arXiv (All)
open access
Efficient Deep Learning Adaptation for Cross-Environment RSS-Based Indoor Localization
Received signal strength (RSS)-based indoor localization has attracted increasing attention due to its low cost and compatibility with existing wireless infrastructures. However, RSS measurements are highly sensitive to environmental variations, making it challenging for deep learning-based localiza
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU#986605
- "What's That Sound?": A Versatile, Robust, and Lightweight Convolutional Transformer for Environment Sound Recognition#1064306
- Modular Deep Learning Mechanisms for Auditable Next-Day Wildfire Spread Prediction#1035614
- Deep-Learning-Based Baseline Evaluation of Public WiFi CSI Datasets for Contactless RF-Based Human Activity Recognition.#318007
- Beyond Contact Sensors: Deep learning with Pseudo-Labeling for remote Photoplethysmography#996824
Record · ID 985964
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive