2,073 research outputs found
The Cell Regulation Mechanism of Neurovascular Unit
Ischemic cerebrovascular disease is one of the three deadly diseases. It is characterised by high mortality and high morbidity. Because of no effective treatments of recombinant tissue plasminogen activator (rt-PA) and neuroprotectant, there are more and more research focus on neurovascular unit (NVU), which is composed of brain microvascular endothelial cells (BMECs), neuron, astrocyte(AS) and so on. Cell-cell signaling and coupling between these different compartments form the basis for normal function and repair of brain injury. In this mini-review, we will describe the relationship of CMECs, neuron and AS
mmHawkeye: Passive UAV Detection with a COTS mmWave Radar
Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to
security-sensitive areas and personal privacy. A UAV can shoot photos at
height, but how to detect such an uninvited intruder is an open problem. This
paper presents mmHawkeye, a passive approach for UAV detection with a COTS
millimeter wave (mmWave) radar. mmHawkeye doesn't require prior knowledge of
the type, motions, and flight trajectory of the UAV, while exploiting the
signal feature induced by the UAV's periodic micro-motion (PMM) for long-range
accurate detection. The design is therefore effective in dealing with low-SNR
and uncertain reflected signals from the UAV. mmHawkeye can further track the
UAV's position with dynamic programming and particle filtering, and identify it
with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on
a commercial mmWave radar and evaluate its performance under varied settings.
The experimental results show that mmHawkeye has a detection accuracy of 95.8%
and can realize detection at a range up to 80m.Comment: 9 pages, 14 figures, IEEE SECON202
Text Mining-Based Patent Analysis for Automated Rule Checking in AEC
Automated rule checking (ARC), which is expected to promote the efficiency of
the compliance checking process in the architecture, engineering, and
construction (AEC) industry, is gaining increasing attention. Throwing light on
the ARC application hotspots and forecasting its trends are useful to the
related research and drive innovations. Therefore, this study takes the patents
from the database of the Derwent Innovations Index database (DII) and China
national knowledge infrastructure (CNKI) as data sources and then carried out a
three-step analysis including (1) quantitative characteristics (i.e., annual
distribution analysis) of patents, (2) identification of ARC topics using a
latent Dirichlet allocation (LDA) and, (3) SNA-based co-occurrence analysis of
ARC topics. The results show that the research hotspots and trends of Chinese
and English patents are different. The contributions of this study have three
aspects: (1) an approach to a comprehensive analysis of patents by integrating
multiple text mining methods (i.e., SNA and LDA) is introduced ; (2) the
application hotspots and development trends of ARC are reviewed based on patent
analysis; and (3) a signpost for technological development and innovation of
ARC is provided
Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
Blind watermarking provides powerful evidence for copyright protection, image
authentication, and tampering identification. However, it remains a challenge
to design a watermarking model with high imperceptibility and robustness
against strong noise attacks. To resolve this issue, we present a framework
Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is
composed of the invertible part to achieve high imperceptibility and the
non-invertible part to strengthen the robustness against strong noise attacks.
For the invertible part, we develop a diffusion and extraction module (DEM) and
a fusion and split module (FSM) to embed and extract watermarks symmetrically
in an invertible way. For the non-invertible part, we introduce a
non-invertible attention-based module (NIAM) and the noise-specific selection
module (NSM) to solve the asymmetric extraction under a strong noise attack.
Extensive experiments demonstrate that our framework outperforms the current
state-of-the-art methods of imperceptibility and robustness significantly. Our
framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under
noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks.
The code will be available in https://github.com/rmpku/CIN.Comment: 9 pages, 9 figures, 5 table
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