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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Yaqeen S. Mezaal
Al-Esraa University College
Computer Science & IT

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Performance analysis of negative group delay network using MIMO technique Yaqeen S. Mezaal; Marwah Al-Ogaidi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.13666

Abstract

This study introduces comparative consequences that determine the bit error rate enhancements, resultant from adopting a proposed MIMO wireless model in this study. The antenna configurations for this model uses new small microstrip slotted patch antenna with multiple frequency bands at strategic operating frequencies of 2.4, 4.4, and 5.55 respectively. The S11 response of the proposed antenna for IEEE802.11 MIMO wireless network has been highly appropriate to be adopted with MIMO antenna system. The negative group delay (NGD) response is the most significant feature for projected MIMO antenna. The NGD stands for a counterintuitive singularity that interacts time advancement with wave propagation. These improvements are employed for increasing a reliability of instantly conveyed data streams, enhance the capacity of the wireless configuration and decrease the bit error rate (BER) of adopted wireless system. In addition to antenna scattering response, the enhancements have been analysed in term of BER for different MIMO topologies.
Gender voice classification with huge accuracy rate Mustafa Sahib Shareef; Thulfiqar Abd; Yaqeen S. Mezaal
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.13717

Abstract

Gender voice recognition stands for an imperative research field in acoustics and speech processing as human voice shows very remarkable aspects. This study investigates speech signals to devise a gender classifier by speech analysis to forecast the gender of the speaker by investigating diverse parameters of the voice sample. A database has 2270 voice samples of celebrities, both male and female. Through Mel frequency cepstrum coefficient (MFCC), vector quantization (VQ), and machine learning algorithm (J 48), an accuracy of about 100% is achieved by the proposed classification technique based on data mining and Java script.
Co-Authors Marwah Al-Ogaidi Mustafa Sahib Shareef Thulfiqar Abd