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  • 1
    Online Resource
    Online Resource
    Cloud-based people counter
    Institute of Advanced Engineering and Science ; 2020
    In:  Bulletin of Electrical Engineering and Informatics Vol. 9, No. 1 ( 2020-02-01), p. 284-291
    Details
    In: Bulletin of Electrical Engineering and Informatics, Institute of Advanced Engineering and Science, Vol. 9, No. 1 ( 2020-02-01), p. 284-291
    Abstract: Emergence of Industry 4.0 in current economic trend promotes the usage of Internet of Things (IoT) in product development. Counting people on streets or at entrances of places is indeed beneficial for security, tracking and marketing purposes. The usage of cameras or closed-circuit television (CCTV) for surveillance purposes has emerged the need of tools for the digital imagery content analysis to improve the system. The purpose of this project is to design a cloud-based people counter using Raspberry Pi embedded system and send the received data to ThingSpeak, IoT platform. The initial stage of the project is simulation and coding development using OpenCV and Python. For the hardware development, a Pi camera is used to capture the video footage and monitor the people movement. Raspberry Pi acts as the microcontroller for the system and process the video to perform people counting. Experiment have been conducted to measure the performance of the system in the actual environment, people counting on saved video footage and visualized the data on ThingSpeak platform.
    Type of Medium: Online Resource
    ISSN: 2302-9285 , 2089-3191
    URL: Issue
    URL: Article
    DOI: 10.11591/eei.v9i1
    DOI: 10.11591/eei.v9i1.1849
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2020
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  • 2
    Online Resource
    Online Resource
    Driving Behaviour Identification based on OBD Speed and GPS Data Analysis
    ASTES Journal ; 2021
    In:  Advances in Science, Technology and Engineering Systems Journal Vol. 6, No. 1 ( 2021-01), p. 550-569
    Details
    In: Advances in Science, Technology and Engineering Systems Journal, ASTES Journal, Vol. 6, No. 1 ( 2021-01), p. 550-569
    Type of Medium: Online Resource
    ISSN: 2415-6698 , 2415-6698
    URL: Issue
    URL: Journal
    URL: Article
    DOI: 10.25046/aj0601
    DOI: 10.25046/astesj
    DOI: 10.25046/aj060160
    Language: Unknown
    Publisher: ASTES Journal
    Publication Date: 2021
    detail.hit.zdb_id: 2901531-5
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  • 3
    Online Resource
    Online Resource
    Feed forward neural network application for classroom reverberation time estimation
    Institute of Advanced Engineering and Science ; 2019
    In:  Indonesian Journal of Electrical Engineering and Computer Science Vol. 15, No. 1 ( 2019-07-01), p. 346-
    Details
    In: Indonesian Journal of Electrical Engineering and Computer Science, Institute of Advanced Engineering and Science, Vol. 15, No. 1 ( 2019-07-01), p. 346-
    Abstract: 〈 span 〉 Acoustic problem is a main issues of the existing classroom due to lack of absorption of surface material. Thus, a feed forward neural network system (FFNN) for classroom Reverberation Time (RT) estimation computation was built. This system was developed to assist the acoustic engineer and consultant to treat and reduce this matter. Data was collected and computed using ODEON12.10 ray tracing method, resulting in a total of 600 rectangular shaped classroom models that were modeled with various length, width, height, as well as different surface material types. The system is able to estimate RT for 500Hz, 1000Hz, and 2000Hz. Using the collected data, FFNN for each frequency were trained and simulated separately (as absorption coefficients are frequency dependent) in order to find the optimum solution. The final system was validated and compared with the actual measurement value from 15 different classrooms in Universiti Tun Hussein Onn Malaysia (UTHM). The developed system show positive results with average validation accuracy of 94.35%, 95.91%, and 96.42% for 500Hz, 1000Hz, and 2000Hz respectively.  〈 /span 〉
    Type of Medium: Online Resource
    ISSN: 2502-4760 , 2502-4752
    URL: Issue
    URL: Article
    DOI: 10.11591/ijeecs.v15.i1
    DOI: 10.11591/ijeecs.v15.i1.pp346-354
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2019
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  • 4
    Online Resource
    Online Resource
    Internet of things (IoT) based traffic management & routing solution for parking space
    Institute of Advanced Engineering and Science ; 2019
    In:  Indonesian Journal of Electrical Engineering and Computer Science Vol. 15, No. 1 ( 2019-07-01), p. 336-
    Details
    In: Indonesian Journal of Electrical Engineering and Computer Science, Institute of Advanced Engineering and Science, Vol. 15, No. 1 ( 2019-07-01), p. 336-
    Abstract: 〈 span lang="EN-MY" 〉 The idea of Internet of Things (IoT) based traffic management & amp; routing solution for parking space is due to the vehicle parking has become major issue in urban areas. The growing number of vehicles has contributed to the traffic problem and vehicle parking issue nowadays. The main purpose of this project is to assist the user to locate the vacant parking space, which help to reduce time and fuel consumption on searching the parking space. This proposed system was used online system via website application, which assist people to find the available parking slot. In fact, the system counted the capacity of the available parking space and notified the user through the website application. Frankly, the system was equipped with an ultrasonic sensor, which acts as the detector that sent data to the microcontroller in order to update into UBIDOTS cloud server for data logger purposes. This system could lessen or solve the time management problem at the parking area, which user could save their time by checking the available parking slots in advance through the website application. 〈 /span 〉
    Type of Medium: Online Resource
    ISSN: 2502-4760 , 2502-4752
    URL: Issue
    URL: Article
    DOI: 10.11591/ijeecs.v15.i1
    DOI: 10.11591/ijeecs.v15.i1.pp336-345
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2019
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  • 5
    Online Resource
    Online Resource
    Pipe leakage detection system with artificial neural network
    Institute of Advanced Engineering and Science ; 2022
    In:  IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 11, No. 3 ( 2022-09-01), p. 977-
    Details
    In: IAES International Journal of Artificial Intelligence (IJ-AI), Institute of Advanced Engineering and Science, Vol. 11, No. 3 ( 2022-09-01), p. 977-
    Abstract: 〈 span lang="EN-US" 〉 This project aims to develop a system that can monitor to detect leaks in water distribution networks. It has been projected that leakage from pipelines may lead to significant economic losses and environmental damage. The loss of water from leaks in pipeline systems accounts for a large portion of the water supply. Pipelines are maintained throughout their lives span; however, it is difficult to avoid a leak occurring at some point. A tremendous amount of water could be saved globally if automated leakage detection systems were introduced. An embedded system that monitors water leaks can efficiently aid in water conservation. This project focuses on developing a real-time water leakage detection system using a few types of sensors: water flow rate sensor, vibration sensor, and water pressure sensor. The data from the sensors is uploaded and stored by the microcontroller (NodeMCU V3) to the database cloud (Google Sheets). The data that is stored in the database is analyzed by artificial neural network (ANN) by using Matlab software. An application is developed based on results from ANN training to detect the leakage event. Implementing the proposed system can increase operations efficiency, reduce delay times, and reduce maintenance costs after leaks are detected. 〈 /span 〉
    Type of Medium: Online Resource
    ISSN: 2252-8938 , 2089-4872
    URL: Issue
    URL: Article
    DOI: 10.11591/ijai.v11.i3
    DOI: 10.11591/ijai.v11.i3.pp977-985
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2022
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