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28,188 Article Results

Adaptive Resource Allocation Algorithm in Wireless Access Network

10.12928/telkomnika.v14i3.3615
Zhanjun; Chongqing University of Posts and Telecommunications Liu , Yue; Chongqing University of Posts and Telecommunications Shen , Zhonghua; Chongqing University of Posts and Telecommunications Yu , Fengxie; Chongqing University of Posts and Telecommunications Qin , Qianbin; Chongqing University of Posts and Telecommunications Chen
Wireless network state varies with the surrounding environment, however, the existing resource allocation algorithm cannot adapt to the varying network state, which results to the underutilization of frequency and power resource. Therefore, in this paper, we propose an adaptive resource allocation algorithm which can efficiently adapt to the varying network state by building an optimal mathematical model and then changing the weighted value of the objective function. Furthermore, the optimal allocation of subcarrier and power is derived by using the Lagrange dual decomposition and the subgradient method. Simulation results show that the proposed algorithm can adaptively allocate the resource to the users according to the varying user density which represents the network state.
Volume: 14
Issue: 3
Page: 887-893
Publish at: 2016-09-01

Scalable Nodes Deployment Algorithm for the Monitoring of Underwater Pipeline

10.12928/telkomnika.v14i3.3464
Muhammad Zahid; Universiti Teknologi Malaysia Abbas , Kamalrulnizam; Universiti Teknologi Malaysia Abu Bakar , Muhammad; University of Tabuk Ayaz Arshad , Muhammad; Universiti Teknologi Malaysia Tayyab , Mohammad Hafiz; Universiti Teknologi Malaysia Mohamed
Underwater Wireless Linear Sensor Networks (UW-LSNs) possess unique features as compared to the terrestrial sensor networks for pipeline monitoring. Other than long propagation delays for long range underwater pipelines and high error probability, homogeneous node deployment also makes it harder to detect and locate the pipeline leakage efficiently. Determining the exact leakage position with minimum delay stays a major issue where pipelines length is extremely long and expensive to deploy many underwater sensors. In order to tackle the problem of large scale pipeline monitoring and unreliable underwater link quality, many algorithms have been proposed and even some of them provided good solutions for these issues but the scalable nodes deployments still need focus and prime attention. In order to handle the problem of nodes deployment, we therefore propose a dynamic nodes deployment algorithm where every node in the network is assigned location in a quick and efficient way without needing any localization scheme. It provides an option to handle the heterogeneous types of nodes, distribute topology and mechanism in which new nodes are easily added to the network without affecting the existing network performance. The proposed distributed topology algorithm divides the pipeline length into segments and sub-segments in order to manage the higher delay issue. Normally nodes are randomly deployed for the long range underwater pipeline inspection yet it requires some proper dynamic nodes deployment algorithm assigning unique position to each node
Volume: 14
Issue: 3
Page: 1183-1191
Publish at: 2016-09-01

MapReduce Integrated Multi-algorithm for HPC Running State Analysis

10.12928/telkomnika.v14i3.3771
ShuRen; Northwest Branch of PetroChina Research Institute of Petroleum Exploration and Development Liu , ChaoMin; Northwest Branch of PetroChina Research Institute of Petroleum Exploration and Development Feng , HongWu; Northwest Branch of PetroChina Research Institute of Petroleum Exploration and Development Luo , Ling; Northwest Branch of PetroChina Research Institute of Petroleum Exploration and Development Wen
High-performance computer clusters are major seismic processing platforms in the oil industry and have a frequent occurrence of failures. In this study, K-means and the Naive Bayes algorithm were programmed into MapReduce and run on Hadoop. The accumulated high-performance computer cluster running status data were first clustered by K-means, and then the results were used for Naive Bayes training. Finally, the test data were discriminated for the knowledge base and equipment failure. Experiments indicate that K-means returned good results, the Naive Bayes algorithm had a high rate of discrimination, and the multi-algorithm used in MapReduce achieved an intelligent prediction mechanism.
Volume: 14
Issue: 3
Page: 1123-1127
Publish at: 2016-09-01

Fuzzy C-Means Clustering Based on Improved Marked Watershed Transformation

10.12928/telkomnika.v14i3.2757
Cuijie; Hebei University of Technology Zhao , Hongdong; Hebei University of Technology Zhao , Wei; Tianjin University of Science and Technology Yao
Currently, the fuzzy c-means algorithm plays a certain role in remote sensing image classification. However, it is easy to fall into local optimal solution, which leads to poor classification. In order to improve the accuracy of classification, this paper, based on the improved marked watershed segmentation, puts forward a fuzzy c-means clustering optimization algorithm. Because the watershed segmentation and fuzzy c-means clustering are sensitive to the noise of the image, this paper uses the adaptive median filtering algorithm to eliminate the noise information. During this process, the classification numbers and initial cluster centers of fuzzy c-means are determined by the result of the fuzzy similar relation clustering. Through a series of comparative simulation experiments, the results show that the method proposed in this paper is more accurate than the ISODATA method, and it is a feasible training method.
Volume: 14
Issue: 3
Page: 981-986
Publish at: 2016-09-01

