Pub Date : 2021-10-13DOI: 10.1109/QIR54354.2021.9716205
Feralia Fitri, R. Munadi, N. Adriansyah
The increasing density of data traffic leads to an increase in the demand for telecommunication services. Therefore, in this study, the LTE network expansion was carried out using a choice of frequencies of 700 MHz, 2100 MHz, and 2300 MHz. The analysis was carried out from the technical, economic, and sensitivity aspects. Sensitivity analysis was used to determine the minimum ARPU for each candidate frequency. This research was conducted using a case study of the city of Yogyakarta. Based on the simulation results, the average RSRP values for the three candidate frequencies are in the very good range. The SINR values for the three candidate frequencies are in the normal category and the throughput values are in the very good category. The techno-economic calculations of IRR, NPV and payback period for the frequencies of 2100 MHz and 2300 are included in a feasible business, while at a frequency of 700 MHz it is not feasible to do so. The results of the sensitivity analysis show that the frequency of 2300 MHz is a feasible frequency to be implemented for LTE networks with the minimum ARPU and the minimum number of users. This research can be used for the operator as a consideration for the implementation of other frequencies on LTE networks.
{"title":"Feasibility Study of LTE Network Implementation on Working Frequency 700 MHz, 2100 MHz, and 2300 MHz in Indonesia","authors":"Feralia Fitri, R. Munadi, N. Adriansyah","doi":"10.1109/QIR54354.2021.9716205","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716205","url":null,"abstract":"The increasing density of data traffic leads to an increase in the demand for telecommunication services. Therefore, in this study, the LTE network expansion was carried out using a choice of frequencies of 700 MHz, 2100 MHz, and 2300 MHz. The analysis was carried out from the technical, economic, and sensitivity aspects. Sensitivity analysis was used to determine the minimum ARPU for each candidate frequency. This research was conducted using a case study of the city of Yogyakarta. Based on the simulation results, the average RSRP values for the three candidate frequencies are in the very good range. The SINR values for the three candidate frequencies are in the normal category and the throughput values are in the very good category. The techno-economic calculations of IRR, NPV and payback period for the frequencies of 2100 MHz and 2300 are included in a feasible business, while at a frequency of 700 MHz it is not feasible to do so. The results of the sensitivity analysis show that the frequency of 2300 MHz is a feasible frequency to be implemented for LTE networks with the minimum ARPU and the minimum number of users. This research can be used for the operator as a consideration for the implementation of other frequencies on LTE networks.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129423154","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716179
Dhanu Pramandita, U. Ubaidillah, M. Nizam, M. Putra
The innovation of braking using the Eddy Current Brake (ECB) system needs to be developed to support the braking system in this era. This study discussed the ECB braking system by utilizing electromagnet. This study aimed to analyze the relationship between the braking torque value produced and the effect of cover added on the performance of the ECB braking system. This study used the Finite Element Method (FEM) in the process of modeling the ECB system with variations in the ECB braking systems without the use of cover, with covers made of pertinax material, and covers made of aluminum at rotary speed variations of 150, 300, 450, 600, and 750 rpm. The results of this study showed that the highest braking torque value produced by a variation without using a cover was 59.81 Nm at a rotary speed of 450 rpm and the lowest braking torque value produced by a variation using a cover made of pertinax material was 32.52 Nm at a rotary speed of 150 rpm. The results of this study showed that the addition of cover materials affected the value of braking torque produced by the ECB system although the value was insignificant.
