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

{"title":"Multi-label Classification of pH Levels using Support Vector Machines","authors":"Raphael Benedict G. Luta, R. Baldovino, N. Bugtai","doi":"10.1109/HNICEM.2018.8666299","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666299","url":null,"abstract":"This paper developed an intelligent system application for the multi-label classification of pH levels. The pH is a measure of how acidic or how basic a substance is. The use of supervised learning methods may serve as a cheaper and more reliable alternative for pH level measurement. In this study, hue-saturation-value (HSV) color data were used for the training and testing the developed model. The obtained dataset has four field attributes including the output. Support vector machine (SVM) classification was the supervised learning tool used to model the classification system. 1410 samples from the dataset were used for the training (987 samples) and the testing (423 samples). Moreover, several kernel functions such as polynomial and radial basis function (RBF) kernel were examined when designing the classification system. Model evaluation through metric functions show that the trained SVM with a polynomial kernel has a 99.41% accuracy. As a result, the developed model was able to produce multiple decision hyperplanes for the multi-label classification task.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"129 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":"121490935","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":"Magneto-Resistive Sensor Nodes for Vehicle Traffic Detection","authors":"F. Cruz, Joseph Bryan G. Ibarra, Kristel Anne D. Jazmin, Jason R. Cirio, Elain Camile B. Buyagao, Steven A. Tobilla","doi":"10.1109/HNICEM.2018.8666293","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666293","url":null,"abstract":"This paper presents the use of magneto-resistive sensor for the detection of vehicle traffic. The developed traffic detection system provides an approximation of the number of vehicles per hour in two-lane roads. Based on the quantity of vehicles in the road, traffic status is classified as light, moderate or heavy. A traffic volume of less than 420 vehicles/hour is considered as a light traffic, between 420 and 1200 vehicles/hour as a moderate traffic, and greater than 1201 vehicles/hour as heavy traffic. The traffic detection in this study uses a threshold algorithm with peak counting. Overall, the study shows that magneto-resistive sensor can be used as a tool in determining traffic volume condition with a percent accuracy of 91.67%.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"23 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114029154","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":"Design Of A Shrouded Rotor With Optimal Thrust And Energy Efficiency In Unmanned Aerial Vehicles","authors":"Jan Chester Chua, Angelo Loveranes, Lester Adrian Macandog, Robert Matthew Reyes, G. Augusto, L. G. Lim","doi":"10.1109/HNICEM.2018.8666271","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666271","url":null,"abstract":"This study aims to improve the energy efficiency and increase the thrust power of an unmanned aerial vehicles by implementing shrouded rotors. First, a shroud design was created based on previous studies into shroud lip design and diffuser configurations. Three parameters, the leading-edge lip radius (LLR), diffuser length (DL), and diffuser angle (DA) were varied and each configuration was simulated in ANSYS CFX to obtain the theoretical thrust. After simulations, the configuration with the largest thrust was fabricated and tested at a variety of rotative speeds, measuring the thrust and energy consumption across each run. The simulations were able to determine that thrust increased with increasing LLR and DL and decreasing DA. The physical test in turn showed that at low RPMs, the thrust increase was more significant but the rotor consumed more energy; at higher RPMs, the thrust gain was lower but the rotor was also more energy-efficient.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"5 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":"121764078","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":"Characterization of EEG Signal Patterns During Visual Imageries of Basic Structures for the Development of Brain-Computer Typing Interface for Locked-In Syndrome Patients","authors":"Jay Patrick M. Nieles, Vince Dennison P. Magdaluyo, Lander Brent A. Mallari, Ram Aaron C. Paliza, Juan Carlos P. Salcedo, Seigfred V. Prado","doi":"10.1109/HNICEM.2018.8666333","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666333","url":null,"abstract":"The paper aims to characterize Electroencephalogram (EEG) signals during visual imagery of basic shapes that includes square, triangle and circle with and without visual stimulus and neutral state using a 14- channel EEG Emotiv EPOC+. Principal Component Analysis (PCA) was utilized to reduce the dimensionality of the features and the transformed features or biomarkers were used to train the classifiers. Classifiers used in this study are Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and