Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298606
R. V. Manurung, Mahadir Marakka, Arifin Pide, E. D. Kurniawan, I. Hermida
The material modification of thick film sensor electrodes is being developed to enhance the performance of sensing capabilities such as stability, sensitivity, and limit detection. The utilization of graphene, ionic liquid, and Nafion has become a key factor to obtain a good material as modifiers in electrochemical sensors. This disposable modified electrode exhibits excellent current enhancement, fast electron transfer kinetics, and chemical stability properties. In this research, a screen-printed electrochemical sensor was fabricated by modifying the carbon working electrode with a combination of Nafion, ionic liquid, and graphene to determine ascorbic acid. The prototype ascorbic acid (AA) sensors show peak oxidation at a potential 0.3 V vs reference Ag|AgCl. Analytical characteristics of the prototype sensors were investigated with a linear calibration curves of AA concentrations over the range from 0.25 to 2 mM (R2 ~ 0.9912). The sensor has sensitivity around 15.95 nA M−1 cm−2 and the limit of detection was 164 μM. The cyclic voltammogram result indicate that the modified working electrode can increase the redox peak current higher than the bare working electrode. Thus, the modified electrode of thick film sensors could provide a promising platform for the sensor of ascorbic acid detection.
为了提高传感器的稳定性、灵敏度和极限检测性能，厚膜传感器电极的材料改性正在得到发展。石墨烯、离子液体和Nafion的利用已成为获得电化学传感器改性材料的关键因素。这种一次性修饰电极具有优异的电流增强、快速的电子转移动力学和化学稳定性。在这项研究中，通过用Nafion、离子液体和石墨烯的组合修饰碳工作电极，制作了一种丝网印刷的电化学传感器，以测定抗坏血酸。原型抗坏血酸(AA)传感器在0.3 V vs参考Ag / AgCl电位下显示峰值氧化。通过在0.25 ~ 2 mM范围内(R2 ~ 0.9912)的AA浓度线性校准曲线考察了原型传感器的分析特性。该传感器的灵敏度约为15.95 nA M−1 cm−2，检测限为164 μM。循环伏安图结果表明，修饰后的工作电极比裸工作电极能提高氧化还原峰值电流。因此，厚膜传感器的修饰电极为抗坏血酸传感器的检测提供了一个很有前景的平台。
{"title":"Screen Printed Electrochemical Sensor for Ascorbic Acid Detection Based on Nafion/Ionic Liquids/Graphene Composite on Carbon Electrodes","authors":"R. V. Manurung, Mahadir Marakka, Arifin Pide, E. D. Kurniawan, I. Hermida","doi":"10.1109/ICRAMET51080.2020.9298606","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298606","url":null,"abstract":"The material modification of thick film sensor electrodes is being developed to enhance the performance of sensing capabilities such as stability, sensitivity, and limit detection. The utilization of graphene, ionic liquid, and Nafion has become a key factor to obtain a good material as modifiers in electrochemical sensors. This disposable modified electrode exhibits excellent current enhancement, fast electron transfer kinetics, and chemical stability properties. In this research, a screen-printed electrochemical sensor was fabricated by modifying the carbon working electrode with a combination of Nafion, ionic liquid, and graphene to determine ascorbic acid. The prototype ascorbic acid (AA) sensors show peak oxidation at a potential 0.3 V vs reference Ag|AgCl. Analytical characteristics of the prototype sensors were investigated with a linear calibration curves of AA concentrations over the range from 0.25 to 2 mM (R2 ~ 0.9912). The sensor has sensitivity around 15.95 nA M−1 cm−2 and the limit of detection was 164 μM. The cyclic voltammogram result indicate that the modified working electrode can increase the redox peak current higher than the bare working electrode. Thus, the modified electrode of thick film sensors could provide a promising platform for the sensor of ascorbic acid detection.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"112 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113995207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298634
Kandi Rahardiyanti, Fitri Yuli Zulkifli, E. Tjipto Rahardjo
in this paper, a simple and small dimension circular corrugated horn antenna at C-band frequency with high gain performance is designed. This antenna is designed for ESM (Electronic Support Measure) application with antenna dimensions is 43 x 155 mm using copper as the antenna material. The circular corrugated horn antenna is designed and simulated using CST Microwave Studio Suite software. The simulation shows results with gain 15.91 dBi, bandwidth 4.3 GHz and linear vertical polarization. This antenna has beamwidth of 45.7° at H-field and 38.9° at E-field.
