2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)最新文献_第3页

Screen Printed Electrochemical Sensor for Ascorbic Acid Detection Based on Nafion/Ionic Liquids/Graphene Composite on Carbon Electrodes 碳电极上基于钠离子/离子液体/石墨烯复合材料的丝网印刷抗坏血酸检测电化学传感器

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298606

R. V. Manurung, Mahadir Marakka, Arifin Pide, E. D. Kurniawan, I. Hermida

{"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}

引用次数: 0

Design of Corrugated Horn Antenna for Electronic Support Measure Application 用于电子支撑测量的波纹喇叭天线设计

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298634

Kandi Rahardiyanti, Fitri Yuli Zulkifli, E. Tjipto Rahardjo

引用次数: 0

A 79GHz Series Fed Microstrip Patch Antenna Array with Bandwidth Enhancement and Sidelobe Suppression 带带宽增强和旁瓣抑制的79GHz系列馈电微带贴片天线阵列

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298602

Yuanzhi Liu, Guo Bai, M. Yagoub

引用次数: 10

Classifying aggravation status of COVID-19 event from short-text using CNN 利用CNN对短文本进行COVID-19事件加重状态分类

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298674

Ekasari Nugraheni, P. Khotimah, Andria Arisal, A. Rozie, D. Riswantini, A. Purwarianti

引用次数: 4

Impact of Dielectric Insertion on Performance of Quad-Ridged Horn Antenna 介质插入对四脊喇叭天线性能的影响

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298628

F. Oktafiani, Effrina Yanti Hamid, A. Munir

引用次数: 0

Notch/Peak filter Design and its FPGA Implementation through Wave Digital Structure 陷波/峰值滤波器设计及其FPGA实现

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298644

Abhay Sharma, T. Rawat

引用次数: 0

ICRAMET 2020 Committees

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/icramet51080.2020.9298693

引用次数: 0

On the Comparison of Social Distancing Violation Detectors with Graphical Processing Unit Support 基于图形处理单元支持的社交距离违例检测器的比较研究

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298574

S. Suryadi, E. Kurniawan, H. Adinanta, B. Sirenden, J. Prakosa, Purwowibowo Purwowibowo

{"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}

引用次数: 6

Transfer Learning and Fine-Tuning for Deep Learning-Based Tea Diseases Detection on Small Datasets 小数据集上基于深度学习的茶病检测的迁移学习和微调

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298575

A. Ramdan, A. Heryana, Andria Arisal, R. B. S. Kusumo, H. Pardede

{"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}

引用次数: 8

A Miniaturized Wideband Antenna for Vehicular Communication, WiMAX, and WLAN Applications 用于车载通信、WiMAX和WLAN应用的小型化宽带天线

2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) Pub Date : 2020-11-18 DOI: 10.1109/ICRAMET51080.2020.9298666

Shobit Agarwal, Ashwani Sharma

引用次数: 1