Pub Date : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954089
Moch. Imam Rifai, R. Sudibyo, Arasy Dafa Sulistya Kurniawan, Moch. Zen Samsono Hadi, H. Mahmudah, Nihayatus Sa’adah
Transportation is very important in life. The width of road is unable to accommodate the total number of vehicles because every year there is a rapid increase in the number of vehicles, causing congestion. In Indonesia, in 2019 the number of motorized vehicles has reached more than 133 million. The process of calculating vehicle volume data which is still done manually has several drawbacks, such as it takes a long time and errors can occur due to human error. In this study, the design of the system used to classify and calculate the number of vehicles automatically utilizes the Deep Learning Convolutional Neural Network with a pre-trained Inception model. The results of this study on the minimum score threshold scenario of 0.4, the highest True Positive (TP) value was 70.75% and the model get 5 FPS during inferencing process.
{"title":"Automatic Vehicle Classification and Counting System Using Inception Model","authors":"Moch. Imam Rifai, R. Sudibyo, Arasy Dafa Sulistya Kurniawan, Moch. Zen Samsono Hadi, H. Mahmudah, Nihayatus Sa’adah","doi":"10.1109/ICITEE56407.2022.9954089","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954089","url":null,"abstract":"Transportation is very important in life. The width of road is unable to accommodate the total number of vehicles because every year there is a rapid increase in the number of vehicles, causing congestion. In Indonesia, in 2019 the number of motorized vehicles has reached more than 133 million. The process of calculating vehicle volume data which is still done manually has several drawbacks, such as it takes a long time and errors can occur due to human error. In this study, the design of the system used to classify and calculate the number of vehicles automatically utilizes the Deep Learning Convolutional Neural Network with a pre-trained Inception model. The results of this study on the minimum score threshold scenario of 0.4, the highest True Positive (TP) value was 70.75% and the model get 5 FPS during inferencing process.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130828111","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954042
C. Nugraha, L. Subekti, Muhammad Yasirroni
In a smart grid, the adequacy of electricity is not only determined by generation, but also by the consumer load. Demand response (DR) is one way to maintain a balance between electricity supply and load by reducing electricity consumption at certain times when necessary or when the system is stressed. However, research on the generation and DR scheduling mostly only discusses the economic impact. In this study, the economic, as well as security impact of DR, is evaluated in a microgrid operating planning. optimization is carried out to obtain the lowest generation costs while maximizing customer benefits from the DR program. The mixed-integer linear programming method is used to determine the optimal generation of each distributed generation and the optimal load reduction throughout the planning period. The results show that the consideration of DR and power flow constraints is not only able to maintain the security of the distribution system, but also results in an economical cost as compared to the scenario without DR.
{"title":"Multi-Objective Microgrid Generation and Demand Response Scheduling Considering Distribution System Security","authors":"C. Nugraha, L. Subekti, Muhammad Yasirroni","doi":"10.1109/ICITEE56407.2022.9954042","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954042","url":null,"abstract":"In a smart grid, the adequacy of electricity is not only determined by generation, but also by the consumer load. Demand response (DR) is one way to maintain a balance between electricity supply and load by reducing electricity consumption at certain times when necessary or when the system is stressed. However, research on the generation and DR scheduling mostly only discusses the economic impact. In this study, the economic, as well as security impact of DR, is evaluated in a microgrid operating planning. optimization is carried out to obtain the lowest generation costs while maximizing customer benefits from the DR program. The mixed-integer linear programming method is used to determine the optimal generation of each distributed generation and the optimal load reduction throughout the planning period. The results show that the consideration of DR and power flow constraints is not only able to maintain the security of the distribution system, but also results in an economical cost as compared to the scenario without DR.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125543994","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954088
Ferra Arik Tridalestari, Mustafid, F. Jie
Today’s business competition is a necessity. Every company tries to provide the best service to its consumers or customers. To increase the number of consumers, many Company are developing and implementing the concept of channel distribution. Channel distribution is an important part of Supply Chain management. Currently, many companies apply the concept of Multichannel distribution or commonly called Multichannel and Omnichannel. Companies are confused about whether to implement Multichannel or Omnichannel. Many people think that both are same or similar. In terms of consumers, sometimes consumers have difficulty when using services that have to repeatedly log in, have difficulty making complaints, and so on. This paper proposes a comparative study between Multichannel and Omnichannel based on two approaches, namely the Information Technology Resources approach and Phillip Kotler’s 9 (nine) Channel Distribution concept. The results of the analysis show that Multichannel has advantages in terms of development costs and business risks, while Omnichannel shows advantages in terms of users, information, negotiation, ordering, payment, physical, and ownership.
