Pub Date : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293716
F. Cruz, Jeremiah A. Ordiales, Malvin Angelo C. Reyes, Pinky T. Salvanera
Municipal fishermen experience collisions and being rammed by larger marine vessels. Automatic identification system (AIS) can be a solution for them, if not for its high cost. Likewise, in emergency situations, due to limited cellular coverage at sea, fishermen do not have the means to communicate with each other and request for assistance. Therefore, this design addresses these concerns with a low-cost AIS receiver embedded with intercommunication using microcomputer and software-defined radio.
{"title":"Automatic Identification System Receiver for Small Fishing Vessels","authors":"F. Cruz, Jeremiah A. Ordiales, Malvin Angelo C. Reyes, Pinky T. Salvanera","doi":"10.1109/TENCON50793.2020.9293716","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293716","url":null,"abstract":"Municipal fishermen experience collisions and being rammed by larger marine vessels. Automatic identification system (AIS) can be a solution for them, if not for its high cost. Likewise, in emergency situations, due to limited cellular coverage at sea, fishermen do not have the means to communicate with each other and request for assistance. Therefore, this design addresses these concerns with a low-cost AIS receiver embedded with intercommunication using microcomputer and software-defined radio.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122540782","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293868
Ma. Jenica M. Autos, Samantha Kaye S. Falculan, J. Fortin, June F. Mendoza, Anna-liza F. Sigue, M. G. Beaño, Oliver A. Medina, Don Juan M. Tuazon
This paper introduces the development of monitoring and maintaining optimal water quality in an aquaponics system. The design is based on the hydroponics system’s Nutrient Film Technique (NFT) in which plant roots are being exposed to a thin layer of nutrient water in a long narrow channel. The design used a developmental and experimental type with the use of microprocessors and sensors for enhanced monitoring and error-correcting within the aquaponics system where standard data was obtained from different reliable sources. Various sensors are calibrated for different measurements to provide accurate and reliable readings of water temperature, pH level, dissolved oxygen, total dissolved solids, water flow, and temperature and humidity. The Arduino Mega reads and analyzes data collected by various sensors, and instructs actuators such as aquarium heater, cooling fan, aerator, grow light, and water pump. The data gathered appears on the built-in LCD screen and will be sent to the owner's mobile phone regarding the condition of the system. Also, the system has a fish feeder that automatically dispenses food at a given time. The device can be controlled wirelessly using a mobile phone and manually using a 4x4 keypad. Also, the owner can monitor the way each actuator was controlled.
{"title":"Automated Aquaponics System and Water Quality Monitoring with SMS Notification for Tilapia Industry","authors":"Ma. Jenica M. Autos, Samantha Kaye S. Falculan, J. Fortin, June F. Mendoza, Anna-liza F. Sigue, M. G. Beaño, Oliver A. Medina, Don Juan M. Tuazon","doi":"10.1109/TENCON50793.2020.9293868","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293868","url":null,"abstract":"This paper introduces the development of monitoring and maintaining optimal water quality in an aquaponics system. The design is based on the hydroponics system’s Nutrient Film Technique (NFT) in which plant roots are being exposed to a thin layer of nutrient water in a long narrow channel. The design used a developmental and experimental type with the use of microprocessors and sensors for enhanced monitoring and error-correcting within the aquaponics system where standard data was obtained from different reliable sources. Various sensors are calibrated for different measurements to provide accurate and reliable readings of water temperature, pH level, dissolved oxygen, total dissolved solids, water flow, and temperature and humidity. The Arduino Mega reads and analyzes data collected by various sensors, and instructs actuators such as aquarium heater, cooling fan, aerator, grow light, and water pump. The data gathered appears on the built-in LCD screen and will be sent to the owner's mobile phone regarding the condition of the system. Also, the system has a fish feeder that automatically dispenses food at a given time. The device can be controlled wirelessly using a mobile phone and manually using a 4x4 keypad. Also, the owner can monitor the way each actuator was controlled.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131783359","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293720
D. Dassanayake, S. Wijesinghe, T.L.C Jayasiri, K.A.S.T. Keenawinna, W. Rankothge, N. Gamage
It is widely recognized that a nation with minimum problems relating to areas such as health, environment, infrastructure, and technology is a developed country [1]. However, the developing/ lower-middle income countries need many improvements in the above-mentioned areas, as they are still lacking in those areas [1]. Apart from the risk associated with these problems, the main challenge faced by developing countries is, making the public aware of these problems. In this paper, we are proposing a mobile game-based learning platform: "AwareME" which focuses on following problems: (1) health awareness (dengue fever), (2) environmental awareness (garbage disposal), (3) cyber security awareness (social media) and (4) safety awareness (road safety). The "AwareME" platform includes quizzes, 2D/3D puzzle games, and 3D action games with activities to improve the cognitive skills and awareness of the public. We have provided the results of an initial performance evaluation of "AwareME" platform.
