Evidence map›Paper›PMID 39599099›Full record

ArticleSensors (Basel, Switzerland)2024

A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge Distillation.

Hamada Rizk, Ahmed Elmogy, Mohamed Rihan, Hirozumi Yamaguchi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hamada RizkComputers & Control Engineering Department, Tanta University, Tanta 31527, Egypt.
Ahmed ElmogyFaculty of Computer Engineering & Sciences, Prince Sattam Bin Abdelaziz University, Al-Kharj 16278, Saudi Arabia.ORCID 0000-0001-7282-6598
Mohamed RihanDepartment of Communications Engineering, University of Bremen, 28725 Bremen, Germany.
Hirozumi YamaguchiGraduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan.

Funding

Prince Sattam Bin Abdulaziz University (PSAU) PSAU-2023- 839 SDG-57
6 · The paper itself

Abstract

User location has emerged as a pivotal factor in human-centered environments, driving applications like tracking, navigation, healthcare, and emergency response that align with Sustainable Development Goals (SDGs). However, accurate indoor localization remains challenging due to the limitations of GPS in indoor settings, where signal interference and reflections disrupt satellite connections. While Received Signal Strength Indicator (RSSI) methods are commonly employed, they are affected by environmental noise, multipath fading, and signal interference. Round-Trip Time (RTT)-based localization techniques provide a more resilient alternative but are not universally supported across access points due to infrastructure limitations. To address these challenges, we introduce

Indexed as

deep learningfingerprintingindoor localizationknowledge distillationround trip time

Identifiers

PMID39599099
PMCPMC11598830

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.