Pub Date : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623174
R. Islam, Fardeen Mahbub, Sayed Abdul Kadir Al-Nahiun, MD. Ismail Hossain Rabbi
Liver cancer has become a prevalent cause of mortality in recent decades. Among various kinds of Liver Cancer, Hepatocellular Carcinoma (HCC) or Hepatoma has become very common. In this paper, a detailed analysis has been made about the human liver through detecting the early stage of HCC using a Monopole Antenna, which has been successfully designed in the CST Studio Suite 2019 software. As a part of the valid analysis, a Human Liver phantom has also been developed in the same software, including the combinations of Skin, Fat, Muscle, Liver. Additionally, using various kinds of dielectric properties, a group of tumor cells was created in the Liver Phantom to determine the Antenna model's effectiveness. After that, the performance of the designed Antenna was determined in free space and including the tumor cells as well. Simulation results have been successfully obtained, including Return Loss (S1, 1) of −27.48 dB, VSWR of 1.088, Efficiency of 70.55%, and Specific Absorption Rate (SAR) of 0.665 W/kg, and many more after applying to the Hepatoma-affected liver phantom. Based on the determined results, a detailed performance analysis of the designed Monopole Antenna has been made in this paper, validating the research's effectiveness and objectives.
近几十年来，肝癌已成为一种普遍的死亡原因。在各种类型的肝癌中，肝细胞癌(HCC)或肝癌(Hepatoma)已经变得非常常见。本文通过在CST Studio Suite 2019软件中成功设计的单极子天线(monpole Antenna)检测早期HCC，对人类肝脏进行了详细分析。作为有效分析的一部分，在同一软件中也开发了一个人体肝脏模型，包括皮肤，脂肪，肌肉，肝脏的组合。此外，利用各种介电特性，在肝幻影中创建了一组肿瘤细胞，以确定天线模型的有效性。然后，对设计的天线在自由空间和包括肿瘤细胞在内的情况下的性能进行了测试。仿真结果表明，回波损耗(S1, 1)为−27.48 dB, VSWR为1.088，效率为70.55%，比吸收率(SAR)为0.665 W/kg，应用于肝癌影响的肝模体后，比吸收率(SAR)更高。在此基础上，对设计的单极子天线进行了详细的性能分析，验证了研究的有效性和目的。
{"title":"Design and Performance Analysis of Monopole Antenna for the Detection of Early-Stage Hepatocellular Carcinoma (HCC) in Human Liver","authors":"R. Islam, Fardeen Mahbub, Sayed Abdul Kadir Al-Nahiun, MD. Ismail Hossain Rabbi","doi":"10.1109/iemcon53756.2021.9623174","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623174","url":null,"abstract":"Liver cancer has become a prevalent cause of mortality in recent decades. Among various kinds of Liver Cancer, Hepatocellular Carcinoma (HCC) or Hepatoma has become very common. In this paper, a detailed analysis has been made about the human liver through detecting the early stage of HCC using a Monopole Antenna, which has been successfully designed in the CST Studio Suite 2019 software. As a part of the valid analysis, a Human Liver phantom has also been developed in the same software, including the combinations of Skin, Fat, Muscle, Liver. Additionally, using various kinds of dielectric properties, a group of tumor cells was created in the Liver Phantom to determine the Antenna model's effectiveness. After that, the performance of the designed Antenna was determined in free space and including the tumor cells as well. Simulation results have been successfully obtained, including Return Loss (S1, 1) of −27.48 dB, VSWR of 1.088, Efficiency of 70.55%, and Specific Absorption Rate (SAR) of 0.665 W/kg, and many more after applying to the Hepatoma-affected liver phantom. Based on the determined results, a detailed performance analysis of the designed Monopole Antenna has been made in this paper, validating the research's effectiveness and objectives.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131960874","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 : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623202
Salma Samy, Karim A. Banawan, M. Azab, Mohamed Rizk
The critical nature of smart grids (SGs) attracts various network attacks and malicious manipulations. Existent SG solutions are less capable of ensuring secure and trustworthy operation. This is due to the large-scale nature of SGs and reliance on network protocols for trust management. A particular example of such severe attacks is the false data injection (FDI). FDI refers to a network attack, where meters' measurements are manipulated before being reported in such a way that the energy system takes flawed decisions. In this paper, we exploit the secure nature of blockchains to construct a data management framework based on public blockchain. Our framework enables trustworthy data storage, verification, and exchange between SG components and decision-makers. Our proposed system enables miners to invest their computational power to verify blockchain transactions in a fully distributed manner. The mining logic employs machine learning (ML) techniques to identify the locations of compromised meters in the network, which are responsible for generating FDI attacks. In return, miners receive virtual credit, which may be used to pay their electric bills. Our design circumvents single points of failure and intentional FDI attempts. Our numerical results compare the accuracy of three different ML-based mining logic techniques in two scenarios: focused and distributed FDI attacks for different attack levels. Finally, we proposed a majority-decision mining technique for the practical case of an unknown FDI attack level.
