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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 46 Documents
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Search results for , issue "Vol 12, No 4: December 2014" : 46 Documents clear
Resolution Improvement in Fabry-Perot Displacement Sensor Based on Fringe Counting Method Nur Izzati Ismail; Nor Hafizah Ngajikin; Nor Fadzlina Mohd Zaman; Maisarah Awang; Asrul Izam Azmi; Nik Noordini Nik Abd. Malik; Norazan Mohd Kassim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.123

Abstract

This paper presents an improved Fringe Counting Method (FCM) technique in order to enhance the displacement resolution of a Fabry-Perot Displacement Sensor (FPDS). A simulation model of a FPDS based on the improved FCM has been developed and simulated for nanometer displacement range by using MATLAB mathematical software. Unlike conventional FCM that analyzed the number of fringes produced over one time period, the improved FCM analyzed the number of fringes for one largest Free Spectral Range (FSR). In this work, the initial length of Fabry-Perot Interferometer (FPI) cavity has been set at 75 μm due to limitation of the machining precision equipment. For the displacement analysis, the improved FCM technique is used as an algorithm. The research results prove that this FPDS could detect displacement at 10nm resolution over a working range of 40 nm. It showed that the improved FCM technique managed to enhance the capability of the conventional FCM in detecting nanometer displacement. 
Image Fuzzy Enhancement Based on Self-Adaptive Bee Colony Algorithm Meng Lei; Yao Fan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.534

Abstract

In the image acquisition or transmission, the image may be damaged and distorted due to various reasons; therefore, in order to satisfy people’s visual effects, these images with degrading quality must be processed to meet practical needs. Integrating artificial bee colony algorithm and fuzzy set, this paper introduces fuzzy entropy into the self-adaptive fuzzy enhancement of image so as to realize the self-adaptive parameter selection. In the meanwhile, based on the exponential properties of information increase, it proposes a new definition of fuzzy entropy and uses artificial bee colony algorithm to realize the self-adaptive contrast enhancement under the maximum entropy criterion. The experimental result shows that the method proposed in this paper can increase the dynamic range compression of the image, enhance the visual effects of the image, enhance the image details, have some color fidelity capacity and effectively overcome the deficiencies of traditional image enhancement methods.
Process Improvement of LSA for Semantic Relatedness Computing Wujian Yang; Lianyue Lin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.811

Abstract

Tang poetry semantic correlation computing is critical in many applications, such as searching, clustering, automatic generation of poetry and so on. Aiming to increase computing efficiency and accuracy of semantic relatedness, we improved the process of latent semantic analysis (LSA). In this paper, we adopted “representation of words semantic” instead of “words-by-poems” to represent the words semantic, which based on the finding that words having similar distribution in poetry categories are almost always semantically related. Meanwhile, we designed experiment which obtained segmentation words from more than 40000 poems, and computed relatedness by cosine value which calculated from decomposed co-occurrence matrix with Singular Value Decomposition (SVD) method. The experimental result shows that this method is good to analyze semantic and emotional relatedness of words in Tang poetry. We can find associated words and the relevance of poetry categories by matrix manipulation of the decomposing matrices as well.
Prediction and Realization of DO in Sewage Treatment Based on Machine Vision and BP Neural Network Liu Liping; Sunjin Sheng; Yin Jing-tao; Liang Na
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.437

Abstract

Dissolved Oxygen (DO) is one of the most important parameters describing biochemical process in wastewater treatment. It is usually measured with dissolved oxygen meters, and currently galvanic and polarographic electrodes are the predominant methods. Expensive, membrane surface inactivation, and especially need of cleaning and calibrating very frequently are common disadvantages of electrode-type measuring sensors. In our work, a novel method for Prediction and Realization dissolved oxygen based-on Machine Vision and BP Neural Network was researched. Pictures of the water-body surface in aeration basins are captured and transformed into HSI space data. These data plus the correspondent measured DO values are processed with a neural network. Using the well-trained neural network, a satisfied result for classifying dissolved oxygen according to the water-body pictures has been realized.
Review of Local Descriptor in RGB-D Object Recognition Ema Rachmawati; Iping Supriana Suwardi; Masayu Leylia Khodra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.388

Abstract

The emergence of an RGB-D (Red-Green-Blue-Depth) sensor which is capable of providing depth and RGB images gives hope to the computer vision community. Moreover, the use of local features began to increase over the last few years and has shown impressive results, especially in the field of object recognition. This article attempts to provide a survey of the recent technical achievements in this area of research. We review the use of local descriptors as the feature representation which is extracted from RGB-D images, in instances and category-level object recognition. We also highlight the involvement of depth images and how they can be combined with RGB images in constructing a local descriptor. Three different approaches are used in involving depth images into compact feature representation, that is classical approach using distribution based, kernel-trick, and feature learning. In this article, we show that the involvement of depth data successfully improves the accuracy of object recognition.
Development of Wireless Electric Field Mill for Atmospheric Electric Field Observation Muhammad Abu Bakar Sidik; Hamizah Shahroom; Zolkafle Buntat; Yanuar Zulardiansyah Arief; Zainuddin Nawawi; Muhammad Irfan Jambak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.977

