2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )最新文献

Recognition of Different Emergency Situation through Body Gesture Using Microsoft Kinect Sensor 利用微软Kinect传感器通过肢体动作识别不同的紧急情况

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9073365

R. Tolentino, Pinky Mae F. Guinto, Dorothy Ysabelle B. Maypa

引用次数: 3

Low Cost High Frequency Tag Antenna for an Inventory Management RFID System 用于库存管理RFID系统的低成本高频标签天线

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9073522

Xyrex A. Calang, Al John S. Timogan, O. J. Gerasta, Najie M. Pandian, V. J. Ylaya

引用次数: 0

Development of a Solar-Powered Closed-Loop Vermicomposting System with Automatic Monitoring and Correction via IoT and Raspberry Pi Module 基于物联网和树莓派模块的太阳能闭环蠕虫堆肥系统的开发

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9073372

Aaron U. Aquino, Dustin G. Baylon, Francis Paul B. Dela Cruz, Ma. Armae Hazel Joy M. Medina, Girlie A. Reyes, Jascha Mae L. Tulauan, Timothy M. Amado, J. M. Ramos, L. K. Tolentino, E. Fernandez

{"title":"Development of a Solar-Powered Closed-Loop Vermicomposting System with Automatic Monitoring and Correction via IoT and Raspberry Pi Module","authors":"Aaron U. Aquino, Dustin G. Baylon, Francis Paul B. Dela Cruz, Ma. Armae Hazel Joy M. Medina, Girlie A. Reyes, Jascha Mae L. Tulauan, Timothy M. Amado, J. M. Ramos, L. K. Tolentino, E. Fernandez","doi":"10.1109/HNICEM48295.2019.9073372","DOIUrl":"https://doi.org/10.1109/HNICEM48295.2019.9073372","url":null,"abstract":"Vermicomposting is a low-cost technology that naturally converts organic wastes into organic fertilizers, commonly called vermicompost, through the combined action of earthworms and mesophilic microorganisms. Vital parameters, such as moisture and temperature, must be considered in the vermicompost production to achieve optimum yield. However, manual monitoring and correction of these said parameters do not give guaranteed results. Also, the traditional process of harvesting vermicompost consumes longer time and requires more human intervention. As a solution, the proponents introduced the development of a system which monitors and corrects these vital parameters, determines the readiness of vermicompost for harvest using digital image processing, and automatically sieves the vermicompost. The system uses Raspberry Pi microcontroller, sensors, and an android phone for monitoring. To measure the system’s reliability and efficiency, the proponents conducted two setups of vermicomposting system – one controlled and the other uncontrolled. From the data gathered, the automated system surpassed the latter in terms of production time, yield quality and quantity.","PeriodicalId":6733,"journal":{"name":"2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75243729","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}

引用次数: 2

Hazard Classification of Toluene, Methane and Carbon Dioxide for Bomb Detection Using Fuzzy Logic 基于模糊逻辑的炸弹探测中甲苯、甲烷和二氧化碳的危害分类

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9073559

Dailyne D. Macasaet, A. Bandala, Ana Antoniette C. Illahi, E. Dadios, Sandy C. Lauguico

引用次数: 2

Development of Plantar Pressure In-sole System for Diabetic Peripheral Neuropathy Analysis using Pressure Mapping Sensors 利用压力映射传感器分析糖尿病周围神经病变的足底压力系统的研制

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9072726

Anne Louise M. Cuenca, Ysabelle Rey C. Dizon, H. A. Espinosa, Ellizer Mernard L. Mendoza, Fhaym C. Sarrondo, Angelo R. dela Cruz, Edison A. Roxas, C. Suarez, Kanny Krizzy D. Serrano

引用次数: 3

Design of HVDC Transmission System Connecting Boracay, Panay, and Negros Islands 连接长滩岛、班乃岛和内格罗斯岛的高压直流输电系统设计

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9073419

Joshua A. Meneses, G. F. Apolinario, Jay-ar Maglente, Jayson G. Cordero

引用次数: 0

Genetic Algorithm Based 3D Motion Planning for Unmanned Aerial Vehicle 基于遗传算法的无人机三维运动规划