{"title":"Smart Blockchain-based Control-data Protection Framework for Trustworthy Smart Grid Operations","authors":"Salma Samy, Karim A. Banawan, M. Azab, Mohamed Rizk","doi":"10.1109/iemcon53756.2021.9623202","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623202","url":null,"abstract":"The critical nature of smart grids (SGs) attracts various network attacks and malicious manipulations. Existent SG solutions are less capable of ensuring secure and trustworthy operation. This is due to the large-scale nature of SGs and reliance on network protocols for trust management. A particular example of such severe attacks is the false data injection (FDI). FDI refers to a network attack, where meters' measurements are manipulated before being reported in such a way that the energy system takes flawed decisions. In this paper, we exploit the secure nature of blockchains to construct a data management framework based on public blockchain. Our framework enables trustworthy data storage, verification, and exchange between SG components and decision-makers. Our proposed system enables miners to invest their computational power to verify blockchain transactions in a fully distributed manner. The mining logic employs machine learning (ML) techniques to identify the locations of compromised meters in the network, which are responsible for generating FDI attacks. In return, miners receive virtual credit, which may be used to pay their electric bills. Our design circumvents single points of failure and intentional FDI attempts. Our numerical results compare the accuracy of three different ML-based mining logic techniques in two scenarios: focused and distributed FDI attacks for different attack levels. Finally, we proposed a majority-decision mining technique for the practical case of an unknown FDI attack level.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133399382","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 : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623169
D. Jayasinghe, W. Rankothge, N. Gamage, T. Gamage, S. Uwanpriya, D. Amarasinghe
Software-Defined Networking (SDN) has become a popular and widely used approach with Cloud Service Providers (CSPs). With the introduction of Virtualized Security Functions (VSFs), and offering them as a service, CSPs are exploring effective and efficient approaches for resource management in the cloud infrastructure, considering specific requirements of VSFs. Network traffic prediction is an important component of cloud resource management, as prediction helps CSPs to take necessary proactive management actions, specifically for VSFs. This research focuses on introducing an algorithm to predict the network traffic traverse via a cloud platform where VSFs are offered as a service, by using the Auto-Regressive Integrated Moving Average (ARIMA) model. In this paper, the implementation and performance of the traffic prediction algorithm are presented. The results show that the network traffic in cloud environments can be effectively predicted by using the introduced algorithm with an accuracy of 96.49%.
