Pub Date : 2020-09-01DOI: 10.1109/IES50839.2020.9231868
Ani Rosyidah, I. Astawa, Anang Budiknrso
Digital Video Broadcasting-Terrestrial Second Generation (DVB-T2) is a digital television broadcasting system that is being implemented in the world. This system can send large amounts of data at high point-to-multipoint speed. Multiple Input Multiple Output (MIMO) is a transmission technique that is implemented in many new technologies nowadays. This technique can increase the data rate without increasing the bandwidth. In this study, a DVB-T2 system simulation was made by applying 2x2 MIMO-OFDM technology. For better system performance, it is necessary to use a detector to minimize the noise in the data transmission process. In this study, simulations and analyzation were performed to determine the performance of MIMO detectors on a DVB-T2 system. The analysis was done by comparing the BER curve to the generated SNR value by each detector system. The simulation results show that the Vertical Bell Labs Layered Space-Time/Minimum Mean Square Error (V-BLAST/MMSE) detection technique is the best technique in improving system performance, followed by V-BLAST/ZF, Minimum Mean Square Error (MMSE), and Zero Forcing (ZF) detectors.
{"title":"Performance Analysis of MIMO Detection Techniques in DVB-T2 Systems","authors":"Ani Rosyidah, I. Astawa, Anang Budiknrso","doi":"10.1109/IES50839.2020.9231868","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231868","url":null,"abstract":"Digital Video Broadcasting-Terrestrial Second Generation (DVB-T2) is a digital television broadcasting system that is being implemented in the world. This system can send large amounts of data at high point-to-multipoint speed. Multiple Input Multiple Output (MIMO) is a transmission technique that is implemented in many new technologies nowadays. This technique can increase the data rate without increasing the bandwidth. In this study, a DVB-T2 system simulation was made by applying 2x2 MIMO-OFDM technology. For better system performance, it is necessary to use a detector to minimize the noise in the data transmission process. In this study, simulations and analyzation were performed to determine the performance of MIMO detectors on a DVB-T2 system. The analysis was done by comparing the BER curve to the generated SNR value by each detector system. The simulation results show that the Vertical Bell Labs Layered Space-Time/Minimum Mean Square Error (V-BLAST/MMSE) detection technique is the best technique in improving system performance, followed by V-BLAST/ZF, Minimum Mean Square Error (MMSE), and Zero Forcing (ZF) detectors.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126851554","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 : 2020-09-01DOI: 10.1109/IES50839.2020.9231557
Netta Divana Vita Sembiring, Irma Wulandari, D. Permatasari
In the learning process, learning media serves to improve the quality of the teaching and learning process. However, so far the determination of learning media in a class has not paid attention to several aspects, namely ease in getting media, characteristics of students to the type of media, learning time in the use of media, as well as funds needed to obtain media. This study aims to assist teachers in determining the optimal learning media based on student learning styles, as well as using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method in the optimization process. Determination of student learning styles is determined through filling questionnaires by students in the system that has been given a knowledge base and rules for determining learning styles. The MOORA method is used as a multi-objective system that optimizes several conflicting attributes simultaneously. The attributes needed in the optimization process in determining learning media are the ease of getting the media and student learning styles as the attributes to be maximized, as well as the time and funds required as the attributes to be minimized. The experimental result demonstrates that as much as 0.0859% for the assessment of each student experiencing a discrepancy because several factors can affect the acquisition of student learning outcomes, namely internal, external, and learning approaches. Besides, the assessment of the grade average shows a mismatch of 0.25%, because the system uses several criteria so that it does not only focus on the assessment criteria.