{"title":"Comparative Study of Multi-channel and Omni-channel on Supply Chain Management","authors":"Ferra Arik Tridalestari, Mustafid, F. Jie","doi":"10.1109/ICITEE56407.2022.9954088","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954088","url":null,"abstract":"Today’s business competition is a necessity. Every company tries to provide the best service to its consumers or customers. To increase the number of consumers, many Company are developing and implementing the concept of channel distribution. Channel distribution is an important part of Supply Chain management. Currently, many companies apply the concept of Multichannel distribution or commonly called Multichannel and Omnichannel. Companies are confused about whether to implement Multichannel or Omnichannel. Many people think that both are same or similar. In terms of consumers, sometimes consumers have difficulty when using services that have to repeatedly log in, have difficulty making complaints, and so on. This paper proposes a comparative study between Multichannel and Omnichannel based on two approaches, namely the Information Technology Resources approach and Phillip Kotler’s 9 (nine) Channel Distribution concept. The results of the analysis show that Multichannel has advantages in terms of development costs and business risks, while Omnichannel shows advantages in terms of users, information, negotiation, ordering, payment, physical, and ownership.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115142204","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954107
S. Prabhavat, Thananop Thongthavorn, Kitsuchart Pasupa
Software-defined Networking (SDN) provides an easy way to monitor network and traffic conditions by employing software-based controllers to communicate with the hardware directly. It provides helpful information that enables efficient routing decisions. This research study attempted to use deep learning techniques—Long Short-term Memory, Bidirectional Long Short-term Memory, and Gated Recurrent Unit—to predict network traffic to allow the controller to early detect congestion. The traffic flow in a network link that will likely be congested will be rerouted to a new path with the largest available bandwidth. Various scenarios were simulated to evaluate our deep learning-based SDN controller (Ryu controller platform). The results show that our proposed deep learning-based SDN controller outperformed the traditional load balancing technique.
{"title":"Deep Learning-Based Early Detection and Avoidance of Traffic Congestion in Software-Defined Networks","authors":"S. Prabhavat, Thananop Thongthavorn, Kitsuchart Pasupa","doi":"10.1109/ICITEE56407.2022.9954107","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954107","url":null,"abstract":"Software-defined Networking (SDN) provides an easy way to monitor network and traffic conditions by employing software-based controllers to communicate with the hardware directly. It provides helpful information that enables efficient routing decisions. This research study attempted to use deep learning techniques—Long Short-term Memory, Bidirectional Long Short-term Memory, and Gated Recurrent Unit—to predict network traffic to allow the controller to early detect congestion. The traffic flow in a network link that will likely be congested will be rerouted to a new path with the largest available bandwidth. Various scenarios were simulated to evaluate our deep learning-based SDN controller (Ryu controller platform). The results show that our proposed deep learning-based SDN controller outperformed the traditional load balancing technique.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125645288","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954111
Arziki Pratama, S. W. Sihwi
Emotions are one of the most influential aspects of everyday life. Everyone has their own way of expressing their emotions. One way to express emotions is through speech or by speaking. This gave rise to a new field of research called speech emotion recognition which aims to understand a person’s emotions through sound. In this study, the Support Vector Machine algorithm will be implemented to create a speech emotion recognition model through the MFCC voice feature obtained from voice processing. The model created can be used to classify six emotions, namely happy, sad, angry, fear, disgust, and neutral. The highest accuracy is obtained from the model created using the Support Vector Machine algorithm using a radial basis function kernel which is considered to be able to properly classify emotions based on sound. The usage of the combined dataset also improved the accuracy of the model and are able to obtain above 70% highest accuracy on each test.
