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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Segun I. Popoola
Covenant University
Computer Science & IT

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Negative resistance amplifier circuit using GaAsFET modelled single MESFET Olasunkanmi Ojewande; Charles Ndujiuba; Adebiyi A. Adelakun; Segun I. Popoola; Aderemi A. Atayero
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.10120

Abstract

Negative resistance devices have attracted much attention in the wireless communication industry because of their low cost, better performance, high speed, and reduced power requirements. Although negative resistance circuits are non-linear circuits, they are associated with distortion, which may either be amplitude-to-amplitude distortion or amplitude-to-phase distortion. In this paper, a unique way of realizing a negative resistance amplifier is proposed using a single metal-semiconductor field-effect transistor (MESFET). Intermodulation distortion test (IMD) is performed to evaluate the characteristic response of the negative resistance circuit amplifier to different bias voltages using the harmonic balance (HB) of the advanced designed software (ADS 2016). The results obtained are compared to those of a conventional distributed amplifier. The findings of this study showed that the negative resistance amplifier spreads over a wider frequency output with reduced power requirements while the conventional distributed amplifier has a direct current (DC) offset with output voltage of 32.34 dBm.
A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system Oluwole A Adegbola; Ismail A Adeyemo; Folasade A Semire; Segun I. Popoola; Aderemi A Atayero
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.11176

Abstract

In Content-Based Image Retrieval (CBIR) system, one approach of image representation is to employ combination of low-level visual features cascaded together into a flat vector. While this presents more descriptive information, it however poses serious challenges in terms of high dimensionality and high computational cost of feature extraction algorithms to deployment of CBIR on platforms (devices) with limited computational and storage resources. Hence, in this work a feature dimensionality reduction technique based on Principal Component Analysis (PCA) is implemented. Each image in a database is indexed using 174 dimensional feature vector comprising of 54-dimensional Colour Moments (CM54), 32-bin HSV-histogram (HIST32), 48-dimensional Gabor Wavelet (GW48) and 40-dimensional Wavelet Moments (MW40). The PCA scheme was incorporated into a CBIR system that utilized the entire feature vector space. The k-largest Eigenvalues that yielded a not more than 5% degradation in mean precision were retained for dimensionality reduction. Three image databases (DB10, DB20 and DB100) were used for testing. The result obtained showed that with 80% reduction in feature dimensions, tolerable loss of 3.45, 4.39 and 7.40% in mean precision value were achieved on DB10, DB20 and DB100.
Co-Authors Adebiyi A. Adelakun Aderemi A Atayero Aderemi A. Atayero Charles Ndujiuba Folasade A Semire Ismail A Adeyemo Olasunkanmi Ojewande Oluwole A Adegbola