Pub Date : 2024-02-12DOI: 10.4108/eetinis.v11i1.4618
Hieu Duong-Trung, Nghia Duong-Trung
This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.
{"title":"Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries","authors":"Hieu Duong-Trung, Nghia Duong-Trung","doi":"10.4108/eetinis.v11i1.4618","DOIUrl":"https://doi.org/10.4108/eetinis.v11i1.4618","url":null,"abstract":"This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843594","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 : 2024-02-12DOI: 10.4108/eetinis.v11i1.4618
Hieu Duong-Trung, Nghia Duong-Trung
This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.
{"title":"Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries","authors":"Hieu Duong-Trung, Nghia Duong-Trung","doi":"10.4108/eetinis.v11i1.4618","DOIUrl":"https://doi.org/10.4108/eetinis.v11i1.4618","url":null,"abstract":"This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139783811","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 : 2024-01-16DOI: 10.4108/eetinis.v11i1.4530
Vu Anh Dao, Tran Tri Thanh Thuy, Vo Nguyen Quoc Bao, Truong Cao Dung, N. X. Quyen
Secured broadband radio communications are becoming increasingly pivotal for high-speed connectivity in radio access networks, playing a crucial role in both mobile information systems and wireless IoT connections. This paper introduces a chaos-based two-channel digital radio communication system utilizing fiber optic radio transmission technology. The system comprises two radio channels operating at up to 1 Gbps using amplitude shift keying (ASK) modulation, followed by modulation with a chaotic sequence before conversion to the optical domain using the MZM modulator. To compensate for fiber loss, the system utilizes an Erbium Doped Fiber Amplifier (EDFA) and employs the optical links through standard ITU-G.655 optical fibers. Numerical simulation of the designed system is performed using the commercialized simulation software Optisystem V.15 to assess and characterize transmission performance. The results demonstrate the system’s effective operation on two channels with a fiber transmission distance of up to 110 km, maintaining a bit error ratio of less than 10−9. This feature ensures reliable performance for high-speed radio connections, particularly in applications such as fronthaul networks in cloud radio access and wireless sensor network connections.
{"title":"Design of A Chaos-based Digital Radio over Fiber Transmission Link using ASK Modulation for Wireless Communication Systems","authors":"Vu Anh Dao, Tran Tri Thanh Thuy, Vo Nguyen Quoc Bao, Truong Cao Dung, N. X. Quyen","doi":"10.4108/eetinis.v11i1.4530","DOIUrl":"https://doi.org/10.4108/eetinis.v11i1.4530","url":null,"abstract":"Secured broadband radio communications are becoming increasingly pivotal for high-speed connectivity in radio access networks, playing a crucial role in both mobile information systems and wireless IoT connections. This paper introduces a chaos-based two-channel digital radio communication system utilizing fiber optic radio transmission technology. The system comprises two radio channels operating at up to 1 Gbps using amplitude shift keying (ASK) modulation, followed by modulation with a chaotic sequence before conversion to the optical domain using the MZM modulator. To compensate for fiber loss, the system utilizes an Erbium Doped Fiber Amplifier (EDFA) and employs the optical links through standard ITU-G.655 optical fibers. Numerical simulation of the designed system is performed using the commercialized simulation software Optisystem V.15 to assess and characterize transmission performance. The results demonstrate the system’s effective operation on two channels with a fiber transmission distance of up to 110 km, maintaining a bit error ratio of less than 10−9. This feature ensures reliable performance for high-speed radio connections, particularly in applications such as fronthaul networks in cloud radio access and wireless sensor network connections.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139618522","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 : 2024-01-08DOI: 10.4108/eetinis.v11i1.4323
Majed Elwardy, H. Zepernick, Thi My Chinh Chu, Yan Hu
Immersive media such as virtual reality, augmented reality, and 360◦ video have seen tremendous technological developments in recent years. Furthermore, the advances in head-mounted displays (HMDs) offer the users increased immersive experiences compared to conventional displays. To develop novel immersive media systems and services that satisfy the expectations of the users, it is essential to conduct subjective tests revealing users’ perceived quality of immersive media. However, due to the new viewing dimensions provided by HMDs and the potential of interacting with the content, a wide range of subjective tests are required to understand the many aspects of user behavior in and quality perception of immersive media. The ground truth obtained by such subjective tests enable the development of optimized immersive media systems that fulfill the expectations of the users. This article focuses on the consistency of 360◦ video quality assessment to reveal whether users’ subjective quality assessment of such immersive visual stimuli changes fundamentally over time or is kept consistent with each user having their own behavior signature. A pilot study was conducted under pandemic conditions with participants given the task of rating the quality of 360◦ video stimuli on an HMD in standing and seated viewing. The choice of conducting a pilot study is motivated by the fact that immersive media impose high cognitive load on the participants and the need to keep the number of participants under pandemic conditions as low as possible. To gain insight into the consistency of the participants’ 360◦ video assessment over time, three sessions were held for each participant and each viewing condition with long and short breaks between sessions. In particular, the opinion scores and head movements were recorded for each participant and each session in standing and seated viewing. The statistical analysis of this data leads to the conjecture that the quality rating stays consistent throughout these sessions with each participant having their own quality assessment signature. The head movements, indicating the participants’ scene exploration during the quality assessment task, also remain consistent for each participant according their individual narrower or wider scene exploration signature. These findings are more pronounced for standing viewing than for seated viewing. This work supports the role of pilot studies being a useful approach of conducting pre-tests on immersive media quality under opportunity-limited conditions and for the planning of subsequent full subjective tests with a large panel of participants. The annotated RQA360 dataset containing the data recorded in the repeated subjective tests is made publicly available to the research community.
