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Found 13 papers, 2 papers with code
MCNet: A crowd denstity estimation network based on integrating multiscale attention module
Finally, this paper integrates IMA module and the lightweight crowd texture feature extraction network to construct the MCNet, and validate the feasibility of this network on image classification dataset: Cifar10 and four crowd density datasets: PETS2009, Mall, QUT and SH_METRO to validate the MCNet whether can be a suitable solution for crowd density estimation in metro video surveillance where there are image processing challenges such as high density, high occlusion, perspective distortion and limited hardware resources.
A Cyclic Small Phase Theorem
This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems.
Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes
Furthermore, we ensure complementary information is learned by deep and radiomic features by designing a novel feature de-correlation loss.
Converse negative imaginary theorems
We also establish a non-existence result that no stable system can robustly stabilise all marginally stable NI uncertainty, thereby showing that the uncertainty class of NI systems is too large as far as robust feedback stability is concerned, thus justifying the consideration of subclasses of NI systems with constrained static or instantaneous gains.
RISC-V Toolchain and Agile Development based Open-source Neuromorphic Processor
Recent works are focusing on accelerating SNN computing.
On the exponential convergence of input-output signals of nonlinear feedback systems
This note studies the exponential convergence of input-output signals of discrete-time nonlinear systems composed of a feedback interconnection of a linear time-invariant system and a nonlinear uncertainty.
When Small Gain Meets Small Phase
In this paper, we investigate the feedback stability of multiple-input multiple-output linear time-invariant systems with combined gain and phase information.
A Frequency-Domain Approach to Nonlinear Negative Imaginary Systems Analysis
In this study, we extend the theory of negative imaginary (NI) systems to a nonlinear framework using a frequency-domain approach.
EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer
To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet.
The Singular Angle of Nonlinear Systems
It is, thus, different from the recently appeared nonlinear system phase which adopts the complexification of real-valued signals using the Hilbert transform.
Phase of Nonlinear Systems
A nonlinear small phase theorem is then established for feedback stability analysis of semi-sectorial systems.
Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack
"Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain.
End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks
When taking photos in dim-light environments, due to the small amount of light entering, the shot images are usually extremely dark, with a great deal of noise, and the color cannot reflect real-world color.