Distributed Target Localization in Wireless Sensor Networks using Diffusion Adaptation

10.11591/ijeecs.v3.i3.pp512-518
Amirhosein Hajihoseini , Seyed Ali Ghorashi
Localization is an important issue for wireless sensor networks. Target localization has attracted many researchers who work on location based services such as navigation, public transportation and so on. Localization algorithms may be performed in a centralized or distributed manner. In this paper we apply diffusion strategy to the Gauss Newton method and introduce a new distributed diffusion based target localization algorithm for wireless sensor networks. In our proposed method, each node knows its own location and estimates the location of target using received signal strength. Then, all nodes cooperate with their neighbors and share their measurements to improve the accuracy of their decisions. In our proposed diffusion based algorithm, each node can localize target individually using its own and neighbor’s measurements, therefore, the power consumption decreases. Simulation results confirm that our proposed method improves the accuracy of target localization compared with alternative distributed consensus based target localization algorithms.  Our proposed algorithm is also shown that is robust against network topology and is insensitive to uncertainty of sensor nodes’ location.
Volume: 3
Issue: 3
Page: 512-518
Publish at: 2016-09-01

Action Recognition of Human’s Lower Limbs Based on a Human Joint

10.12928/telkomnika.v14i3.3556
Feng Liang , Zhili Zhang , Xiangyang Li , Yong Long , Zhao Tong
In order to recognize the actions of human’s lower limbs, a novel action recognition method based on a human joint was proposed. Firstly, hip joint was chosen as the recognition object, its y coordinates were as recognition parameter, and human action characteristics were achieved based on filtering and wavelet transform. Secondly, an improved self-organizing competitive neural network was proposed, which could classify the action characteristics automatically according to the classification number. The classification results of motion capture data proved the validity of the neural network. Finally,an action recognition method based on hidden Markov model (HMM) was introduced to realize the recognition of classification results of human action characteristicswith the change direction of y coordinates. The proposed action recognition method needs less action information and has a fast calculation speed. Experiments proved the method hada high recognition rate and a good application prospect.
Volume: 14
Issue: 3
Page: 1192-1202
Publish at: 2016-09-01

Metamorphic Malware Detection Based on Support Vector Machine Classification of Malware Sub-Signatures

10.12928/telkomnika.v14i3.3850
Ban Mohammed; Universiti Teknologi Malaysia Khammas , Alireza; Universiti Teknologi Malaysia Monemi , Ismahani; Universiti Teknologi Malaysia Ismail , Sulaiman; Universiti Teknologi Malaysia Mohd Nor , M.N.; Universiti Teknologi Malaysia Marsono
Achieving accurate and efficient metamorphic malware detection remains a challenge. Metamorphic malware is able to mutate and alter its code structure in each infection, with some vital functionality and codesegment remain unchanged. We exploit these unchanged features for detecting metamorphic malware detection using Support Vector Machine(SVM) classifier. n-gram features are extracted directly from sample malware binaries to avoid disassembly, which are then masked with the extracted Snort signature n-grams. These masked features reduce considerably the number of selected n-gram features. Our method is capable to accurately detect metamorphic malware with ~99 % accuracy and low false positive rate. The proposed method is also superior than commercially available anti-viruses in detecting metamorphicmalware.
Volume: 14
Issue: 3
Page: 1157-1165
Publish at: 2016-09-01

