Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172037
Shan Ali, A. Khan, Shafaq Zia, Mayyda Mukhtar
Human Activity Recognition (HAR) has gained significance importance due to its wide range of applications in security, healthcare, surveillance, virtual reality, control systems and automation. Sensors embedded in modern mobile phones enable unobtrusive detection of Activities of Daily Living (ADL). Various statistical and deep learning techniques for the automated detection of human activity have been presented recently. In this study, we have collected accelerometry data through a mobile phone carried by a user for number of days to classify ADL on the basis of exhibited movement into stationary, light ambulatory, intense ambulatory and abnormal classes. ADL such as walking, sitting and jogging etc. are performed and classified simultaneously by mobile phone application and users for comparative analysis. Collected data is given as an input to the trained model and analyzed by implementing the J48 classifier. Results reveal an accuracy score of around 70% for each activity class and it is noted that the classification was performed with an accuracy of above 80% for stationary activity. It is shown that ADL can be recognized with high accuracy using accelerometry data collected in a constrained environment and a single sensor. J48 classifier also correctly classified activities that have a strong correlation between them such as sitting on a chair and standing in stationary position. This work is significant for utilization in long term health monitoring systems that are capable of ensuring neurological health for masses through HAR and mobile phones embedded with accelerometers.
{"title":"Human Activity Recognition System using Smart Phone based Accelerometer and Machine Learning","authors":"Shan Ali, A. Khan, Shafaq Zia, Mayyda Mukhtar","doi":"10.1109/IAICT50021.2020.9172037","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172037","url":null,"abstract":"Human Activity Recognition (HAR) has gained significance importance due to its wide range of applications in security, healthcare, surveillance, virtual reality, control systems and automation. Sensors embedded in modern mobile phones enable unobtrusive detection of Activities of Daily Living (ADL). Various statistical and deep learning techniques for the automated detection of human activity have been presented recently. In this study, we have collected accelerometry data through a mobile phone carried by a user for number of days to classify ADL on the basis of exhibited movement into stationary, light ambulatory, intense ambulatory and abnormal classes. ADL such as walking, sitting and jogging etc. are performed and classified simultaneously by mobile phone application and users for comparative analysis. Collected data is given as an input to the trained model and analyzed by implementing the J48 classifier. Results reveal an accuracy score of around 70% for each activity class and it is noted that the classification was performed with an accuracy of above 80% for stationary activity. It is shown that ADL can be recognized with high accuracy using accelerometry data collected in a constrained environment and a single sensor. J48 classifier also correctly classified activities that have a strong correlation between them such as sitting on a chair and standing in stationary position. This work is significant for utilization in long term health monitoring systems that are capable of ensuring neurological health for masses through HAR and mobile phones embedded with accelerometers.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114281607","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172022
Arrizky Ayu Faradila Purnama, M. I. Nashiruddin
SigFox is a Low Power Wide Area Network (LPWAN) technology using unlicensed frequency bands with Ultra Narrow Band technology. It has advantages in terms of very low power consumption, high receiver sensitivity, and the cheap cost of end devices. SigFox technology is based on Link Quality Control (LQI). One parameter is the division of zones is based on radio configuration, where Indonesia included in zone 2 with radio configuration RC4. SigFox is very suitable as a solution in terms of radio connectivity. In this study, an analysis of the Internet of Thing (IoT) network design was carried out in the East Java province of Indonesia, particularly in the cities of Surabaya, Sidoarjo, and Gresik as Urban Scenario. The Advanced Metering Infrastructure services include electricity, water, gas, and fuel. From the simulations that have been carried out, the optimal number of gateways obtained respectively 34 sites for the Surabaya area with the average signal level received was −78 dBm, and SNR value was 17.27 dB. While five gateways need for the Sidoarjo area with the average signal level received was −89.68 dBm, and SNR value was −1.06 dB, and eight sites for the Gresik area with the average signal level received was −89.14 dBm, and SNR value was −1.12 dB.
