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Found 15 papers, 7 papers with code
Intelligent Reflecting Surfaces vs. Full-Duplex Relays: A Comparison in the Air
This letter aims to provide a fundamental analytical comparison for the two major types of relaying methods: intelligent reflecting surfaces and full-duplex relays, particularly focusing on unmanned aerial vehicle communication scenarios.
Towards the THz Networks in the 6G Era
This commentary dedicates to envision what role THz is going to play in the coming human-centric 6G era.
One-stage Modality Distillation for Incomplete Multimodal Learning
Specifically, we propose the joint adaptation network for the modality transfer task to preserve the privileged information.
MMANet: Margin-aware Distillation and Modality-aware Regularization for Incomplete Multimodal Learning
It consists of three components: the deployment network used for inference, the teacher network transferring comprehensive multimodal information to the deployment network, and the regularization network guiding the deployment network to balance weak modality combinations.
Ranked #56 on
Semantic Segmentation
on NYU Depth v2
Behavioural Reports of Multi-Stage Malware
The extensive damage caused by malware requires anti-malware systems to be constantly improved to prevent new threats.
Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges
Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing.
DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes
Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images.
An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation
Automatic modulation recognition (AMR) is a promising technology for intelligent communication receivers to detect signal modulation schemes.
Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity
In this paper we propose a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images.
Change Detection
Change detection for remote sensing images
+1
The effects of regularisation on RNN models for time series forecasting: Covid-19 as an example
Applying Dropout to a GRU model trained on only 28 days of data reduced the RMSE by 23%.
A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition
Automatic modulation recognition (AMR) plays a vital role in modern communication systems.
Fractional order graph neural network
This paper proposes fractional order graph neural networks (FGNNs), optimized by the approximation strategy to address the challenges of local optimum of classic and fractional graph neural networks which are specialised at aggregating information from the feature and adjacent matrices of connected nodes and their neighbours to solve learning tasks on non-Euclidean data such as graphs.
A Model-driven and Data-driven Fusion Framework for Accurate Air Quality Prediction
Then, we deploy a long-short-term-memory (LSTM) to fetch the preliminary results, which will be further corrected by a neural network (NN) involving the meteorological index as well as other pollutants concentrations.
A DoA Estimation Based Robust Beam Forming Method for UAV-BS Communication
Then, we propose a DoA estimation algorithm and a steering vector adaptive receiving beam forming method.
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation.
