Pub Date : 2021-10-27DOI: 10.1109/iemcon53756.2021.9623192
Milon Hossain, Khuder Sadik, Md. Musfiqur Rahman, Fahad Ahmed, Md. Nur Hossain Bhuiyan, Mohammad Monirujjaman Khan
Skin cancer is very dangerous and deadly diseases in today's world. Between Malignant and Benign skin cancers, Malignant is the deadliest and Benign is curable. Due to the significant growth rate of Malignant and Benign skin cancer, its high treatment costs, and the mortality rate, the need for early detection of skin cancer has been increased. In most cases, these cells are manually identified and it takes time to cure them. In this paper it has been addressed the requirement for a cheap and fast detection of skin disease (Malignant and Benign) applying more effective CNN, PyTorch and to increase the accuracy four different ResNet models has been used. In this method, a pre-trained model named ResNet is used for image classification. It has been used four different version of ResNet model (ResNet18, ResNet50, ResNet101 and ResNet152) to increase the accuracy of our project. ResNet model is a specific type and advance version of deep convolutional neural network. It is better and faster than previously used VGG-16 per-trained model for image classification. Dataset used in this project is collected from Kaggle.com which contains almost 6,599 images to train the model and measure the accuracy. By using different version of ResNet model respectively observed different test result (86.34% for ResNet18 model, 88.78% for ResNet50, 89.09% for ResNet101 and 89.65% for ResNet152). It has been compared the accuracy from our proposed method with the existing method and obtained better accuracy rather than the existing method. The existing system gave an accuracy which is about 83.02% and this system gives more than 89.65% accuracy and it's higher than previously done on skin cancer detection project.
{"title":"Convolutional Neural Network Based Skin Cancer Detection (Malignant vs Benign)","authors":"Milon Hossain, Khuder Sadik, Md. Musfiqur Rahman, Fahad Ahmed, Md. Nur Hossain Bhuiyan, Mohammad Monirujjaman Khan","doi":"10.1109/iemcon53756.2021.9623192","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623192","url":null,"abstract":"Skin cancer is very dangerous and deadly diseases in today's world. Between Malignant and Benign skin cancers, Malignant is the deadliest and Benign is curable. Due to the significant growth rate of Malignant and Benign skin cancer, its high treatment costs, and the mortality rate, the need for early detection of skin cancer has been increased. In most cases, these cells are manually identified and it takes time to cure them. In this paper it has been addressed the requirement for a cheap and fast detection of skin disease (Malignant and Benign) applying more effective CNN, PyTorch and to increase the accuracy four different ResNet models has been used. In this method, a pre-trained model named ResNet is used for image classification. It has been used four different version of ResNet model (ResNet18, ResNet50, ResNet101 and ResNet152) to increase the accuracy of our project. ResNet model is a specific type and advance version of deep convolutional neural network. It is better and faster than previously used VGG-16 per-trained model for image classification. Dataset used in this project is collected from Kaggle.com which contains almost 6,599 images to train the model and measure the accuracy. By using different version of ResNet model respectively observed different test result (86.34% for ResNet18 model, 88.78% for ResNet50, 89.09% for ResNet101 and 89.65% for ResNet152). It has been compared the accuracy from our proposed method with the existing method and obtained better accuracy rather than the existing method. The existing system gave an accuracy which is about 83.02% and this system gives more than 89.65% accuracy and it's higher than previously done on skin cancer detection project.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"193 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":"128843621","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/iemcon53756.2021.9623110
Homayra Alam, Damian Valles
The road debris clean-up process can be improved by utilizing drones, Deep Learning, and object detection to optimize the operation and re-open roads for traffic. Common debris is unsecured items that fly out from vehicles after a vehicle accident. The cleaning procedure of the road debris after an accident is cumbersome and sensitive. It demands much workforce and a time-consuming process to haul debris properly. The paper aims to detect debris on the road using a drone to minimize the time cleaning procedure. Object detection API with the pre-trained model of SSD and Faster R-CNN is used for object detection. The accuracy graphs, evaluation matrix, and detection box score determine the efficient model for debris detection.
