Search Results for author:
Found 40 papers, 11 papers with code
Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization
For the extended target case, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form.
ISAC from the Sky: UAV Trajectory Design for Joint Communication and Target Localization
In this paper, we consider an airborne integrated sensing and communications (ISAC) system where a UAV, which acts both as a communication BS and a mono-static radar, flies over a given area to transmit downlink signal to a ground communication user.
Prototypical Contrastive Language Image Pretraining
In this paper, we show a representation grouping effect during this process: the InfoNCE objective indirectly groups semantically similar representations together via randomly emerged within-modal anchors.
A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting
Specifically, training of FloodDAN includes two stages: in the first stage, we train a rainfall encoder and a prediction head to learn general transferable hydrological knowledge on large-scale source domain data; in the second stage, we transfer the knowledge in the pretrained encoder into the rainfall encoder of target domain through adversarial domain alignment.
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors.
Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware ISAC Beam-tracking
In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries.
Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization
Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the DoA estimation error in terms of Cram\'er-Rao lower bound (CRLB).
Analysis Method of Strapdown Inertial Navigation Error Distribution Based on Covariance Matrix Decomposition
Compared with the Monte-Carlo method and other method based on covariance matrix, the proposed method uses more complete error model, considers the interaction effect of error sources and can be easily realized with less computation.
Disentangled Multimodal Representation Learning for Recommendation
In particular, we adopt a disentangled representation technique to ensure the features of different factors in each modality are independent to each other.
Sensing as A Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation
Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services.
Integrated Human Activity Sensing and Communications
In this article, we provide a review of human related sensing in the context of ISAC.
An Experimental Proof of Concept for Integrated Sensing and Communications Waveform Design
This experimental work focuses on a dual-functional radar sensing and communication framework where a single radiation waveform, either omnidirectional or directional, can realize both radar sensing and communication functions.
Disentangled Graph Neural Networks for Session-based Recommendation
The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors.
Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets
The first stage is exploited for ISAC transmission, where a wide beam is adopted for both communication and sensing.
MIMO-OFDM Dual-Functional Radar-Communication Systems: Low-PAPR Waveform Design
In this paper, we explore a dual-functional radar-communication (DFRC) system for achieving integrated sensing and communications (ISAC).
Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond
As the standardization of 5G is being solidified, researchers are speculating what 6G will be.
VirtualConductor: Music-driven Conducting Video Generation System
In this demo, we present VirtualConductor, a system that can generate conducting video from any given music and a single user's image.
Accelerating Edge Intelligence via Integrated Sensing and Communication
Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.
Significant Wave Height Prediction based on Wavelet Graph Neural Network
Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction.
Rethinking the Tradeoff in Integrated Sensing and Communication: Recognition Accuracy versus Communication Rate
Integrated sensing and communication (ISAC) is a promising technology to improve the band-utilization efficiency via spectrum sharing or hardware sharing between radar and communication systems.
Towards Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication and Security
At the same time, the sensing capability incorporated in the ISAC transmission offers unique opportunities to design secure ISAC techniques.
Secure Dual-Functional Radar-Communication Transmission: Exploiting Interference for Resilience Against Target Eavesdropping
We study security solutions for dual-functional radar communication (DFRC) systems, which detect the radar target and communicate with downlink cellular users in millimeter-wave (mmWave) wireless networks simultaneously.
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks
Although adversarial training (AT) methods such as Adversarial Query (AQ) can improve the adversarially robust performance of meta-learning models, AT is still computationally expensive training.
Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges
Meanwhile, triggered by ISAC, we are also witnessing a paradigm shift in the ubiquitous IoT architecture, in which the sensing and communication layers are tending to converge into a new layer, namely, the signaling layer.
Hardware Efficient Joint Radar-Communications with Hybrid Precoding and RF Chain Optimization
In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems.
An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing
Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, sharing a majority of hardware and signal processing modules and, in a typical case, sharing a single transmitted signal.
Feature-level Attentive ICF for Recommendation
To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and feature-level attention for neural ICF models.
Interest-aware Message-Passing GCN for Recommendation
To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure.
Cramér-Rao Bound Optimization for Joint Radar-Communication Design
We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios.
Learning to Select for MIMO Radar based on Hybrid Analog-Digital Beamforming
In this paper, we propose an energy-efficient radar beampattern design framework for a Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming structure.
The GALAH+ Survey: Third Data Release
Based on kinematics, 4% are halo stars.
Astrophysics of Galaxies Solar and Stellar Astrophysics
Deep Learning Based Single Sample Per Person Face Recognition: A Survey
Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models.
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically.
Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach
To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation.
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible.
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Considering the fact that for different users, the attributes of an item have different influence on their preference for this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information.
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items.
Truncated nuclear norm regularization for low-rank tensor completion
It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as an improved approximation to the rank of a matrix.
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion
The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios.
Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization
Currently, low-rank tensor completion has gained cumulative attention in recovering incomplete visual data whose partial elements are missing.
