Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach

This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31...
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Autor*in:	Kumar Rai, Jayant [verfasserIn] Anuragi, Khemchandra [verfasserIn] Mishra, Naveen [verfasserIn] Chowdhury, Rakesh [verfasserIn] Kumar, Somesh [verfasserIn] Ranjan, Pinku [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	CRLH-TL Dual-Band Machine Learning Miniaturized Antenna ZOR

Übergeordnetes Werk:	Enthalten in: International journal of electronics and communications - München : Elsevier, 2011, 176
Übergeordnetes Werk:	volume:176

DOI / URN:	10.1016/j.aeue.2024.155120

Katalog-ID:	ELV067026508

Internformat


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100	1		\|a Kumar Rai, Jayant \|e verfasserin \|4 aut
245	1	0	\|a Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach
264		1	\|c 2024
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications.
650		4	\|a CRLH-TL
650		4	\|a Dual-Band
650		4	\|a Machine Learning
650		4	\|a Miniaturized Antenna
650		4	\|a ZOR
700	1		\|a Anuragi, Khemchandra \|e verfasserin \|4 aut
700	1		\|a Mishra, Naveen \|e verfasserin \|4 aut
700	1		\|a Chowdhury, Rakesh \|e verfasserin \|4 aut
700	1		\|a Kumar, Somesh \|e verfasserin \|4 aut
700	1		\|a Ranjan, Pinku \|e verfasserin \|0 (orcid)0000-0002-1422-5943 \|4 aut
773	0	8	\|i Enthalten in \|t International journal of electronics and communications \|d München : Elsevier, 2011 \|g 176 \|w (DE-627)329270273 \|w (DE-600)2046900-7 \|x 143-48411 \|7 nnns
773	1	8	\|g volume:176
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 176