本文设计了一种结构简单、尺寸小、具有高增益性能的c波段圆形波纹喇叭天线。该天线专为ESM(电子支持测量)应用而设计，天线尺寸为43 x 155 mm，采用铜作为天线材料。利用CST Microwave Studio Suite软件对圆形波纹喇叭天线进行了设计和仿真。仿真结果显示，增益15.91 dBi，带宽4.3 GHz，垂直线性极化。该天线在h场和e场的波束宽度分别为45.7°和38.9°。
{"title":"Design of Corrugated Horn Antenna for Electronic Support Measure Application","authors":"Kandi Rahardiyanti, Fitri Yuli Zulkifli, E. Tjipto Rahardjo","doi":"10.1109/ICRAMET51080.2020.9298634","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298634","url":null,"abstract":"in this paper, a simple and small dimension circular corrugated horn antenna at C-band frequency with high gain performance is designed. This antenna is designed for ESM (Electronic Support Measure) application with antenna dimensions is 43 x 155 mm using copper as the antenna material. The circular corrugated horn antenna is designed and simulated using CST Microwave Studio Suite software. The simulation shows results with gain 15.91 dBi, bandwidth 4.3 GHz and linear vertical polarization. This antenna has beamwidth of 45.7° at H-field and 38.9° at E-field.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123059806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298602
Yuanzhi Liu, Guo Bai, M. Yagoub
Series fed microstrip patch antenna arrays are widely used for millimeter-wave radar applications due to their simple structure, high gain, and low cost. However, they usually suffer from narrow impedance bandwidth. In this paper, a 79GHz series fed microstrip patch antenna array with bandwidth enhancement and sidelobe suppression is presented. The obtained simulation results showed a maximum sidelobe level of -19dB and -17dB at E-Plane and H-Plane, respectively, as well as a -10dB impedance bandwidth of 4.14GHz and a high gain of 22dBi.
{"title":"A 79GHz Series Fed Microstrip Patch Antenna Array with Bandwidth Enhancement and Sidelobe Suppression","authors":"Yuanzhi Liu, Guo Bai, M. Yagoub","doi":"10.1109/ICRAMET51080.2020.9298602","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298602","url":null,"abstract":"Series fed microstrip patch antenna arrays are widely used for millimeter-wave radar applications due to their simple structure, high gain, and low cost. However, they usually suffer from narrow impedance bandwidth. In this paper, a 79GHz series fed microstrip patch antenna array with bandwidth enhancement and sidelobe suppression is presented. The obtained simulation results showed a maximum sidelobe level of -19dB and -17dB at E-Plane and H-Plane, respectively, as well as a -10dB impedance bandwidth of 4.14GHz and a high gain of 22dBi.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121785388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298674
Ekasari Nugraheni, P. Khotimah, Andria Arisal, A. Rozie, D. Riswantini, A. Purwarianti
COVID-19 pandemic is a new precedent that has changed many aspects of human life. With the uncertainty of vaccine availability, stakeholders are required to track the dynamics of COVID-19 events to prepare the necessary response. One sub-task in tracking the dynamics of an event is to identify the aggravation status of the event (i.e., whether an event is worsening or getting better). We experimented with convolutional neural network (CNN) models to classify the status of COVID-19 aggravation status from a short text. CNN without one hot encoding prevailed. Furthermore, we conduct tuning to achieve better performance of CNN. The highest performance was achieved by tuning some of the configuration parameters. As the final result, the model performed at best (accuracy = 87.585% and F1-score = 76%) when using 80 nodes, SGD optimizer, lr = 0.1, and momentum = 0.9.
