Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement
Authors: Yifeng Wang, Yi Zhao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We comprehensively compare the existing IMU signal enhancement methods in terms of Allan variance analysis and four downstream tasks. The experimental results show that the proposed WDSNet achieves state-of-the-art performance and has significant superiority compared with all current IMU signal enhancement methods. |
| Researcher Affiliation | Academia | School of Science, Harbin Institute of Technology, Shenzhen wangyifeng@stu.hit.edu.cn, zhao.yi@hit.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper states, "We take 15 smartphones with the built-in IMUs to collect the inertial dataset", but it does not provide concrete access information (link, DOI, repository) for this collected dataset to make it publicly available. |
| Dataset Splits | No | The paper mentions that "one type of smartphone is employed for collecting the training set, while the data of all the other phones are used for testing", but it does not provide specific details on dataset splits (e.g., percentages, sample counts, or clear validation set information). |
| Hardware Specification | Yes | All experiments are implemented by Pytorch 1.10.1 with an Nvidia RTX 2080TI GPU and Intel(R) Xeon(R) W-2133 CPU. |
| Software Dependencies | Yes | All experiments are implemented by Pytorch 1.10.1 with an Nvidia RTX 2080TI GPU and Intel(R) Xeon(R) W-2133 CPU. |
| Experiment Setup | No | The paper describes the overall framework and components (e.g., 1D-Res Net, FC layer, regularization terms) but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations required for replication. |