TELKOMNIKA (Telecommunication Computing Electronics and Control)
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)
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Algorithm performance comparison for earthquake signal recognition on smartphone’s accelerometer
Hapsoro Agung Nugroho;
Haryas Subyantara Wicaksana;
Hariyanto Hariyanto;
Rista H. Virgianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14708
Micro-electro-mechanical-system accelerometer is able to detect acceleration signal caused by earthquake. Such type of accelerometer is also used by smartphones. There are few algorithms that can be used to recognize the type of acceleration signal from smartphone. This study aims to find signal recognition algorithm in order to consider the most proper algorithm for earthquake signal detection. The initial stage of designing the recognizer is data collection for each type of signal classification. The next step is to apply a highpass filter to separate the signals collected from the gravitational acceleration signal. The signal is divided into several segments. The system will extract features of each signal segment in the time and frequency domain. Each signal segment is then classified according to the type of signal using the classifier through a series of training data processes. The classifier which has the highest accuracy value is exported into the new input signal modeling. As the result, fine K-NN algorithm has the highest level of accuracy in the classification. The fine K-NN algorithm has an accuracy rate of 99.75% in the classification of human activity signals and earthquake signals with a memory capacity of 6,044 kilobytes and processing time of 15.93 seconds. This algorithm has the best classifier criteria compared to decision tree, support vector machine and linear discriminant analysis algorithms.
The influences of calcium fluoride and silica particles on improving color homogeneity of WLEDs
Anh-Minh D. Tran;
Nguyen Doan Quoc Anh;
Nguyen Thi Phuong Loan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.13571
The LEDs lighting device with phosphor ingredient (pcLEDs) is among the most common lighting methods in recent years and evaluated by chromatic uniformity and lighting capacity. Therefore, we introduce the phosphor particles that can improve the scattering efficiency (SEPs) to apply in pcLEDs at 8500 K correlated color temperature (CCT) with the expectation to produce better pcLEDs by enhancing both quantity and quality of emitted light. Combining various materials such as CaF2 and SiO2 with yellow Y3Al5O12:Ce3+ phosphor composition in the pcLEDs simulation created by the LightTools program is the mechanism of this research. The simulated pcLEDs are tested and the results will be verified with Mie-scattering theory. The observation of the simulation leads to the conclusion about the scattering coefficients of SEPs at 455 nm and 595 nm wavelengths. The calculation showed that CaF2 is better for color homogeneity yet suffer from luminous flux deficiency as the concentration gets higher. On the other hand, SiO2 is the scattering enhancement material that can maintain high luminous flux regardless of its concentration.
A power efficient delta-sigma ADC with series-bilinear switch capacitor voltage-controlled oscillator
D. S. Shylu;
P. Sam Paul;
D. Jackuline Moni;
J. Arolin Monica Helan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14034
In low-power VLSI design applications non-linearity and harmonics are a major dominant factor which affects the performance of the ADC. To avoid this, the new architecture of voltage-controlled oscillator (VCO) was required to solve the non-linearity issues and harmonic distortion. In this work, a 12-bit, 200MS/s low power delta-sigma analog to digital converter (ADC) VCO based quantizer was designed using switched capacitor technique. The proposed technique uses frequency to current conversion technique as a linearization method to reduce the non-linearity issue. Simulation result show that the proposed 12-bit delta-sigma ADC consumes the power of 2.68 mW and a total area of 0.09 mm² in 90 nm CMOS process.
Performance assessment of an optimization strategy proposed for power systems
Harold Puin;
Cesar Hernandez
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14396
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
Designing and configuring context-aware semantic web applications
Haider Hadi Abbas;
Suha Sahib Oleiwi;
Haider Rasheed Abdulshaheed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.15277
Context-aware services are attracting attention of world as the use of web services are rapidly growing. We designed an architecture of context-aware semantic web which provides on demand flexibility and scalability in extracting and mining the research papers from well-known digital libraries i.e. ACM, IEEE and SpringerLink. This paper proposes a context-aware administrations system, which supports programmed revelation and incorporation of setting dependent on Semantic Web administrations. This work has been done using the python programming language with a dedicated library for the semantic web analysis named as “Cubic-Web” on any defined dataset, in our case as we have used a dataset for extracting and studying several publications to measure the impact of context aware semantic web application on the results. We have found the average recall and averge accuracy for all the context aware research journals in our research work. Moreover, as this study is limited journal documents, other future studies can be approached by examining different types of publications using this advance research. An efficient system has been designed considering the parameters of research article meta-data to find out the papers from the web using semantic web technology. Parameters like year of publication, type of publication, number of contributors, evaluation methods and analysis method used in publication. All this data has been extracted using the designed context-aware semantic web technology.
