ArticleSensors (Basel, Switzerland)2019
A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- A Dual-Modality CNN Approach for RSS-Based Indoor Positioning Using Spatial and Frequency Fingerprints.Sensors (Basel, Switzerland) · 2025Article
- Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients.Cancer medicine · 2023Article
- Development of Machine-Learning Models for Tinnitus-Related Distress Classification Using Wavelet-Transformed Auditory Evoked Potential Signals and Clinical Data.Journal of clinical medicine · 2023Article
- A Survey of Recent Indoor Localization Scenarios and Methodologies.Sensors (Basel, Switzerland) · 2021Review
- Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date, there is not adequate feature extraction method that can significantly mitigate the influence of the receiver signal strength indicator (RSSI) variation that degrades the performance of the ANN-based indoor fingerprinting algorithm. In this work, a wavelet scattering transform is used to extract reliable features that are stable to small deformation and rotation invariant. The extracted features are used by a deep neural network (DNN) model to predict the location. The zero
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.