2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)最新文献

{"title":"Hazardous Gas Detection and Notification System","authors":"Maribelle Jualayba, Kristian Regio, Harold Quiozon, Adrian Destreza","doi":"10.1109/HNICEM.2018.8666358","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666358","url":null,"abstract":"Hazardous gases like propane and methane are combustible and could cause explosion if confined in a close room. The study described in this paper comprises a system that involves detection and notification of hazardous gases present in an area. The system has three sensors; hydrogen sensor, Liquefied Petroleum Gas (LPG) sensor, and methane sensor that serve as switches with different set-points. Every level of gas detected is send to the Arduino that serves as the controller which analyze the level of gas present. The controller is also in charge for triggering the lamp, exhaust fan, buzzer, Liquid Crystal Display (LCD) monitor and Global System Mobile (GSM) module. The system has three lamps that indicate the level of gas being detected. Green light means “safe level”, yellow means “medium level”, and red for “danger level”. The exhaust fan is activated as the controller detects medium level, which is simultaneous with the lighting of the yellow light and will only be deactivated when the system detects again safe level. The buzzer is activated as the controller detects danger level which is synchronized with the lighting of the red light. The LCD monitor displays the level of gas being detected and the GSM module sends Short Message Service (SMS) as notification to concern people. The system was tested and is capable of detecting LPG, methane gas and hydrogen gas with a maximum distance of 50, 30 and 30 inches, respectively.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115223679","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}

{"title":"Retinal Disease Screening through Statistical Texture Analysis and Local Binary Patterns using Machine Vision","authors":"Rommel M. Anacan, Jason Cedrick L. Batimana, Francesca Louise P. Bulanon, L. Cubillan, Mike Miguel B. Magalang, Eduardo E. Punto, Joshua E. Refe, Kristian Reubenson M. Tagalog","doi":"10.1109/HNICEM.2018.8666278","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666278","url":null,"abstract":"This paper scrutinizes the most prominent area of machine vision which is medical imaging application to detect high risk diseases for allowing screening method for referral to eye care practitioner and the capabilities of Local Binary Patterns and Statistical Texture Analysis in the analysis of fundus images to distinguish healthy and diseased retina. The goal is to discern between diabetic retinopathy (DR), glaucoma (G), and healthy fundus images analyzing the texture of the retina background using NI Vision. This paper also studied the efficacy and efficiency of the algorithm by measuring the sensitivity and specificity of the diagnosis developed by the researchers and validated by the Philippine Eye Research Institute, University of the Philippines-Manila. Finally, the images were classified as Healthy, DR or G. The results show that the sensitivity, specificity, and precision of Local Binary Patterns and Statistical Texture Analysis are 70.00%, 93.33%, 91.30% respectively. By adding a color matching classifier it will make the screening process more accurate resulting to the following values, 83.33%, 93.33%, and 83.33%.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115617615","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}

{"title":"Sleep Onset Period Detection Using Slow Eyelid Movement (SEM) Through Eye Aspect Ratio with Electroencephalogram (EEG)","authors":"Glenn O. Avendaño, A. Ballado, Jennifer C. Dela Cruz, Sarah Alma P. Bentir, Juan Christian B. Camposanto, Alexis L. Carreos, Lorenz Albert B. Domingo, Kendrick Dale M.Garcia","doi":"10.1109/HNICEM.2018.8666429","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666429","url":null,"abstract":"This study presents the development of sleep onset period detection using SEM through eye aspect ratio with EEG. The researchers made use of a camera module, Neurosky Mindwave headset and a microcontroller coupled up with an improvised alarm system composed of a buzzer and vibration motors, to detect drowsiness of a subject and to alert the same. Raspberry Pi Camera Module was utilized for eyelid movement detection, Neurosky Mindwave headset for brain wave monitoring and a microcontroller to manage and activate the alarm system of the device. The results of the study showed that the integration of eyelid movement and electroencephalogram provides a more accurate method of determining sleep onset period compared to previous studies. The integrated SEM and EEG parameters provided 97.5% accuracy. This research will greatly benefit the safety of the drivers. Also, this will be beneficial to companies which require its employees to have a high level of alertness as demanded by certain occupations.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121824113","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}

