Volume 2, Issue 4

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Volume 2, Issue 4 - 3 Articles

This issue presents three new research papers about improving technology in different areas.
The first paper talks about a new tool that checks how well a big internet network is working. This tool makes it faster and easier to see if the network is giving customers the right amount of internet speed they paid for. The second paper describes new software for designing special electronic parts called amplifiers. These amplifiers are used in things like cell phones and radios. The software helps engineers make better amplifiers that use less power and work more efficiently. The third paper looks at ways to make computers better at understanding special pictures called hyperspectral images. These images contain a lot of information and are used in things like studying the environment or finding minerals. The researchers found new ways to help computers understand these complex images more accurately.

Editorial
Editorial
1 Page, 5,078 KB Download PDF
Front Cover

Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # i–i, 2023

Editorial
1 Page, 686 KB Download PDF
Editorial Board

Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # ii–ii, 2023

Editorial
2 Pages, 697 KB Download PDF
Editorial

by Paul Andrew

Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # iii–iv, 2023

Editorial
1 Page, 669 KB Download PDF
Table of Contents

Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # v–v, 2023

Articles
Open AccessArticle
13 Pages, 1,026 KB Download PDF
Graph-based Tool for Bandwidth Estimation, Health Monitoring and Update Planning in Broadband Networks

by Gian Paolo Jesi and Andrea Odorizzi
Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # 1-13, 2023; DOI: 10.55708/js0204001
Abstract: This paper focuses on the genesis and evolution of our specific Company tool. It is aimed to tackle the problem of verifying the health status and availability of residual bandwidth between any node over the Lepida ScpA broadband network. In fact, there must be a correspondence between active contractual obligations signed by local network operators… Read More

(This article belongs to the Special Issue on SP2 (Special Issue on Computing, Engineering and Sciences 2022-23) and the Section Telecommunications (TEL))

Open AccessArticle
8 Pages, 2,795 KB Download PDF
An Advanced Load-Line Analysis Software for use in the Design and Simulation of Microwave Low-Distortion, High-Efficiency and High-Power GaN HEMT Amplifiers

by Yasushi Itoh, Takana Kaho and Koji Matsunaga
Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # 14-21, 2023; DOI: 10.55708/js0204002
Abstract: An advanced load-line analysis software is devised for nonlinear circuit design and simulation of microwave low-distortion, high-efficiency and high-power GaN HEMT amplifiers. A single software package can incorporate DC, small- and large-signal performances of GaN HEMT devices, and then analyze nonlinear performance of amplitude-to-amplitude (AM-AM) and amplitude-to-phase (AM-PM) modulations, and finally evaluate intermodulation distortion (IMD)… Read More

(This article belongs to the Section Electronic Engineering (EEE))

Open AccessArticle
11 Pages, 3,612 KB Download PDF
Classification of Rethinking Hyperspectral Images using 2D and 3D CNN with Channel and Spatial Attention: A Review

by Muhammad Ahsan Aslam, Muhammad Tariq Ali, Sunwan Nawaz, Saima Shahzadi and Muhammad Ali Fazal
Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # 22-32, 2023; DOI: 10.55708/js0204003
Abstract: It has been demonstrated that 3D Convolutional Neural Networks (CNN) are an effective technique for classifying hyperspectral images (HSI). Conventional 3D CNNs produce too many parameters to extract the spectral-spatial properties of HSIs. A channel service module and a spatial service module are utilized to optimize characteristic maps and enhance sorting performance in order to… Read More

(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))

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