AIMC Topic: Radar
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Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals.
Computational intelligence and neuroscience
Oct 23, 2017
Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlie...
Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks.
Sensors (Basel, Switzerland)
Nov 24, 2016
Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on using radar has not been extensively studied, unl...
Cognitive bio-radar: The natural evolution of bio-signals measurement.
Journal of medical systems
Aug 30, 2016
In this article we discuss a novel approach to Bio-Radar, contactless measurement of bio-signals, called Cognitive Bio-Radar. This new approach implements the Bio-Radar in a Software Defined Radio (SDR) platform in order to obtain awareness of the en...
Clinical evaluation of the newly developed infectious disease/fever screening radar system using the neural network and fuzzy grouping method for travellers with suspected infectious diseases at Narita International Airport Clinic.
The Journal of infection
Sep 28, 2015
An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method.
The Journal of infection
Dec 23, 2014
OBJECTIVES: To classify higher-risk influenza patients within 10 s, we developed an infectious disease and fever screening radar system.
Collision risk prediction and takeover requirements assessment based on radar-video integrated sensors data: A system framework based on LLM.
Accident; analysis and prevention
Aug 1, 2025
There are safety risks when drivers take over the control of autonomous driving vehicles, and reducing unnecessary takeovers is essential to improve driving safety. This study seeks to develop an interpretable system framework for collision risk pred...
Machine learning assisted noncontact neonatal anthropometry using FMCW radar.
Scientific reports
May 8, 2025
This study proposes a method for measuring the height and weight of a neonate conveniently, safely, and accurately by applying a convolutional neural network to frequency-modulated continuous-wave (FMCW) radar sensor data. Fifteen neonates, with pare...
Automated Fall Detection in Smart Homes Using Multiple Radars and Machine Learning Classifiers.
Studies in health technology and informatics
Apr 8, 2025
Falls pose a significant risk, especially among elderly persons. Recently, radar sensors have been explored for fall detection. In this study, an attempt has been made to classify fall detection using multiple radars, machine learning (ML) classifier...
A novel deep learning model for obstructive sleep apnea diagnosis: hybrid CNN-Transformer approach for radar-based detection of apnea-hypopnea events.
Sleep
Dec 11, 2024
STUDY OBJECTIVES: The demand for cost-effective and accessible alternatives to polysomnography (PSG), the conventional diagnostic method for obstructive sleep apnea (OSA), has surged. In this study, we have developed and validated a deep learning mod...
Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Aug 1, 2024
STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likeliho...
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