{"title":"Design Study of The Effect of Cover Addition on Eddy Current Brake Type Half Circle Slotted: A Computational Approach","authors":"Dhanu Pramandita, U. Ubaidillah, M. Nizam, M. Putra","doi":"10.1109/QIR54354.2021.9716179","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716179","url":null,"abstract":"The innovation of braking using the Eddy Current Brake (ECB) system needs to be developed to support the braking system in this era. This study discussed the ECB braking system by utilizing electromagnet. This study aimed to analyze the relationship between the braking torque value produced and the effect of cover added on the performance of the ECB braking system. This study used the Finite Element Method (FEM) in the process of modeling the ECB system with variations in the ECB braking systems without the use of cover, with covers made of pertinax material, and covers made of aluminum at rotary speed variations of 150, 300, 450, 600, and 750 rpm. The results of this study showed that the highest braking torque value produced by a variation without using a cover was 59.81 Nm at a rotary speed of 450 rpm and the lowest braking torque value produced by a variation using a cover made of pertinax material was 32.52 Nm at a rotary speed of 150 rpm. The results of this study showed that the addition of cover materials affected the value of braking torque produced by the ECB system although the value was insignificant.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128733958","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716199
Muhamad Aditya Royandi, J. Hung
The Internet-of-Things (IoT) is becoming popular in almost every field of industry. It can send the data through wired or wireless connections and integrate data acquisition, communication, processing, and visualizing on a real-time network. The real-time capability of it can be considered as the main idea for designing an online monitoring system. This article mainly aims to provide the design process of the IoT system using some open-source platforms such as Arduino microcontroller, backup-and-sync-from-google desktop application, Firebase API, C# programming language, WhatsApp API, and MIT App inventor. All of these are integrated to become an affordable IoT-based monitoring system. This system can provide a monitoring system that can show data in two different end-user devices. Some features that this system can provide are visualizing, processing, and analyzing the data. Furthermore, this IoT-based monitoring system will be applied in the machine tool application as a versatile system.
物联网(IoT)在几乎所有工业领域都变得越来越流行。它可以通过有线或无线连接发送数据，并在实时网络上集成数据采集、通信、处理和可视化。它的实时性可以作为设计在线监控系统的主要思想。本文主要旨在提供物联网系统的设计过程，使用一些开源平台，如Arduino微控制器，备份和同步从谷歌桌面应用程序，Firebase API, c#编程语言，WhatsApp API, MIT App inventor。所有这些集成成为一个经济实惠的基于物联网的监控系统。该系统可以提供一个监控系统，可以在两个不同的终端用户设备中显示数据。该系统所能提供的一些功能是数据的可视化、处理和分析。此外，这种基于物联网的监控系统将作为一个多功能系统应用于机床应用中。
{"title":"Design of an Affordable IoT-Based Monitoring System for Versatile Application in Machine Tool","authors":"Muhamad Aditya Royandi, J. Hung","doi":"10.1109/QIR54354.2021.9716199","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716199","url":null,"abstract":"The Internet-of-Things (IoT) is becoming popular in almost every field of industry. It can send the data through wired or wireless connections and integrate data acquisition, communication, processing, and visualizing on a real-time network. The real-time capability of it can be considered as the main idea for designing an online monitoring system. This article mainly aims to provide the design process of the IoT system using some open-source platforms such as Arduino microcontroller, backup-and-sync-from-google desktop application, Firebase API, C# programming language, WhatsApp API, and MIT App inventor. All of these are integrated to become an affordable IoT-based monitoring system. This system can provide a monitoring system that can show data in two different end-user devices. Some features that this system can provide are visualizing, processing, and analyzing the data. Furthermore, this IoT-based monitoring system will be applied in the machine tool application as a versatile system.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124147385","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716200
M. Ariyanto, Prima Dewi Purnamasari
This paper will discuss an alternative to replace barcodes in the form of object detection based on deep learning which reads the overall feature of an object, so defects and irregularly shaped products do not hinder the reading of the product in processing purchases. Two deep learning-based object detection models, Faster Regional Convolutional Neural Network (Faster R-CNN) and You Only Look Once 9000 (YOLO9000), were tested for their performance in the training phase and real-time implementation. The result of training phase testing shows that the Faster R-CNN model is more accurate and efficient with an mAP of 88.2%, a training time of 1175.6 seconds/epoch, and memory usage of 1.611 GB. The result of the real-time testing of the model shows that Faster RCNN has a high accuracy of 67.1%, but YOLO9000 has a very fast prediction speed of 0.023 seconds/frame. The result of simulation testing shows that YOLO9000 can read products at a speed of 67.40 seconds which is comparable to the speed of a barcode scanner-based cash register that can read products at a speed of 65.77 seconds.