{"title":"Speech Emotion Recognition Model using Support Vector Machine Through MFCC Audio Feature","authors":"Arziki Pratama, S. W. Sihwi","doi":"10.1109/ICITEE56407.2022.9954111","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954111","url":null,"abstract":"Emotions are one of the most influential aspects of everyday life. Everyone has their own way of expressing their emotions. One way to express emotions is through speech or by speaking. This gave rise to a new field of research called speech emotion recognition which aims to understand a person’s emotions through sound. In this study, the Support Vector Machine algorithm will be implemented to create a speech emotion recognition model through the MFCC voice feature obtained from voice processing. The model created can be used to classify six emotions, namely happy, sad, angry, fear, disgust, and neutral. The highest accuracy is obtained from the model created using the Support Vector Machine algorithm using a radial basis function kernel which is considered to be able to properly classify emotions based on sound. The usage of the combined dataset also improved the accuracy of the model and are able to obtain above 70% highest accuracy on each test.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123062753","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954103
D. Wardani, Masya Marshallia, A. Wijayanto, Haryono Setyadi
Along with the development of the knowledge graph, it required more significant structured data to build it. Publishing massive structured data, such as relational databases, require substantial effort. Therefore, this work pro-poses a framework for semi-automatic generating structured data and creating a semantic matching algorithm for the table’s attributes and schema.org’s properties. The algorithm uses Wu Palmer Similarity (WUP) and WordNet as semantic similarity measurements. Although the obtained scores are still pretty fair, the whole framework runs well at 0.4285 for WUP+k and 0,3833 for WUP.
{"title":"Semi-Automatic Generating Semantic Markup Webpage from Structured Data with Semantic Matching","authors":"D. Wardani, Masya Marshallia, A. Wijayanto, Haryono Setyadi","doi":"10.1109/ICITEE56407.2022.9954103","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954103","url":null,"abstract":"Along with the development of the knowledge graph, it required more significant structured data to build it. Publishing massive structured data, such as relational databases, require substantial effort. Therefore, this work pro-poses a framework for semi-automatic generating structured data and creating a semantic matching algorithm for the table’s attributes and schema.org’s properties. The algorithm uses Wu Palmer Similarity (WUP) and WordNet as semantic similarity measurements. Although the obtained scores are still pretty fair, the whole framework runs well at 0.4285 for WUP+k and 0,3833 for WUP.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117111817","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954098
Aries Pratiarso, Muhammad Hafidz Hismawan, Inna Ladayna Mazida, M. Z. S. Hadi, Mike Yuliana
Self-isolation is step or effort to stop the spread of the Covid-19 virus that can carried out by individuals infected with the corona virus. However, they do not show enough symptoms seriously. This is one method to push amount Covid-19 cases. People who do self-isolation must stay at home around 7 days until they are free from Covid-19. To help monitoring by effective condition patient in self-isolation at home and reduce risk the symptoms of Covid-19 experienced, it requires a support system. In this research, it makes a system that can help inhabitant of village to monitor condition patients in the room during self-isolation through camera-based detection object and some sensors to monitor their health such as temperature, heart rate, and oxygen saturation. Camera can classify condition patient based on real-time video recording. If patient is detected lie down or fall on the floor, it will be assumed need help and message emergency sent to the telegram bot. However, if the patient is in a position like stand up, it will be assumed that patient in health condition. By using Mobilenet V2 320x320 SSD object model the average of accuracy is obtained by 86.8%. The results in this system could be monitored through web page.