{"title":"SigFox-based Internet of Things Network Planning for Advanced Metering Infrastructure Services in Urban Scenario","authors":"Arrizky Ayu Faradila Purnama, M. I. Nashiruddin","doi":"10.1109/IAICT50021.2020.9172022","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172022","url":null,"abstract":"SigFox is a Low Power Wide Area Network (LPWAN) technology using unlicensed frequency bands with Ultra Narrow Band technology. It has advantages in terms of very low power consumption, high receiver sensitivity, and the cheap cost of end devices. SigFox technology is based on Link Quality Control (LQI). One parameter is the division of zones is based on radio configuration, where Indonesia included in zone 2 with radio configuration RC4. SigFox is very suitable as a solution in terms of radio connectivity. In this study, an analysis of the Internet of Thing (IoT) network design was carried out in the East Java province of Indonesia, particularly in the cities of Surabaya, Sidoarjo, and Gresik as Urban Scenario. The Advanced Metering Infrastructure services include electricity, water, gas, and fuel. From the simulations that have been carried out, the optimal number of gateways obtained respectively 34 sites for the Surabaya area with the average signal level received was −78 dBm, and SNR value was 17.27 dB. While five gateways need for the Sidoarjo area with the average signal level received was −89.68 dBm, and SNR value was −1.06 dB, and eight sites for the Gresik area with the average signal level received was −89.14 dBm, and SNR value was −1.12 dB.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129351408","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172028
Shafaq Zia, A. Khan, Mayyda Mukhtar, Shan Ali, Jibran Shahid, Mobeen Sohail
We have developed a healthcare mobile application, for human activity recognition, monitoring of well-being and detection of individuals going towards a health hazard based on the data collected from sensors embedded in mobile phones and wearables. The data from sensors are processed within the mobile application to detect and classify different Activities of Daily Living. The developed framework is used to collect data in an unconstraint environment from individuals suffering from neurological disorders. The data is further tested using signal processing and machine learning algorithms. Results of in-app processing and classification are stored in a dedicated mobile database for later reference and analysis. This paper shows that statistical and Machine Learning methods can also be used within a mobile application for classification of ADLs. MyNeuroHealth has been designed in accordance with the scale of the prevalence of neurological disorders among the general population of developing countries and has become more relevant in COVID-19 pandemic as it offers real-time nonintrusive monitoring. Results show that MyNeuroHealth can detect and classify Motor Seizures and falls with an accuracy of 99%. The app is also able to detect if a patient had stumbled or fallen due to any reason and notifies caregiver accordingly.
{"title":"Detection of Motor Seizures and Falls in Mobile Application using Machine Learning Classifiers","authors":"Shafaq Zia, A. Khan, Mayyda Mukhtar, Shan Ali, Jibran Shahid, Mobeen Sohail","doi":"10.1109/IAICT50021.2020.9172028","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172028","url":null,"abstract":"We have developed a healthcare mobile application, for human activity recognition, monitoring of well-being and detection of individuals going towards a health hazard based on the data collected from sensors embedded in mobile phones and wearables. The data from sensors are processed within the mobile application to detect and classify different Activities of Daily Living. The developed framework is used to collect data in an unconstraint environment from individuals suffering from neurological disorders. The data is further tested using signal processing and machine learning algorithms. Results of in-app processing and classification are stored in a dedicated mobile database for later reference and analysis. This paper shows that statistical and Machine Learning methods can also be used within a mobile application for classification of ADLs. MyNeuroHealth has been designed in accordance with the scale of the prevalence of neurological disorders among the general population of developing countries and has become more relevant in COVID-19 pandemic as it offers real-time nonintrusive monitoring. Results show that MyNeuroHealth can detect and classify Motor Seizures and falls with an accuracy of 99%. The app is also able to detect if a patient had stumbled or fallen due to any reason and notifies caregiver accordingly.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131949196","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172024
A. Ramdani, Agus Virgono, C. Setianingsih
Currently, the payment process at restaurants is still manual and inefficient because it uses a cash register. A cashier will check what food is ordered, then count it with the cash register. This is not efficient. So food detection devices and automatic food price estimates have the answer to these deficiencies. Food detection aims to facilitate payment at restaurants, and automatic food price estimation using the Convolutional Neural Network (CNN) classification method. The detection accuracy of 6 types of food using the CNN method was obtained 100% with 80% data partition training data and 20% test data with epoch 9000 and learning rate 0.0002, with a detection time of fewer than 10 seconds.