{"title":"AwareME: Public Awareness through Game-Based Learning","authors":"D. Dassanayake, S. Wijesinghe, T.L.C Jayasiri, K.A.S.T. Keenawinna, W. Rankothge, N. Gamage","doi":"10.1109/TENCON50793.2020.9293720","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293720","url":null,"abstract":"It is widely recognized that a nation with minimum problems relating to areas such as health, environment, infrastructure, and technology is a developed country [1]. However, the developing/ lower-middle income countries need many improvements in the above-mentioned areas, as they are still lacking in those areas [1]. Apart from the risk associated with these problems, the main challenge faced by developing countries is, making the public aware of these problems. In this paper, we are proposing a mobile game-based learning platform: \"AwareME\" which focuses on following problems: (1) health awareness (dengue fever), (2) environmental awareness (garbage disposal), (3) cyber security awareness (social media) and (4) safety awareness (road safety). The \"AwareME\" platform includes quizzes, 2D/3D puzzle games, and 3D action games with activities to improve the cognitive skills and awareness of the public. We have provided the results of an initial performance evaluation of \"AwareME\" platform.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134572786","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293707
Sayan Sarkar, W. Ki
This research studies the effect of a starving resistor in various pre-driver schemes for shoot-through loss reduction in the buffer of an integrated DC-DC converter and explores how the efficiency is affected. The starving resistor (Bidirectional delay element) reduces the short circuit current of an inverter by developing time skewed gate driving signals for the driven stage NMOS and PMOS inside a buffer. The starving resistor scheme enhances the efficiency if it is inside the buffer of a switch, but is not as efficient if it is inside the buffer of an active diode. The efficiency of the buffer can be further enhanced by adding delay generator schemes within a buffer. Results are validated via extensive SPICE simulations.
{"title":"A Study on Shoot-Through Reduction of DC-DC Converter Pre-Driver using Starving Resistor","authors":"Sayan Sarkar, W. Ki","doi":"10.1109/TENCON50793.2020.9293707","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293707","url":null,"abstract":"This research studies the effect of a starving resistor in various pre-driver schemes for shoot-through loss reduction in the buffer of an integrated DC-DC converter and explores how the efficiency is affected. The starving resistor (Bidirectional delay element) reduces the short circuit current of an inverter by developing time skewed gate driving signals for the driven stage NMOS and PMOS inside a buffer. The starving resistor scheme enhances the efficiency if it is inside the buffer of a switch, but is not as efficient if it is inside the buffer of an active diode. The efficiency of the buffer can be further enhanced by adding delay generator schemes within a buffer. Results are validated via extensive SPICE simulations.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133176019","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293925
M. A. Hossain, Mubtasim Islam Sabik, Ikramuzzaman Muntasir, A. Islam, Salekul Islam, Ashir Ahmed
It is reported that since 2016 there are over sixty thousand diagnosed cases of Leukemia in the United States of America alone. It also suggests that Leukemia is the most common type of cancer seen in the age of twenty. Although the study is based on a Western country, it is equally alarming for an Asian country like Bangladesh where healthcare system is not up to the standard. Researches show that the Chronic Lymphocytic Leukemia has about 83% five-year long survival rates. This paper focuses on Acute Lymphocytic Leukemia (ALL) as this is the most common type of Leukemia in Bangladesh. It is common knowledge among oncologists, that cancer is much easier to treat if it is detected in the early stages. Thus the treatment needs to begin as early as possible. We propose a hands-on approach in detecting the irregular blood components (e.g., Neutrophils, Eosinophils, Basophils, Lymphocytes and Monocytes) that are typically found in a cancer patient. In this work, we first identify 14 attributes to prepare the dataset and determine 4 major attributes that play a significant role in determining a Leukemia patient. We have also collected 256 primary data from Leukemia patient. The data is then processed using microscope to obtain images and fetch into Faster-RCNN machine learning algorithm to predict the odds of cancer cells forming. Here we have applied two loss functions to both the RPN (Region Convolutional Neural Network) model and the classifier model to detect the similar blood object. After identifying the object, we have calculated the corresponding object and based on the count of the corresponding object we finally detect Leukemia. The mean average precision observed are 0.10, 0.16 and 0, where the epochs are 40, 60 and 120, respectively.