{"title":"Nonlinear Autoregressive with Exogenous InputsNeural Network for Water Level Prediction","authors":"Mary Anne M. Sahagun, Jennifer C. Dela Cruz, Ramon G. Garcia","doi":"10.1109/HNICEM.2018.8666406","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666406","url":null,"abstract":"Water level prediction is a flood disaster issues which needs to have sufficient time for low-land communities to prepare and secure their lives and properties. This study aims to predict flood water level for longer term forecasting using Artificial Neural Network (ANN). Specifically, it aims todetermine exogenous inputs that contribute to the prediction of flood water level; determine additional set of criterion in the selection of optimized ANN model and determine the best fit mathematical model for each optimized ANN model. The ANN models were trained using NNtool of Matlab with the default Levenberg-Marquardt as training algorithm. Eleven cases were formulated to determine the optimized model. Nonlinear autoregressive network with exogenous input (NARX) was used in modellingall cases. Selection of optimized models was based on multi-criteria approach such as: Pearson R, mean absolute error (MAE), root-mean-square error (RMSE), Akiake’s Final Prediction Error (FPE), and Nash-Sutcliffe’s coefficient of efficiency (NSE). Result of the study shows that aside from upper stream, the tide level and rainfall were the exogenous inputs that contribute in predicting the water level at flood risk areas. The optimize model for both flood risk areas is a NARX model having upper stream water as the exogenous input that has the following multi-criteria values:for medium risk flood area, the optimized model has the following criterion values: MSE=0.000153, R=0.89519, FPE= 7.1355e-7, MAE=0.0066, RMSE=0.0089 and NSE=0.7868; and for high risk flood area: MSE=1.68e-5, R=0.85153, FPE=3.5245e-7, MAE=0.0027, RMSE=0.0035, and MSE=0.5729. This study is beneficial tool to local government unitas an alternative way of predicting and interpreting water level data to have an immediate warning to the community.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121072887","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}

{"title":"Efficacy of Learning Scaffolds and Learner-User Experience (UX) in the Zone of Proximal Development","authors":"Arlene Mae C. Valderama, Joel B. Mangaba, Roel C. Traballo, Paulino H. Gatpandan, Mary Ann B. Taduyo, D. Dasig, Mengvi P. Gatpandan, Rudolph Val F. Gua, A. Vinluan","doi":"10.1109/HNICEM.2018.8666337","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666337","url":null,"abstract":"This paper exhibits the learner sited in the zone of proximal development as it aimed to display the performance efficacy in the two levels of the zone of proximal development: the actual development level, where a learner performs independently, and the level of potential development, where a learner performs with the assistance of a more skilled individual. Execution viability in this study mounts learner experiences through the use of learning scaffolds and determine efficacy performance using a pretest and posttest scheme and the mean scores of a controlled and experimental course learning activities. The study employed descriptive-quantitative design to ascertain efficacy in learners’ feat. The experimental method of research was employed across researcher-identified tracking phases. It consisted of student participants taking Computer Programming course under the 1st year level of the Bachelor of Science in Information Technology program curriculum and were purposely selected based on qualifiers. Learning scaffold approaches included human computer interaction in reference to user experience and usability of a university acquired learning management system as it presents how learners employ the application and assessment prove its efficiency. The study revealed that the utilization of scaffolding strategies was operative amidst the tracking phases as the competency of a learner results reflect incremental percentage frequency distribution in the learner-user experiences and considerably significant differences in the mean scores of a pre-test and post-test, and in the mean scores of the controlled and experimental laboratory activities. Henceforth, the researcher recommended that learners in areas across and outside information technology courses and curriculum may protrude distinctive scaffolding strategies as they manifest.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127477288","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}

{"title":"An Intelligent Road Traffic Information System using Text Analysis in the Most Congested Roads in Metro Manila","authors":"Erika Ritzelle P. Bondoc, Francis Percival M. Caparas, John Eddie D. Macias, Vileser T. Naculangga, Jheanel E. Estrada","doi":"10.1109/HNICEM.2018.8666416","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666416","url":null,"abstract":"The increasing number of vehicles for every year in the Philippines produces a bigger percentage of causing severe traffic congestion in the three leading roads (Circumferential Road 5, EDSA, and Commonwealth Avenue) in Metro Manila. The researchers observed that most of the implemented technology used to detect traffic is by using a surveillance camera which requires labor and costly, while other web and mobile traffic information system used crowdsourcing and mostly owned by private. The researchers aim to develop a real-time road traffic information system by using the traffic-related tweets from MMDA (Metropolitan Manila Development Authority) official Twitter account to aid the current problem of providing sufficient road traffic information in the country. In this research, the traffic-related tweets from MMDA data are fetched using Twitter Streaming API, filtered by using Named Entity Recognition, preprocessed by applying tokenization, frequency counting and removal of unnecessary symbols, features were extracted by using Latent Dirichlet Allocation to identify the significant topic segments (time, day, lane of road, road direction, location and traffic mode) and Linear Regression was used for pattern recognition. Also, this study compares four machine learning algorithms (Naïve Bayes, Decision Tree, Random Tree, and k-Nearest Neighbor) for the proper identification of the most effective algorithm that will be used to for traffic mode classification. The experimental result shows that k-NN produced the best performance compared to other algorithms with 84.00% classification accuracy in anticipating the traffic congestion modes (Light, Light to Moderate, Moderate, Moderate to Heavy, and Heavy) in Metro Manila, Philippines.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125093464","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}

