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Scopus YÖKSİS DOI Eşleşti SJR Q1

Classification of human target movements behind walls using multi-channel range-doppler images

Multimedia Tools and Applications · Mayıs 2024

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YÖKSİS Kayıtları
Classification of human target movements behind walls using multi-channel range-doppler images
Multimedia Tools and Applications · 2024 SCI-Expanded
Dr. Öğr. Üyesi YUNUS EMRE ACAR →
Classification of human target movements behind walls using multi-channel range-doppler images
Multimedia Tools and Applications · 2024 SCI-Expanded
Dr. Öğr. Üyesi KÜRŞAD UÇAR →
Classification of human target movements behind walls using multi-channel range-doppler images
Multimedia Tools and Applications · 2024 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 5 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Improved affine encryption algorithm for color images using LFSR and XOR encryption
2023 ISSN: 1380-7501 SCI-Expanded Q2
Prof. Dr. NURETTİN DOĞAN →
The current state and future of mobile security in the light of the recent mobile security threat reports
2023 ISSN: 1380-7501 SCI-Expanded Q2
Doç. Dr. AHMET CEVAHİR ÇINAR →
Machine learning based detection of depression from task-based fMRI using weighted-3D-DWT denoising method
2024 ISSN: 1380-7501 SCI-Expanded Q2
Dr. Öğr. Üyesi RUKİYE TEKDEMİR →
A hybrid color image encryption method based on extended logistic map
2024 ISSN: 1380-7501 SCI-Expanded Q2
Prof. Dr. NURETTİN DOĞAN →
Classification of human target movements behind walls using multi-channel range-doppler images
2024 ISSN: 1380-7501 SCI-Expanded Q2
Dr. Öğr. Üyesi YUNUS EMRE ACAR →

Makale Bilgileri

Dergi Multimedia Tools and Applications
ISSN13807501
Yayın TarihiMayıs 2024
Cilt / Sayfa83 · 56021-56038
DOI10.1007/s11042-023-17759-8
Scopus ID2-s2.0-85178916655
Özet Range-Doppler images represent one of the most informative radar data forms, providing range and frequency information. This study explores the performance of machine learning and deep learning techniques in classifying human activities behind walls using Range-Doppler images. Therefore, we input the HOG features of Range-Doppler images into various machine-learning approaches. Although the HOG feature enhances the performance of machine learning methods, we observe the superior performance of Convolutional Neural Network (CNN) architectures in a more complex scenario in which the number of target activity classes is higher. To obtain sufficient data for the CNN architecture, we combine the Uniform Linear Array (ULA) and stepped-frequency continuous-wave (SFCW) structures, enabling the acquisition of multi-channel data. The experimental results demonstrate both the improvement of the machine learning accuracy from 95.33% to 98.67% through the HOG + Range-Doppler approach and an approximately 6% enhancement in CNN performance achieved through the SFCW-ULA combination.

Yazarlar (4)

1
Yunus Emre Acar
2
Kursad Ucar
3
Ismail Saritas
4
Ercan Yaldiz

Anahtar Kelimeler

Histogram of oriented gradients (HOG) Human activity classification Radar Range-Doppler images Through-the- wall (TTW) Uniform Linear Array (ULA)

Kurumlar

Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Multimedia Tools and Applications
Q1
SJR Skoru0,798
H-Index134
YayıncıSpringer
ÜlkeUnited States
Media Technology (Q1)
Computer Networks and Communications (Q2)
Hardware and Architecture (Q2)
Software (Q2)
Dergi sayfasına git

Metrikler

3
Atıf
4
Yazar
6
Anahtar Kelime

Sistemimizdeki Yazarlar

İS
İSMAİL SARITAŞ
Prof. Dr.

Hızlı Erişim

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