{"title":"Network Traffic Prediction for a Software Defined Network Based Virtualized Security Functions Platform","authors":"D. Jayasinghe, W. Rankothge, N. Gamage, T. Gamage, S. Uwanpriya, D. Amarasinghe","doi":"10.1109/iemcon53756.2021.9623169","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623169","url":null,"abstract":"Software-Defined Networking (SDN) has become a popular and widely used approach with Cloud Service Providers (CSPs). With the introduction of Virtualized Security Functions (VSFs), and offering them as a service, CSPs are exploring effective and efficient approaches for resource management in the cloud infrastructure, considering specific requirements of VSFs. Network traffic prediction is an important component of cloud resource management, as prediction helps CSPs to take necessary proactive management actions, specifically for VSFs. This research focuses on introducing an algorithm to predict the network traffic traverse via a cloud platform where VSFs are offered as a service, by using the Auto-Regressive Integrated Moving Average (ARIMA) model. In this paper, the implementation and performance of the traffic prediction algorithm are presented. The results show that the network traffic in cloud environments can be effectively predicted by using the introduced algorithm with an accuracy of 96.49%.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133599267","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 : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623085
B. K. Murthy, S. Shiva
The Internet of Things (IoT) has evolved at a faster rate in recent years. Fog computing can perform a substantial amount of computation at a faster rate and is capable of dealing with IoT applications with huge traffic demands and stringent Quality of Service (QoS) requirements. Resource provisioning issues in fog computing are addressed using double-state-temporal difference learning. By considering double Q-states while propagating the temporal difference error, high quality resource provisioning policies can be formed. The proposed resource-provisioning scheme outperforms one of the recent works in terms of the performance metrics such as execution time, learning rate, accuracy and resource utilization rate under varying uncertainties of the task and grid resource parameters.
{"title":"Double-State-Temporal Difference Learning for Resource Provisioning in Uncertain Fog Computing Environment","authors":"B. K. Murthy, S. Shiva","doi":"10.1109/iemcon53756.2021.9623085","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623085","url":null,"abstract":"The Internet of Things (IoT) has evolved at a faster rate in recent years. Fog computing can perform a substantial amount of computation at a faster rate and is capable of dealing with IoT applications with huge traffic demands and stringent Quality of Service (QoS) requirements. Resource provisioning issues in fog computing are addressed using double-state-temporal difference learning. By considering double Q-states while propagating the temporal difference error, high quality resource provisioning policies can be formed. The proposed resource-provisioning scheme outperforms one of the recent works in terms of the performance metrics such as execution time, learning rate, accuracy and resource utilization rate under varying uncertainties of the task and grid resource parameters.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114458097","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 : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623067
Adedayo Aribisala, Mohammad S. Khan, G. Husari
Smart grid architecture and Software-defined Networking (SDN) have evolved into a centrally controlled infrastructure that captures and extracts data in real-time through sensors, smart-meters, and virtual machines. These advances pose a risk and increase the vulnerabilities of these infrastructures to sophisticated cyberattacks like distributed denial of service (DDoS), false data injection attack (FDIA), and Data replay. Integrating machine learning with a network intrusion detection system (NIDS) can improve the system's accuracy and precision when detecting suspicious signatures and network anomalies. Analyzing data in real-time using trained and tested hyperparameters on a network traffic dataset applies to most network infrastructures. The NSL-KDD dataset implemented holds various classes, attack types, protocol suites like TCP, HTTP, and POP, which are critical to packet transmission on a smart grid network. In this paper, we leveraged existing machine learning (ML) algorithms, Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), and Bagging; to perform a detailed performance comparison of selected classifiers. We propose a multi-level hybrid model of SVM integrated with RF for improved accuracy and precision during network filtering. The hybrid model SVM-RF returned an average accuracy of 94% in 10-fold cross-validation and 92.75%in an 80-20% split during class classification.