{"title":"Smart Self-Isolation System to Monitor the Condition of Covid-19 Patients at Home","authors":"Aries Pratiarso, Muhammad Hafidz Hismawan, Inna Ladayna Mazida, M. Z. S. Hadi, Mike Yuliana","doi":"10.1109/ICITEE56407.2022.9954098","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954098","url":null,"abstract":"Self-isolation is step or effort to stop the spread of the Covid-19 virus that can carried out by individuals infected with the corona virus. However, they do not show enough symptoms seriously. This is one method to push amount Covid-19 cases. People who do self-isolation must stay at home around 7 days until they are free from Covid-19. To help monitoring by effective condition patient in self-isolation at home and reduce risk the symptoms of Covid-19 experienced, it requires a support system. In this research, it makes a system that can help inhabitant of village to monitor condition patients in the room during self-isolation through camera-based detection object and some sensors to monitor their health such as temperature, heart rate, and oxygen saturation. Camera can classify condition patient based on real-time video recording. If patient is detected lie down or fall on the floor, it will be assumed need help and message emergency sent to the telegram bot. However, if the patient is in a position like stand up, it will be assumed that patient in health condition. By using Mobilenet V2 320x320 SSD object model the average of accuracy is obtained by 86.8%. The results in this system could be monitored through web page.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"2675 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116192909","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954093
R. Putro, M. Riasetiawan
Traditional Indonesian Pasar has become a symbolic representation of the hardworking backbone of the Indonesian economy. In an era where the implementation of innovative technologies is becoming prevalent, Indonesian pasar must be willing to adapt to the changing times. Hence, A mobile application will be developed to showcase products and traditional pasars shown around their area, users are able to purchase said products and negotiate for better prices similar to their real-life pasar counterparts, all in an effort to make the transition from physical to digital more convenient for old and new customers alike.Early Survey and Final Targeted Surveys results show that the creation of the mobile application has been a pleasant and endeavor for the users. Ultimately leading to a net positive response in terms of convenience and accessibility and achieving significant steps towards innovating in the Indonesian pasar market through an e-grocery mobile application. However, features such as user reviews and an order/delivery logistics system be considered in future reiterations of the application; in order to encompass a more complete flow of the user journey and transforming the way we currently think of Traditional Pasar.
{"title":"The Digitization of the Indonesian Pasar Market Through An E-Grocery Based Mobile App Solution","authors":"R. Putro, M. Riasetiawan","doi":"10.1109/ICITEE56407.2022.9954093","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954093","url":null,"abstract":"Traditional Indonesian Pasar has become a symbolic representation of the hardworking backbone of the Indonesian economy. In an era where the implementation of innovative technologies is becoming prevalent, Indonesian pasar must be willing to adapt to the changing times. Hence, A mobile application will be developed to showcase products and traditional pasars shown around their area, users are able to purchase said products and negotiate for better prices similar to their real-life pasar counterparts, all in an effort to make the transition from physical to digital more convenient for old and new customers alike.Early Survey and Final Targeted Surveys results show that the creation of the mobile application has been a pleasant and endeavor for the users. Ultimately leading to a net positive response in terms of convenience and accessibility and achieving significant steps towards innovating in the Indonesian pasar market through an e-grocery mobile application. However, features such as user reviews and an order/delivery logistics system be considered in future reiterations of the application; in order to encompass a more complete flow of the user journey and transforming the way we currently think of Traditional Pasar.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129201771","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954084
R. Dijaya, N. Suciati, Ahmad Saikhu
Feature selection is the important step in image classification due to its influence on accuracy. The objective of this study is to diagnose corn plant diseases using visual features extracted from leaf images with Bag of visual words (BoVW) and the Support Vector Machine (SVM) classification approach. The Speeded up Robust Feature (SURF) approach is implemented to extract and describe the key points of each corn leaf image in the training dataset. The K-Means clustering is utilized to generate k Centroids representing visual words. The arrangement of the BoVW feature based on the histogram of k clusters of visual words provides the input for the SVM classification algorithm. The original contribution of this study is to investigate the impact of number of clusters and proportion of the involved strongest key points toward classification accuracy. The experiment was conducted using the plantvillage public dataset. The experiment results indicate that the best classification accuracy is 85%, with the number of clusters 800 and the proportion of the strongest key points 80%.
