Pub Date : 2022-12-19DOI: 10.1109/jac-ecc56395.2022.10043973
H. M. Marzouk, A. A. El-Hameed, A. Allam, A. Abdel-Rahman
This study suggests a small, low-cost, and adequate blood glucose sensor. A triangle-shaped defective ground structure (DGS) and a parallel linked microstrip line on the upper surface make up its architecture. The bandwidth and sensitivity of the suggested sensor can be greatly enhanced by optimizing the dimensions and position. In this configuration, 14 GHz operation is possible with a reflection coefficient of -39dB. A detailed sensitivity analysis is being conducted for each concentration of several glucose concentrations, referred to be material under test (MUT), from 80 to 4000 mg/dL. The simulated results reveal a frequency shift from 14 to 2.5 GHz, due to the loading effect of the blood and the container. An amplitude shift occurs due to the change in the blood glucose level when operating in the reflection mode. The applied sensor has an average sensitivity of 1.07%.
{"title":"Design of Non-Invasive Glucose Measurement Sensor","authors":"H. M. Marzouk, A. A. El-Hameed, A. Allam, A. Abdel-Rahman","doi":"10.1109/jac-ecc56395.2022.10043973","DOIUrl":"https://doi.org/10.1109/jac-ecc56395.2022.10043973","url":null,"abstract":"This study suggests a small, low-cost, and adequate blood glucose sensor. A triangle-shaped defective ground structure (DGS) and a parallel linked microstrip line on the upper surface make up its architecture. The bandwidth and sensitivity of the suggested sensor can be greatly enhanced by optimizing the dimensions and position. In this configuration, 14 GHz operation is possible with a reflection coefficient of -39dB. A detailed sensitivity analysis is being conducted for each concentration of several glucose concentrations, referred to be material under test (MUT), from 80 to 4000 mg/dL. The simulated results reveal a frequency shift from 14 to 2.5 GHz, due to the loading effect of the blood and the container. An amplitude shift occurs due to the change in the blood glucose level when operating in the reflection mode. The applied sensor has an average sensitivity of 1.07%.","PeriodicalId":326002,"journal":{"name":"2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125373114","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 : 2022-10-11DOI: 10.1109/JAC-ECC56395.2022.10043955
A. Elrahman, A. Taloba, Mohammed F. Farghally, T. H. Soliman
the growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students’ learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students’ responses to the course exercises. We applied Item Response Theory (IRT) analysis and a Latent Trait Mode (LTM) to identify the most difficult exercises. To evaluate the quality of the course exercises we applied the IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention.
{"title":"Identifying Difficult exercises in an eTextbook Using Item Response Theory and Logged Data Analysis","authors":"A. Elrahman, A. Taloba, Mohammed F. Farghally, T. H. Soliman","doi":"10.1109/JAC-ECC56395.2022.10043955","DOIUrl":"https://doi.org/10.1109/JAC-ECC56395.2022.10043955","url":null,"abstract":"the growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students’ learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students’ responses to the course exercises. We applied Item Response Theory (IRT) analysis and a Latent Trait Mode (LTM) to identify the most difficult exercises. To evaluate the quality of the course exercises we applied the IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention.","PeriodicalId":326002,"journal":{"name":"2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133337970","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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