{"title":"MACHINE LEARNING ALGORITHMS AND THEIR APPLICATIONS IN CLASSIFYING CYBER-ATTACKS ON A SMART GRID NETWORK","authors":"Adedayo Aribisala, Mohammad S. Khan, G. Husari","doi":"10.1109/iemcon53756.2021.9623067","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623067","url":null,"abstract":"Smart grid architecture and Software-defined Networking (SDN) have evolved into a centrally controlled infrastructure that captures and extracts data in real-time through sensors, smart-meters, and virtual machines. These advances pose a risk and increase the vulnerabilities of these infrastructures to sophisticated cyberattacks like distributed denial of service (DDoS), false data injection attack (FDIA), and Data replay. Integrating machine learning with a network intrusion detection system (NIDS) can improve the system's accuracy and precision when detecting suspicious signatures and network anomalies. Analyzing data in real-time using trained and tested hyperparameters on a network traffic dataset applies to most network infrastructures. The NSL-KDD dataset implemented holds various classes, attack types, protocol suites like TCP, HTTP, and POP, which are critical to packet transmission on a smart grid network. In this paper, we leveraged existing machine learning (ML) algorithms, Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), and Bagging; to perform a detailed performance comparison of selected classifiers. We propose a multi-level hybrid model of SVM integrated with RF for improved accuracy and precision during network filtering. The hybrid model SVM-RF returned an average accuracy of 94% in 10-fold cross-validation and 92.75%in an 80-20% split during class classification.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117066222","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 : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623179
Hung-Chin Jang, Ying-Tzu Chen
Moving towards Industry 5.0, the Industrial Internet of Things (IIoT) has become an indispensable role. IIoT devices could help manufacturing companies to observe various production statuses hence improving production efficiency. However, its limitation on resources and features makes robust security implementation a big challenge. Implementing a secure, general and scalable system to adopt across industries is one of the most critical topics for IIoT. This paper proposed a blockchain-based system architecture to auto-update, monitor, and fix the IIoT devices' software. We take the software status snapshots and store them as a blockchain ledger to protect the software's integrity and the software from unauthorized modification. To achieve auto-update and auto fix, we also simulate the concept of a blockchain contract to create the ledger whenever there are changes. As a result, the changes could continuously be tracked. The performance and scalability are also evaluated. The result shows that the system could also be deployed in larger-scale IIoT devices. The software update and fix could be guaranteed authenticated, and the unauthorized software could be monitored and detected.
{"title":"A Blockchain-Based Software Always-On System","authors":"Hung-Chin Jang, Ying-Tzu Chen","doi":"10.1109/iemcon53756.2021.9623179","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623179","url":null,"abstract":"Moving towards Industry 5.0, the Industrial Internet of Things (IIoT) has become an indispensable role. IIoT devices could help manufacturing companies to observe various production statuses hence improving production efficiency. However, its limitation on resources and features makes robust security implementation a big challenge. Implementing a secure, general and scalable system to adopt across industries is one of the most critical topics for IIoT. This paper proposed a blockchain-based system architecture to auto-update, monitor, and fix the IIoT devices' software. We take the software status snapshots and store them as a blockchain ledger to protect the software's integrity and the software from unauthorized modification. To achieve auto-update and auto fix, we also simulate the concept of a blockchain contract to create the ledger whenever there are changes. As a result, the changes could continuously be tracked. The performance and scalability are also evaluated. The result shows that the system could also be deployed in larger-scale IIoT devices. The software update and fix could be guaranteed authenticated, and the unauthorized software could be monitored and detected.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123679106","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 : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623258
Jules Guiliary Ravanne, Y. L. Then, H. T. Su, I. Hijazin
Power detector chip design and fabrication have experienced significant advancement with the emergence of various technological processes such as BiCMOS and CMOS. With the continuous downscaling of semiconductor devices, chip fabrication has become more complex and less accessible. This paper investigates the design and analysis of RMS power detectors using power MOSFET. A BSIM3 model developed from extracted parameters of the power MOSFET datasheet was employed to design and simulate the RMS power detector. A cascode structure with a current-source load was used to realize high conversion gain and sensitivity. RMS detection is realized by exploiting the square-law principles of MOS transistors in the strong inversion region. The proposed RMS detector targets practical applications in the agricultural sector and educational institutions. The RMS power detector was fabricated using FR4 PCB substrate. The measurement results at 2 GHz suggest that the RMS power detector employed using power MOSFET on FR4 PCB substrate can detect RF power.