{"title":"Comparative Evaluation of NFC Tags for the NFC-Controlled Door Lock with Automated Circuit Breaker","authors":"Romeo L. Jorda, Joshua Renz A. Coballes, Lejan Alfred C. Enriquez, Mark Lester S. Millan, Angelo J. Mora, Melbert Neil G. Teodoro, N. Arago, August C. Thio-ac, L. K. Tolentino","doi":"10.1109/HNICEM.2018.8666375","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666375","url":null,"abstract":"In the age of automation where almost all household appliances are having the term “smart”, the need for a secured door system together with an automated circuit breaker is indeed a must have for being in the trend. With the use of the NFC technology, entering door hassle-free without countless keys and open any door with just a tap of a single card is possible. The NFC-Controlled Door Lock also comes with an automatic circuit breaker that activates when a person entered, and it deactivates when the person exits therefore reducing power costs due to idle appliances. Moreover, 4 NFC tags/cards were tested for its detection speed and distance to determine the most suitable tag to be used. The system prototype is evaluated, and it is 100% accurate when recognizing and authorizing an NFC card. It also comes with a database that records the person who enters and exits the room which is accessible easily thru a browser-based database.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126052689","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}

{"title":"Resilient Network and Data Transmission between Philippines and Vietnam via Software-Defined Network using OpenDaylight Controller and Mininet","authors":"Allysa Carla P. Bonifacio, Patrick Jaycee C. Galo, J. D. Lopena, J. Villaverde, G. Magwili","doi":"10.1109/HNICEM.2018.8666230","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666230","url":null,"abstract":"With Software-Defined Network’s resiliency and efficiency, it helps us to keep up with the pace of this advancement of technology. In this paper, an SDN connection from Philippines to Vietnam was established. Moreover, SDN has been successfully integrated with a sensor to transmit data on a different SDN testbed. Moreover, SDN has been proven to be better than Traditional Network in accordance to the following network parameters: latency, packet loss, bandwidth, and convergence time.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125274156","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}

{"title":"Filipino Sign Language Recognition for Beginners using Kinect","authors":"Kyle Elijah Oliva, Love Lee Ortaliz, Maria Aurielle Tobias, L. Vea","doi":"10.1109/HNICEM.2018.8666346","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666346","url":null,"abstract":"There exists a shortcoming called communication gap between deaf people and people who can hear. Most people use verbal language as a form of communication while deaf people use non-verbal communication called sign language. Several studies were already conducted in the field of signal processing focusing on sign language recognition. This study covers Filipino sign language and used Kinect V2. The proponents of this research analyzed the performance between two different methods. In the first method, the location of the joints of interest are tracked via the Cartesian coordinates of the joints while the second method tracks the location of the joints via spherical coordinates that includes normalization. Normalization is applied because the size and position of the user from the Kinect V2 may vary resulting to poor recognition. Both of these methods used the Dynamic Time Warping algorithm and the Support Vector Machine individually. In total there are four results: results for the Spherical-DTW, Spherical-SVM, Cartesian-DTW and Cartesian-SVM. The scope of this study includes basic Filipino words provided by sign language experts wherein the hands are in open palm shape. Fingers and facial expression are not considered therefore, these gaps can be an opportunity for future studies. The proposed method in this study reached a peak accuracy of 95.00%, recall of 95.00%, and precision of 95.89%","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126636512","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}

{"title":"Development of Street Lighting System with Object Detection","authors":"Juvy Amor M. Galindo, M. V. Caya","doi":"10.1109/HNICEM.2018.8666410","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666410","url":null,"abstract":"Streetlight that emits too much light or shines when and where it’s not needed is wasteful. Wasting energy has huge economic and environmental consequences. Automated street light management system is needed to resolve this problem. This study aims to develop an energy management methodology applied in the streetlights of a school campus. The study is a street lighting that has the dimming capability to minimize the cost of energy consumption. The LED light will illuminate when an object is detected. Raspberry Pi and Pi camera module were used to control the dimming of the LED lights. Furthermore, it has the capability of detecting objects like people walking in the streets. The object detection was made possible in identifying human from other objects using computer vision technique. Dimming capability of streetlight has proven that it can minimize power consumption of electricity.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116015291","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}