本文将讨论一种以基于深度学习的物体检测形式取代条形码的替代方案，该方法可以读取物体的整体特征，因此在处理采购时，缺陷和不规则形状的产品不会妨碍对产品的读取。两种基于深度学习的目标检测模型，Faster区域卷积神经网络(Faster R-CNN)和You Only Look Once 9000 (YOLO9000)，在训练阶段和实时实现中测试了它们的性能。训练阶段测试结果表明，更快的R-CNN模型具有更高的准确率和效率，mAP为88.2%，训练时间为1175.6秒/epoch，内存使用量为1.611 GB。模型的实时测试结果表明，Faster RCNN的预测准确率高达67.1%，而YOLO9000的预测速度非常快，为0.023秒/帧。仿真测试结果表明，YOLO9000读取产品的速度为67.40秒，与基于条形码扫描器的收银机读取产品的速度为65.77秒相当。
{"title":"Object Detection System for Self-Checkout Cashier System Based on Faster Region-Based Convolution Neural Network and YOLO9000","authors":"M. Ariyanto, Prima Dewi Purnamasari","doi":"10.1109/QIR54354.2021.9716200","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716200","url":null,"abstract":"This paper will discuss an alternative to replace barcodes in the form of object detection based on deep learning which reads the overall feature of an object, so defects and irregularly shaped products do not hinder the reading of the product in processing purchases. Two deep learning-based object detection models, Faster Regional Convolutional Neural Network (Faster R-CNN) and You Only Look Once 9000 (YOLO9000), were tested for their performance in the training phase and real-time implementation. The result of training phase testing shows that the Faster R-CNN model is more accurate and efficient with an mAP of 88.2%, a training time of 1175.6 seconds/epoch, and memory usage of 1.611 GB. The result of the real-time testing of the model shows that Faster RCNN has a high accuracy of 67.1%, but YOLO9000 has a very fast prediction speed of 0.023 seconds/frame. The result of simulation testing shows that YOLO9000 can read products at a speed of 67.40 seconds which is comparable to the speed of a barcode scanner-based cash register that can read products at a speed of 65.77 seconds.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126678135","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716169
Nova Kristian, Fikri Adzikri, M. Rizkinia
Machine learning (ML) algorithms have been widely used to predict future financial trends. It has become a tool for predicting future trends based on what is known beforehand. Like other financial stock markets, cryptocurrency has become a new sensation and challenge for investors to predict its behaviour. However, unlike other financial instruments, cryptocurrency has been renowned because of the difficulty to predict the price due to its volatility behaviour that changes so rapidly and since there is no fundamental economy for its value. This paper presents a performance comparison of two ML algorithms in predicting Ethereum price with non-time series analysis, which are k- Nearest Neighbors (k-NN) and multiple polynomial regression (MPR). The experiment used independent variables from related real-world economic fundamentals such as Dow Jones Index, gold price, oil price, and Ethereum volume. The experiment data was collected from the records from April 2017 until April 2021. For each algorithm, several methods of preprocessing data were used to match all independent data with the dependent data. Three different preprocessing scenarios were also used to find the maximum accuracy model. scenario 1 (feature selection based on correlation matrix), scenario 2 (feature selection based on correlation with the dependent variables and among independent variables), and scenario 3 (scenario 1 extracted with PCA). The performance of the compared methods was evaluated by using MSE and MAE. From the experiment, a comparison of results using two different models with k-NN and multiple polynomial regression is obtained. It is found that k-NN with a hyperparameter K = 2 have the best prediction with MSE = 449.032 and MAE = 14.282 compared with multiple polynomial regression with the best MSE = 13953.96 and MAE = 84.923.
机器学习(ML)算法已被广泛用于预测未来的金融趋势。它已经成为一种基于事先已知的预测未来趋势的工具。与其他金融股票市场一样，加密货币已经成为投资者预测其行为的新轰动和挑战。然而，与其他金融工具不同，加密货币之所以闻名，是因为其波动行为变化如此之快，难以预测价格，而且其价值没有基本的经济性。本文介绍了两种ML算法在使用非时间序列分析预测以太坊价格方面的性能比较，这两种算法分别是k-近邻(k- nn)和多元多项式回归(MPR)。该实验使用了来自相关现实世界经济基本面的独立变量，如道琼斯指数、黄金价格、油价和以太坊交易量。实验数据收集自2017年4月至2021年4月的记录。对于每种算法，使用几种预处理数据的方法将所有独立数据与相关数据进行匹配。采用三种不同的预处理方案来寻找最大精度模型。场景1(基于关联矩阵的特征选择)、场景2(基于因变量与自变量之间的相关性的特征选择)、场景3(基于PCA提取的场景1)。通过MSE和MAE对比较方法的性能进行了评价。通过实验，比较了k-NN和多元多项式回归两种不同模型的结果。结果表明，超参数K = 2的K - nn的预测效果最好，MSE = 449.032, MAE = 14.282，而多元多项式回归的预测效果最好，MSE = 13953.96, MAE = 84.923。