{"title":"On the Consistency of 360 Video Quality Assessment in Repeated Subjective Tests: A Pilot Study","authors":"Majed Elwardy, H. Zepernick, Thi My Chinh Chu, Yan Hu","doi":"10.4108/eetinis.v11i1.4323","DOIUrl":"https://doi.org/10.4108/eetinis.v11i1.4323","url":null,"abstract":"Immersive media such as virtual reality, augmented reality, and 360◦ video have seen tremendous technological developments in recent years. Furthermore, the advances in head-mounted displays (HMDs) offer the users increased immersive experiences compared to conventional displays. To develop novel immersive media systems and services that satisfy the expectations of the users, it is essential to conduct subjective tests revealing users’ perceived quality of immersive media. However, due to the new viewing dimensions provided by HMDs and the potential of interacting with the content, a wide range of subjective tests are required to understand the many aspects of user behavior in and quality perception of immersive media. The ground truth obtained by such subjective tests enable the development of optimized immersive media systems that fulfill the expectations of the users. This article focuses on the consistency of 360◦ video quality assessment to reveal whether users’ subjective quality assessment of such immersive visual stimuli changes fundamentally over time or is kept consistent with each user having their own behavior signature. A pilot study was conducted under pandemic conditions with participants given the task of rating the quality of 360◦ video stimuli on an HMD in standing and seated viewing. The choice of conducting a pilot study is motivated by the fact that immersive media impose high cognitive load on the participants and the need to keep the number of participants under pandemic conditions as low as possible. To gain insight into the consistency of the participants’ 360◦ video assessment over time, three sessions were held for each participant and each viewing condition with long and short breaks between sessions. In particular, the opinion scores and head movements were recorded for each participant and each session in standing and seated viewing. The statistical analysis of this data leads to the conjecture that the quality rating stays consistent throughout these sessions with each participant having their own quality assessment signature. The head movements, indicating the participants’ scene exploration during the quality assessment task, also remain consistent for each participant according their individual narrower or wider scene exploration signature. These findings are more pronounced for standing viewing than for seated viewing. This work supports the role of pilot studies being a useful approach of conducting pre-tests on immersive media quality under opportunity-limited conditions and for the planning of subsequent full subjective tests with a large panel of participants. The annotated RQA360 dataset containing the data recorded in the repeated subjective tests is made publicly available to the research community.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445514","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 : 2023-12-07DOI: 10.4108/eetinis.v10i4.4192
Q.-S. Nguyen, Van Hien Nguyen, Trung Duy Tran, Luong Nhat Nguyen, L. Tu
The performance of the satellite-terrestrial network with Fountain codes (FCs) is conducted in the present work. More precisely, the air-to-ground link is modeled according to the shadow-Rician distribution to capture the strong light-of-sight (LOS) path as well as the impact of the shadowing. As a result, we employ the directional beamforming at both the satellite and relay to mitigate such an ultra-long transmission distance. We investigate the trade-off between the reliability and security aspects. Particularly, we derive the outage probability (OP) and intercept probability (IP) in the closed-form expressions. To further facilitate the security of the considered networks, the friendly jamming scheme is deployed as well. Finally, simulation results based on the Monte-Carlo method are given to corroborate the exactness of the developed mathematical framework and to identify key parameters such as antenna gain, and transmit power that have a big impact on the considered networks.