{"title":"Design and Analysis of a Practical RMS Power Detector Using Power MOSFET","authors":"Jules Guiliary Ravanne, Y. L. Then, H. T. Su, I. Hijazin","doi":"10.1109/iemcon53756.2021.9623258","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623258","url":null,"abstract":"Power detector chip design and fabrication have experienced significant advancement with the emergence of various technological processes such as BiCMOS and CMOS. With the continuous downscaling of semiconductor devices, chip fabrication has become more complex and less accessible. This paper investigates the design and analysis of RMS power detectors using power MOSFET. A BSIM3 model developed from extracted parameters of the power MOSFET datasheet was employed to design and simulate the RMS power detector. A cascode structure with a current-source load was used to realize high conversion gain and sensitivity. RMS detection is realized by exploiting the square-law principles of MOS transistors in the strong inversion region. The proposed RMS detector targets practical applications in the agricultural sector and educational institutions. The RMS power detector was fabricated using FR4 PCB substrate. The measurement results at 2 GHz suggest that the RMS power detector employed using power MOSFET on FR4 PCB substrate can detect RF power.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939082","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 : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623170
Gemilang Kurniawan Soejantono, M. I. Nashiruddin, S. N. Hertiana, M. Nugraha
Internet use has been exceptional during the previous several decades. As a result, Wide Area Network (WAN), employed in all types of inter-data center, enterprise, and carrier networks, has become a vital infrastructure since it serves as one of the internet's most crucial transmission mediums. However, due to the transfer of enterprise data to the cloud, internet traffic in the future is changing. As a result, traditional WAN connectivity will be inadequate. One of the viable solutions to this is to implement Software-Defined WAN (SD-WAN) for enterprises, particularly in Business-to-Business (B2B). SD-WAN replaces existing Multi-Protocol Label Switching (MPLS) with multi-broadband internet with high bandwidth, encrypted with Virtual Private Network (VPN) IP Sec standard, and simplifies branch management with a single dashboard covering multiple branches. This study aims to evaluate the performance of deploying SD-WAN on mining enterprises' public links such as Direct Internet Access (DIA), fixed broadband, and 4G LTE, and private links, namely, Very Small Aperture Terminal (VSAT) and Multi-Protocol Layer Switching (MPLS), by utilizing SD-WAN's architecture and scenarios testing with the head office in Jakarta and branch office in Balikpapan. The results obtained that SD-WAN works appropriately when one of the multiple links is down. Meanwhile, when multiple links are down, the SD-WAN can still access a Voice over IP (VoIP) and video conference between the head office in Jakarta and a branch office in Balikpapan.
在过去的几十年里，互联网的使用一直是例外。因此，广域网(WAN)作为互联网最重要的传输媒介之一，已成为重要的基础设施，用于所有类型的数据中心间、企业和运营商网络。然而，由于企业数据向云的传输，未来的互联网流量正在发生变化。因此，传统的广域网连接将是不够的。一个可行的解决方案是为企业实现软件定义广域网(SD-WAN)，特别是在企业对企业(B2B)中。SD-WAN以高带宽的多宽带互联网取代现有的多协议标签交换(MPLS)，使用虚拟专用网(VPN) IP Sec标准加密，并通过单个仪表板覆盖多个分支机构来简化分支机构管理。本研究旨在通过SD-WAN的架构和在雅加达总部和巴厘巴潘分公司的场景测试，评估SD-WAN在矿业企业直接互联网接入(DIA)、固定宽带和4G LTE等公共链路以及在甚小孔径终端(VSAT)和多协议层交换(MPLS)等私有链路上部署的性能。结果表明，当多条链路中有一条链路断开时，SD-WAN可以正常工作。同时，当多条链路中断时，SD-WAN仍然可以访问雅加达总部和巴厘巴盘分支机构之间的VoIP和视频会议。
