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Investigation of Pi3 pulsations as a primary scientific source using combined satellite and ground-based measurements, highlighting ionospheric and magnetospheric contributions during a moderate magnetic storm.
Year: 2022
Region / City: Global observations, data from Canada, Russia
Subject: Geomagnetic Pi3 pulsations
Document Type: Research article
Institutions: Polar Geophysical Institute, Institute of Solar-Terrestrial Physics of the Siberian Branch of the RAS, Institute of Physics of the Earth
Authors: Belakhovsky V.B., Pilipenko V.A.
Data Sources: CARISMA and THEMIS magnetometer networks, NORSTAR riometers, GOES, MMS, THEMIS, and Cluster satellites
Event Type: Moderate-intensity magnetic storm (Dst = -71 nT) with substorms (~1500 nT)
Observation Date: September 4, 2022
Funding: Russian Science Foundation, grant № 24-77-10012
Analysis Focus: Ionospheric contribution to geomagnetic Pi3 pulsations and their interaction with magnetospheric waves
Document Scope: Space physics and geomagnetic storm phenomena
Research Method: Satellite and ground-based measurements, data detrending and smoothing, comparison of conjugated stations and satellites
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The Importance of Ionospheric Correction to the InSAR Measurement: Application to the 2018 Palu Earthquake
Year: 2021
Region / City: Indonesia, Yogyakarta
Theme: Ionospheric Correction, InSAR, Earthquake Studies
Document Type: Research Paper
Institution: Indonesian Journal of Geography
Author: Hidayat Panuntun
Target Audience: Researchers, Geoscientists
Period of Activity: October 2018 – February 2021
Date of Approval: 7 September 2021
Date of Last Modification: N/A
OCR Algorithm Using Raspberry Pi3 for Vision-Impaired Assistance
Authors: Praveena S., Sowmiya N., Thenmozhi M., Manoharan L.
Institution: Jeppiaar SRR Engineering College
Department: Department of Electronics and Communication Engineering
Author roles: UG Student; Assistant Professor
Academic field: Electronics and Communication Engineering
Topic: Optical Character Recognition and assistive technology for visually impaired users
Technologies mentioned: Raspberry Pi 3; Tesseract algorithm; Python; HDMI to VGA converter; Ultrasonic sensor
Keywords: OCR Technology; Raspberry Pi3; HDMI to VGA converter; Tesseract algorithm
Document type: Academic research paper
Research area: Computer vision; Artificial Intelligence; Digital Image Analysis
Application domain: Assistive reading system for blind and visually impaired people
Input data: Images of printed or handwritten text captured by camera
Output format: Speech audio generated from recognized text
Recognition method: Text localization with gradient features and Adaboost model followed by Tesseract OCR recognition
Algorithm features: Motion-based object detection and region-of-interest localization
Reported performance: Approximately 90% success rate on the test set
Hardware platform: Raspberry Pi device with camera and sensors
Topics: pulsations Ionospheric Pi3