Sound event detection using deep neural networks
Suk-Hwan Jung;
Yong-Joo Chung
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14246
We applied various architectures of deep neural networks for sound event detection and compared their performance using two different datasets. Feed forward neural network (FNN), convolutional neural network (CNN), recurrent neural network (RNN) and convolutional recurrent neural network (CRNN) were implemented using hyper-parameters optimized for each architecture and dataset. The results show that the performance of deep neural networks varied significantly depending on the learning rate, which can be optimized by conducting a series of experiments on the validation data over predetermined ranges. Among the implemented architectures, the CRNN performed best under all testing conditions, followed by CNN. Although RNN was effective in tracking the time-correlation information in audio signals,it exhibited inferior performance compared to the CNN and the CRNN. Accordingly, it is necessary to develop more optimization strategies for implementing RNN in sound event detection.
Brain-computer interface of focus and motor imagery using wavelet and recurrent neural networks
Esmeralda C. Djamal;
Rifqi D. Putra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14899
Brain-computer interface is a technology that allows operating a device without involving muscles and sound, but directly from the brain through the processed electrical signals. The technology works by capturing electrical or magnetic signals from the brain, which are then processed to obtain information contained therein. Usually, BCI uses information from electroencephalogram (EEG) signals based on various variables reviewed. This study proposed BCI to move external devices such as a drone simulator based on EEG signal information. From the EEG signal was extracted to get motor imagery (MI) and focus variable using wavelet. Then, they were classified by recurrent neural networks (RNN). In overcoming the problem of vanishing memory from RNN, was used long short-term memory (LSTM). The results showed that BCI used wavelet, and RNN can drive external devices of non-training data with an accuracy of 79.6%. The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses. Whereas in computational learning time, Adam's model is faster than AdaDelta's model.
Developed approach for phase-based Eulerian video magnification
Haider Ismael Shahadi;
Zaid Jabbar Al-allaq;
Hayder Jawad Albattat
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14321
This paper proposes a modification approach for phased-based EVM in order to reduce the processing time without effect the quality of the magnified video. The proposed approach applies a resizing process on the input video using Lanczos-3 algorithm. Then, it decomposes video frames using steerable pyramid to obtain multi-scale frame with its orientation. Subsequently, the resulted frames are filtered by temporal filters for specific bands and the filtered frames are multiplied by a magnification factor. Now, both the magnified regions and the unmagnified regions for each frame are added together. Finally, reconstructing the produced magnified multi-scale frames using the inverse steerable pyramid. The experimental results show that superiority of the proposed approach compares to the conventional phase-based EVM in processing time, where the processing time reduction about 60-65%. Furthermore, this approach does not affect on the video quality, which maintain it in the boundary of the conventional Phase-based EVM.
Determination of flexibility of workers working time through Taguchi method approach
Suhaila Saidat;
Ahmad Kadri Junoh;
Wan Zuki Azman Wan Muhamad;
Zainab Yahya
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.13979
Human factor is one of the important elements in manufacturing world, despite their important role in improvement the production flow, they have been neglected while scheduling for many decades. In this paper the researchers taken the human factor throughout their job performance weightage into consideration while using job shop scheduling (JSS) for a factory of glass industry, in order to improving the workers' flexibility. In other hand, the researchers suggested a new sequence of workers' weightage by using Taguchi method, which present the best flexibility that workers can have, while decreasing the total time that the factory need to complete the whole production flow.
Balancing a Segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms
Ibrahim K. Mohammed;
Abdulla I. Abdulla
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14717
A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.