{"title":"Debris Object Detection Caused by Vehicle Accidents Using UAV and Deep Learning Techniques","authors":"Homayra Alam, Damian Valles","doi":"10.1109/iemcon53756.2021.9623110","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623110","url":null,"abstract":"The road debris clean-up process can be improved by utilizing drones, Deep Learning, and object detection to optimize the operation and re-open roads for traffic. Common debris is unsecured items that fly out from vehicles after a vehicle accident. The cleaning procedure of the road debris after an accident is cumbersome and sensitive. It demands much workforce and a time-consuming process to haul debris properly. The paper aims to detect debris on the road using a drone to minimize the time cleaning procedure. Object detection API with the pre-trained model of SSD and Faster R-CNN is used for object detection. The accuracy graphs, evaluation matrix, and detection box score determine the efficient model for debris detection.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"384 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":"129144827","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/iemcon53756.2021.9623105
B.A. Akalanka, K. Senevirathne, M.H.V Dias, W.A.R Nimantha, K. Chathurika, Chamari Silva
COVID -19 is one of the most contagious diseases in the 21st century. Therefore, there's an emerging need to contrive an accurate, gradual new method to identify this deadly virus. Apropos, we present “Smart assistance to ease the process of COVID -19/pneumonia detection” mobile application that can use to identify covid-19 contemplating patient's symptoms, health history, breathing information, chest CT scan and chest X-ray images. Stage 1 of the proposed application will prognosticate the danger level of the patient utilizing symptoms, breathing information, health history using machine learning techniques. Recognition and drawing out of patient's health background information by engaging the user to maximize the accuracy of the outcome is the main objective of this stage. Stage 2 of the application will identify COVID-19 by a chest X-ray/CT scan image, and it predicts the danger level using deep learning techniques. Classify the image to predict the danger level for COVID-19 is the main objective of this phase. Subsequently, all the predictions are sent to a physician and validate the outcome. Finally, patient will be notified about the results. This automatized application is built with the intention of reducing the cost of covid-19 identification tests like PCR tests and to give precise results as soon as possible. Our motive is to show that the proposed application could be a finer alternative for already existing COVID -19 identification tests. As a result, we achieved the best accuracy of 92%, 96% for CT scan, X-ray images classification and 94.08%, 74.19% accuracy for health history information analysis and breathing information analysis. We also achieved 94%, 71% accuracies for the COVID-19 prediction model and severity level prediction model based on symptoms.
{"title":"Smart Assistant to Ease the Process of COVID-19 and Pneumonia Detection","authors":"B.A. Akalanka, K. Senevirathne, M.H.V Dias, W.A.R Nimantha, K. Chathurika, Chamari Silva","doi":"10.1109/iemcon53756.2021.9623105","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623105","url":null,"abstract":"COVID -19 is one of the most contagious diseases in the 21st century. Therefore, there's an emerging need to contrive an accurate, gradual new method to identify this deadly virus. Apropos, we present “Smart assistance to ease the process of COVID -19/pneumonia detection” mobile application that can use to identify covid-19 contemplating patient's symptoms, health history, breathing information, chest CT scan and chest X-ray images. Stage 1 of the proposed application will prognosticate the danger level of the patient utilizing symptoms, breathing information, health history using machine learning techniques. Recognition and drawing out of patient's health background information by engaging the user to maximize the accuracy of the outcome is the main objective of this stage. Stage 2 of the application will identify COVID-19 by a chest X-ray/CT scan image, and it predicts the danger level using deep learning techniques. Classify the image to predict the danger level for COVID-19 is the main objective of this phase. Subsequently, all the predictions are sent to a physician and validate the outcome. Finally, patient will be notified about the results. This automatized application is built with the intention of reducing the cost of covid-19 identification tests like PCR tests and to give precise results as soon as possible. Our motive is to show that the proposed application could be a finer alternative for already existing COVID -19 identification tests. As a result, we achieved the best accuracy of 92%, 96% for CT scan, X-ray images classification and 94.08%, 74.19% accuracy for health history information analysis and breathing information analysis. We also achieved 94%, 71% accuracies for the COVID-19 prediction model and severity level prediction model based on symptoms.","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":"129995125","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/iemcon53756.2021.9623225
Jules Guiliary Ravanne, Y. L. Then, H. T. Su, I. Hijazin
This paper investigates the implementation and design of a low-power linear-in-decibel RF power detector using a 180-nm standard CMOS process for applications in the S-band frequency. The proposed circuit aims at applications in wireless communication and as sensing devices in the agricultural sector. A logarithmic amplifier is employed to achieve wide dynamic range linear-in-decibel output. A current-source-load RMS power detector is placed before the logarithmic amplifier to improve the RF power detector sensitivity. MOSFETS square-law principle in the saturation region is exploited to perform power detection. The logarithmic amplifier is realized using five identical differential limiting amplifiers, amplifying and compressing the wide dynamic range input signal. Each limiting amplifier is designed as 11.2 dB gain cells. The circuit is designed and simulated using 180-nm CMOS process parameters. The simulation results demonstrate that the RF power detector can detect power from −50 dBm to 0 dBm. The power detector operating frequency is from 2 GHz to 4 GHz, and its supply voltage is 1.8 V. The total power dissipation is 0.610 mW.