k Nearest Neighbors (KNN). The results obtained from 5 study volunteers indicated that the highest contributions to the 56 new biomarkers are the higher order even central moments and more predominantly from channel T7. Also, the extracted features from EEG signals during visual imagery of different shapes with and without visual stimuli consistently showed a low degree of correlation. Furthermore, the dataset used to train the classifiers were subdivided into two: one containing neutral state with visual stimulus, and the other comprising neutral state without visual stimulus. Performance of different classifiers trained with and without visual stimulus yielded similar accuracies; however, the dataset with the absence of visual stimulus exhibit higher classification accuracies for all classifiers. In addition, all classifiers obtained high classification accuracies (>96%) for both datasets and the SVM performed best among the classifiers having accuracies of 97.5% and 99.5% for datasets with and without visual stimulus respectively. The study supports the feasibility of a brain-computer typing interface that utilizes visual imagery as an input modality. Furthermore, the findings of this study will serve as a basis for the development of a brain-computer typing interface using visual imagery of characters and letters.","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":"115802877","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":"Proximity Tracker using Received Signal Strength, Particle Filter and Extended Kalman Filter","authors":"Jennifer C. Dela Cruz, Ramon G. Garcia, A. Garcia, Khrysielle Anne A. Manalo, Vincent I. Nworgu, Jan Bernard M. Payumo","doi":"10.1109/HNICEM.2018.8666403","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666403","url":null,"abstract":"Received Signal Strength Indicator (RSSI) is a relative variable related to signal strength. The strength of the signal transmitted is used in continuous close-proximity tracking applications. The approximate exact location of a node is estimated using various kinds of filtering techniques. The demand for close-proximity tracking or location and proximity integration increases with the society’s dependency to mobile devices but using such geolocation services are power-hungry and is directly affected by atmospheric conditions. Present tracking services are made relative to the earth’s axis. To address these gaps, this study focuses on integrating Extended Kalman Filter (EKF) and Particle Filter (PF) into a system of Bluetooth-enabled nodes that are capable of relative positioning in three-dimensional space. Three nodes made of Raspberry Pi Zero are tracked by a parent device made of Raspberry Pi 3 Model B. The nodes’ coordinates are displayed in the parent device’s dashboard. Detection of the nodes are done using the library BIueZ. These nodes broadcast themselves to the parent device to determine their range and location using a 750-meter range Bluetooth dongle. The device is tested in both open areas, a memorial park and a beach resort. Using spiral method, the weakest RSSI value measured is -142dB at 776.43 meters and -23dB at 0 meter. RSSI value in an area with obstruction or interference is -168dB at a distance of693.1 meters. The output shows very promising results indicating effectiveness and efficiency in the field of proximity tracking.","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":"132204503","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":"Denoising of Spatiotemporal Gait Waveforms from Motion-Sensing Depth Camera using Least Mean Square (LMS) Adaptive Filter","authors":"Karl Vincent G. Castillo, N. Mendoza, Chelsea Andrea S. Morales, Allen Dominic A. Perez, Jansen Yna L. Unisa, A. Cruz","doi":"10.1109/HNICEM.2018.8666294","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666294","url":null,"abstract":"Quantitative gait assessment is made possible through the use of instruments such as 3D motion capture systems (Mo-Cap) and its cheaper counterpart, an RGB-D camera. The cost-effectiveness of the RGB-D camera makes it a more practical instrument to use in most clinical settings but it is not as accurate as the Mo-Cap. This paper presents denoising of gait waveforms obtained from the most common parameters produced by RGB-D camera using LMS adaptive filter. The data used came from 14 study volunteers whose normal walking gait were recorded using VICON cameras and Microsoft Kinect v2 sensors. Spatiotemporal gait parameters were calculated from the two gait waveforms. The adaptive filter was trained using training dataset to create a filter model that was then used for the testing phase. Two given data sets, unfiltered and filtered gait parameters, were compared to the motion capture system gait parameters using statistical tools. Unfiltered parameters from the RGB-D camera exhibit significant difference with the Mo-Cap parameters at an average percent error of 26.83%. By calculating statistical values such as mean, standard deviation and root mean square error (RMSE), the findings confirmed that filtered parameter data improved at an average of 11.34% and there is a reduction of rejected parameters by 6 using t-test.