Indexfelder

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matchkey_str	article:14348411:2024----::uladiitrzdopstrgtetaddrnmsinieoatnaomcoaeomnc
hierarchy_sort_str	2024
publishDate	2024
allfields	10.1016/j.aeue.2024.155120 doi (DE-627)ELV067026508 (ELSEVIER)S1434-8411(24)00005-0 DE-627 ger DE-627 rda eng 004 620 VZ Kumar Rai, Jayant verfasserin aut Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications. CRLH-TL Dual-Band Machine Learning Miniaturized Antenna ZOR Anuragi, Khemchandra verfasserin aut Mishra, Naveen verfasserin aut Chowdhury, Rakesh verfasserin aut Kumar, Somesh verfasserin aut Ranjan, Pinku verfasserin (orcid)0000-0002-1422-5943 aut Enthalten in International journal of electronics and communications München : Elsevier, 2011 176 (DE-627)329270273 (DE-600)2046900-7 143-48411 nnns volume:176 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 176
spelling	10.1016/j.aeue.2024.155120 doi (DE-627)ELV067026508 (ELSEVIER)S1434-8411(24)00005-0 DE-627 ger DE-627 rda eng 004 620 VZ Kumar Rai, Jayant verfasserin aut Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications. CRLH-TL Dual-Band Machine Learning Miniaturized Antenna ZOR Anuragi, Khemchandra verfasserin aut Mishra, Naveen verfasserin aut Chowdhury, Rakesh verfasserin aut Kumar, Somesh verfasserin aut Ranjan, Pinku verfasserin (orcid)0000-0002-1422-5943 aut Enthalten in International journal of electronics and communications München : Elsevier, 2011 176 (DE-627)329270273 (DE-600)2046900-7 143-48411 nnns volume:176 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 176
allfields_unstemmed	10.1016/j.aeue.2024.155120 doi (DE-627)ELV067026508 (ELSEVIER)S1434-8411(24)00005-0 DE-627 ger DE-627 rda eng 004 620 VZ Kumar Rai, Jayant verfasserin aut Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications. CRLH-TL Dual-Band Machine Learning Miniaturized Antenna ZOR Anuragi, Khemchandra verfasserin aut Mishra, Naveen verfasserin aut Chowdhury, Rakesh verfasserin aut Kumar, Somesh verfasserin aut Ranjan, Pinku verfasserin (orcid)0000-0002-1422-5943 aut Enthalten in International journal of electronics and communications München : Elsevier, 2011 176 (DE-627)329270273 (DE-600)2046900-7 143-48411 nnns volume:176 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 176
allfieldsGer	10.1016/j.aeue.2024.155120 doi (DE-627)ELV067026508 (ELSEVIER)S1434-8411(24)00005-0 DE-627 ger DE-627 rda eng 004 620 VZ Kumar Rai, Jayant verfasserin aut Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications. CRLH-TL Dual-Band Machine Learning Miniaturized Antenna ZOR Anuragi, Khemchandra verfasserin aut Mishra, Naveen verfasserin aut Chowdhury, Rakesh verfasserin aut Kumar, Somesh verfasserin aut Ranjan, Pinku verfasserin (orcid)0000-0002-1422-5943 aut Enthalten in International journal of electronics and communications München : Elsevier, 2011 176 (DE-627)329270273 (DE-600)2046900-7 143-48411 nnns volume:176 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 176
allfieldsSound	10.1016/j.aeue.2024.155120 doi (DE-627)ELV067026508 (ELSEVIER)S1434-8411(24)00005-0 DE-627 ger DE-627 rda eng 004 620 VZ Kumar Rai, Jayant verfasserin aut Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications. CRLH-TL Dual-Band Machine Learning Miniaturized Antenna ZOR Anuragi, Khemchandra verfasserin aut Mishra, Naveen verfasserin aut Chowdhury, Rakesh verfasserin aut Kumar, Somesh verfasserin aut Ranjan, Pinku verfasserin (orcid)0000-0002-1422-5943 aut Enthalten in International journal of electronics and communications München : Elsevier, 2011 176 (DE-627)329270273 (DE-600)2046900-7 143-48411 nnns volume:176 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 176
language	English
source	Enthalten in International journal of electronics and communications 176 volume:176
sourceStr	Enthalten in International journal of electronics and communications 176 volume:176
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	CRLH-TL Dual-Band Machine Learning Miniaturized Antenna ZOR
dewey-raw	004
isfreeaccess_bool	false
container_title	International journal of electronics and communications
authorswithroles_txt_mv	Kumar Rai, Jayant @@aut@@ Anuragi, Khemchandra @@aut@@ Mishra, Naveen @@aut@@ Chowdhury, Rakesh @@aut@@ Kumar, Somesh @@aut@@ Ranjan, Pinku @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	329270273
dewey-sort	14
id	ELV067026508
language_de	englisch
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author	Kumar Rai, Jayant
spellingShingle	Kumar Rai, Jayant ddc 004 misc CRLH-TL misc Dual-Band misc Machine Learning misc Miniaturized Antenna misc ZOR Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach
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topic_title	004 620 VZ Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach CRLH-TL Dual-Band Machine Learning Miniaturized Antenna ZOR
topic	ddc 004 misc CRLH-TL misc Dual-Band misc Machine Learning misc Miniaturized Antenna misc ZOR
topic_unstemmed	ddc 004 misc CRLH-TL misc Dual-Band misc Machine Learning misc Miniaturized Antenna misc ZOR
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title	Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach
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title_full	Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach
author_sort	Kumar Rai, Jayant
journal	International journal of electronics and communications
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author_browse	Kumar Rai, Jayant Anuragi, Khemchandra Mishra, Naveen Chowdhury, Rakesh Kumar, Somesh Ranjan, Pinku
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author-letter	Kumar Rai, Jayant
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title_sort	dual-band miniaturized composite right left handed transmission line zor antenna for microwave communication with machine learning approach
title_auth	Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach
abstract	This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications.
abstractGer	This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications.
abstract_unstemmed	This article presents a dual-band miniaturized Composite Right-Left-Handed Transmission Line (CRLH-TL) in an open-ended terminal, employing the Machine Learning (ML) technique. The CRLH-TL antenna is designed on the FR4 epoxy substrate. The substrate size is 0.31 λ 0 × 0.09 λ 0 , where λ 0 is the free space wavelength. The proposed antenna offers dual-band functionality with resonant frequencies at 2.49 GHz and 5.33 GHz. The measured dual-band impedance bandwidths are 14.46 % and 14.74 %, with gains of 0.85 dB and 1.67 dB, and radiation efficiencies of 91.78 % and 95.45 % obtained at resonating frequencies of 2.49 GHz and 5.33 GHz, respectively. The proposed antenna also offers bipolar-type radiation patterns in the E-plane, and omnidirectional radiation patterns in the H-plane, along with compactness and constant gain. Several ML methods, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are used to optimize the antenna. Compared to other ML algorithms, RF ML techniques estimate reflection coefficient S 11 with an accuracy of above 98 %. The proposed antenna is utilized in WLAN (5.15–5.35, 5.47–5.725 GHz) and Wi-MAX (5.2–5.8 GHz) microwave applications.
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title_short	Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach
remote_bool	true
author2	Anuragi, Khemchandra Mishra, Naveen Chowdhury, Rakesh Kumar, Somesh Ranjan, Pinku
author2Str	Anuragi, Khemchandra Mishra, Naveen Chowdhury, Rakesh Kumar, Somesh Ranjan, Pinku
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doi_str	10.1016/j.aeue.2024.155120
up_date	2024-07-06T19:49:35.293Z
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score	7.402011

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Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach

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