Abstract

Rotating-vane electric field mill (REFM) sensor is one of the popular methods to measure atmospheric quasi-static electric field. Lightning incident occasion can be predicted by observing electric fields strength in atmosphere. In this paper an integration of REFM with an online wireless data monitoring system for long distance observation and data collection is presented. This method reduces the required man-hour to gather data from the REFM as well as system costs by removing the computer and data logger on-site. The development includes hardware and software design in order to improve efficiency the atmospheric electric field measurement system. The contribution of this work is the design of the electronic circuit which converts the ac signal from the REFM sensor to dc signal and then correlates the signal to the electric field strength in the vicinity. Subsequently the information is transmitted via a wireless data transmission system, using the Global System Mobile Communication (GSM) network. Using the proposed method, all the data from sensors can be observed and analysed immediately from any location.
Cooperative Avoidance Control-based Interval Fuzzy Kohonen Networks Algorithm in Simple Swarm Robots Siti Nurmaini; Siti Zaiton; Ricy Firnando
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.495

Abstract

A novel technique to control swarm robot’s movement is presented and analyzed in this paper. It allows a group of robots to move as a unique entity performing the following function such as obstacle avoidance at group level. The control strategy enhances the mobile robot’s performance whereby their forthcoming decisions are impacted by its previous experiences during the navigation apart from the current range inputs. Interval Fuzzy-Kohonen Network (IFKN) algorithm is utilized in this strategy. By employing a small number of rules, the IFKN algorithms can be adapted to swarms reactive control. The control strategy provides much faster response compare to Fuzzy Kohonen Network (FKN) algorithm to expected events. The effectiveness of the proposed technique is also demonstrated in a series of practical test on our experimental by using five low cost robots with limited sensor abilities and low computational effort on each single robot in the swarm. The results show that swarm robots based on proposed technique have the ability to perform cooperative behavior, produces minimum collision and capable to navigate around square shapes obstacles.
Unambiguous Acquisition for Galileo E1 OS Signal Based on Delay-And-Multiply Deng Zhongliang; Xi Yue; Jiao Jichao; Yin Lu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.530

Abstract

Galileo E1 Open Service (OS) signal is transmitted with the modulation of Composite Binary Offset Carrier (CBOC). CBOC has a main drawback that is the autocorrelation function has multiple side-peaks, which will lead to ambiguous acquisition. The high rate of data bit and secondary code makes it very difficult to increase coherent integration time. This paper will propose a new scheme based on the delay-and-multiply concept. And also this scheme combines the data channel and pilot channel. Finally, the theoretical results will be given to prove that the new scheme will accomplish unambiguous acquisition and also eliminate the influence of bit transition.
Diagnostic Study Based on Wavelet Packet Entropy and Wear Loss of Support Vector Machine Yunjie Xu; Shudong Xiu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.305

Abstract

Against the problems, the ratio of signal to noise of bearing wear is low, the feature extraction is difficult, there are few fault samples and it is difficult to establish the reliable fault recognition model, the diagnostic method is put forward based on wavelet packet features and bearing wear loss of SVM. Firstly, choose comentropy with strong fault tolerance as characteristic parameter, then through wavelet packet decomposition, extract feature entropy of wavelet packet in fault sensitivity band as input vector and finally, apply the Wrapper method of least square SVM to choose optimal character subset. The application in actual bearing fault diagnosis indicates the effectiveness of the proposed method in the article.
A Novel Intrusion Detection Approach using Multi-Kernel Functions Lijiao Pan; Weijian Jin; Jun Wu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.889

Abstract

Network intrusion detection finds variant applications in computer and network industry. How to achieve high intrusion detection accuracy and speed is still received considerable attentions in this field. To address this issue, this work presents a novel method that takes advantages of multi-kernel computation technique to realize speedy and precise network intrusion detection and isolation. In this new development the multi-kernel function based kernel direct discriminant analysis (MKDDA) and quantum particle swarm optimization (QPSO) optimized kernel extreme learning machine (KELM) were appropriately integrated and thus form a novel method with strong intrusion detection ability. The MKDDA herein was firstly employed to extract distinct features by projecting the original high dimensionality of the intrusion features into a low dimensionality space. A few distinct and efficient features were then selected out from the low dimensionality space. Secondly, the KELM was proposed to provide quick and accurate intrusion recognition on the extracted features. The only parameter need be determined in KELM is the neuron number of hidden layer. Literature review indicates that very limited work has addressed the optimization of this parameter. Hence, the QPSO was used for the first time to optimize the KELM parameter in this paper. Lastly, experiments have been implemented to verify the performance of the proposed method. The test results indicate that the proposed LLE-PSO-KELM method outperforms its rivals in terms of both recognition accuracy and speed. Thus, the proposed intrusion detection method has great practical importance.

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