{"title":"Ethereum Price Prediction Comparison Using k-NN and Multiple Polynomial Regression","authors":"Nova Kristian, Fikri Adzikri, M. Rizkinia","doi":"10.1109/QIR54354.2021.9716169","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716169","url":null,"abstract":"Machine learning (ML) algorithms have been widely used to predict future financial trends. It has become a tool for predicting future trends based on what is known beforehand. Like other financial stock markets, cryptocurrency has become a new sensation and challenge for investors to predict its behaviour. However, unlike other financial instruments, cryptocurrency has been renowned because of the difficulty to predict the price due to its volatility behaviour that changes so rapidly and since there is no fundamental economy for its value. This paper presents a performance comparison of two ML algorithms in predicting Ethereum price with non-time series analysis, which are k- Nearest Neighbors (k-NN) and multiple polynomial regression (MPR). The experiment used independent variables from related real-world economic fundamentals such as Dow Jones Index, gold price, oil price, and Ethereum volume. The experiment data was collected from the records from April 2017 until April 2021. For each algorithm, several methods of preprocessing data were used to match all independent data with the dependent data. Three different preprocessing scenarios were also used to find the maximum accuracy model. scenario 1 (feature selection based on correlation matrix), scenario 2 (feature selection based on correlation with the dependent variables and among independent variables), and scenario 3 (scenario 1 extracted with PCA). The performance of the compared methods was evaluated by using MSE and MAE. From the experiment, a comparison of results using two different models with k-NN and multiple polynomial regression is obtained. It is found that k-NN with a hyperparameter K = 2 have the best prediction with MSE = 449.032 and MAE = 14.282 compared with multiple polynomial regression with the best MSE = 13953.96 and MAE = 84.923.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130638905","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716186
Muhammad Reza Fairuzi, F. Zulkifli
Indonesia is a country that has heavy air traffic every day. Therefore, safety is a very important thing to pay attention to, one of them is the runway safety. The runway is an important component in aviation activities because aircraft use it for takeoff and landing. Foreign objects or FOD (Foreign Object Debris) could appear on the runway which can cause damage to the aircraft and may result in an accident. Therefore, we need a security system that can detect foreign objects in real-time. One approach that can be done is to use Computer Vision technology by using a camera. This method utilizes Artificial Intelligence (AI) technology for FOD detection. Various methods or algorithms have been developed for Computer Vision, SSD and YOLO are the most frequently used methods for real-time detection because of their high FPS and accuracy performance. Where in this study it was found that SSD MobileNet V2 can reach up to 12 FPS with mAP 0.5 value of 86.8% and for YOLOv4 can reach up to 31 FPS with mAP 0.5 value of 98.73%.
{"title":"Performance Analysis of YOLOv4 and SSD Mobilenet V2 for Foreign Object Debris (FOD) Detection at Airport Runway Using Custom Dataset","authors":"Muhammad Reza Fairuzi, F. Zulkifli","doi":"10.1109/QIR54354.2021.9716186","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716186","url":null,"abstract":"Indonesia is a country that has heavy air traffic every day. Therefore, safety is a very important thing to pay attention to, one of them is the runway safety. The runway is an important component in aviation activities because aircraft use it for takeoff and landing. Foreign objects or FOD (Foreign Object Debris) could appear on the runway which can cause damage to the aircraft and may result in an accident. Therefore, we need a security system that can detect foreign objects in real-time. One approach that can be done is to use Computer Vision technology by using a camera. This method utilizes Artificial Intelligence (AI) technology for FOD detection. Various methods or algorithms have been developed for Computer Vision, SSD and YOLO are the most frequently used methods for real-time detection because of their high FPS and accuracy performance. Where in this study it was found that SSD MobileNet V2 can reach up to 12 FPS with mAP 0.5 value of 86.8% and for YOLOv4 can reach up to 31 FPS with mAP 0.5 value of 98.73%.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"42 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115876080","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716160
A. P. Aji, H. Satoh, C. Apriono, E. Rahardjo, H. Inokawa
Optical input power calculation is critical in the performance evaluation of any kind of photodetector, including bolometers. By using a reference detector, the optical power from a light source can be determined based on the calibrated responsivity. We present the responsivity calibration of a reference pyroelectric detector based on the radiation from a blackbody source. The detector is operated in the infrared wavelength region and calibration is performed by using three different types of filter to evaluate the responsivity in the terahertz region. We consider the effect of the transmittance of the filters, atmospheric attenuation of the propagation medium, and optical characteristics of the parabolic mirror to accurately calculated the incident power from the blackbody source.