{"title":"A Linear-in-decibel RF Power Detector for Microwave Measurements in the S-band Frequency using CMOS Technology","authors":"Jules Guiliary Ravanne, Y. L. Then, H. T. Su, I. Hijazin","doi":"10.1109/iemcon53756.2021.9623225","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623225","url":null,"abstract":"This paper investigates the implementation and design of a low-power linear-in-decibel RF power detector using a 180-nm standard CMOS process for applications in the S-band frequency. The proposed circuit aims at applications in wireless communication and as sensing devices in the agricultural sector. A logarithmic amplifier is employed to achieve wide dynamic range linear-in-decibel output. A current-source-load RMS power detector is placed before the logarithmic amplifier to improve the RF power detector sensitivity. MOSFETS square-law principle in the saturation region is exploited to perform power detection. The logarithmic amplifier is realized using five identical differential limiting amplifiers, amplifying and compressing the wide dynamic range input signal. Each limiting amplifier is designed as 11.2 dB gain cells. The circuit is designed and simulated using 180-nm CMOS process parameters. The simulation results demonstrate that the RF power detector can detect power from −50 dBm to 0 dBm. The power detector operating frequency is from 2 GHz to 4 GHz, and its supply voltage is 1.8 V. The total power dissipation is 0.610 mW.","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":"129868056","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/iemcon53756.2021.9623119
Esther Jakubowicz, Eman Abdelfattah
Bitcoin's dominance in the cryptocurrency market has only increased in recent years. However, it experiences rapid spikes and declines that creates difficulty in predicting its future behavior. Much research has been done to find efficient models that predict with high accuracy, but with limited results. The goal of this study was to determine if higher accuracy can be achieved by focusing on a broader perspective of numeric ranges as opposed to specific time series price predictions. The predictions were concentrated on reporting the expected market direction for the following hour. In using one hour interval trading data and creating discrete classes of levels of hourly changes, five different Machine Learning models were trained and tested. Except for one model, cross validation accuracy ranging from 96-100% was achieved.
{"title":"The Rise and Fall of Bitcoin: Predicting Market Direction Using Machine Learning Models","authors":"Esther Jakubowicz, Eman Abdelfattah","doi":"10.1109/iemcon53756.2021.9623119","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623119","url":null,"abstract":"Bitcoin's dominance in the cryptocurrency market has only increased in recent years. However, it experiences rapid spikes and declines that creates difficulty in predicting its future behavior. Much research has been done to find efficient models that predict with high accuracy, but with limited results. The goal of this study was to determine if higher accuracy can be achieved by focusing on a broader perspective of numeric ranges as opposed to specific time series price predictions. The predictions were concentrated on reporting the expected market direction for the following hour. In using one hour interval trading data and creating discrete classes of levels of hourly changes, five different Machine Learning models were trained and tested. Except for one model, cross validation accuracy ranging from 96-100% was achieved.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"81 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":"126370211","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/iemcon53756.2021.9623121
M.R.L.Y Menikrama, C.S Liyanagunawardhana, H.G.D.M.I Amarasekara, M.S Ramasinghe, L. Weerasinghe, I. Weerasinghe
One of the technologies that has been gaining ground in recent years is Augmented Reality (AR), which allows to insert virtual objects into a real-world view using a device's camera and screen. This form of interaction associated with education can improve teaching and experiencing practical knowledge in schools, especially in more difficult subjects such as Chemistry. This study focused on virtual education by providing a platform for students to follow practical oriented subjects like Chemistry. As a result, a mobile application is created with four main functions that assist students during their learning process of Chemistry using the AR technique. The main functions are, AR with Artificial Intelligence (AI), Chemical equation identification and correction with Image Processing, Chabot with sentiment analysis and text summarization. The application is developed by using Machine Learning, AI with Deep Learning and Mobile Application development technologies. ARChem shows 3D models of flasks with important descriptions with the use and also features a Chabot with text summarization for frequently asked questions.
