This work introduces a novel "near" real-time (real-time after an initial short delay) MRI solution that handles motion well and is generalizable. Here, real-time means the algorithm works well on a highly a...
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Advancements in compact, high-performance filtering structures are crucial for high performance communication systems. Applications such as massive MIMO antenna arrays require filters which are at the same time strong...
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The global energy environment is shifting toward sustainability and carbon neutrality. Internet of Things (IoT) technology with Carbon Capture and Storage (CCS) in Enhanced Oil Recovery (EOR) may reduce carbon emissio...
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In this work, we demonstrated upconversion imagers integrated with shortwave infrared photodetectors paired with an electron blocking layer. The use of electron blocking layer screened charge injection to prevent reco...
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We propose a novel intensity diffraction tomography reconstruction algorithm based on the split-step non-paraxial model for recovering the 3D refractive index distribution of multiple-scattering biological samples. ...
Polyvinyl alcohol cryogel (PVA-C) is a widely used tissue-mimicking material in medical imaging phantoms, essential for evaluating imaging techniques, training clinicians, and simulating anatomical tissue structures. ...
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Integrating solar PV inverters and storage devices into the modern power grid generates multiple power profiles with varying magnitudes. The intermittent nature of PV necessitates installing storage devices to reduce ...
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Coherence-based ultrasound imaging has demonstrated potential to improve breast mass diagnosis by distinguishing solid from fluid-filled masses. Harmonic imaging, which is known to reduce acoustic clutter, has the pot...
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ISBN:
(纸本)9781510660311
Coherence-based ultrasound imaging has demonstrated potential to improve breast mass diagnosis by distinguishing solid from fluid-filled masses. Harmonic imaging, which is known to reduce acoustic clutter, has the potential to offer additional improvements. However, the lack of a theoretical basis to describe these improvements precludes clinical recommendations based on physics and engineering principles. This work is the first to develop a theoretical model of coherence-based ultrasound imaging to describe both solid vs. fluid mass distinction and the effects of harmonic short-lag spatial coherence (SLSC) imaging. The scattering function and the transmit ultrasound beam of the van Cittert-Zernike theorem applied to ultrasound imaging were redefined to generate the theoretical model for solid vs. fluid mass distinction and for harmonic imaging, respectively. The derived theory was used to compare fundamental and harmonic SLSC images for hypoechoic solid, hypoechoic fluid, hyperechoic, and point targets. Theoretical simulations showed improved resolution, mitigated dark-region artifacts around hyperechoic targets, and increased spatial coherence of fluid masses in harmonic SLSC images when compared to fundamental SLSC images. Experimental data from tissue-mimicking phantoms and in vivo breast ultrasound images agreed with theoretical results. In particular, when compared to fundamental SLSC imaging, harmonic SLSC imaging improved resolution by 0.19 ± 0.25 mm, mitigated dark region artifacts by 0.55 ± 0.54 mm, and increased the spatial coherence of fluid-filled masses, resulting in a 6.50 ± 4.28 dB decrease in contrast. Results will enable future clinical recommendations supporting the use of fundamental or harmonic SLSC imaging for analyses of fluid or solid masses, respectively. These contributions establish a theoretical foundation to combine fundamental and harmonic coherence-based imaging with harmonic B-mode imaging to improve the accuracy of breast mass diagno
This paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class na...
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ISBN:
(数字)9798331516147
ISBN:
(纸本)9798331516154
This paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class names for each endmember and their corresponding columns in the signature matrix are identified, followed by the visualization of their spectral profiles. Abundance maps for the endmembers are then generated using the fully constrained least squares (FCLS) method. Afterward, the maximum abundance classifier is applied, and the resulting classified image is displayed with color-coded pixels. The abundance maps illustrate the spatial distribution of endmembers across the hyperspectral image, where the abundance values of each pixel represent the proportion of each endmember present. By determining the highest abundance value for each pixel and assigning it to the corresponding endmember class, the pixels within the hyperspectral images are classified. Experimental results demonstrate that the proposed MAC method effectively handles mixed pixels. In addition, it can effectively deal with the mixed pixel problem in hyperspectral images because it identifies components by calculating the abundance values for each pixel rather than relying solely on single spectral features.
Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usag...
ISBN:
(纸本)9798331314385
Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.
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