{"title":"Performance Evaluation of SD-WAN Deployment for XYZ Enterprise Company in Indonesia","authors":"Gemilang Kurniawan Soejantono, M. I. Nashiruddin, S. N. Hertiana, M. Nugraha","doi":"10.1109/iemcon53756.2021.9623170","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623170","url":null,"abstract":"Internet use has been exceptional during the previous several decades. As a result, Wide Area Network (WAN), employed in all types of inter-data center, enterprise, and carrier networks, has become a vital infrastructure since it serves as one of the internet's most crucial transmission mediums. However, due to the transfer of enterprise data to the cloud, internet traffic in the future is changing. As a result, traditional WAN connectivity will be inadequate. One of the viable solutions to this is to implement Software-Defined WAN (SD-WAN) for enterprises, particularly in Business-to-Business (B2B). SD-WAN replaces existing Multi-Protocol Label Switching (MPLS) with multi-broadband internet with high bandwidth, encrypted with Virtual Private Network (VPN) IP Sec standard, and simplifies branch management with a single dashboard covering multiple branches. This study aims to evaluate the performance of deploying SD-WAN on mining enterprises' public links such as Direct Internet Access (DIA), fixed broadband, and 4G LTE, and private links, namely, Very Small Aperture Terminal (VSAT) and Multi-Protocol Layer Switching (MPLS), by utilizing SD-WAN's architecture and scenarios testing with the head office in Jakarta and branch office in Balikpapan. The results obtained that SD-WAN works appropriately when one of the multiple links is down. Meanwhile, when multiple links are down, the SD-WAN can still access a Voice over IP (VoIP) and video conference between the head office in Jakarta and a branch office in Balikpapan.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129531125","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}
The purpose of visual-semantic embedding is to respectively map image and text to a common embedding space and perform cross-modal semantic alignment learning. Image-text matching is also the main research content of visual semantic embedding. Existing researches have confirmed that in visual-semantic embedding, a simple pooling strategy can also achieve a good performance. However, the existing visual semantic pooling strategies (aggregators) generally have some problems, including adding additional training parameters, increasing training time, ignoring intra-modal semantic-related information, and so on. In this paper, we propose a Super Visual Semantic Embedding (SVSE) Model based on Softmax Pooling (SoftPool). We introduced the softmax pooling strategy into visual semantic embedding for the first time. SoftPool is not only simple to implement but also doesn't introduce new additional training parameters. It can adaptively calculate the weights between different feature values and preserve more intra-modal correlation information between different features. At the same time, we combine the enhanced semantic representation module and our softmax pooling strategy to construct the intra-modal semantic association, which is used to improve the performance of the visual semantic embedding in image-text matching. Undoubtedly, our proposed method possesses a higher engineering application value than other methods. Experiments are conducted on two widely used cross-modal image-text datasets, namely MS-COCO and Flickr-30K. Comparing with the best pooling strategy, our proposed softmax pooling strategy not only is better in training time but also outperforms by 0.48% (5K) on MS-COCO and 1.95% on Flickr-30K at R@1 (image retrieval). Moreover, comparing with the best visual semantic embedding model, our proposed SVSE outperforms by 2.83% (5K) on MS-COCO and 4.89% (1K) on Flickr-30K at R@1 (image retrieval), respectively. Our code is available at https://github.com/zengzhixian/SoftPool_SVSE.git.