{"title":"ARChem: Augmented Reality Chemistry Lab","authors":"M.R.L.Y Menikrama, C.S Liyanagunawardhana, H.G.D.M.I Amarasekara, M.S Ramasinghe, L. Weerasinghe, I. Weerasinghe","doi":"10.1109/iemcon53756.2021.9623121","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623121","url":null,"abstract":"One of the technologies that has been gaining ground in recent years is Augmented Reality (AR), which allows to insert virtual objects into a real-world view using a device's camera and screen. This form of interaction associated with education can improve teaching and experiencing practical knowledge in schools, especially in more difficult subjects such as Chemistry. This study focused on virtual education by providing a platform for students to follow practical oriented subjects like Chemistry. As a result, a mobile application is created with four main functions that assist students during their learning process of Chemistry using the AR technique. The main functions are, AR with Artificial Intelligence (AI), Chemical equation identification and correction with Image Processing, Chabot with sentiment analysis and text summarization. The application is developed by using Machine Learning, AI with Deep Learning and Mobile Application development technologies. ARChem shows 3D models of flasks with important descriptions with the use and also features a Chabot with text summarization for frequently asked questions.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"10 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":"126427949","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/iemcon53756.2021.9623106
Alex Williams, Deepak K. Tosh
Provenance in computing systems is the key to establishing data integrity. It provides a historical ledger of data's life cycle through creation, ownership, consumption, and manipulation. With provenance in hand, it is possible to reverse engineer the state of the data that can lead to understanding how it was derived and verify its accuracy. This need for data integrity is extremely critical in scientific workflows to ensure verifiability and repeatability of the derived results. Due to the vast computational power required by scientific workflows, many operate within high performance computing (HPC) environments, where data is consumed and manipulated by a multitude of processes running on highly distributed infrastructure. The current landscape of HPC environments range from on-premise systems to cloud and grid based solutions. While the majority of research in digital provenance has been focused on standalone HPC environments, provenance in a heterogeneous HPC environment remains a challenge. In this paper we propose HyperProvenance, a high level system architecture especially for next generation heterogeneous HPC environments, which aims to increase confidence in workflow result accuracy through secure provenance collection.