{"title":"On the Security and Reliability Trade-off of the Satellite Terrestrial Networks with Fountain Codes and Friendly Jamming","authors":"Q.-S. Nguyen, Van Hien Nguyen, Trung Duy Tran, Luong Nhat Nguyen, L. Tu","doi":"10.4108/eetinis.v10i4.4192","DOIUrl":"https://doi.org/10.4108/eetinis.v10i4.4192","url":null,"abstract":"The performance of the satellite-terrestrial network with Fountain codes (FCs) is conducted in the present work. More precisely, the air-to-ground link is modeled according to the shadow-Rician distribution to capture the strong light-of-sight (LOS) path as well as the impact of the shadowing. As a result, we employ the directional beamforming at both the satellite and relay to mitigate such an ultra-long transmission distance. We investigate the trade-off between the reliability and security aspects. Particularly, we derive the outage probability (OP) and intercept probability (IP) in the closed-form expressions. To further facilitate the security of the considered networks, the friendly jamming scheme is deployed as well. Finally, simulation results based on the Monte-Carlo method are given to corroborate the exactness of the developed mathematical framework and to identify key parameters such as antenna gain, and transmit power that have a big impact on the considered networks.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138592541","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 : 2023-11-06DOI: 10.4108/eetinis.v10i4.4315
Debabrata Swain, Kaxit Pandya, Jay Sanghvi, Yugandhar Manchala
Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled persons. Because of visual improvement, these people face lots of issues. Hence for this group of people, there is a high need for an assistance system in terms of object recognition. For specially-abled people sometimes it becomes really difficult to identify clothing-related items from one another because of high similarity. For better object classification we use a model which includes computer vision and CNN. Computer vision is the area of AI that helps to identify visual objects. Here a CNN-based model is used for better classification of clothing and fashion items. Another model known as Lenet is used which has a stronger architectural structure. Lenet is a multi-layer convolution neural network that is mainly used for image classification tasks. For model building and validation MNIST fashion dataset is used.
{"title":"An Intelligent Fashion Object Classification Using CNN","authors":"Debabrata Swain, Kaxit Pandya, Jay Sanghvi, Yugandhar Manchala","doi":"10.4108/eetinis.v10i4.4315","DOIUrl":"https://doi.org/10.4108/eetinis.v10i4.4315","url":null,"abstract":"Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled persons. Because of visual improvement, these people face lots of issues. Hence for this group of people, there is a high need for an assistance system in terms of object recognition. For specially-abled people sometimes it becomes really difficult to identify clothing-related items from one another because of high similarity. For better object classification we use a model which includes computer vision and CNN. Computer vision is the area of AI that helps to identify visual objects. Here a CNN-based model is used for better classification of clothing and fashion items. Another model known as Lenet is used which has a stronger architectural structure. Lenet is a multi-layer convolution neural network that is mainly used for image classification tasks. For model building and validation MNIST fashion dataset is used.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135634117","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 : 2023-11-06DOI: 10.4108/eetinis.v10i4.4294
Raghav Agarwal, Gaurav Sharma, Nirdesh Singh, Hrishikesh S Nair, Yash Daga, D Venkata Lakshmi
A growing population necessitates more transportation, which pressures car parking spots. Parking is a problem for public places in cities, such as theatres, malls, parks, and temples. Even though several techniques have been suggested in publications, manual parking systems are still used in most places. For large locations where it is challenging to find open spaces, traditional parking arrangements need to be more archaic and convoluted. This might lead to heavy traffic, minor mishaps, and widespread accidents. In the modern era of sophisticated parking management systems, an automatic parking spot-detecting system has been introduced in an innovative format. Experts in computer vision are drawn to this emerging field to contribute. The system could tell if the automobile was fully or partially parked. Neither during the process nor afterward, human oversight is required. As parking management enters the modern era, computer vision is becoming increasingly critical. The parking system will not only make it easier for drivers to identify parking spaces but also enhance parking administration and monitoring. Vehicles will be able to observe available parking spots due to technology that monitors parking spaces. India and other emerging nations, as well as industrialized ones, have recently shown interest in smart cities. This article's smart auto parking system was conceived and implemented utilizing a Raspberry Pi and cameras placed in various parking spaces. Using a website and an Android app, this project creates and deploys a real-time system that enables vehicles to efficiently find and reclaim open parking spaces.