{"title":"Softmax Pooling for Super Visual Semantic Embedding*","authors":"Zhixian Zeng, Jianjun Cao, Nianfeng Weng, Guoquan Jiang, Yizhuo Rao, Yuxin Xu","doi":"10.1109/iemcon53756.2021.9623131","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623131","url":null,"abstract":"The purpose of visual-semantic embedding is to respectively map image and text to a common embedding space and perform cross-modal semantic alignment learning. Image-text matching is also the main research content of visual semantic embedding. Existing researches have confirmed that in visual-semantic embedding, a simple pooling strategy can also achieve a good performance. However, the existing visual semantic pooling strategies (aggregators) generally have some problems, including adding additional training parameters, increasing training time, ignoring intra-modal semantic-related information, and so on. In this paper, we propose a Super Visual Semantic Embedding (SVSE) Model based on Softmax Pooling (SoftPool). We introduced the softmax pooling strategy into visual semantic embedding for the first time. SoftPool is not only simple to implement but also doesn't introduce new additional training parameters. It can adaptively calculate the weights between different feature values and preserve more intra-modal correlation information between different features. At the same time, we combine the enhanced semantic representation module and our softmax pooling strategy to construct the intra-modal semantic association, which is used to improve the performance of the visual semantic embedding in image-text matching. Undoubtedly, our proposed method possesses a higher engineering application value than other methods. Experiments are conducted on two widely used cross-modal image-text datasets, namely MS-COCO and Flickr-30K. Comparing with the best pooling strategy, our proposed softmax pooling strategy not only is better in training time but also outperforms by 0.48% (5K) on MS-COCO and 1.95% on Flickr-30K at R@1 (image retrieval). Moreover, comparing with the best visual semantic embedding model, our proposed SVSE outperforms by 2.83% (5K) on MS-COCO and 4.89% (1K) on Flickr-30K at R@1 (image retrieval), respectively. Our code is available at https://github.com/zengzhixian/SoftPool_SVSE.git.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128420938","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 : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623177
Taslima Akter Tamanna, S. Choudhury, Afsana, Mohammad Monirujjaman Khan
In Bangladesh, there is a shortage of legitimate nourishment data frameworks that can give fitting sustenance messages dependent on various rules for pregnant ladies and newborn children. Lack of healthy sustenance devastatingly affects people's wellbeing and prosperity and the monetary improvement of nations. Conversely, essential or tertiary health laborers couldn't offer vital assistance to them. With so many people becoming ill from the (COVID-19), poor weight control plans exacerbate pre-existing conditions, putting them at greater risk. Individuals living with chronic illnesses who have been diagnosed with COVID-19 must improve their mental health and count calories to ensure that they remain in good health. Look for direct and psychosocial support from suitably prepared wellbeing care experts, including community-based lay and peer guides. Venturing into nourishment counsel, advancing breastfeeding, and battling deception around COVID-19 transmission will offer assistance to protect the role of nutritious nourishment as a partner against sickness. Any health worker in Bangladesh can easily use this application. Our health laborers regularly neglect to convey legitimate nourishment data to moms. Such an instrument can be helpful in giving a proper method to show particular nourishment messages to mothers dependent on their wellbeing stages and dependent on their baby's age. The design of this application can provide a legitimate office for conveying sustenance messages to mothers and workers. This framework may have to be examined occasionally to meet the progression of client prerequisites and be applied properly.
{"title":"Mobile Application Based Teli-nutrition System for Covid-19 Pandemic","authors":"Taslima Akter Tamanna, S. Choudhury, Afsana, Mohammad Monirujjaman Khan","doi":"10.1109/iemcon53756.2021.9623177","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623177","url":null,"abstract":"In Bangladesh, there is a shortage of legitimate nourishment data frameworks that can give fitting sustenance messages dependent on various rules for pregnant ladies and newborn children. Lack of healthy sustenance devastatingly affects people's wellbeing and prosperity and the monetary improvement of nations. Conversely, essential or tertiary health laborers couldn't offer vital assistance to them. With so many people becoming ill from the (COVID-19), poor weight control plans exacerbate pre-existing conditions, putting them at greater risk. Individuals living with chronic illnesses who have been diagnosed with COVID-19 must improve their mental health and count calories to ensure that they remain in good health. Look for direct and psychosocial support from suitably prepared wellbeing care experts, including community-based lay and peer guides. Venturing into nourishment counsel, advancing breastfeeding, and battling deception around COVID-19 transmission will offer assistance to protect the role of nutritious nourishment as a partner against sickness. Any health worker in Bangladesh can easily use this application. Our health laborers regularly neglect to convey legitimate nourishment data to moms. Such an instrument can be helpful in giving a proper method to show particular nourishment messages to mothers dependent on their wellbeing stages and dependent on their baby's age. The design of this application can provide a legitimate office for conveying sustenance messages to mothers and workers. This framework may have to be examined occasionally to meet the progression of client prerequisites and be applied properly.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127326729","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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