{"title":"Scientific Workflow Provenance Architecture for Heterogeneous HPC Environments","authors":"Alex Williams, Deepak K. Tosh","doi":"10.1109/iemcon53756.2021.9623106","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623106","url":null,"abstract":"Provenance in computing systems is the key to establishing data integrity. It provides a historical ledger of data's life cycle through creation, ownership, consumption, and manipulation. With provenance in hand, it is possible to reverse engineer the state of the data that can lead to understanding how it was derived and verify its accuracy. This need for data integrity is extremely critical in scientific workflows to ensure verifiability and repeatability of the derived results. Due to the vast computational power required by scientific workflows, many operate within high performance computing (HPC) environments, where data is consumed and manipulated by a multitude of processes running on highly distributed infrastructure. The current landscape of HPC environments range from on-premise systems to cloud and grid based solutions. While the majority of research in digital provenance has been focused on standalone HPC environments, provenance in a heterogeneous HPC environment remains a challenge. In this paper we propose HyperProvenance, a high level system architecture especially for next generation heterogeneous HPC environments, which aims to increase confidence in workflow result accuracy through secure provenance collection.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"197 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":"121193281","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/iemcon53756.2021.9623246
Amin Qassoud, Chung-Horng Lung
Traffic Engineering (TE) is a critical topic in network routing and switching. The topic has been intensively investigated. New network architecture has also been proposed to improve TE, e.g., Multi-Protocol Label Switching (MPLS) architecture. MPLS has been widely used to in the past 15 years or so. However, the overhead associated with MPLS architecture is high, particularly the Resource Reservation Protocol (RSVP)-TE protocol used for signaling and path creation/maintenance. Segment Routing (SR) is a relatively new network solution to mitigate the high overhead issue of MPLS/RSVP- TE and it has gained increasing attention. SR for IPv6 (SRv6) has drawn a great deal of attention recently for efficient and flexible TE features. However, more research is still needed for SRv6-based bandwidth reservation for TE, as RSVP- TE used for bandwidth reservation is no longer part of SR. The objective of this paper is to develop bandwidth reservation algorithms for SR-based solutions and investigate the performance of those algorithms. The current focus is on depth-first search (DFS) and breath first search (BFS) bandwidth reservation algorithms. The preliminary outcomes show that BFS results in higher bandwidth usage, whereas DFS is more time efficient in path computations.
流量工程(TE)是网络路由与交换中的一个重要课题。这个话题已被深入研究。新的网络架构也被提出来改进TE，例如多协议标签交换(MPLS)架构。MPLS在过去的15年里得到了广泛的应用。但是，与MPLS体系结构相关的开销很高，特别是用于信令和路径创建/维护的资源保留协议(RSVP)-TE协议。SR (Segment Routing，分段路由)是一种较新的解决MPLS/RSVP- TE高开销问题的网络解决方案，越来越受到人们的关注。SR for IPv6 (SRv6)最近因其高效和灵活的TE特性而引起了极大的关注。然而，由于用于带宽预留的RSVP- TE不再是sr的一部分，因此基于srv6的TE带宽预留还需要更多的研究。本文的目的是为基于sr的解决方案开发带宽预留算法并研究这些算法的性能。目前的研究重点是深度优先搜索(DFS)和呼吸优先搜索(BFS)带宽预留算法。初步结果表明，BFS具有更高的带宽利用率，而DFS在路径计算方面具有更高的时间效率。
{"title":"Investigation of Bandwidth Reservation for Segment Routing","authors":"Amin Qassoud, Chung-Horng Lung","doi":"10.1109/iemcon53756.2021.9623246","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623246","url":null,"abstract":"Traffic Engineering (TE) is a critical topic in network routing and switching. The topic has been intensively investigated. New network architecture has also been proposed to improve TE, e.g., Multi-Protocol Label Switching (MPLS) architecture. MPLS has been widely used to in the past 15 years or so. However, the overhead associated with MPLS architecture is high, particularly the Resource Reservation Protocol (RSVP)-TE protocol used for signaling and path creation/maintenance. Segment Routing (SR) is a relatively new network solution to mitigate the high overhead issue of MPLS/RSVP- TE and it has gained increasing attention. SR for IPv6 (SRv6) has drawn a great deal of attention recently for efficient and flexible TE features. However, more research is still needed for SRv6-based bandwidth reservation for TE, as RSVP- TE used for bandwidth reservation is no longer part of SR. The objective of this paper is to develop bandwidth reservation algorithms for SR-based solutions and investigate the performance of those algorithms. The current focus is on depth-first search (DFS) and breath first search (BFS) bandwidth reservation algorithms. The preliminary outcomes show that BFS results in higher bandwidth usage, whereas DFS is more time efficient in path computations.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"31 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":"117118208","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 digital transformation journey in the public sector has become a common agenda for elected leaders, public administrators as well as academics and researchers in the past few years. However, evidence suggests that the efforts to achieve the anticipated benefits from digital transformation proved challenging. Prior studies suggest that several issues related to the introduction of new information systems have unfavourably affected digital public service delivery processes. This paper presents the result of a single case study conducted at one of the most digitalised Ministries of the Ethiopian Federal Government. Using interviews and publicly available documents, we identified a list of factors that could determine the success of digital transformation in public organisations. The findings indicate that the Ministry is struggling from a lack of clearly articulated and shared IT strategic vision and conducive organisational structure fostering digital transformation. Besides, the dysfunctional communications between the IT and remaining departments, lack of information security awareness and measures to mitigate information security risks, the incomplete utilisation of IT solutions due to low skill sets or non-existing culture encouraging digital literacy have all contributed to the bumpy digital transformation journey. The result of our study contributes to research and practice by pointing out various areas of concern that need to be monitored as digital services are continuously rolled out.