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"11 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":"132520558","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":"Implementation of On-chip OVP, OCP and OTP Circuits for DC-DC Converter Design","authors":"Jennifer Ian G. Ligtao, C. M. Overstreet, Robert T. Nericua, O. J. Gerasta, J. Hora","doi":"10.1109/HNICEM.2018.8666254","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666254","url":null,"abstract":"The integration of the three protection circuits namely, over-temperature, over-current, and over-voltage protection circuits for DC-DC Converter is implemented. The over-temperature protection circuit activates whenever the device temperature reaches 150 °C. Over-current condition is reached when current is equal to or more than 500mA which initializes the over-current protection circuit. An improved sensing circuit, which utilizes a gate driver, is implemented to boost its efficiency without using a conventional operational amplifier. When the converter produces an output voltage greater than the specified voltage requirement, the over-voltage protection circuit turns off the converter. The chip core design has a total area of 0.0137 mm2 and implemented in TSMC 90nm CMOS technology. All expected outputs are achieved based on the simulation results.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"39 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":"134355197","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":"Portable Stress Level Detector based on Galvanic Skin Response, Heart Rate, and Body Temperature","authors":"John Paul D. Serrano, Jamie Mitchelle A. Soltez, Rodney Karlo C. Pascual, John Christopher D. Castillo, J. L. Torres, F. Cruz","doi":"10.1109/HNICEM.2018.8666352","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666352","url":null,"abstract":"A portable device was designed and developed that measures stress level based from physiological signals gathered from a person. The device uses three physiological signals, the galvanic skin response (GSR), heart rate (HR), and body temperature (BT). The system uses a microcomputer to fetch and process the data, and to compute the stress level using the applied machine learning algorithm. From the results, it was verified that the system provides accurate measurements of the physiological signals with an average percent difference of 0.68338% and 0.19327% for HR and BT respectively.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"40 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":"132574034","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":"Geometric Analysis of Skin Lesion for Skin Cancer Using Image Processing","authors":"N. Linsangan, J. Adtoon, J. L. Torres","doi":"10.1109/HNICEM.2018.8666296","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666296","url":null,"abstract":"This study focuses on the geometric features of skin lesions for detecting and classifying skin cancer. Geometric features of the skin lesion are extracted following the asymmetry, border, and diameter parameters of the ABCD-Rule of Dermoscopy. In particular, area, perimeter, circularity index, greatest and shortest diameter, irregularity index and equivalent diameter are the parameters loaded in the dataset for classification. Three classifications of skin lesion are considered in this study such as malignant melanoma, benign melanoma, and unknown. Classification of skin lesion images is done through k-Nearest Neighbors (k-NN) algorithm and shows an accuracy of 90.0% in the functionality testing.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"30 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":"132705253","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":"Threat Object Classification in X-ray Images Using Transfer Learning","authors":"Reagan L. Galvez, E. Dadios, A. Bandala, R. R. Vicerra","doi":"10.1109/HNICEM.2018.8666344","DOIUrl":"https://doi.org/10.1109/HNICEM.2018.8666344","url":null,"abstract":"Automatic classification of threat objects in X-ray images is important because of terrorist incidents happening in every country especially in the Philippines. Manual inspection using X-ray machine is prone to human error due limited amount of time given to the operator to check the baggage. This task is also stressful because there are lots of objects to be identified and needs full attention. Over long period of time, the performance of human inspector decreases and the chance of missed detection increases. As a solution to the problem, this paper used the concept of transfer learning in classification of threat objects. The threat objects used in the experiment consists of 4 classes such as blade, gun, knife and shuriken. The dataset came from the GDXray database, a public database of X-ray images. Experiment results showed that by using the concept of transfer learning with data augmentation and fine-tuning, threat objects can be classified at 99.5% accuracy.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"76 2 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":"123180384","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}