Wirawan Wirawan
Institut Teknologi Sepuluh Nopember Surabaya

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

An Adaptive Modulation in Millimeter-Wave Communication System for Tropical Region Suwadi Suwadi; Gamantyo Hendrantoro; Wirawan Wirawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 8, No 3: December 2010
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v8i3.634

Abstract

 The dominant propagation factor affecting the outage and the spectral efficiency of millimeter-wave communication systems operating at frequencies 30 GHz is rain attenuation. An adaptive modulation is proposed to improve the outage and spectral efficiency performance of the system. This paper presents an analytical procedure for the evaluation of the outage and spectral efficiency of the system in Indonesia with heavy rain rate. By comparing analytic and simulation a validation was conducted. The results show that adaptive modulation can significantly improve the outage and the spectral efficiency performance of the system, for links with long distance.
Gabor-based Face Recognition with Illumination Variation using Subspace-Linear Discriminant Analysis Hendra Kusuma; Wirawan Wirawan; Adi Soeprijanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 1: March 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i1.767

Abstract

            Face recognition has been an active research topic in the past few decades due to its potential applications. Accurate face recognition is still a difficult task, especially in the case that illumination is unconstrained. This paper presents an efficient method for the recognition of faces with different illumination by using Gabor features, which are extracted by using log-Gabor filters of six orientations and four scales. By Using sliding window algorithm, these features are extracted at image block-regions. Extracted features are passed to the principal component analysis (PCA) and then to linear discriminant analysis (LDA). For development and testing we used facial images from the Yale-B databases. The proposed method achieved 86–100 % rank 1 recognition rate.