{"title":"Determination of Learning Media in Elementary School using Multi-Objective Optimization on the Basis of Ratio Analysis Method","authors":"Netta Divana Vita Sembiring, Irma Wulandari, D. Permatasari","doi":"10.1109/IES50839.2020.9231557","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231557","url":null,"abstract":"In the learning process, learning media serves to improve the quality of the teaching and learning process. However, so far the determination of learning media in a class has not paid attention to several aspects, namely ease in getting media, characteristics of students to the type of media, learning time in the use of media, as well as funds needed to obtain media. This study aims to assist teachers in determining the optimal learning media based on student learning styles, as well as using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method in the optimization process. Determination of student learning styles is determined through filling questionnaires by students in the system that has been given a knowledge base and rules for determining learning styles. The MOORA method is used as a multi-objective system that optimizes several conflicting attributes simultaneously. The attributes needed in the optimization process in determining learning media are the ease of getting the media and student learning styles as the attributes to be maximized, as well as the time and funds required as the attributes to be minimized. The experimental result demonstrates that as much as 0.0859% for the assessment of each student experiencing a discrepancy because several factors can affect the acquisition of student learning outcomes, namely internal, external, and learning approaches. Besides, the assessment of the grade average shows a mismatch of 0.25%, because the system uses several criteria so that it does not only focus on the assessment criteria.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126943593","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 : 2020-09-01DOI: 10.1109/IES50839.2020.9231651
Nur Khomairoh, R. Sigit, T. Harsono, Y. Hernaningsih, A. Anwar
Leukemia is a blood cancer that attacks human white blood cells. This disease is divided into four types, including Acute Myeloid Leukemia (AML). AML is the most common type of acute leukemia, and it has eight types of subtypes distinguished by the level of cell maturation. Medical personnel determines the type of AML based on microscopic images of blood cell smears that contain white blood cells, red blood cells, and pieces of blood. This research builds a segmentation system that can determine the boundary of an object with the surrounding area, where the object sought is white blood cells contained in microscopic images of blood cell smears. White blood cells are sought based on ROI using the Haar Cascade Classifier, and then segmentation is carried out on the nucleus and cytoplasm. AML sub-types used as objects in this study are M4, M5, and M7. Based on the results of experimental data on the segmentation system, the nucleus segmentation in each cell of M4, M5, and M7 with an accuracy of 87.5%, 90.4%, 84.6% in sequence, and the results of cytoplasm segmentation are 75%, 71.4%, and 80.76%, respectively.
{"title":"Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image","authors":"Nur Khomairoh, R. Sigit, T. Harsono, Y. Hernaningsih, A. Anwar","doi":"10.1109/IES50839.2020.9231651","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231651","url":null,"abstract":"Leukemia is a blood cancer that attacks human white blood cells. This disease is divided into four types, including Acute Myeloid Leukemia (AML). AML is the most common type of acute leukemia, and it has eight types of subtypes distinguished by the level of cell maturation. Medical personnel determines the type of AML based on microscopic images of blood cell smears that contain white blood cells, red blood cells, and pieces of blood. This research builds a segmentation system that can determine the boundary of an object with the surrounding area, where the object sought is white blood cells contained in microscopic images of blood cell smears. White blood cells are sought based on ROI using the Haar Cascade Classifier, and then segmentation is carried out on the nucleus and cytoplasm. AML sub-types used as objects in this study are M4, M5, and M7. Based on the results of experimental data on the segmentation system, the nucleus segmentation in each cell of M4, M5, and M7 with an accuracy of 87.5%, 90.4%, 84.6% in sequence, and the results of cytoplasm segmentation are 75%, 71.4%, and 80.76%, respectively.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116564925","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 : 2020-09-01DOI: 10.1109/IES50839.2020.9231774
Muhammad Alifudin Fahmi, I. Sudiharto, I. Ferdiansyah
The increasing need for electrical energy at the rate of an era, to meet the increase in the use of many alternative energy such as solar energy. The availability solar energy will never run out and solar energy can also be used as an alternative energy that can convert to electrical energy. Solar energy has a fluctuating nature where there is always a change in the amount of energy over time. By maximizing the utilization of solar panel energy can be achieved by the existence of methods such as MPPT (Maximum Power Point Tracking). Particle Swarm Optimization (PSO) is an algorithm that can be used as an MPPT, where PSO will learn every irradiation change that occurs and get maximum power which will then be used as a source for the battery charger. In this paper, using a hybrid power system that uses a source from PV and the grid 220Vac PLN. The sources obtained from the PLN grid will be used as a backup source. Using the Particle Swarm Optimization method as MPPT is able to get power of 198.85 Watt with efficiencies above 95% in the hybrid power system for battery chargers, and the presence of the PLN Grid as a backup source, when the PV system does not meet the load power requirements.
