{"title":"Development of simultaneous localization and mapping algorithm using optical sensor for multi-rotor UAV","authors":"Z. Ng, S. K. Phang","doi":"10.1063/5.0001374","DOIUrl":"https://doi.org/10.1063/5.0001374","url":null,"abstract":"The advancement of an autonomous Unmanned Aerial Vehicle (UAV) has permeated throughout various aspect of our lives, from military to home entertainment. Autonomous UAV has bought a new level of sensation which enables the operator of the autonomous UAV to fly the UAV to the predetermined location just by selecting the location on the map projected on the screen as this can be done by implementing the GPS module on the drone. However, a slight interference will cause a tremendous effect on the GPS signal and it is impossible to operate the autonomous UAV indoor due to the weak signal. Therefore, the objective of this research paper is to develop an alternative for UAV to detect its own location up to 25cm of accuracy without the use of GPS module in a closed environment or under the tree canopy. It is also to perform a highly accurate and precise Simultaneous Localization and Mapping (SLAM) algorithm on a low-power processing unit yet robust for UAV to map and navigate. In order to have further understand on how the SLAM algorithm works, the offline simulation was carried out on a ground computer. On top of that, a monocular offline data was downloaded and simulation of monocular SLAM was carried out with the data. Once the offline data simulation was completed, Robot Operating System (ROS) was then installed in the ground computer to perform real time monocular SLAM using a webcam. From the real time monocular SLAM, the webcam was used to capture the images and pinpoint the feature points of each image. This process will slowly generating a 3D map by each key frame. The real time SLAM was then performed with a low-powered processing unit on top of the UAV for mapping and navigation. In a nutshell, the expected outcome of this research paper is to develop a low-powered yet robust processing unit for SLAM algorithm in autonomous UAV to determine its own location up to the accuracy of 25cm for navigation and mapping purpose.","PeriodicalId":282583,"journal":{"name":"13TH INTERNATIONAL ENGINEERING RESEARCH CONFERENCE (13TH EURECA 2019)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115261467","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}
Hung Ming Christon Lee, Douglas Tong Kum Tien, Jun Wai Ee
{"title":"Design a data collection system for palm kernel screw press","authors":"Hung Ming Christon Lee, Douglas Tong Kum Tien, Jun Wai Ee","doi":"10.1063/5.0001728","DOIUrl":"https://doi.org/10.1063/5.0001728","url":null,"abstract":"The paper describes the various materials and methods used to create a functioning prototype of a mass flow rate data collection system which is applicable to the palm kernel screw press machine only. The system was then used to obtain crucial information through a series of tests which include the load cell calibration test which resulted in a maximum error of 5% deviation from the actual value of mass at higher loads. The Bluetooth Module and Arduino Uno communication test however, brought about great results as the prototype was found to function as intended and had a 100% success rate to perform optimally in a real-life situation. The mass flow rate data collection system will be able to notify the user when the screw press machine has reached a critical point for maintenance and also help in the troubleshooting process to locate the source of the decrease in performance of the screw press.","PeriodicalId":282583,"journal":{"name":"13TH INTERNATIONAL ENGINEERING RESEARCH CONFERENCE (13TH EURECA 2019)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116566487","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}
Hasmath Farhana Thariq Ahmed, Hafisoh Ahmad, S. K. Phang, C. Vaithilingam, Houda Harkat, Kulasekharan Narasingamurthi
{"title":"Sign language gesture recognition with bispectrum features using SVM","authors":"Hasmath Farhana Thariq Ahmed, Hafisoh Ahmad, S. K. Phang, C. Vaithilingam, Houda Harkat, Kulasekharan Narasingamurthi","doi":"10.1063/5.0002344","DOIUrl":"https://doi.org/10.1063/5.0002344","url":null,"abstract":". Wi-Fi based sensing system captures the signal reflections due to human gestures as Channel State Information (CSI) values in subcarrier level for accurately predicting the fine-grained gestures. The proposed work explores the Higher Order Statistical (HOS) method by deriving bispectrum features (BF) from raw signal by adopting a Conditional Informative Feature Extraction (CIFE) technique from information theory to form a subset of informative and best features. Support Vector Machine (SVM) classifier is adopted in the present work for classifying the gesture and to measure the prediction accuracy. The present work is validated on a secondary dataset, SignFi, having data collected from two different environments with varying number of users and sign gestures. SVM reports an overall accuracy of 83.8%, 94.1%, 74.9% and 75.6% in different environments/scenarios.","PeriodicalId":282583,"journal":{"name":"13TH INTERNATIONAL ENGINEERING RESEARCH CONFERENCE (13TH EURECA 2019)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130841579","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":"Optimisation of ultrasonic-assisted extraction conditions of Citrus hystrix for the total phenolic content","authors":"Hong Yeow Liew, B. L. Chua, Y. Chow","doi":"10.1063/5.0001538","DOIUrl":"https://doi.org/10.1063/5.0001538","url":null,"abstract":"","PeriodicalId":282583,"journal":{"name":"13TH INTERNATIONAL ENGINEERING RESEARCH CONFERENCE (13TH EURECA 2019)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131455236","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}