237 research outputs found
Link Scheduling in UAV-Aided Networks
Unmanned Aerial Vehicles (UAVs) or drones are a type of low altitude aerial mobile vehicles. They can be integrated into existing networks; e.g., cellular, Internet of Things (IoT) and satellite networks. Moreover, they can leverage existing cellular or Wi-Fi infrastructures to communicate with one another. A popular application of UAVs is to deploy them as mobile base stations and/or relays to assist terrestrial wireless communications. Another application is data collection, whereby they act as mobile sinks for wireless sensor networks or sensor devices operating in IoT networks. Advantageously, UAVs are cost-effective and they are able to establish line-of-sight links, which help improve data rate. A key concern, however, is that the uplink communications to a UAV may be limited, where it is only able to receive from one device at a time. Further, ground devices, such as those in IoT networks, may have limited energy, which limit their transmit power. To this end, there are three promising approaches to address these concerns, including (i) trajectory optimization, (ii) link scheduling, and (iii) equipping UAVs with a Successive Interference Cancellation (SIC) radio.
Henceforth, this thesis considers data collection in UAV-aided, TDMA and SICequipped wireless networks. Its main aim is to develop novel link schedulers to schedule uplink communications to a SIC-capable UAV. In particular, it considers two types of networks: (i) one-tier UAV communications networks, where a SIC-enabled rotary-wing UAV collects data from multiple ground devices, and (ii) Space-Air-Ground Integrated Networks (SAGINs), where a SIC-enabled rotary-wing UAV offloads collected data from ground devices to a swarm of CubeSats. A CubeSat then downloads its data to a terrestrial gateway. Compared to one-tier UAV communications networks, SAGINs are able to provide wide coverage and seamless connectivity to ground devices in remote and/or sparsely populated areas
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Background: View planning for the acquisition of cardiac magnetic resonance
(CMR) imaging remains a demanding task in clinical practice. Purpose: Existing
approaches to its automation relied either on an additional volumetric image
not typically acquired in clinic routine, or on laborious manual annotations of
cardiac structural landmarks. This work presents a clinic-compatible,
annotation-free system for automatic CMR view planning. Methods: The system
mines the spatial relationship, more specifically, locates the intersecting
lines, between the target planes and source views, and trains deep networks to
regress heatmaps defined by distances from the intersecting lines. The
intersection lines are the prescription lines prescribed by the technologists
at the time of image acquisition using cardiac landmarks, and retrospectively
identified from the spatial relationship. As the spatial relationship is
self-contained in properly stored data, the need for additional manual
annotation is eliminated. In addition, the interplay of multiple target planes
predicted in a source view is utilized in a stacked hourglass architecture to
gradually improve the regression. Then, a multi-view planning strategy is
proposed to aggregate information from the predicted heatmaps for all the
source views of a target plane, for a globally optimal prescription, mimicking
the similar strategy practiced by skilled human prescribers. Results: The
experiments include 181 CMR exams. Our system yields the mean angular
difference and point-to-plane distance of 5.68 degrees and 3.12 mm,
respectively. It not only achieves superior accuracy to existing approaches
including conventional atlas-based and newer deep-learning-based in prescribing
the four standard CMR planes but also demonstrates prescription of the first
cardiac-anatomy-oriented plane(s) from the body-oriented scout.Comment: Medical Physics. arXiv admin note: text overlap with arXiv:2109.1171
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
The problem of how to assess cross-modality medical image synthesis has been
largely unexplored. The most used measures like PSNR and SSIM focus on
analyzing the structural features but neglect the crucial lesion location and
fundamental k-space speciality of medical images. To overcome this problem, we
propose a new metric K-CROSS to spur progress on this challenging problem.
Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to
predict the lesion location, together with a tumor encoder for representing
features, such as texture details and brightness intensities. To further
reflect the frequency-specific information from the magnetic resonance imaging
principles, both k-space features and vision features are obtained and employed
in our comprehensive encoders with a frequency reconstruction penalty. The
structure-shared encoders are designed and constrained with a similarity loss
to capture the intrinsic common structural information for both modalities. As
a consequence, the features learned from lesion regions, k-space, and
anatomical structures are all captured, which serve as our quality evaluators.
We evaluate the performance by constructing a large-scale cross-modality
neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist
judgments. Extensive experiments demonstrate that the proposed method
outperforms other metrics, especially in comparison with the radiologists on
NIRPS
BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
Recent text-to-image diffusion models have demonstrated an astonishing
capacity to generate high-quality images. However, researchers mainly studied
the way of synthesizing images with only text prompts. While some works have
explored using other modalities as conditions, considerable paired data, e.g.,
box/mask-image pairs, and fine-tuning time are required for nurturing models.
As such paired data is time-consuming and labor-intensive to acquire and
restricted to a closed set, this potentially becomes the bottleneck for
applications in an open world. This paper focuses on the simplest form of
user-provided conditions, e.g., box or scribble. To mitigate the aforementioned
problem, we propose a training-free method to control objects and contexts in
the synthesized images adhering to the given spatial conditions. Specifically,
three spatial constraints, i.e., Inner-Box, Outer-Box, and Corner Constraints,
are designed and seamlessly integrated into the denoising step of diffusion
models, requiring no additional training and massive annotated layout data.
Extensive results show that the proposed constraints can control what and where
to present in the images while retaining the ability of the Stable Diffusion
model to synthesize with high fidelity and diverse concept coverage. The code
is publicly available at https://github.com/Sierkinhane/BoxDiff.Comment: Accepted by ICCV 2023. The paper is still being revised for better
organization and comparison. Code is available at:
https://github.com/Sierkinhane/BoxDif
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