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Found 25 papers, 7 papers with code
DRL-Based Orchestration of Multi-User MISO Systems with Stacked Intelligent Metasurfaces
Stacked intelligent metasurfaces (SIM) represents an advanced signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light.
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets
Named Entity Recognition (NER) models play a crucial role in various NLP tasks, including information extraction (IE) and text understanding.
Ranked #1 on
Scholarly Named Entity Recognition
on GSAP-NER
Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition
Deep neural networks have been widely used in communication signal recognition and achieved remarkable performance, but this superiority typically depends on using massive examples for supervised learning, whereas training a deep neural network on small datasets with few labels generally falls into overfitting, resulting in degenerated performance.
RGB-X Object Detection via Scene-Specific Fusion Modules
Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions.
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments
Speech super-resolution (SSR) aims to predict a high resolution (HR) speech signal from its low resolution (LR) corresponding part.
Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles
We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cues as supervisory signals.
Radio Generation Using Generative Adversarial Networks with An Unrolled Design
Experimental results with extensive simulations demonstrate that our proposed GAN framework can effectively learn transmitter characteristics and various channel effects, thus accurately modeling for an underlying sampling distribution to synthesize radio signals of high quality.
Open-Set RF Fingerprinting via Improved Prototype Learning
Deep learning has been widely used in radio frequency (RF) fingerprinting.
mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectra
Speech super-resolution (SSR) aims to recover a high resolution (HR) speech from its corresponding low resolution (LR) counterpart.
Semi-Supervised RF Fingerprinting with Consistency-Based Regularization
As a promising non-password authentication technology, radio frequency (RF) fingerprinting can greatly improve wireless security.
Environment-Aware Codebook for Reconfigurable Intelligent Surface-Aided MISO Communications
Reconfigurable intelligent surface (RIS) is a revolutionary technology that can customize the wireless channel and improve the energy efficiency of next-generation cellular networks.
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks
To improve the efficiency of the D-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead.
Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network
This work presents a new method for unsupervised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention network.
Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems
Reconfigurable intelligent surface (RIS) has recently gained popularity as a promising solution for improving the signal transmission quality of wireless communications with less hardware cost and energy consumption.
RNGDet: Road Network Graph Detection by Transformer in Aerial Images
To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper.
A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints
Specifically, for the situation that the discrete phase shifts and perfect CSI are considered, we first develop a deep quantization neural network (DQNN) to simultaneously design the active and passive beamforming while most reported works design them alternatively.
csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps
To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation.
Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning
To mitigate the effects of shadow fading and obstacle blocking, reconfigurable intelligent surface (RIS) has become a promising technology to improve the signal transmission quality of wireless communications by controlling the reconfigurable passive elements with less hardware cost and lower power consumption.
Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference
This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model.
A Unified Model of Feature Extraction and Clustering for Spike Sorting
Finally, taking the best of the clustering validity indices into the proposed model, we derive an automatic spike sorting method.
Signal Processing
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
Bayesian Spatial Kernel Smoothing for ScalableDense Semantic Mapping
This paper develops a Bayesian continuous 3D semantic occupancy map from noisy point cloud measurements.
Robotics
Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation Using Factor Graphs
The factor graph framework is a convenient modeling technique for robotic state estimation where states are represented as nodes, and measurements are modeled as factors.
Robotics
Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors
We introduce forward kinematic factors and preintegrated contact factors into a factor graph framework that can be incrementally solved in real-time.
Robotics
Subsampled terahertz data reconstruction based on spatio-temporal dictionary learning
In this paper, the problem of terahertz pulsed imaging and reconstruction is addressed.