{"title":"Food Detection with Image Processing Using Convolutional Neural Network (CNN) Method","authors":"A. Ramdani, Agus Virgono, C. Setianingsih","doi":"10.1109/IAICT50021.2020.9172024","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172024","url":null,"abstract":"Currently, the payment process at restaurants is still manual and inefficient because it uses a cash register. A cashier will check what food is ordered, then count it with the cash register. This is not efficient. So food detection devices and automatic food price estimates have the answer to these deficiencies. Food detection aims to facilitate payment at restaurants, and automatic food price estimation using the Convolutional Neural Network (CNN) classification method. The detection accuracy of 6 types of food using the CNN method was obtained 100% with 80% data partition training data and 20% test data with epoch 9000 and learning rate 0.0002, with a detection time of fewer than 10 seconds.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130076787","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172012
Fenta Febriyandi, A. S. Arifin, M. I. Nashiruddin
Sigfox is a radio protocol Low Power Wide Area Network (LPWAN) technology that has a characteristic global reach, cost-effective, energy efficiency, and simplicity. Sigfox operates in the unlicensed spectrum frequency band, during transmitting and receiving messages, Sigfox uses ultra-narrow band (UNB) modulation technique. This paper analyzes Sigfox based network planning for public Internet of Things (IoT) services in a metropolitan area that has 10.37 million population and 662.3 km2. The result of the study stated that 33 access points are needed to cover the entire city. The simulation result shows that 99.9% of the area has a Reference Signal Received Power (RSRP) Downlink level above −134 dBm. The mean values of RSRP Downlink and Signal to Interference and Noise Ratio (SINR) Downlink are −91.81 dBm and 7.98 dB, respectively. Meanwhile, for the Received Signal Strength Indicator (RSSI) Downlink parameter, 98.7% metropolitan area is covered and has a mean value of −87.91 dBm. Okumura-Hata radio propagation model is used and gives results that allowed propagation loss and shadow fading margin are 152.5 dB and 7.5 dB, respectively, thus cell range for one access point reaches 3.28 km.
{"title":"Sigfox Based Network Planning Analysis for Public Internet of Things Services in Metropolitan Area","authors":"Fenta Febriyandi, A. S. Arifin, M. I. Nashiruddin","doi":"10.1109/IAICT50021.2020.9172012","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172012","url":null,"abstract":"Sigfox is a radio protocol Low Power Wide Area Network (LPWAN) technology that has a characteristic global reach, cost-effective, energy efficiency, and simplicity. Sigfox operates in the unlicensed spectrum frequency band, during transmitting and receiving messages, Sigfox uses ultra-narrow band (UNB) modulation technique. This paper analyzes Sigfox based network planning for public Internet of Things (IoT) services in a metropolitan area that has 10.37 million population and 662.3 km2. The result of the study stated that 33 access points are needed to cover the entire city. The simulation result shows that 99.9% of the area has a Reference Signal Received Power (RSRP) Downlink level above −134 dBm. The mean values of RSRP Downlink and Signal to Interference and Noise Ratio (SINR) Downlink are −91.81 dBm and 7.98 dB, respectively. Meanwhile, for the Received Signal Strength Indicator (RSSI) Downlink parameter, 98.7% metropolitan area is covered and has a mean value of −87.91 dBm. Okumura-Hata radio propagation model is used and gives results that allowed propagation loss and shadow fading margin are 152.5 dB and 7.5 dB, respectively, thus cell range for one access point reaches 3.28 km.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627427","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172017
C. Apriono, Farida Ulfah
The size of a detector is the primary consideration to obtain a high-resolution imaging quality. A THz quasi optic can combine an optical component of a hemispherical dielectric lens and an antenna-based sensor to capture effectively incoming radiation. The use of the hemispherical lens can contribute to sensor size. This paper investigates an immersion technique for dielectric lens size reduction to provide radiation performances of gain and radiation efficiency on the purpose of antenna size miniaturization at Terahertz (THz) frequency. This investigation is using the CST Microwave Studio simulation software. Gain and radiation efficiency show a decreasing pattern as the dielectric thickness increases. The obtained gain is still 30 dB by adding thickness until half of the hemispherical radius once combined with matching layers and 0.6 of the radius once without matching layers. Therefore, a smaller size than a hemispherical structure can still provide excellent radiation performance. This information is useful to design as small as a THz detector to obtain high-resolution imaging.