{"title":"Responsivity Calibration of Terahertz Pyroelectric Detector Based on Blackbody Radiator","authors":"A. P. Aji, H. Satoh, C. Apriono, E. Rahardjo, H. Inokawa","doi":"10.1109/QIR54354.2021.9716160","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716160","url":null,"abstract":"Optical input power calculation is critical in the performance evaluation of any kind of photodetector, including bolometers. By using a reference detector, the optical power from a light source can be determined based on the calibrated responsivity. We present the responsivity calibration of a reference pyroelectric detector based on the radiation from a blackbody source. The detector is operated in the infrared wavelength region and calibration is performed by using three different types of filter to evaluate the responsivity in the terahertz region. We consider the effect of the transmittance of the filters, atmospheric attenuation of the propagation medium, and optical characteristics of the parabolic mirror to accurately calculated the incident power from the blackbody source.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128297778","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716176
U. Darusalam, P. Priambodo, E. Rahardjo
The optical relaying networks (ORN) on free-space optical (FSO) communications give beneficial prospect in the development to reach the last mile users in the order of tenth kilometer. However, the ORN on FSO that is configured in many nodes of transmission has a major drawback. This major problem is risen by the induce of turbulence effects in every node that degrades the performance of system. In this work, the reception of signal by the receiver system in the ORN on FSO is investigated. We propose a method of noise filtering in the output of photodetector in order to enhance the performance of system. The proposed method is intended to enhance the performance for application of wavelength division multiplexing signal on the ORN on FSO. Four types of filters are investigated which are Gaussian-, Butterworth-, Bessel-and Chebyshev-filter. Two modulation formats of signal also implemented which are non-return to zero and return to zero. From the results of simulation, noise filtering through low-pass filter in the output of photodetector has the capability to improve the performance of bit-error-rate (BER) in the range value of 101 to 10-3 in comparison with non-filtering setup.
{"title":"Noise Filtering in the Output of Photodetector to Enhance the Performance of Optical Relaying Networks on FSO communications","authors":"U. Darusalam, P. Priambodo, E. Rahardjo","doi":"10.1109/QIR54354.2021.9716176","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716176","url":null,"abstract":"The optical relaying networks (ORN) on free-space optical (FSO) communications give beneficial prospect in the development to reach the last mile users in the order of tenth kilometer. However, the ORN on FSO that is configured in many nodes of transmission has a major drawback. This major problem is risen by the induce of turbulence effects in every node that degrades the performance of system. In this work, the reception of signal by the receiver system in the ORN on FSO is investigated. We propose a method of noise filtering in the output of photodetector in order to enhance the performance of system. The proposed method is intended to enhance the performance for application of wavelength division multiplexing signal on the ORN on FSO. Four types of filters are investigated which are Gaussian-, Butterworth-, Bessel-and Chebyshev-filter. Two modulation formats of signal also implemented which are non-return to zero and return to zero. From the results of simulation, noise filtering through low-pass filter in the output of photodetector has the capability to improve the performance of bit-error-rate (BER) in the range value of 101 to 10-3 in comparison with non-filtering setup.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125407135","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716196
Ihsan Ibrahim, A. A. P. Ratna, Naoki Fukuta
There were still some issues regarding human-interface occurred in multi-agent field or artificial intelligence. In general, due to the rapid advancement of technology into more interactive and intelligent, better human-interface improved the needs of multi-agent systems and artificial intelligence for more use of technologies that can help solve human problems. Vice versa, the human-interface could be improved and eased by using multi-agent or artificial intelligence. In the future aspect, there is also a prospect from the perspective that the human-interface itself could be used to improve the multi-agent or artificial intelligence’s internal process. It needs a learning process as a part of their life cycle to achieve the end goal. It could be a more efficient way to give the agent or the AI some knowledge to choose what is best or meaningful for its next generation of learning. This improvement concept hopes can reduce the duration of the learning process and cut the computation cost of the processing. In this paper, we would explain about the related issues of human-interface aspect in the multi-agent or artificial intelligence field that we got into a study as the specific works of this research into the discussion.
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Pub Date : 2021-10-13DOI: 10.1109/qir54354.2021.9716202
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