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RISS 인기검색어
- 검색키워드
- 저자 : Jen Aldwayne B. Delmo (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Helmet for Road Hazard Warning and Wireless Motorcycle Authentication
Leonardo N. Miranda Jr.,Lean Jan Carlo T. Oseña,Jen Aldwayne B. Delmo ASCONS 2019 IJASC Vol.1 No.1
Background/Objectives: Helmet for Road Hazard Warning and Wireless Motorcycle Authentication was designed for the safety and prevention of accidents for motorcycle riders by making them wear a helmet before driving, automatically warns them about the road hazards that the riders encounters, and sending SMS to the emergency contact numbers of the rider to call medical help immediately when an accident occurs. Methods/Statistical analysis: The project consists of two devices: the Helmet and the Navigation Device. Navigation Device equipped with Raspberry Pi 3 B+ as the main controller, which was connected on the LCD Touch Screen that displays wherein the user can delete the saved audio files and edit numbers that receives the SMS in case of emergency. Arduino Uno was connected to the Raspberry Pi via USB serial where the GSM Module was connected for texting, Piezo Vibration Sensor for calculation of the impact to identify an accident, and the Alarm for the rider to notify to wear the helmet. Findings: On the Helmet Device, it was equipped with an Arduino Nano that serves as the main controller of the Copper Switch to detect if the rider is wearing a helmet or not. Improvements/Applications: The Helmet Device has a microphone and speaker for the recording and automatically plays the road hazards encountered by the rider. Dash cams for recording of the front and rear view of the rider for future evidence. Both Helmet and Navigation devices work in synchronization using Bluetooth modules.
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Image to Text Conversion Technique for Anti-Plagiarism System
Mark B. Batomalaque,Chella May R. Camacho,Maria Jewella P. Dalida,Jen Aldwayne B. Delmo ASCONS 2019 IJASC Vol.1 No.2
Background/Objectives: The IMAGE TO TEXT CONVERSION TECHNIQUE FOR ANTI-PLAGIARISM SYSTEM is a design project on how the Optical Character Recognition will be utilized in order to extract text from images that can be used to increase the accuracy rate of an anti-plagiarism checker. It also highlights the integration of Convolutional Neural Network and its effect in the result of the conversion. Methods/Statistical analysis: Optical Character Recognition is a technology that recognizes text within an image. It is commonly used to recognize text in scanned documents, but it serves many other purposes as well. While Convolutional Neural network is a category of neural networks that have been proven very effective in performing image recognition and classification. The main objective of the study is to design a software that will convert images of text into plain editable text. The study aims to use a specific algorithm to extract useful information from the images. Findings: It will integrate the two algorithm, convolutional neural network and optical character recognition technology in order to develop a software. The input of the software is a document in .docx format and will generate an output in the same format. Improvements/Applications: This software will be an aid to the existing anti-plagiarism checkers to generate a more thorough and better plagiarism
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