{"title":"Intelligent Raspberry-Pi-Based Parking Slot Identification System","authors":"Raghav Agarwal, Gaurav Sharma, Nirdesh Singh, Hrishikesh S Nair, Yash Daga, D Venkata Lakshmi","doi":"10.4108/eetinis.v10i4.4294","DOIUrl":"https://doi.org/10.4108/eetinis.v10i4.4294","url":null,"abstract":"A growing population necessitates more transportation, which pressures car parking spots. Parking is a problem for public places in cities, such as theatres, malls, parks, and temples. Even though several techniques have been suggested in publications, manual parking systems are still used in most places. For large locations where it is challenging to find open spaces, traditional parking arrangements need to be more archaic and convoluted. This might lead to heavy traffic, minor mishaps, and widespread accidents. In the modern era of sophisticated parking management systems, an automatic parking spot-detecting system has been introduced in an innovative format. Experts in computer vision are drawn to this emerging field to contribute. The system could tell if the automobile was fully or partially parked. Neither during the process nor afterward, human oversight is required. As parking management enters the modern era, computer vision is becoming increasingly critical. The parking system will not only make it easier for drivers to identify parking spaces but also enhance parking administration and monitoring. Vehicles will be able to observe available parking spots due to technology that monitors parking spaces. India and other emerging nations, as well as industrialized ones, have recently shown interest in smart cities. This article's smart auto parking system was conceived and implemented utilizing a Raspberry Pi and cameras placed in various parking spaces. Using a website and an Android app, this project creates and deploys a real-time system that enables vehicles to efficiently find and reclaim open parking spaces.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135636439","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 : 2023-10-02DOI: 10.4108/eetinis.v10i3.3874
Priyanka More, None Sachin Sakhare
With the increasing prevalence of the Internet of Things (IoT), there is a growing need for effective access control methods to secure IoT systems and data. Traditional access control models often prove inadequate when dealing with the specific challenges presented by IoT, characterized by a variety of heterogeneous devices, ever-changing network structures, and diverse contextual elements. Managing IoT devices effectively is a complex task in maintaining network security.This study introduces a context-driven approach for IoT Device Classification and Clustering, aiming to address the unique characteristics of IoT systems and the limitations of existing access control methods. The proposed context-based model utilizes contextual information such as device attributes, location, time, and communication patterns to dynamically establish clusters and cluster leaders. By incorporating contextual factors, the model provides a more accurate and adaptable clustering mechanism that aligns with the dynamic nature of IoT systems. Consequently, network administrators can configure dynamic access policies for these clusters.
{"title":"Context-Aware Device Classification and Clustering for Smarter and Secure Connectivity in Internet of Things","authors":"Priyanka More, None Sachin Sakhare","doi":"10.4108/eetinis.v10i3.3874","DOIUrl":"https://doi.org/10.4108/eetinis.v10i3.3874","url":null,"abstract":"With the increasing prevalence of the Internet of Things (IoT), there is a growing need for effective access control methods to secure IoT systems and data. Traditional access control models often prove inadequate when dealing with the specific challenges presented by IoT, characterized by a variety of heterogeneous devices, ever-changing network structures, and diverse contextual elements. Managing IoT devices effectively is a complex task in maintaining network security.This study introduces a context-driven approach for IoT Device Classification and Clustering, aiming to address the unique characteristics of IoT systems and the limitations of existing access control methods. The proposed context-based model utilizes contextual information such as device attributes, location, time, and communication patterns to dynamically establish clusters and cluster leaders. By incorporating contextual factors, the model provides a more accurate and adaptable clustering mechanism that aligns with the dynamic nature of IoT systems. Consequently, network administrators can configure dynamic access policies for these clusters.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135834934","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 : 2023-09-27DOI: 10.4108/eetinis.v10i3.3752
Pablo Flores-Siguenza, Jose Antonio Marmolejo-Saucedo, Rodrigo Guamán
Textile MSMEs are characterized by their high influence on the economy of the countries, both for their contribution to the gross domestic product as well as for the generation of employment, in recent years the complexity of their operations, instability and lack of balance between economic, environmental and social factors, axes of sustainable development, stand out. It is necessary to implement approaches such as sustainable manufacturing and production planning, which seeks the creation of products with minimal environmental impact, safe for workers, and economically robust. In this context, this study aims to develop a multi-objective optimization model that enhances sustainable production planning in textile MSMEs. The methodology is based on two phases, the first one focused on the acquisition of information and the second one dedicated to the mathematical formulation of the model, where three objective functions focused on economic, environmental and social factors are proposed. The model is validated with real data from a textile MSME in Ecuador and different production alternatives are generated by proposing the implementation and use of photovoltaic energy as well as a greater use of personal protective equipment. One of the relevant conclusions of the study is the contribution to the textile industry with a sustainable decision support tool, where different scenarios for production planning and their respective economic, environmental and social consequences are shown.
