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Found 13 papers, 2 papers with code
Scale-Semantic Joint Decoupling Network for Image-text Retrieval in Remote Sensing
To address these issues, we propose a novel Scale-Semantic Joint Decoupling Network (SSJDN) for remote sensing image-text retrieval.
Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach
With the proposed predictive approach, we can avoid full-scale CSI estimation and facilitate low-dimensional CE for transmit beamforming design such that the signaling overhead is reduced by a scale of $\frac{1}{N}$, where $N$ is the number of IRS elements.
Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks
Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e. g., locations and speeds, based on the reflected echoes.
Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems
OTFS channel estimation is first formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation.
Information Theory Information Theory
Cross Domain Iterative Detection for Orthogonal Time Frequency Space Modulation
Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation.
Information Theory Information Theory
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications
Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI).
Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications.
PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field
Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications.
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols.
Drug–target affinity prediction using graph neural network and contact maps
Computer-aided drug design uses high-performance computers to simulate the tasks in drug design, which is a promising research area.
Ranked #3 on
Drug Discovery
on LIT-PCBA(ALDH1)
Sum-Rate Maximization for Multiuser MISO Downlink Systems with Self-sustainable IRS
This paper investigates multiuser multi-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can harvest power from the received signals.
UW-NET: AN INCEPTION-ATTENTION NETWORK FOR UNDERWATER IMAGE CLASSIFICATION
By substituting the inception module with the I-A module, the Inception-ResnetV2 network achieves a 10. 7% top1 error rate and a 0% top5 error rate on the subset of ILSVRC-2012, which further illustrates the function of the background attention in the image classifications.
Molecule Property Prediction Based on Spatial Graph Embedding
And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning.
Ranked #2 on
Graph Regression
on Lipophilicity
