synergy between computational methods and human vision to
perform tasks that neither can perform alone
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool
Accelerated by the increasing attention drawn by 5G, 6G, and Internet of
Things applications, communication and sensing technologies have rapidly
evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years.
Enabled by significant advancements in electromagnetic (EM) hardware, mmWave
and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz,
respectively, can be employed for a host of applications. The main feature of
THz systems is high-bandwidth transmission, enabling ultra-high-resolution
imaging and high-throughput communications; however, challenges in both the
hardware and algorithmic arenas remain for the ubiquitous adoption of THz
technology. Spectra comprising mmWave and THz frequencies are well-suited for
synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide
spectrum of tasks like material characterization and nondestructive testing
(NDT). This article provides a tutorial review of systems and algorithms for
THz SAR in the near-field with an emphasis on emerging algorithms that combine
signal processing and machine learning techniques. As part of this study, an
overview of classical and data-driven THz SAR algorithms is provided, focusing
on object detection for security applications and SAR image super-resolution.
We also discuss relevant issues, challenges, and future research directions for
emerging algorithms and THz SAR, including standardization of system and
algorithm benchmarking, adoption of state-of-the-art deep learning techniques,
signal processing-optimized machine learning, and hybrid data-driven signal
processing algorithms...Comment: Submitted to Proceedings of IEE
Millimetre wave imaging for concealed target detection
PhDConcealed weapon detection (CWD) has been a hot topic as the concern about pub-
lic safety increases. A variety of approaches for the detection of concealed objects
on the human body based on earth magnetic ¯eld distortion, inductive magnetic
¯eld, acoustic and ultrasonic, electromagnetic resonance, MMW (millimetre wave),
THz, Infrared, x-ray technologies have been suggested and developed. Among all
of them, MMW holographic imaging is considered as a promising approach due
to the relatively high penetration and high resolution that it can o®er. Typical
concealed target detection methods are classi¯ed into 2 categories, the ¯rst one is a
resonance based target identi¯cation technique, and the second one is an imaging
based system. For the former, the complex natural resonance (CNR) frequencies
associated with a certain target are extracted and used for identi¯cation, but this
technique has an issue of high false alarm rate. The microwave/millimetre wave
imaging systems can be categorized into two types: passive systems and active sys-
tems. For the active microwave/millimetre wave imaging systems, the microwave
holographic imaging approach was adopted in this thesis. Such a system can oper-
ate at either a single frequency or multiple frequencies (wide band). An active,
coherent, single frequency operation millimetre wave imaging system based on the
theory of microwave holography was developed. Based on literature surveys and
¯rst hand experimental results, this thesis aims to provide system level parame-
ter determination to aid the development of a target detection imager. The goal
is approached step by step in 7 chapters, with topics and issues addressed rang-
ing from reviewing the past work, ¯nding out the best candidate technology, i.e.
the MMW holographic imaging combined with the resonance based target recog-
i
nition technique, the construction of the 94 GHz MMW holographic prototype
imager, experimental trade-o® investigation of system parameters, imager per-
formance evaluation, low pro¯le components and image enhancement techniques,
feasibility investigation of resonance based technique, to system implementation
based on the parameters and results achieved. The task set forth in the beginning
is completed by coming up with an entire system design in the end.
Towards joint communication and sensing (Chapter 4)
Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed
OCM 2023 - Optical Characterization of Materials : Conference Proceedings
The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving.
The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field
Learning to Detect Open Carry and Concealed Object with 77GHz Radar
Detecting harmful carried objects plays a key role in intelligent
surveillance systems and has widespread applications, for example, in airport
security. In this paper, we focus on the relatively unexplored area of using
low-cost 77GHz mmWave radar for the carried objects detection problem. The
proposed system is capable of real-time detecting three classes of objects -
laptop, phone, and knife - under open carry and concealed cases where objects
are hidden with clothes or bags. This capability is achieved by the initial
signal processing for localization and generating range-azimuth-elevation image
cubes, followed by a deep learning-based prediction network and a multi-shot
post-processing module for detecting objects. Extensive experiments for
validating the system performance on detecting open carry and concealed objects
have been presented with a self-built radar-camera testbed and collected
dataset. Additionally, the influence of different input formats, factors, and
parameters on system performance is analyzed, providing an intuitive
understanding of the system. This system would be the very first baseline for
other future works aiming to detect carried objects using 77GHz radar.Comment: 12 page
Photometric Depth Super-Resolution
This study explores the use of photometric techniques (shape-from-shading and
uncalibrated photometric stereo) for upsampling the low-resolution depth map
from an RGB-D sensor to the higher resolution of the companion RGB image. A
single-shot variational approach is first put forward, which is effective as
long as the target's reflectance is piecewise-constant. It is then shown that
this dependency upon a specific reflectance model can be relaxed by focusing on
a specific class of objects (e.g., faces), and delegate reflectance estimation
to a deep neural network. A multi-shot strategy based on randomly varying
lighting conditions is eventually discussed. It requires no training or prior
on the reflectance, yet this comes at the price of a dedicated acquisition
setup. Both quantitative and qualitative evaluations illustrate the
effectiveness of the proposed methods on synthetic and real-world scenarios.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(T-PAMI), 2019. First three authors contribute equall
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