{"title":"Multi-objective optimization model for sustainable production planning in textile MSMEs","authors":"Pablo Flores-Siguenza, Jose Antonio Marmolejo-Saucedo, Rodrigo Guamán","doi":"10.4108/eetinis.v10i3.3752","DOIUrl":"https://doi.org/10.4108/eetinis.v10i3.3752","url":null,"abstract":"Textile MSMEs are characterized by their high influence on the economy of the countries, both for their contribution to the gross domestic product as well as for the generation of employment, in recent years the complexity of their operations, instability and lack of balance between economic, environmental and social factors, axes of sustainable development, stand out. It is necessary to implement approaches such as sustainable manufacturing and production planning, which seeks the creation of products with minimal environmental impact, safe for workers, and economically robust. In this context, this study aims to develop a multi-objective optimization model that enhances sustainable production planning in textile MSMEs. The methodology is based on two phases, the first one focused on the acquisition of information and the second one dedicated to the mathematical formulation of the model, where three objective functions focused on economic, environmental and social factors are proposed. The model is validated with real data from a textile MSME in Ecuador and different production alternatives are generated by proposing the implementation and use of photovoltaic energy as well as a greater use of personal protective equipment. One of the relevant conclusions of the study is the contribution to the textile industry with a sustainable decision support tool, where different scenarios for production planning and their respective economic, environmental and social consequences are shown.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135538093","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 : 2023-09-26DOI: 10.4108/eetinis.v10i3.3863
Ngo Thi Ngoc Quyen, Tran Duy Linh, Vu Hong Phuc, Nguyen Van Nam
Today, facial emotion recognition is widely adopted in many intelligent applications including the driver monitoring system, the smart customer care as well as the e-learning system. In fact, the human emotions can be well represented by facial landmarks which are hard to be detected from images, due to the high number of discrete landmarks, the variation of shapes and poses of the human face in real world. Over decades, many methods have been proposed for facial landmark detection including the shape fitting, the coordinate regression such as ASMNet and AnchorFace. However, their performance is still limited for real-time applications in terms of both accuracy and efficiency. In this paper, we propose a novel method called SHELF which is the first to combine the shape fitting and heatmap regression approaches for landmark detection in human face. The heatmap model aims to generate the landmarks that fit to the common shapes. The method has been evaluated on three datasets 300W-Challenging, WFLW, 300VW-E with 31557 images and achieved a normalized mean error (NME) of 6.67% , 7.34%, 12.55% correspondingly, which overcomes most existing methods. For the first two datasets, the method is also comparable to the state of the art AnchorFace with a NME of 6.19%, 4.62%, respectively.
{"title":"SHELF: Combination of Shape Fitting and Heatmap Regression for Landmark Detection in Human Face","authors":"Ngo Thi Ngoc Quyen, Tran Duy Linh, Vu Hong Phuc, Nguyen Van Nam","doi":"10.4108/eetinis.v10i3.3863","DOIUrl":"https://doi.org/10.4108/eetinis.v10i3.3863","url":null,"abstract":"Today, facial emotion recognition is widely adopted in many intelligent applications including the driver monitoring system, the smart customer care as well as the e-learning system. In fact, the human emotions can be well represented by facial landmarks which are hard to be detected from images, due to the high number of discrete landmarks, the variation of shapes and poses of the human face in real world. Over decades, many methods have been proposed for facial landmark detection including the shape fitting, the coordinate regression such as ASMNet and AnchorFace. However, their performance is still limited for real-time applications in terms of both accuracy and efficiency. In this paper, we propose a novel method called SHELF which is the first to combine the shape fitting and heatmap regression approaches for landmark detection in human face. The heatmap model aims to generate the landmarks that fit to the common shapes. The method has been evaluated on three datasets 300W-Challenging, WFLW, 300VW-E with 31557 images and achieved a normalized mean error (NME) of 6.67% , 7.34%, 12.55% correspondingly, which overcomes most existing methods. For the first two datasets, the method is also comparable to the state of the art AnchorFace with a NME of 6.19%, 4.62%, respectively.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960310","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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