{"title":"Classifying aggravation status of COVID-19 event from short-text using CNN","authors":"Ekasari Nugraheni, P. Khotimah, Andria Arisal, A. Rozie, D. Riswantini, A. Purwarianti","doi":"10.1109/ICRAMET51080.2020.9298674","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298674","url":null,"abstract":"COVID-19 pandemic is a new precedent that has changed many aspects of human life. With the uncertainty of vaccine availability, stakeholders are required to track the dynamics of COVID-19 events to prepare the necessary response. One sub-task in tracking the dynamics of an event is to identify the aggravation status of the event (i.e., whether an event is worsening or getting better). We experimented with convolutional neural network (CNN) models to classify the status of COVID-19 aggravation status from a short text. CNN without one hot encoding prevailed. Furthermore, we conduct tuning to achieve better performance of CNN. The highest performance was achieved by tuning some of the configuration parameters. As the final result, the model performed at best (accuracy = 87.585% and F1-score = 76%) when using 80 nodes, SGD optimizer, lr = 0.1, and momentum = 0.9.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"121 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116377007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298628
F. Oktafiani, Effrina Yanti Hamid, A. Munir
In this paper, the impact of dielectric insertion on the performance of quad-ridged horn antenna (QRHA) is investigated. The dielectric material which is inserted into the QRHA is aimed to enhance the performance of QRHA, in particular, the bandwidth response with an extension into the lower operating frequency. To achieve the optimum performance, four kinds of dielectric material with different relative permittivity are utilized. The used dielectric materials are Teflon, Paraffin, Epoxy, and Duroid with the relative permittivity of 2, 2.26, 4, and 6, respectively. The characterization results show that Teflon could exhibit better performance than other dielectric materials yielding the bandwidth response of 11.5 GHz in the frequency range of 2.5 GHz to 14 GHz. Meanwhile, the bandwidth response of QRHA without dielectric insertion is 9.6 GHz in the frequency range of 4 GHz to 13.6 GHz.
{"title":"Impact of Dielectric Insertion on Performance of Quad-Ridged Horn Antenna","authors":"F. Oktafiani, Effrina Yanti Hamid, A. Munir","doi":"10.1109/ICRAMET51080.2020.9298628","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298628","url":null,"abstract":"In this paper, the impact of dielectric insertion on the performance of quad-ridged horn antenna (QRHA) is investigated. The dielectric material which is inserted into the QRHA is aimed to enhance the performance of QRHA, in particular, the bandwidth response with an extension into the lower operating frequency. To achieve the optimum performance, four kinds of dielectric material with different relative permittivity are utilized. The used dielectric materials are Teflon, Paraffin, Epoxy, and Duroid with the relative permittivity of 2, 2.26, 4, and 6, respectively. The characterization results show that Teflon could exhibit better performance than other dielectric materials yielding the bandwidth response of 11.5 GHz in the frequency range of 2.5 GHz to 14 GHz. Meanwhile, the bandwidth response of QRHA without dielectric insertion is 9.6 GHz in the frequency range of 4 GHz to 13.6 GHz.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129161481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298644
Abhay Sharma, T. Rawat
In this paper, complimentary notch/peak filter is designed using wave digital structure and then implemented on FPGA using Xilinx System Generator for DSP EDA tool. The existing theory of all-pass filter based design is used to realize second order all-pass function using wave digital equivalent of second order resonance circuit. It is also shown that the wave digital adaptor coefficients can directly tune the frequency and bandwidth of the filter.
