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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 64 Documents
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Search results for , issue "Vol 17, No 5: October 2019" : 64 Documents clear
A new two-scroll chaotic system with two nonlinearities: dynamical analysis and circuit simulation Sundarapandian Vaidyanathan; Aceng Sambas; Sen Zhang; Yicheng Zeng; Mohamad Afendee Mohamed; Mustafa Mamat
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.10650

Abstract

Chaos theory has several applications in science and engineering. In this work, we announce a new two-scroll chaotic system with two nonlinearities. The dynamical properties of the system such as dissipativity, equilibrium points, Lyapunov exponents, Kaplan-Yorke dimension and bifurcation diagram are explored in detail. The presence of coexisting chaotic attractors, coexisting chaotic and periodic attractors in the system is also investigated. In addition, the offset boosting of a variable in the new chaotic system is achieved by adding a single controlled constant. It is shown that the new chaotic system has rotation symmetry about the z-axis. An electronic circuit simulation of the new two-scroll chaotic system is built using Multisim to check the feasibility of the theoretical model. 
Blind multi-signature scheme based on factoring and discrete logarithm problem Duc Nguyen Tan; Hai Nguyen Nam; Minh Nguyen Hieu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.10525

Abstract

One of the important objectives of information security systems is providing authentication of the electronic documents and messages. In that, blind signature schemes are an important solution to protect the privacy of users in security electronic transactions by highlighting the anonymity of participating parties. Many studies have focused on blind signature schemes, however, most of the studied schemes are based on single computationally difficult problem. Also digital signature schemes from two difficult problems were proposed but the fact is that only finding solution to single hard problem then these digital signature schemes are breakable. In this paper, we propose a new signature schemes base on the combination of the RSA and Schnorr signature schemes which are based on two hard problems: IFP and DLP. Then expanding to propose a single blind signature scheme, a blind multi-signature scheme, which are based on new baseline schemes.
Energy extraction method for EEG channel selection Hilman Fauzi; M. Abdullah Azzam; Mohd. Ibrahim Shapiai; Masaki Kyoso; Uswah Khairuddin; Tadayasu Komura
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.12805

Abstract

Channel selection is an improvement technique to optimize EEG-based BCI performance. In previous studies, many channel selection methods—mostly based on spatial information of signals—have been introduced. One of these channel selection techniques is the energy calculation method. In this paper, we introduce an energy optimization calculation method, called the energy extraction method. Energy extraction is an extension of the energy calculation method, and is divided into two steps. The first step is energy calculation and the second is energy selection. In the energy calculation step, l2-norm is used to calculate channel energy, while in the energy selection method we propose three techniques: “high value” (HV), “close to mean” (CM), and “automatic”. All proposed framework schemes for energy extraction are applied in two types of datasets. Two classes of datasets i.e. motor movement (hand and foot movement) and motor imagery (imagination of left and right hand movement) were used. The system used a Common Spatial Pattern (CSP) method to extract EEG signal features and k-NN as a classification method to classify the signal features with k = 3. Based on the test results, all schemes for the proposed energy extraction method yielded improved BCI performance of up to 58%. In summary, the energy extraction approach using the CM energy selection method was found to be the best channel selection technique.
Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network Romi Fadillah Rahmat; Dennis Dennis; Opim Salim Sitompul; Sarah Purnamawati; Rahmat Budiarto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.11276

Abstract

In this paper, we propose an approach to detect and geotag advertisement billboard in real-time condition. Our approach is using AlexNet’s Deep Convolutional Neural Network (DCNN) as a pre-trained neural network with 1000 categories for image classification. To improve the performance of the pre-trained neural network, we retrain the network by adding more advertisement billboard images using inductive transfer learning approach. Then, we fine-tuned the output layer into advertisement billboard related categories. Furthermore, the detected advertisement billboard images will be geotagged by inserting Exif metadata into the image file. Experimental results show that the approach achieves 92.7% training accuracy for advertisement billboard detection, while for overall testing results it will give 71,86% testing accuracy.

Page 7 of 7 | Total Record : 64

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