Musical Genre Classification Using SVM and Audio Features

10.12928/telkomnika.v14i3.3281
Achmad Benny; Faculty of Computer Science and Information Technology, Gunadarma University Mutiara , Rina; Faculty of Computer Science and Information Technology, Gunadarma University Refianti , Nadia R.A.; Faculty of Computer Science and Information Technology, Gunadarma University Mukarromah
The need of advance Music Information Retrieval increases as well asa huge amount of digital music files distribution on the internet.Musical genres are the main top-level descriptors used to organize digital music files. Most of work in labeling genre done manually. Thus, an automatic way for labeling a genre to digital music files is needed.The most standard approach to do automatic musical genre classification is feature extraction followed by supervised machine-learning. This research aims to find the best combination of audio features using several kernels of non-linear Support Vector Machines (SVM). The 31 different  combinations of proposed audio features are dissimilar compared in any other related research. Furthermore, among the proposed audio features, Linear Predictive Coefficients (LPC) has not been used in another works related to musical genre classiffication. LPC was originally used for speech coding. An experimentation in classifying digital music file into a genre is carried out. The experiments are done by extracting feature sets related to timbre, rhythm, tonality and LPC from music files. All possible combination of the extracted features are classified using three different kernel of SVM classifier that are Radial Basis Function (RBF), polynomial and sigmoid.The result shows that the most appropriate kernel for automatic musical genre classification is polynomial kernel and the best combination of audio features is the combination of musical surface, Mel-Frequency Cepstrum Coefficients (MFFC), tonality and LPC. It achieves 76.6 % in classification accuracy.
Volume: 14
Issue: 3
Page: 1024-1034
Publish at: 2016-09-01

A Review on Modulation Strategies of Multi Level Inverter

10.11591/ijeecs.v3.i3.pp681-705
Chinnapettai Ramalingam Balamurugan , S.P. Natarajan , R. Bensraj , B. Shanthi
This review develop different switching methods for Multi Level Inverter (MLI). The switching methods proposed in this paper are to compare various methods and to predict exact switching method for different application based upon its quality of the outputs.  The performance of the inverter is analyzed with the parameters like THD (Total Harmonic Injection), VRMS (fundamental), CF (Crest Factor), FF (Form Factor) and DF (Distortion Factor). From the various non PWM (Pulse Width Modulation) and PWM methods the analysis are method to identify the exact PWM strategies for specific applications. 
Volume: 3
Issue: 3
Page: 681-705
Publish at: 2016-09-01

Ventricular Tachyarrhythmia Prediction based on Heart Rate Variability and Genetic Algorithm

10.12928/telkomnika.v14i3.3665
Khang Hua; Universiti Teknologi Malaysia Boon , Malarvili; Universiti Teknologi Malaysia Bala Krishnan , Mohamed; Universiti Teknologi Malaysia Khalil-Hani
Predicting ventricular tachyarrhythmia (VTA) provides opportunities to reduce casualties due to sudden cardiac death. However, prediction accuracy is still need improvement. In this paper, we propose a method that can predict VTA events using support vector machine (SVM) that trained with HRV features from heart rate variability (HRV). The Spontaneous Ventricular Tachyarrhythmia Database (Medtronic Version 1.0), comprising 106 pre-VT records, 26 pre-VF records, and 135 control data, is used.  Fifty percent of the data was used to train the SVM, and the remainder was used to verify the performance. Each data set was subjected to preprocessing and HRV feature extraction. After correcting the ectopic beats, 5 minutes RR intervals prior to each event was cropped for feature extraction. Extraction of the time domain, spectral, non-linear and bispectrum features were performed subsequently. Furthermore, both t-test and genetic algorithm (GA) were used to optimize the HRV feature subset. With optimized feature subset by GA, proposed method of current work able to outperform previous works with 77.94%, 80.88% and 79.41 % for senstivity, specificity and accuracy respectively.
Volume: 14
Issue: 3
Page: 999-1008
Publish at: 2016-09-01

Towards Smooth and High-Quality Bitrate Adaptation for HTTP Adaptive Streaming

10.12928/telkomnika.v14i3.3517
Lihong; Chinese Academy of Sciences Geng , Liang; Chinese Academy of Sciences Pan , Yiqiang; Chinese Academy of Sciences Sheng , Zhichuan; Chinese Academy of Sciences Guo
Although HTTP adaptive streaming has been well documented for the cost-effective delivery of video streaming, it is still a great challenge to play back video smoothly with high quality under the fluctuating network conditions. In this paper, we proposed a novel bitrate adaptation algorithm for HTTP adaptive streaming. Our algorithm employed two approaches for throughput estimation and bitrate selection, which was evaluated on our testbed (a fully functional HTTP Live Streaming system) over a network, emulated using DummyNet. First, the throughput estimation method, based on the prediction of the difference between the estimated and instantaneous throughputs, was observed to respond smoothly to short-term fluctuations and rapidly to large fluctuations. Second, the bitrate selection algorithm, based on piecewise functions to define the variation range of the current bitrate, was found to result in smoother changes in quality with a higher average quality. The results of our experiments demonstrated the prospects of our bitrate adaptation algorithm for HTTP adaptive streaming.
Volume: 14
Issue: 3
Page: 904-915
Publish at: 2016-09-01