本文采用波形数字结构设计了互补陷峰滤波器，并利用Xilinx System Generator for DSP EDA工具在FPGA上实现。利用现有的全通滤波器设计理论，利用二阶谐振电路的波数字等效实现二阶全通功能。波形数字适配器系数可以直接调节滤波器的频率和带宽。
{"title":"Notch/Peak filter Design and its FPGA Implementation through Wave Digital Structure","authors":"Abhay Sharma, T. Rawat","doi":"10.1109/ICRAMET51080.2020.9298644","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298644","url":null,"abstract":"In this paper, complimentary notch/peak filter is designed using wave digital structure and then implemented on FPGA using Xilinx System Generator for DSP EDA tool. The existing theory of all-pass filter based design is used to realize second order all-pass function using wave digital equivalent of second order resonance circuit. It is also shown that the wave digital adaptor coefficients can directly tune the frequency and bandwidth of the filter.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132403982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/icramet51080.2020.9298693
{"title":"ICRAMET 2020 Committees","authors":"","doi":"10.1109/icramet51080.2020.9298693","DOIUrl":"https://doi.org/10.1109/icramet51080.2020.9298693","url":null,"abstract":"","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122225429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298574
S. Suryadi, E. Kurniawan, H. Adinanta, B. Sirenden, J. Prakosa, Purwowibowo Purwowibowo
Social distancing or sometimes referred as physical distancing is claimed as the best spread stopper in the present COVID-19 pandemic. Social distancing monitoring by using computer vision becomes an important technological aspect in the current pandemic. This type of technology ensures automatic human object detection followed by physical distance measurement. The actual distances are measured as the number of pixels separating two centroids. The social distancing violations are known based on the measured distances. In this works, we compare three deep learning methods used for social distancing monitoring i.e YOLOv3, YOLOv3-Tiny, and MobileNetSSD. Those methods are executed with and without GPU support, and we assess the their performances in terms of speed and detection accuracies. The results show that the use of GPU significantly increases the speed of both YOLOv3 and YOLOv3-Tiny, but not for MobilenetSSD. GPU support increases about 300 % the Frame-per-Second (FPS) rate of YOLOv3 and the highest FPS rate is achieved for YOLOv3-Tiny. The results also indicate that YOLOv3 offers the best detection accuracies compared to YOLOv3-Tiny and MobilenetSSD, but in the exchange of heavy computational process.
{"title":"On the Comparison of Social Distancing Violation Detectors with Graphical Processing Unit Support","authors":"S. Suryadi, E. Kurniawan, H. Adinanta, B. Sirenden, J. Prakosa, Purwowibowo Purwowibowo","doi":"10.1109/ICRAMET51080.2020.9298574","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298574","url":null,"abstract":"Social distancing or sometimes referred as physical distancing is claimed as the best spread stopper in the present COVID-19 pandemic. Social distancing monitoring by using computer vision becomes an important technological aspect in the current pandemic. This type of technology ensures automatic human object detection followed by physical distance measurement. The actual distances are measured as the number of pixels separating two centroids. The social distancing violations are known based on the measured distances. In this works, we compare three deep learning methods used for social distancing monitoring i.e YOLOv3, YOLOv3-Tiny, and MobileNetSSD. Those methods are executed with and without GPU support, and we assess the their performances in terms of speed and detection accuracies. The results show that the use of GPU significantly increases the speed of both YOLOv3 and YOLOv3-Tiny, but not for MobilenetSSD. GPU support increases about 300 % the Frame-per-Second (FPS) rate of YOLOv3 and the highest FPS rate is achieved for YOLOv3-Tiny. The results also indicate that YOLOv3 offers the best detection accuracies compared to YOLOv3-Tiny and MobilenetSSD, but in the exchange of heavy computational process.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116642321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298575
A. Ramdan, A. Heryana, Andria Arisal, R. B. S. Kusumo, H. Pardede
It is well-known that a large amount of data is required to train deep learning systems. However, data collection is very costly if it is not impossible to do. To overcome the limited data problem, one can use models that have been trained with a large dataset and apply them in the target domain with a limited dataset. In this paper, we use pre-trained models on imageNet data and re-train them on our data to detect tea leaf diseases. Those pre-trained models use deep convolutional neural network (DCNN) architectures: VGGNet, ResNet, and Xception. To mitigate the difference tasks of ImageNet and ours, we apply fine-tuning on the pre-trained models by replacing some parts of the pre-trained models with new structures. We evaluate the performance using various re-training and fine-tuning schema. The vanilla pre-trained model is used as the baseline while other techniques such as re-training the models on the appended structures, partially re-training the pre-trained models, and fully re-training the whole networks where the pre-trained models are used in the initialization as the evaluator. Our experiments show that applying transfer learning only on our data may not be effective due to the difference in our task to ImageNet. Applying fine-tuning on pre-trained DCNN models is found to be effective. It is consistently better than that of using transfer learning only or partial fine-tuning. It is also better than training the model from scratch, i.e., without using pre-trained models.
