Enhancing Data Storage and Access in CSN Labs with Raspberry Pi 3B+ and Open Media Vault NAS
Journal Title: Information Dynamics and Applications - Year 2023, Vol 2, Issue 2
Abstract
The purpose of this study was to devise a more efficient system for data storage and exchange in the Computer System and Network (CSN) Laboratory at Ibn Khaldun Bogor University. Open Media Vault (OMV) software and Raspberry Pi 3B+ were employed to establish a Network Attached Storage (NAS) system. The performance and file transfer speeds of the Raspberry Pi were evaluated in the context of this implementation. The implementation of the NAS system was intended to offer students of the CSN laboratory swifter and more efficient access to data, thereby reducing dependence on USB media. The findings of this study could hold substantial implications for enhancing the efficiency and effectiveness of data storage and exchange in educational environments.
Authors and Affiliations
Ritzkal Ritzkal, Kodarsyah Kodarsyah, Asep Ramdan Sopyan Nudin, Ibnu Hanafi Setiadi, Freza Riana, Berlina Wulandari
MR Image Feature Analysis for Alzheimer’s Disease Detection Using Machine Learning Approaches
Alzheimer’s disease (AD), a progressive neurological disorder, predominantly impacts cognitive functions, manifesting as memory loss and deteriorating thinking abilities. Recognized as the primary form of dementia, this...
Bridging Fundamental Physics and Practical Applications: Advances in Quantum-Enhanced Sensing
Quantum-enhanced sensing has emerged as a transformative technology with the potential to surpass classical sensing modalities in precision and sensitivity. This study explores the advancements and applications of quan...
Enhancing Pneumonia Diagnosis with Transfer Learning: A Deep Learning Approach
The significant impact of pneumonia on public health, particularly among vulnerable populations, underscores the critical need for early detection and treatment. This research leverages the National Institutes of Health...
Detection of Fruit Ripeness and Defectiveness Using Convolutional Neural Networks
The classification of fruit ripeness and detection of defects are critical processes in the agricultural industry to minimize losses during commercialization. This study evaluated the performance of three Convolutional N...
Optimizing Energy Storage and Hybrid Inverter Performance in Smart Grids Through Machine Learning
The effective integration of renewable energy sources (RES), such as solar and wind power, into smart grids is essential for advancing sustainable energy management. Hybrid inverters play a pivotal role in the conversio...