{"title":"Particle Swarm Optimization Implementation as MPPT on Hybrid Power System","authors":"Muhammad Alifudin Fahmi, I. Sudiharto, I. Ferdiansyah","doi":"10.1109/IES50839.2020.9231774","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231774","url":null,"abstract":"The increasing need for electrical energy at the rate of an era, to meet the increase in the use of many alternative energy such as solar energy. The availability solar energy will never run out and solar energy can also be used as an alternative energy that can convert to electrical energy. Solar energy has a fluctuating nature where there is always a change in the amount of energy over time. By maximizing the utilization of solar panel energy can be achieved by the existence of methods such as MPPT (Maximum Power Point Tracking). Particle Swarm Optimization (PSO) is an algorithm that can be used as an MPPT, where PSO will learn every irradiation change that occurs and get maximum power which will then be used as a source for the battery charger. In this paper, using a hybrid power system that uses a source from PV and the grid 220Vac PLN. The sources obtained from the PLN grid will be used as a backup source. Using the Particle Swarm Optimization method as MPPT is able to get power of 198.85 Watt with efficiencies above 95% in the hybrid power system for battery chargers, and the presence of the PLN Grid as a backup source, when the PV system does not meet the load power requirements.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114747417","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 : 2020-09-01DOI: 10.1109/IES50839.2020.9231869
Syahrul Arifiiddin Kholid, Ferry Astika Saputra, A. Barakbah
Quick response service and emergency reports handling is one of the main aspects in the data-driven government system, oriented to people service in the city of Surabaya through an emergency center called as Command Center 112. Our idea is to implement descriptive and predictive analytics to be able to provide a detailed picture of the intensity of the number of reports of each category and sub-district in the city of Surabaya as well as make predictions to find out future public report projections by analyzing spatial and temporal data. For descriptive analysis, we apply the unsupervised learning method with agglomerative hierarchical clustering combined with K-Means clustering for centroid initialization. After the data is preprocessed, such as imputation and data structure improvement, the data is then transformed into a report number format for each month and category, then segmented with the K-Means clustering hierarchical model, this model will get 3 final labels. These labels will be projected (grounding) to the level of intensity of community reports in the month and category, ranging from the low, medium and high categories. As for the prediction model, in this study we use combination of timeseries prediction methods, such as Exponential Smoothing, Moving Average and Auto Regressive Integrated Moving Average (ARIMA) by modifying the parameters according to the characteristics of movement, trends and seasonal data. We applied the model that we proposed for research purposes with a dataset of reports from the people of Surabaya to the Command Center 112 in 2019 with a total of 169,937 data.
{"title":"Data Analytics Implementation for Surabaya City Emergency Center","authors":"Syahrul Arifiiddin Kholid, Ferry Astika Saputra, A. Barakbah","doi":"10.1109/IES50839.2020.9231869","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231869","url":null,"abstract":"Quick response service and emergency reports handling is one of the main aspects in the data-driven government system, oriented to people service in the city of Surabaya through an emergency center called as Command Center 112. Our idea is to implement descriptive and predictive analytics to be able to provide a detailed picture of the intensity of the number of reports of each category and sub-district in the city of Surabaya as well as make predictions to find out future public report projections by analyzing spatial and temporal data. For descriptive analysis, we apply the unsupervised learning method with agglomerative hierarchical clustering combined with K-Means clustering for centroid initialization. After the data is preprocessed, such as imputation and data structure improvement, the data is then transformed into a report number format for each month and category, then segmented with the K-Means clustering hierarchical model, this model will get 3 final labels. These labels will be projected (grounding) to the level of intensity of community reports in the month and category, ranging from the low, medium and high categories. As for the prediction model, in this study we use combination of timeseries prediction methods, such as Exponential Smoothing, Moving Average and Auto Regressive Integrated Moving Average (ARIMA) by modifying the parameters according to the characteristics of movement, trends and seasonal data. We applied the model that we proposed for research purposes with a dataset of reports from the people of Surabaya to the Command Center 112 in 2019 with a total of 169,937 data.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114771042","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 : 2020-09-01DOI: 10.1109/IES50839.2020.9231873
Happy Yaumil Fitri Rozaana, I. Gede Puja Astawa, Arifin
MIMO technology is needed in the world of wireless communication (wireless) which acts as a pair of antennas on the transmitter and receiver sides that work on multipath components. The MIMO system design is able to maximize data rates to support data flow and improve overall system reliability. So, it is concluded that this technology has access to data communication that is reliable, fast, and efficient in bandwidth usage. However, the disadvantage of MIMO is that any use of an antenna on the receiver side requires a RF front-end that is as large as the number of antennas. So, for a large size antenna, it is not effective. Therefore, RF front-end on the receiver side requires a single RF technique with the number of antennas as needed that aims to minimize the number of RF front-end. In this research, a simulation is carried out to analyze the performance of the single RF technique at the receiver side of the MIMO system using V-BLAST detection technique. V-BLAST is a channel coding technique that is used to minimize errors during large and fast data transmission processes. So, this technique aims to reduce the quantity of the Bit Error Rate (BER). The antenna to be used has a 2x2 dimension based on a single RF. The modulation that will be used is 64- QAM by using a 1/3 rate convolution encoder code. The results obtained by this research are improving performance and minimizing RF front-end by using the V-BLAST technique in the MIMO system where the observed parameters are the comparison of Bit Error Rate (BER) to Signal to Noise Ratio (SNR) which will be shown in the BER towards SNR curve.