{"title":"Immersion Effect of Dielectric Lens Radiation Performances Fed with Double Crossed Terahertz Planar Bow-Tie Antenna","authors":"C. Apriono, Farida Ulfah","doi":"10.1109/IAICT50021.2020.9172017","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172017","url":null,"abstract":"The size of a detector is the primary consideration to obtain a high-resolution imaging quality. A THz quasi optic can combine an optical component of a hemispherical dielectric lens and an antenna-based sensor to capture effectively incoming radiation. The use of the hemispherical lens can contribute to sensor size. This paper investigates an immersion technique for dielectric lens size reduction to provide radiation performances of gain and radiation efficiency on the purpose of antenna size miniaturization at Terahertz (THz) frequency. This investigation is using the CST Microwave Studio simulation software. Gain and radiation efficiency show a decreasing pattern as the dielectric thickness increases. The obtained gain is still 30 dB by adding thickness until half of the hemispherical radius once combined with matching layers and 0.6 of the radius once without matching layers. Therefore, a smaller size than a hemispherical structure can still provide excellent radiation performance. This information is useful to design as small as a THz detector to obtain high-resolution imaging.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121052507","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172018
Surya Darmawan, Budhi Irawan, C. Setianingsih, Muhammad Ary Murty
The Gyro sensor works with the principle of determination of angular momentum, and this tool works in conjunction with an accelerometer. The mechanism is a spinning wheel with a disc inside that remains stable. This tool is often used on robots or drones and other sophisticated tools. In addition to being used on robots or drones, the gyro sensor can be an early detection tool for seawater waves. In addition to being cheaper, the benefits that can be provided by this tool are being able to help the community, especially in the seaside or coastal areas, to find out the anomalies that occur in the sea. With that the community can know what is happening at sea, besides being able to anticipate disasters, it can also provide information if the sea conditions, especially fishermen who will go to sea and tourists who will visit the beach.
{"title":"Design of Detection Device for Sea Water Waves with Fuzzy Algorithm Based on Internet of Things","authors":"Surya Darmawan, Budhi Irawan, C. Setianingsih, Muhammad Ary Murty","doi":"10.1109/IAICT50021.2020.9172018","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172018","url":null,"abstract":"The Gyro sensor works with the principle of determination of angular momentum, and this tool works in conjunction with an accelerometer. The mechanism is a spinning wheel with a disc inside that remains stable. This tool is often used on robots or drones and other sophisticated tools. In addition to being used on robots or drones, the gyro sensor can be an early detection tool for seawater waves. In addition to being cheaper, the benefits that can be provided by this tool are being able to help the community, especially in the seaside or coastal areas, to find out the anomalies that occur in the sea. With that the community can know what is happening at sea, besides being able to anticipate disasters, it can also provide information if the sea conditions, especially fishermen who will go to sea and tourists who will visit the beach.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127306922","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172010
Khalil Esper, Frank Schnicke
Current markets are characterized by rapid changing in requirements and new customer needs. Thus, fast changeable production is a fundamental goal of Industry 4.0 scheme, which aims to make the manufacturing more changeable and efficient by interconnecting the various factory components, and representing the factory assets virtually in digital twins.In order to study the Industry 4.0 changeability capability, we apply scenario-based evaluation. We derive three change scenarios that can be observed in a plant: Change of product flow depending on quality, depending on product type and the introduction of a new product. Using these scenarios, we compare between the third industrial revolution (Industry 3) and Industry 4.0 architectures based on the resultant change impact.For our evaluation, we utilize the Architecture-Level Modifiability Analysis (ALMA) method and describe its instantiation to the given context, providing ALMA 4.0, a guideline for Industry 4.0 Maintainability scenario-based evaluation. The result shows that the change impact on Industry 4.0 is less than Industry 3. Thus, we provide quantitative evidence that changing Industry 4.0 architecture incurs less efforts than Industry 3 changes.
