83 research outputs found
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Indoor And Outdoor Real Time Information Collection in Disaster Scenario
A disaster usually severely harms human health and property. After a disaster, great amount of information of a disaster area is needed urgently. The information not only indicates the severity of the disaster, but also is crucial for an efficient search and rescue process. In order to quickly and accurately collect real time information in a disaster scenario, a mobile platform is developed for an outdoor scenario and a localization and navigation system for responders is introduced for an indoor scenario.
The mobile platform has been integrated to the DIORAMA system. It is built with a 6-wheel robot chassis along with an Arduino microcontroller. Controlled by a mounted Android smartphone, the mobile platform can receive commands from incident commanders and quickly respond to the commands. While patrolling in a disaster area, a constant RFID signal is collected to improve the localization accuracy of victims. Pictures and videos are also captured in order to enhance the situational awareness of rescuers.
The design of the indoor information collection is focused on the responder side. During a disaster scenario, it is hard to track responders’ locations in an indoor environment. In this thesis, an indoor localization and navigation system based on Bluetooth low energy and Android is developed for helping responders report current location and quickly find the right path in the environment. Different localization algorithms are investigated and implemented. A navigation system based on A* is also proposed
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning
Cross-domain Sequential Recommendation (CSR) which leverages user sequence
data from multiple domains has received extensive attention in recent years.
However, the existing CSR methods require sharing origin user data across
domains, which violates the General Data Protection Regulation (GDPR). Thus, it
is necessary to combine federated learning (FL) and CSR to fully utilize
knowledge from different domains while preserving data privacy. Nonetheless,
the sequence feature heterogeneity across different domains significantly
impacts the overall performance of FL. In this paper, we propose FedDCSR, a
novel federated cross-domain sequential recommendation framework via
disentangled representation learning. Specifically, to address the sequence
feature heterogeneity across domains, we introduce an approach called
inter-intra domain sequence representation disentanglement (SRD) to disentangle
the user sequence features into domain-shared and domain-exclusive features. In
addition, we design an intra domain contrastive infomax (CIM) strategy to learn
richer domain-exclusive features of users by performing data augmentation on
user sequences. Extensive experiments on three real-world scenarios demonstrate
that FedDCSR achieves significant improvements over existing baselines
MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences
The measurement of retinal blood flow (RBF) in capillaries can provide a
powerful biomarker for the early diagnosis and treatment of ocular diseases.
However, no single modality can determine capillary flowrates with high
precision. Combining erythrocyte-mediated angiography (EMA) with optical
coherence tomography angiography (OCTA) has the potential to achieve this goal,
as EMA can measure the absolute 2D RBF of retinal microvasculature and OCTA can
provide the 3D structural images of capillaries. However, multimodal retinal
image registration between these two modalities remains largely unexplored. To
fill this gap, we establish MEMO, the first public multimodal EMA and OCTA
retinal image dataset. A unique challenge in multimodal retinal image
registration between these modalities is the relatively large difference in
vessel density (VD). To address this challenge, we propose a segmentation-based
deep-learning framework (VDD-Reg) and a new evaluation metric (MSD), which
provide robust results despite differences in vessel density. VDD-Reg consists
of a vessel segmentation module and a registration module. To train the vessel
segmentation module, we further designed a two-stage semi-supervised learning
framework (LVD-Seg) combining supervised and unsupervised losses. We
demonstrate that VDD-Reg outperforms baseline methods quantitatively and
qualitatively for cases of both small VD differences (using the CF-FA dataset)
and large VD differences (using our MEMO dataset). Moreover, VDD-Reg requires
as few as three annotated vessel segmentation masks to maintain its accuracy,
demonstrating its feasibility.Comment: Submitted to IEEE JBH
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