{"title":"MIMO V-BLAST Detection Performances on Single RF Using Convolutional Code with Rate of 1/3","authors":"Happy Yaumil Fitri Rozaana, I. Gede Puja Astawa, Arifin","doi":"10.1109/IES50839.2020.9231873","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231873","url":null,"abstract":"MIMO technology is needed in the world of wireless communication (wireless) which acts as a pair of antennas on the transmitter and receiver sides that work on multipath components. The MIMO system design is able to maximize data rates to support data flow and improve overall system reliability. So, it is concluded that this technology has access to data communication that is reliable, fast, and efficient in bandwidth usage. However, the disadvantage of MIMO is that any use of an antenna on the receiver side requires a RF front-end that is as large as the number of antennas. So, for a large size antenna, it is not effective. Therefore, RF front-end on the receiver side requires a single RF technique with the number of antennas as needed that aims to minimize the number of RF front-end. In this research, a simulation is carried out to analyze the performance of the single RF technique at the receiver side of the MIMO system using V-BLAST detection technique. V-BLAST is a channel coding technique that is used to minimize errors during large and fast data transmission processes. So, this technique aims to reduce the quantity of the Bit Error Rate (BER). The antenna to be used has a 2x2 dimension based on a single RF. The modulation that will be used is 64- QAM by using a 1/3 rate convolution encoder code. The results obtained by this research are improving performance and minimizing RF front-end by using the V-BLAST technique in the MIMO system where the observed parameters are the comparison of Bit Error Rate (BER) to Signal to Noise Ratio (SNR) which will be shown in the BER towards SNR curve.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130272681","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 : 2020-09-01DOI: 10.1109/IES50839.2020.9231935
Rika Rokhana, Wiwiet Herulambang, R. Indraswari
The agricultural sector really needs an application that able to estimate the effect of fertilization on plant growth patterns. The paper proposed the three dimensional (3D) simulation plant growth’s model of Glycine Max (L) Merrill/soybean plant using machine learning Multi-Layered Perceptron (MLP) method combine with Polynomial-Lindenmayer (Poly-L) system. The modeling parameters are the trunk/branches growth (L), the leaves width (W), and the number of branching (B) as the function of changes Nitrogen (N), Phosphate (P), and Potassium (K) elements in the fertilization process. The L, W, and B are modeled as the function of N, P, and K input using MLP method. Then, L, W, and B output are used as a variable to visualize plant growth into a 3D plant’s structure using the Poly-L System interpretation. The polynomial equation is used as a weighted factor according to the iteration of the L-System routine. The experimental results show that the MLP method is quite adaptable to the various changes of N, P, and K values and able to estimate the L, W, and B output. The average error of the trunk's growth prediction is 3.63%, the average error of leaf's width prediction is 3.72%, and the average error on the prediction of the branching's growth is 4.27%. The final result proved that the change of N, P, and K composition influenced the Poly-L System frames. Overall, the system has been running as expected.