{"title":"Evaluation of the Maintainability Aspect of Industry 4.0 Service-oriented Production","authors":"Khalil Esper, Frank Schnicke","doi":"10.1109/IAICT50021.2020.9172010","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172010","url":null,"abstract":"Current markets are characterized by rapid changing in requirements and new customer needs. Thus, fast changeable production is a fundamental goal of Industry 4.0 scheme, which aims to make the manufacturing more changeable and efficient by interconnecting the various factory components, and representing the factory assets virtually in digital twins.In order to study the Industry 4.0 changeability capability, we apply scenario-based evaluation. We derive three change scenarios that can be observed in a plant: Change of product flow depending on quality, depending on product type and the introduction of a new product. Using these scenarios, we compare between the third industrial revolution (Industry 3) and Industry 4.0 architectures based on the resultant change impact.For our evaluation, we utilize the Architecture-Level Modifiability Analysis (ALMA) method and describe its instantiation to the given context, providing ALMA 4.0, a guideline for Industry 4.0 Maintainability scenario-based evaluation. The result shows that the change impact on Industry 4.0 is less than Industry 3. Thus, we provide quantitative evidence that changing Industry 4.0 architecture incurs less efforts than Industry 3 changes.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117340624","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172034
A. F. Isnawati, M. Afandi, J. Hendry
Nowadays, the use of barcode and QR code has been very common. The implementation of barcode and QR code is not only to ease the identification and inventory of goods but it is also used for goods tracking, place tracking, document management, and others. The performance of FBMC-OQAM communication system for image transmission of barcode and QR code become important to be researched considering both image data input have different characteristics. This study uses Zero Forcing (ZF) equalization as a symbol detection. The result of the study shows that in general, the accepted image data on system which used ZF equalization is better than the other without using ZF. The result of simulation also showed BER on barcode image as much as 1.94e-03 and on QR code as much as 8.125e-05 which meant that the performance of QR code transmission system earns better result compared to barcode. Based on the reading process of the data by reader or scanner, it showed that barcode image needed higher SNR, that was 27 dB compared to QR code image that only needed SNR 20 dB. In addition, barcode image enables data misreading even though the reader or scanner could detect the code that was on SNR 19 dB, it is different from QR code which the result of image reading earned correct information if it reached threshold.
{"title":"Performance Analysis of FBMC-OQAM System for Barcode and QR Code Image Transmission","authors":"A. F. Isnawati, M. Afandi, J. Hendry","doi":"10.1109/IAICT50021.2020.9172034","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172034","url":null,"abstract":"Nowadays, the use of barcode and QR code has been very common. The implementation of barcode and QR code is not only to ease the identification and inventory of goods but it is also used for goods tracking, place tracking, document management, and others. The performance of FBMC-OQAM communication system for image transmission of barcode and QR code become important to be researched considering both image data input have different characteristics. This study uses Zero Forcing (ZF) equalization as a symbol detection. The result of the study shows that in general, the accepted image data on system which used ZF equalization is better than the other without using ZF. The result of simulation also showed BER on barcode image as much as 1.94e-03 and on QR code as much as 8.125e-05 which meant that the performance of QR code transmission system earns better result compared to barcode. Based on the reading process of the data by reader or scanner, it showed that barcode image needed higher SNR, that was 27 dB compared to QR code image that only needed SNR 20 dB. In addition, barcode image enables data misreading even though the reader or scanner could detect the code that was on SNR 19 dB, it is different from QR code which the result of image reading earned correct information if it reached threshold.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123737348","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 : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172026
Jampu Raju, C. V. Raghu, S. N. George, T. Bindiya
This paper describes the details of development of a hand held security device to help the security people at the entrances of big institutions/industries/apartments. The security people can scan the number plate of vehicles come at entrance using this device and the device will display whether the vehicle is authorised or unauthorised to enter to the premises. Provision is given to add/remove the registration number to/from the database. This device is designed around onboard computer, which is commonly termed as Raspberry Pi. The optical character recognition (OCR) technique implemented on this device is used for the identification of the registration number.
{"title":"Development of a Hand held device for Automatic License Plate Recognition","authors":"Jampu Raju, C. V. Raghu, S. N. George, T. Bindiya","doi":"10.1109/IAICT50021.2020.9172026","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172026","url":null,"abstract":"This paper describes the details of development of a hand held security device to help the security people at the entrances of big institutions/industries/apartments. The security people can scan the number plate of vehicles come at entrance using this device and the device will display whether the vehicle is authorised or unauthorised to enter to the premises. Provision is given to add/remove the registration number to/from the database. This device is designed around onboard computer, which is commonly termed as Raspberry Pi. The optical character recognition (OCR) technique implemented on this device is used for the identification of the registration number.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120977439","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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