{"title":"Leukemia Detection Mechanism through Microscopic Image and ML Techniques","authors":"M. A. Hossain, Mubtasim Islam Sabik, Ikramuzzaman Muntasir, A. Islam, Salekul Islam, Ashir Ahmed","doi":"10.1109/TENCON50793.2020.9293925","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293925","url":null,"abstract":"It is reported that since 2016 there are over sixty thousand diagnosed cases of Leukemia in the United States of America alone. It also suggests that Leukemia is the most common type of cancer seen in the age of twenty. Although the study is based on a Western country, it is equally alarming for an Asian country like Bangladesh where healthcare system is not up to the standard. Researches show that the Chronic Lymphocytic Leukemia has about 83% five-year long survival rates. This paper focuses on Acute Lymphocytic Leukemia (ALL) as this is the most common type of Leukemia in Bangladesh. It is common knowledge among oncologists, that cancer is much easier to treat if it is detected in the early stages. Thus the treatment needs to begin as early as possible. We propose a hands-on approach in detecting the irregular blood components (e.g., Neutrophils, Eosinophils, Basophils, Lymphocytes and Monocytes) that are typically found in a cancer patient. In this work, we first identify 14 attributes to prepare the dataset and determine 4 major attributes that play a significant role in determining a Leukemia patient. We have also collected 256 primary data from Leukemia patient. The data is then processed using microscope to obtain images and fetch into Faster-RCNN machine learning algorithm to predict the odds of cancer cells forming. Here we have applied two loss functions to both the RPN (Region Convolutional Neural Network) model and the classifier model to detect the similar blood object. After identifying the object, we have calculated the corresponding object and based on the count of the corresponding object we finally detect Leukemia. The mean average precision observed are 0.10, 0.16 and 0, where the epochs are 40, 60 and 120, respectively.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114197117","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293839
M. Aqib, A. Ukil
Agent-based models (ABM) are a kind of micro scale model that imitate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex process. Netlogo is a real time simulation software tool to design this model with the help of programming and coding. This paper identifies decision variables based on electric vehicles (EVs) charging statistics and the heuristic decisions in EVs charging at public charging stations, commercial place and offices are converted into constraints of (ABM). This unique model is the version of real time charging scenario at the charging stations. With the help of programmed model in Netlogo, the behaviour of EVs user under different real life scenarios are observed and recorded. The proposed system is implemented and designed in Netlogo to test the results.
{"title":"Modelling of Electric Vehicle Charging and Discharging Profile to Mimic Real life Scenario at Charging Stations","authors":"M. Aqib, A. Ukil","doi":"10.1109/TENCON50793.2020.9293839","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293839","url":null,"abstract":"Agent-based models (ABM) are a kind of micro scale model that imitate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex process. Netlogo is a real time simulation software tool to design this model with the help of programming and coding. This paper identifies decision variables based on electric vehicles (EVs) charging statistics and the heuristic decisions in EVs charging at public charging stations, commercial place and offices are converted into constraints of (ABM). This unique model is the version of real time charging scenario at the charging stations. With the help of programmed model in Netlogo, the behaviour of EVs user under different real life scenarios are observed and recorded. The proposed system is implemented and designed in Netlogo to test the results.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115813043","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293765
Dhruv Verma, Sunaina Puri, S. Prabhu, Komal Smriti
Automated anomaly detection in panoramic dental x-rays is a crucial step in streamlining post diagnosis treatment. It can reduce clinical time for a patient and also aid in giving them faster access to medical care. In this paper, we propose a hybrid deep learning and machine learning based approach to detect evident dental caries/periapical infection, altered periodontal bone height, and third molar impactions using panoramic dental radiographs. We use a Convolutional Neural Network as a feature extractor for an input image and use a Support Vector Machine to classify the image as either "Normal" or "Anomalous" based on the extracted features. We compare the performance of this model with the performance of a Convolutional Neural Network and a Support Vector Machine for the same classification task. We also compare our best model with other existing models trained to detect carries and periodontal bone loss. The results obtained with the hybrid deep learning and machine learning approach outperformed the existing methods in the literature.