{"title":"Machine Learning and Polynomial – L System Algorithm for Modeling and Simulation of Glycine Max (L) Merrill Growth","authors":"Rika Rokhana, Wiwiet Herulambang, R. Indraswari","doi":"10.1109/IES50839.2020.9231935","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231935","url":null,"abstract":"The agricultural sector really needs an application that able to estimate the effect of fertilization on plant growth patterns. The paper proposed the three dimensional (3D) simulation plant growth’s model of Glycine Max (L) Merrill/soybean plant using machine learning Multi-Layered Perceptron (MLP) method combine with Polynomial-Lindenmayer (Poly-L) system. The modeling parameters are the trunk/branches growth (L), the leaves width (W), and the number of branching (B) as the function of changes Nitrogen (N), Phosphate (P), and Potassium (K) elements in the fertilization process. The L, W, and B are modeled as the function of N, P, and K input using MLP method. Then, L, W, and B output are used as a variable to visualize plant growth into a 3D plant’s structure using the Poly-L System interpretation. The polynomial equation is used as a weighted factor according to the iteration of the L-System routine. The experimental results show that the MLP method is quite adaptable to the various changes of N, P, and K values and able to estimate the L, W, and B output. The average error of the trunk's growth prediction is 3.63%, the average error of leaf's width prediction is 3.72%, and the average error on the prediction of the branching's growth is 4.27%. The final result proved that the change of N, P, and K composition influenced the Poly-L System frames. Overall, the system has been running as expected.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130307609","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 : 2020-09-01DOI: 10.1109/IES50839.2020.9231686
S. Sasaki, Shogo Shibahara
This paper presents a spatiotemporal and multidimensional analysis method for threatened species by various factors; spatial factors, temporal factors, natural-phenomenal factors and human’s socio-economic factors with 5D World Map System, which is for understanding the cause- and-effect relations and factors between threatened species and human activities. The objective of this study is to provide a systematic way to analyze and visualize the history and the current situation among biodiversity loss with a geographical analysis, and the causal relations between threatened species and human activities with a multidimensional way. In this paper, we verify the feasibility of our method by applying to a globally collected and accessible dataset of extinct or endangered/threatened species and analyzing the effects of human activities to those species, which has a potential for implementations of Sustainable Development Goals (SDGs).
{"title":"Spatiotemporal and Multidimensional Factor Analysis of Threatened Species with 5D World Map System","authors":"S. Sasaki, Shogo Shibahara","doi":"10.1109/IES50839.2020.9231686","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231686","url":null,"abstract":"This paper presents a spatiotemporal and multidimensional analysis method for threatened species by various factors; spatial factors, temporal factors, natural-phenomenal factors and human’s socio-economic factors with 5D World Map System, which is for understanding the cause- and-effect relations and factors between threatened species and human activities. The objective of this study is to provide a systematic way to analyze and visualize the history and the current situation among biodiversity loss with a geographical analysis, and the causal relations between threatened species and human activities with a multidimensional way. In this paper, we verify the feasibility of our method by applying to a globally collected and accessible dataset of extinct or endangered/threatened species and analyzing the effects of human activities to those species, which has a potential for implementations of Sustainable Development Goals (SDGs).","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130842624","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 Robot Soccer uses the vision system to look for the ball continuously. The quality of vision object detection is the main factor that considered by the robot. Beside the quality, the performance of the detection process also affects the robot performance. The object detection is the heaviest process in entire ERSOW’s robot process. In this paper, we addressed the ways optimizing the vision object detection process that enhanced by the tracking method using Kaiman Filter. The Kaiman filter is also widely used for robotic purposes. The object has been equipped with a local ROI around them to limit the scanning on the entire frame when detection method is running. The local ROI will reduce the computation process and keeping the process in the sufficient resources that processor can handle. The Kaiman filter will predicted the object position and the direction of the object by considered the previous position and the times was taken. The Kaiman filter will lock the object and will follow the object without using detection feature anymore. From the results of tests conducted, the predicted value in several position has showed promising result. The average error on x-axis is 1.425 pixels and in y-axis 1.7226 pixels. This system can also reduce the average computation time from 31.67 Ms into 20.4 Ms. This research is expected to overcome the overwhelmed of the ERSOW’s computation and increased the performance of the robot