{"title":"Transfer Learning and Fine-Tuning for Deep Learning-Based Tea Diseases Detection on Small Datasets","authors":"A. Ramdan, A. Heryana, Andria Arisal, R. B. S. Kusumo, H. Pardede","doi":"10.1109/ICRAMET51080.2020.9298575","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298575","url":null,"abstract":"It is well-known that a large amount of data is required to train deep learning systems. However, data collection is very costly if it is not impossible to do. To overcome the limited data problem, one can use models that have been trained with a large dataset and apply them in the target domain with a limited dataset. In this paper, we use pre-trained models on imageNet data and re-train them on our data to detect tea leaf diseases. Those pre-trained models use deep convolutional neural network (DCNN) architectures: VGGNet, ResNet, and Xception. To mitigate the difference tasks of ImageNet and ours, we apply fine-tuning on the pre-trained models by replacing some parts of the pre-trained models with new structures. We evaluate the performance using various re-training and fine-tuning schema. The vanilla pre-trained model is used as the baseline while other techniques such as re-training the models on the appended structures, partially re-training the pre-trained models, and fully re-training the whole networks where the pre-trained models are used in the initialization as the evaluator. Our experiments show that applying transfer learning only on our data may not be effective due to the difference in our task to ImageNet. Applying fine-tuning on pre-trained DCNN models is found to be effective. It is consistently better than that of using transfer learning only or partial fine-tuning. It is also better than training the model from scratch, i.e., without using pre-trained models.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117021790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1109/ICRAMET51080.2020.9298666
Shobit Agarwal, Ashwani Sharma
A compact wideband microstrip antenna operating over 5.45 GHz - 7.47 GHz bandwidth is reported. The design uses double rectangular type annular-ring structured patch, along with a circular arc at each corner acting as defected metal structure (DMS) and two triangular-shaped defects in the ground plane acting as defected ground structures (DGS). The annular ring contributes to increase bandwidth of basic microstrip antenna and further enhancement in bandwidth is achieved by combination of both DMS and DGS. The design is implemented on a RT/Duroid 5880 TM substrate. The overall design size is 14 mm×18 mm. The maximum simulated gain of the proposed antenna is 7.8 dBi. Hence, the proposed design is found suitable for vehicle to vehicle communication in Intelligent Transport System (ITS), WiMAX, WLAN, downlink of X-band and satellite communication and other applications operating in ultra wideband frequency range.
{"title":"A Miniaturized Wideband Antenna for Vehicular Communication, WiMAX, and WLAN Applications","authors":"Shobit Agarwal, Ashwani Sharma","doi":"10.1109/ICRAMET51080.2020.9298666","DOIUrl":"https://doi.org/10.1109/ICRAMET51080.2020.9298666","url":null,"abstract":"A compact wideband microstrip antenna operating over 5.45 GHz - 7.47 GHz bandwidth is reported. The design uses double rectangular type annular-ring structured patch, along with a circular arc at each corner acting as defected metal structure (DMS) and two triangular-shaped defects in the ground plane acting as defected ground structures (DGS). The annular ring contributes to increase bandwidth of basic microstrip antenna and further enhancement in bandwidth is achieved by combination of both DMS and DGS. The design is implemented on a RT/Duroid 5880 TM substrate. The overall design size is 14 mm×18 mm. The maximum simulated gain of the proposed antenna is 7.8 dBi. Hence, the proposed design is found suitable for vehicle to vehicle communication in Intelligent Transport System (ITS), WiMAX, WLAN, downlink of X-band and satellite communication and other applications operating in ultra wideband frequency range.","PeriodicalId":228482,"journal":{"name":"2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133965989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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