{"title":"Anomaly detection in panoramic dental x-rays using a hybrid Deep Learning and Machine Learning approach","authors":"Dhruv Verma, Sunaina Puri, S. Prabhu, Komal Smriti","doi":"10.1109/TENCON50793.2020.9293765","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293765","url":null,"abstract":"Automated anomaly detection in panoramic dental x-rays is a crucial step in streamlining post diagnosis treatment. It can reduce clinical time for a patient and also aid in giving them faster access to medical care. In this paper, we propose a hybrid deep learning and machine learning based approach to detect evident dental caries/periapical infection, altered periodontal bone height, and third molar impactions using panoramic dental radiographs. We use a Convolutional Neural Network as a feature extractor for an input image and use a Support Vector Machine to classify the image as either \"Normal\" or \"Anomalous\" based on the extracted features. We compare the performance of this model with the performance of a Convolutional Neural Network and a Support Vector Machine for the same classification task. We also compare our best model with other existing models trained to detect carries and periodontal bone loss. The results obtained with the hybrid deep learning and machine learning approach outperformed the existing methods in the literature.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123199748","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293841
Michael Pareja, A. Bandala
Irrigation is essential for growing crops and leads to gradual growth in the economy. This research proposal aims to resolve the issue of scarcity and proper water management in the tank system through the Fuzzy Irrigation System. Fuzzy logic improves the irrigation system that includes three input parameters, such as soil moisture, soil temperature, and the water level. The combinations of these parameters will produce the time duration to have an efficient flow of water to the crop fields. Likewise, the Rain Detection Model (RDM) and the Fertilizer Control Model (FCM) are other features that support, strengthen, and innovate the system. The pilot test conducted by the researcher through MATLAB simulations were performed to check the effectiveness of the proposed system before its actual implementation.
{"title":"Fuzzy Irrigation System with Rain Detection and Fertilizer Control","authors":"Michael Pareja, A. Bandala","doi":"10.1109/TENCON50793.2020.9293841","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293841","url":null,"abstract":"Irrigation is essential for growing crops and leads to gradual growth in the economy. This research proposal aims to resolve the issue of scarcity and proper water management in the tank system through the Fuzzy Irrigation System. Fuzzy logic improves the irrigation system that includes three input parameters, such as soil moisture, soil temperature, and the water level. The combinations of these parameters will produce the time duration to have an efficient flow of water to the crop fields. Likewise, the Rain Detection Model (RDM) and the Fertilizer Control Model (FCM) are other features that support, strengthen, and innovate the system. The pilot test conducted by the researcher through MATLAB simulations were performed to check the effectiveness of the proposed system before its actual implementation.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123229604","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293866
Sandy C. Lauguico, Ronnie S. Concepcion, Rogelio Ruzcko Tobias, A. Bandala, R. R. Vicerra, E. Dadios
Identifying variant diseases in leaves is a significant method for optimizing food production. As the global population continues to arise and agricultural space continues to decline, every possible way of increasing the supply of food in any given condition and limited resources will address the above-mentioned problems. This study proposes a way for detecting three different diseases from grape leaves apart from the healthy leaves and considers the confidence value of the system in correctly identifying the classes. The diseases are namely: Black Rot, Black Measles, and Isariopsis. The system conducted a comparative analysis to determine which among the three pre-trained networks, AlexNet, GoogLeNet, and ResNet-18 will be the most suitable network to be integrated with Regions with Convolutional Neural Networks (RCNN) in performing multiple object detection in a given image. The data used in training the models comprised of annotated image data represented as a ground truth table with image files and their corresponding bounding boxes coordinates. The models evaluated resulted to AlexNet being the best pre-trained network to be working on the RCNN with an accuracy of 95.65%. The other two models from GoogLeNet and ResNet-18 only obtained accuracies of 92.29% and 89.49% respectively.
