1,033 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
An Examination of Environmental Applications for Uncooled Thermal Infrared Remote Sensing Instruments
Advancements in system design for thermal instruments require assessment of potential environmental applications and appropriate data processing techniques. A novel multi-band thermal imaging system was proposed by DRS Leonardo for the National Aeronautics and Space Administration Earth Science Technology Office Instrument Incubator Program, for which these criteria were assessed. The Multi-Band Uncooled Radiometer Imager (MURI) is a six spectral channel instrument designed to collect images in the thermal infrared, specifically in the range of 7.5 to 12.5 μm. The work detailed in this thesis characterizes the ability of a thermal imager with an uncooled microbolometer focal plane array to provide valuable data for environmental science applications. Here, a pair of studies using simulated data demonstrates the ability of a multispectral instrument such as MURI to detect enhanced levels of atmospheric methane using a novel approach that performs similarly to a state of the art algorithm when applied to MURI data. The novel method is evaluated using a controlled concentration simulated dataset to determine the extent of its detection capabilities and its dependence on atmospheric conditions. The methane investigations reveal the system is capable of detecting a 20 m thick CH4 plume of 10-20 ppm above background levels when column water vapor is low using both the NDMI and matched filter approaches. Additionally, land surface temperature and emissivity retrieval techniques were applied to experimental MURI data recorded during initial test flights to assess their accuracy with MURI data. Utilizing split window and Temperature Emissivity Separation make this examination distinct as this establishes that proven methods can be applied to uncooled multiband imager data. Application of these methods to MURI data demonstrated the system is capable of temperature retrievals with Root Mean Square Errors of less than 1 K to measured reference values and surface emissivity retrievals within 2% of reference database values. The definition and application of the Normalized Differential Methane Index in this thesis demonstrates a novel approach for detection of enhanced plumes of methane utilizing a multispectral system with only a single band allocated to methane absorption features
Investigation of natural environment by space means. Geobotany, Geomorphology, soil sciences, agricultural lands, landscape study
Reports given by Soviet specialists at a meeting of Socialist countries on remote sensing of the earth using aerospace methods are presented
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
Multisource and Multitemporal Data Fusion in Remote Sensing
The sharp and recent increase in the availability of data captured by
different sensors combined with their considerably heterogeneous natures poses
a serious challenge for the effective and efficient processing of remotely
sensed data. Such an increase in remote sensing and ancillary datasets,
however, opens up the possibility of utilizing multimodal datasets in a joint
manner to further improve the performance of the processing approaches with
respect to the application at hand. Multisource data fusion has, therefore,
received enormous attention from researchers worldwide for a wide variety of
applications. Moreover, thanks to the revisit capability of several spaceborne
sensors, the integration of the temporal information with the spatial and/or
spectral/backscattering information of the remotely sensed data is possible and
helps to move from a representation of 2D/3D data to 4D data structures, where
the time variable adds new information as well as challenges for the
information extraction algorithms. There are a huge number of research works
dedicated to multisource and multitemporal data fusion, but the methods for the
fusion of different modalities have expanded in different paths according to
each research community. This paper brings together the advances of multisource
and multitemporal data fusion approaches with respect to different research
communities and provides a thorough and discipline-specific starting point for
researchers at different levels (i.e., students, researchers, and senior
researchers) willing to conduct novel investigations on this challenging topic
by supplying sufficient detail and references
Application of Multi-Sensor Fusion Technology in Target Detection and Recognition
Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems
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