{"title":"Dynamic Local Ball Tracking in Middle Size League Robot Soccer ERSOW based on Kaiman Filter","authors":"M. Bachtiar, Iwan Kurnianto Wibowo, Rangga Dikarinata, Renardi Adryantoro Priambudi, Khoirul Anwar","doi":"10.1109/IES50839.2020.9231877","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231877","url":null,"abstract":"The Robot Soccer uses the vision system to look for the ball continuously. The quality of vision object detection is the main factor that considered by the robot. Beside the quality, the performance of the detection process also affects the robot performance. The object detection is the heaviest process in entire ERSOW’s robot process. In this paper, we addressed the ways optimizing the vision object detection process that enhanced by the tracking method using Kaiman Filter. The Kaiman filter is also widely used for robotic purposes. The object has been equipped with a local ROI around them to limit the scanning on the entire frame when detection method is running. The local ROI will reduce the computation process and keeping the process in the sufficient resources that processor can handle. The Kaiman filter will predicted the object position and the direction of the object by considered the previous position and the times was taken. The Kaiman filter will lock the object and will follow the object without using detection feature anymore. From the results of tests conducted, the predicted value in several position has showed promising result. The average error on x-axis is 1.425 pixels and in y-axis 1.7226 pixels. This system can also reduce the average computation time from 31.67 Ms into 20.4 Ms. This research is expected to overcome the overwhelmed of the ERSOW’s computation and increased the performance of the robot","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125409385","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 : 2020-09-01DOI: 10.1109/IES50839.2020.9231533
Yolanda Dwi Paramitha, R. Sigit, T. Harsono, A. Anwar
Liver cancer is a type of cancer that affects the largest organs of the stomach, where some are grown from the liver and some grow in other organs, then spread to the liver. One of the technologies used to analyze and diagnose liver cancer is CT Scan (Computer Tomography Scanner). The CT Scan is often preferred for diagnosing liver cancer, especially as being considered of high accurate imaging, high imaging speed and relatively lower cost. However, the results of the CT Scan are often different depending on the accuracy and experience of the doctor so that it can lead to different diagnoses. In this study, a system was created that was able to extract features from CT Scan images of liver cancer to recognize the object of cancer and distinguish it from other objects. This system will be tested on 50 data abdominal CT Scan images with a diagnosis of liver cancer, where 21 data for benign liver cancer and 29 data for malignant liver cancer. This research has three main stages, that is preprocessing to improve image quality using scaling image, histogram equalization, and median filtering. Segmentation to identify the object being observed and separate it from the background using watershed method and binary thresholding with accuracy is 90%. The last is feature extraction based on cancer area, edge irregularity, and texture to identify liver cancer.
{"title":"Feature Extraction in Liver Cancer Based on Abdominal CT Scan Images using Contour Analysis and GLCM Method","authors":"Yolanda Dwi Paramitha, R. Sigit, T. Harsono, A. Anwar","doi":"10.1109/IES50839.2020.9231533","DOIUrl":"https://doi.org/10.1109/IES50839.2020.9231533","url":null,"abstract":"Liver cancer is a type of cancer that affects the largest organs of the stomach, where some are grown from the liver and some grow in other organs, then spread to the liver. One of the technologies used to analyze and diagnose liver cancer is CT Scan (Computer Tomography Scanner). The CT Scan is often preferred for diagnosing liver cancer, especially as being considered of high accurate imaging, high imaging speed and relatively lower cost. However, the results of the CT Scan are often different depending on the accuracy and experience of the doctor so that it can lead to different diagnoses. In this study, a system was created that was able to extract features from CT Scan images of liver cancer to recognize the object of cancer and distinguish it from other objects. This system will be tested on 50 data abdominal CT Scan images with a diagnosis of liver cancer, where 21 data for benign liver cancer and 29 data for malignant liver cancer. This research has three main stages, that is preprocessing to improve image quality using scaling image, histogram equalization, and median filtering. Segmentation to identify the object being observed and separate it from the background using watershed method and binary thresholding with accuracy is 90%. The last is feature extraction based on cancer area, edge irregularity, and texture to identify liver cancer.","PeriodicalId":344685,"journal":{"name":"2020 International Electronics Symposium (IES)","volume":"346 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133791045","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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