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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 63 Documents
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Search results for , issue "Vol 17, No 4: August 2019" : 63 Documents clear
Co-clustering algorithm for the identification of cancer subtypes from gene expression data Logenthiran Machap; Afnizanfaizal Abdullah; Zuraini Ali Shah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.12773

Abstract

Cancer has been classified as a heterogeneous genetic disease comprising various different subtypes based on gene expression data. Early stages of diagnosis and prognosis for cancer type have become an essential requirement in cancer informatics research because it is helpful for the clinical treatment of patients. Besides this, gene network interaction which is the significant in order to understand the cellular and progressive mechanisms of cancer has been barely considered in current research. Hence, applications of machine learning methods become an important area for researchers to explore in order to categorize cancer genes into high and low risk groups or subtypes. Presently co-clustering is an extensively used data mining technique for analyzing gene expression data. This paper presents an improved network assisted co-clustering for the identification of cancer subtypes (iNCIS) where it combines gene network information with gene expression data to obtain co-clusters. The effectiveness of iNCIS was evaluated on large-scale Breast Cancer (BRCA) and Glioblastoma Multiforme (GBM). This weighted co-clustering approach in iNCIS delivers a distinctive result to integrate gene network into the clustering procedure.
A 28 GHz 0.18-μm CMOS cascade power amplifier with reverse body bias technique A. F. Hasan; S. A. Z. Murad; F. A. Bakar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.12761

Abstract

A 28 GHz power amplifier (PA) using CMOS 0.18 μm Silterra process technology is reported. The cascade configuration has been adopted to obtain high Power Added Efficiency (PAE). To achieve low power consumption, the input stage adopts reverse body bias technique. The simulation results show that the proposed PA consumes 32.03mW and power gain (S21) of 9.51 dB is achieved at 28 GHz. The PA achieves saturated power (Psat) of 11.10 dBm and maximum PAE of 16.55% with output 1-dB compression point (OP1dB) 8.44 dBm. These results demonstrate the proposed power amplifier architecture is suitable for 5G applications.
Design and implementation of single bit error correction linear block code system based on FPGA Abdullah Mohammed A. Hamdoon; Zaid Ghanim Mohammed; Emad A. Mohammed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.12033

Abstract

Linear block code (LBC) is an error detection and correction code that is widely used in communication systems. In this paper a special type of LBC called Hamming code was implemented and debugged using FPGA kit with integrated software environments ISE for simulation and tests the results of the hardware system. The implemented system has the ability to correct single bit error and detect two bits error. The data segments length was considered to give high reliability to the system and make an aggregation between the speed of processing and the hardware ability to be implemented. An adaptive length of input data has been consider, up to 248 bits of information can be handled using Spartan 3E500 with 43% as a maximum slices utilization. Input/output data buses in FPGA have been customized to meet the requirements where 34% of input/output resources have been used as maximum ratio. The overall hardware design can be considerable to give an optimum hardware size for the suitable information rate.

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