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Found 11 papers, 2 papers with code
Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable.
Wireless Localisation in WiFi using Novel Deep Architectures
Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates.
Deep Transfer Learning for WiFi Localization
Finally, an ablation study of the training dataset shows that, in both office and sport hall scenarios, after reusing the feature extraction layers of the base model, only 55% of the training data is required to obtain the models' accuracy similar to the base models.
OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors
This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
We provide a taxonomy for the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.).
Wi-Fi Based Passive Human Motion Sensing for In-Home Healthcare Applications
The experiment results offer potential for promising healthcare applications using Wi-Fi passive sensing in the home to monitor daily activities, to gather health data and detect emergency situations.
Sim2real for Reinforcement Learning Driven Next Generation Networks
One of the main reasons is the modelling gap between the simulation and the real environment, which could make the RL agent trained by simulation ill-equipped for the real environment.
Variational Autoencoder Assisted Neural Network Likelihood RSRP Prediction Model
Measuring customer experience on mobile data is of utmost importance for global mobile operators.
Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning
The model will also be used as the base model for adaptive training in the new environment.
Federated Meta-Learning for Traffic Steering in O-RAN
In order to provide such services, 5G systems will support various combinations of access technologies such as LTE, NR, NR-U and Wi-Fi.
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations
In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations.