{"title":"Corn Plant Disease Identification Using SURF-based Bag of Visual Words Feature","authors":"R. Dijaya, N. Suciati, Ahmad Saikhu","doi":"10.1109/ICITEE56407.2022.9954084","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954084","url":null,"abstract":"Feature selection is the important step in image classification due to its influence on accuracy. The objective of this study is to diagnose corn plant diseases using visual features extracted from leaf images with Bag of visual words (BoVW) and the Support Vector Machine (SVM) classification approach. The Speeded up Robust Feature (SURF) approach is implemented to extract and describe the key points of each corn leaf image in the training dataset. The K-Means clustering is utilized to generate k Centroids representing visual words. The arrangement of the BoVW feature based on the histogram of k clusters of visual words provides the input for the SVM classification algorithm. The original contribution of this study is to investigate the impact of number of clusters and proportion of the involved strongest key points toward classification accuracy. The experiment was conducted using the plantvillage public dataset. The experiment results indicate that the best classification accuracy is 85%, with the number of clusters 800 and the proportion of the strongest key points 80%.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132934527","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 : 2022-10-18DOI: 10.1109/ICITEE56407.2022.9954120
Izzun Nafis Ibadik, Ahmad Ashari, D. D. Ariananda, Wahyu Dewanto
The rapid development of wireless technology compels to efficient use of frequency bands. One possible way to achieve this goal is the cognitive radio (CR) systems, which allow a frequency band, possessed by a primary user (PU), to be borrowed by the secondary users (SUs) who can dynamically access the frequency band. Spectrum sensing process plays a key part in the CR system as SU has to gauge multiple frequency bands to decide if a band is occupied or not. This paper proposed a spectrum sensing approach for a multiband channel scenario. First, the power spectral density (PSD) of the received signal is computed and then the detection process is carried out separately in each subband in the frequency domain. By analyzing the estimated PSD and the noise power, a threshold is applied on the received signal to conclude the existence of PU. The performance of this method was evaluated and compared with the existing energy detection and k-means clustering methods. The result shows that the proposed method has a better performance compared to the other two existing methods. The method can detect the presence of PU in a lower signal-to-noise ratio while maintaining the acceptable detection performance.
{"title":"Frequency Domain Energy Detection for Multiband Spectrum Sensing in Cognitive Radio System","authors":"Izzun Nafis Ibadik, Ahmad Ashari, D. D. Ariananda, Wahyu Dewanto","doi":"10.1109/ICITEE56407.2022.9954120","DOIUrl":"https://doi.org/10.1109/ICITEE56407.2022.9954120","url":null,"abstract":"The rapid development of wireless technology compels to efficient use of frequency bands. One possible way to achieve this goal is the cognitive radio (CR) systems, which allow a frequency band, possessed by a primary user (PU), to be borrowed by the secondary users (SUs) who can dynamically access the frequency band. Spectrum sensing process plays a key part in the CR system as SU has to gauge multiple frequency bands to decide if a band is occupied or not. This paper proposed a spectrum sensing approach for a multiband channel scenario. First, the power spectral density (PSD) of the received signal is computed and then the detection process is carried out separately in each subband in the frequency domain. By analyzing the estimated PSD and the noise power, a threshold is applied on the received signal to conclude the existence of PU. The performance of this method was evaluated and compared with the existing energy detection and k-means clustering methods. The result shows that the proposed method has a better performance compared to the other two existing methods. The method can detect the presence of PU in a lower signal-to-noise ratio while maintaining the acceptable detection performance.","PeriodicalId":246279,"journal":{"name":"2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117235504","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：