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9072878

Maverick Rivera, J. R. D. del Rosario, A. Bandala

引用次数: 1

Single-Inductor Multiple-Output (SIMO) Boost DC-DC Converter using Ripple-Based Control for Ultra-Low Power Indoor Light Energy Harvesting Applications 采用纹波控制的单电感多输出(SIMO)升压DC-DC变换器用于超低功耗室内光能收集应用

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9072862

Paul Adrian E. Divino, Michelle P. Prunes, Kevin O. Maglinte

{"title":"Single-Inductor Multiple-Output (SIMO) Boost DC-DC Converter using Ripple-Based Control for Ultra-Low Power Indoor Light Energy Harvesting Applications","authors":"Paul Adrian E. Divino, Michelle P. Prunes, Kevin O. Maglinte","doi":"10.1109/HNICEM48295.2019.9072862","DOIUrl":"https://doi.org/10.1109/HNICEM48295.2019.9072862","url":null,"abstract":"A Single-Inductor Multiple-Output (SIMO) Boost DC-DC Converter that operates for Ultra-Low Power (ULP) Applications is designed in 65nm TSMC technology. The converter boosts and regulates three output voltages using Ripple-Based Control of Pulse Frequency Modulation (PFM) while operating in Discontinuous Conduction Mode (DCM). To control the regulation of the output voltages, its control system follows an algorithm that is converted into digital logic circuits and helps in synchronizing the power MOS switching in the power stage. Aside from using Ripple- Based Control and DCM operation, the designed converter also uses a Strong-Arm comparator, which has no power consumption during its idle state to help ensure good power conversion efficiency of the system, making it suitable for the target application. This converter is suitable for indoor applications, for its input voltage could vary from 0.4V, for low light intensity situation, to 0.6V. With output voltages of 1.2V for analog core and two 1 V for digital cores, the converter shows excellent overall transient response and a peak power efficiency of 86% at 1mA load current.","PeriodicalId":6733,"journal":{"name":"2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80938075","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}

引用次数: 1

Development of Predictive Models using Machine Learning Algorithms for Food Adulterants Bacteria Detection 利用机器学习算法开发食品掺假细菌检测预测模型

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9072907

Timothy M. Amado, Ma. Rica Bunuan, Relamae F. Chicote, Sheila May C. Espenida, Honeyleth L. Masangcay, Camille H. Ventura, L. K. Tolentino, M. V. Padilla, G. A. Madrigal, Lejan Alfred C. Enriquez

{"title":"Development of Predictive Models using Machine Learning Algorithms for Food Adulterants Bacteria Detection","authors":"Timothy M. Amado, Ma. Rica Bunuan, Relamae F. Chicote, Sheila May C. Espenida, Honeyleth L. Masangcay, Camille H. Ventura, L. K. Tolentino, M. V. Padilla, G. A. Madrigal, Lejan Alfred C. Enriquez","doi":"10.1109/HNICEM48295.2019.9072907","DOIUrl":"https://doi.org/10.1109/HNICEM48295.2019.9072907","url":null,"abstract":"One of the necessities of human to survive is food and meat is one of mainly consumed food by humans. Thus, a level of quality of food is a must to be safely consumed. There have been some cases of adulteration of meats, which can cause harm to consumers. Adulteration can lead to bacteria contamination which are difficult to determine the presence of bacteria without an instrument or food laboratory tests. Nowadays, the idea of applying machine learning in the field of food microbiology is becoming a trend. And one of these applications is on detection and classification of bacteria in food products. Hence, this study aims to apply machine learning algorithms to construct predictive models to detect the presence of bacteria such as Escherichia Coli and Staphylococcus Aureus in raw meat and determine which model is best through accuracy and cross-validation. In this study, five machine learning algorithms are used which are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Naïve-Bayes Classifier (NB), and Artificial Neural Network (ANN). All models are implemented effectively each having an accuracy of 94.97%, 91.84%, 97.57%, 61.46%, and 66.84% respectively. A web application is created using the shiny package in R to attain a standalone application used to show the detected bacteria.","PeriodicalId":6733,"journal":{"name":"2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78563589","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}

引用次数: 7

Real-time Class Attendance Monitoring using Smart Face Recognition 使用智能人脸识别的实时课堂出勤监测

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI: 10.1109/HNICEM48295.2019.9072846

Ma. Ian P. Delos Trinos, Jozar H. Rios, Keith Gabriel O. Portades, Paulo Rae O. Portades, Renielle Miguel P. Langreo, Mideth B. Abisado

引用次数: 0