{"title":"Grape Leaf Multi-disease Detection with Confidence Value Using Transfer Learning Integrated to Regions with Convolutional Neural Networks","authors":"Sandy C. Lauguico, Ronnie S. Concepcion, Rogelio Ruzcko Tobias, A. Bandala, R. R. Vicerra, E. Dadios","doi":"10.1109/TENCON50793.2020.9293866","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293866","url":null,"abstract":"Identifying variant diseases in leaves is a significant method for optimizing food production. As the global population continues to arise and agricultural space continues to decline, every possible way of increasing the supply of food in any given condition and limited resources will address the above-mentioned problems. This study proposes a way for detecting three different diseases from grape leaves apart from the healthy leaves and considers the confidence value of the system in correctly identifying the classes. The diseases are namely: Black Rot, Black Measles, and Isariopsis. The system conducted a comparative analysis to determine which among the three pre-trained networks, AlexNet, GoogLeNet, and ResNet-18 will be the most suitable network to be integrated with Regions with Convolutional Neural Networks (RCNN) in performing multiple object detection in a given image. The data used in training the models comprised of annotated image data represented as a ground truth table with image files and their corresponding bounding boxes coordinates. The models evaluated resulted to AlexNet being the best pre-trained network to be working on the RCNN with an accuracy of 95.65%. The other two models from GoogLeNet and ResNet-18 only obtained accuracies of 92.29% and 89.49% respectively.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121802469","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293893
A. Bandala, E. Sybingco, Jose Martin Z. Maningo, E. Dadios, Gann Isaac Isidro, Rocez Deanne Jurilla, Chia-Yu Lai
Fire incidents often result to associated deaths, injuries, and losses occurring structures and properties, particularly in homes every year. In this study, the researchers proposed a 4-wheeled fire extinguishing robot with the ability to detect human presence in the area even when there is fire. Multiple sensors are utilized in this study to detect nearby flame, smoke, temperature and humidity, and obstacles through integration with Arduino and Raspberry Pi. The proposed robot is remotely controlled by the user over Wi-Fi through the graphical user interface created by the researchers in Python for easy monitoring of data and control. A camera is also mounted to the robot for surveillance purposes. The human detection system of the robot is implemented through using ultra-wide band radar (UWB) by utilizing the X4M300 presence sensor, which could detect human presence based on their respiration movement. Initial testing and four experiments were conducted to test the radar sensor's capabilities compared to the existing methods of human detection. The researchers yielded an accuracy of 97.29% in the testing of human detection system, proving that the implementation of UWB radar sensor is successful.
{"title":"Human Presence Detection using Ultra Wide Band Signal for Fire Extinguishing Robot","authors":"A. Bandala, E. Sybingco, Jose Martin Z. Maningo, E. Dadios, Gann Isaac Isidro, Rocez Deanne Jurilla, Chia-Yu Lai","doi":"10.1109/TENCON50793.2020.9293893","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293893","url":null,"abstract":"Fire incidents often result to associated deaths, injuries, and losses occurring structures and properties, particularly in homes every year. In this study, the researchers proposed a 4-wheeled fire extinguishing robot with the ability to detect human presence in the area even when there is fire. Multiple sensors are utilized in this study to detect nearby flame, smoke, temperature and humidity, and obstacles through integration with Arduino and Raspberry Pi. The proposed robot is remotely controlled by the user over Wi-Fi through the graphical user interface created by the researchers in Python for easy monitoring of data and control. A camera is also mounted to the robot for surveillance purposes. The human detection system of the robot is implemented through using ultra-wide band radar (UWB) by utilizing the X4M300 presence sensor, which could detect human presence based on their respiration movement. Initial testing and four experiments were conducted to test the radar sensor's capabilities compared to the existing methods of human detection. The researchers yielded an accuracy of 97.29% in the testing of human detection system, proving that the implementation of UWB radar sensor is successful.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122668850","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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