An Improved Artificial Bee Colony Algorithm for Staged Search

10.12928/telkomnika.v14i3.3609
Shoulin; Shenyang Normal University Yin , Jie; Shenyang Normal University Liu , Lin; Shenyang Normal University Teng
Artificial Bee Colony(ABC) or its improved algorithms used in solving high dimensional complex function optimization issues has some disadvantages, such as lower convergence, lower solution precision, lots of control parameters of improved algorithms, easy to fall into a local optimum solution. In this letter, we propose an improved ABC of staged search. This new algorithm designs staged employed bee search strategy which makes that employed bee has different search characters in different stages. That reduces probability of falling into local extreme value. It defines the escape radius which can guide precocious individual to jump local extreme value and avoid the blindness of flight behavior. Meanwhile, we adopt initialization strategy combining uniform distribution and backward learning to prompt initial solution with uniform distribution and better quality. Finally, we make simulation experiments for eight typical high dimensional complex functions. Results show that the improved algorithm has a higher solution precision and faster convergence rate which is more suitable for solving high dimensional complex functions.
Volume: 14
Issue: 3
Page: 1099-1104
Publish at: 2016-09-01

An Equivalent Electrode System for Efficient Charging of Filtration Media

10.11591/ijeecs.v3.i3.pp646-654
Mohamed Anwar Abouelatta , Abdelhadi R Salama
This paper concerns the influence of moving an auxiliary limiting cylinder in X-Y directions on the electrostatic field and corona onset voltage of the dual electrode system employed in the electrostatic filtration process resulting in a “Tri-electrode” system. The Tri-electrode system is applied in order to control the field around the ionized wire and on the ground plate. Accurate calculation of the electrostatic field is obtained using the charge simulation method coupled with genetic algorithms. The calculated field values are utilized in computing the corona onset voltage of the ionized electrode. Laboratory measurements of the onset voltage of the ionized electrode are applied. It is found that the limiting cylinder controls the onset voltage of the ionized wire such that the ionized wire may be in ionized or non-ionized state without changing the position of the ionized wire itself. The numerical onset voltage values agreed satisfactorily with those measured experimentally. 
Volume: 3
Issue: 3
Page: 646-654
Publish at: 2016-09-01

Hadoop Performance Analysis on Raspberry Pi for DNA Sequence Alignment

10.12928/telkomnika.v14i3.1886
Jaya Sena; Bogor Agricultural University Turana , Heru; Bogor Agricultural University Sukoco , Wisnu Ananta; Bogor Agricultural University Kusuma
The rapid development of electronic data has brought two major challenges, namely, how to store big data and how to process it. Two main problems in processing big data are the high cost and the computational power. Hadoop, one of the open source frameworks for processing big data, uses distributed computational model designed to be able to run on commodity hardware. The aim of this research is to analyze Hadoop cluster on Raspberry Pi as a commodity hardware for DNA sequence alignment. Six B Model Raspberry Pi and a Biodoop library were used in this research for DNA sequence alignment. The length of the DNA used in this research is between 5,639 bp and 13,271 bp. The results showed that the Hadoop cluster was running on the Raspberry Pi with average usage of processor 73.08%, 334.69 MB of memory and 19.89 minutes of job time completion. The distribution of Hadoop data file blocks was found to reduce processor usage as much as 24.14% and memory usage as much as 8.49%. However this increased job processing time as much as 31.53%.
Volume: 14
Issue: 3
Page: 1059-1066
Publish at: 2016-09-01

Survival Analysis of Hemodialysis Patients

10.11591/ijphs.v5i3.4800
Efri Tri Ardianto , Hari Basuki Notobroto , Windhu Purnomo
Survival analysis as a collection of statistical procedures for analyzing the data that its outcome variable was the time to occurrence of an event. Kaplan-Meier method is a type of survival analysis technique, this method is often called the Product Limit Method. Chronic Kidney Disease (CKD) became one of the public health problem throughout the world, including Indonesia. The number of hemodialysis patients has increased every year and have an impact on increasing the number of death in General Hospital Ibnu Sina Gresik. This study was determine the survival of hemodialysis patients using Kaplan-Meier analysis techniques. Non-reactive research with a retrospective cohort using the calculations right censoring. 155 population were taken randomly and sample size of 111. Data were collected using a checklist. The estimated survival time of female, adult age, further education, patients work, patients without insurance, patients with normal nutritional status, patients with a history of disease, patient with hypertention and patient with diabetic had a better survival time. The insurance status, nutritional status, hypertension, and diabetes mellitus were significant difference to the survival time (p-value <0.05). It was necessary special treatment for CKD patients through giving information, education to families and patients to maintain healthy lifestyle.
Volume: 5
Issue: 3
Page: 306-312
Publish at: 2016-09-01
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