{"title":"Public Sector Digital Transformation: Challenges for Information Technology Leaders","authors":"Gideon Mekonnen Jonathan, K. Hailemariam, Bemenet Kasahun Gebremeskel, Sileshi Demesie Yalew","doi":"10.1109/iemcon53756.2021.9623161","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623161","url":null,"abstract":"The digital transformation journey in the public sector has become a common agenda for elected leaders, public administrators as well as academics and researchers in the past few years. However, evidence suggests that the efforts to achieve the anticipated benefits from digital transformation proved challenging. Prior studies suggest that several issues related to the introduction of new information systems have unfavourably affected digital public service delivery processes. This paper presents the result of a single case study conducted at one of the most digitalised Ministries of the Ethiopian Federal Government. Using interviews and publicly available documents, we identified a list of factors that could determine the success of digital transformation in public organisations. The findings indicate that the Ministry is struggling from a lack of clearly articulated and shared IT strategic vision and conducive organisational structure fostering digital transformation. Besides, the dysfunctional communications between the IT and remaining departments, lack of information security awareness and measures to mitigate information security risks, the incomplete utilisation of IT solutions due to low skill sets or non-existing culture encouraging digital literacy have all contributed to the bumpy digital transformation journey. The result of our study contributes to research and practice by pointing out various areas of concern that need to be monitored as digital services are continuously rolled out.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"31 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":"128382281","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}
This paper aims to solve the optimization problems in far-field wireless power transfer systems using machine learning techniques. We assembled the RF power transfer robot, which can emit the electromagnetic wave to charge the energy harvesters that are deployed in the experimental field. The wireless transmitter intends to charge all the energy harvesters in a fair manner. Since the energy harvesters can be either stationary or mobile, a multi-armed bandit(MAB) problem is formulated and we use Upper Confidence Bound(UCB) algorithm to determine the optimal transmission strategy. As the number of the transmitters is increased, multiple wireless transmitters coordinate with each other to boost the levels of energy harvesting at all energy harvesters. Correspondingly, we formulate a combinational MAB problem and UCB algorithm is applied to determine the optimal transmission strategy for each transmitter. The simulation results prove the superiority of the Multi-armed bandit approach in solving the proposed optimization problems.
{"title":"Optimization of Transmission Strategy for Wireless Power Transfer Using Multi-Armed Bandit Algorithm","authors":"Yuan Xing, Riley Young, Giaolong Nguyen, Maxwell Lefebvre, Tianchi Zhao, Haowen Pan","doi":"10.1109/iemcon53756.2021.9623190","DOIUrl":"https://doi.org/10.1109/iemcon53756.2021.9623190","url":null,"abstract":"This paper aims to solve the optimization problems in far-field wireless power transfer systems using machine learning techniques. We assembled the RF power transfer robot, which can emit the electromagnetic wave to charge the energy harvesters that are deployed in the experimental field. The wireless transmitter intends to charge all the energy harvesters in a fair manner. Since the energy harvesters can be either stationary or mobile, a multi-armed bandit(MAB) problem is formulated and we use Upper Confidence Bound(UCB) algorithm to determine the optimal transmission strategy. As the number of the transmitters is increased, multiple wireless transmitters coordinate with each other to boost the levels of energy harvesting at all energy harvesters. Correspondingly, we formulate a combinational MAB problem and UCB algorithm is applied to determine the optimal transmission strategy for each transmitter. The simulation results prove the superiority of the Multi-armed bandit approach in solving the proposed optimization problems.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"20 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":"116862375","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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