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31,456 Article Results

BER performance in NOMA downlink transmission using AWGN, Rayleigh, and Rician fading channels

10.12928/telkomnika.v23i1.26050
Abdelbari; Ibn Tofail University Falloun , Abdessalam; Ibn Tofail University Ait Madi
5G wireless technology is accessible to a much larger user base compared to previous cellular networks. Users can connect to base stations (BSs) in a variety of methods, including of frequency division multiplexing (FDM), orthogonal frequency division multiplexing (OFDM), orthogonal multiple access (OMA), and non-orthogonal multiple access (NOMA). NOMA is considered to be the best one for 5G. Additionally, NOMA employs radio resource optimization and interference control techniques to enhance spectrum network efficiency and support extremely large connections, such as successive interference cancellation (SIC). Users can be divided into two categories: strong and weak. While weak users may experience a low data rate, strong users need a high one. In this paper, we examine the two users in relation to transmit power, the indicative bit error rate (BER), outage probability (OP), and rate attainable capacity using additive white gaussian noise (AWGN) channel. Moreover, we compare the BER and signal-to-noise ratio (SNR) per bit for two users, showing that the Rician fading channel performs better than the Rayleigh fading channel. The use of NOMA technology is the optimal choice and indicates an improvement in the precision of data transmission within the communication system, particularly in the presence of noise and interference.
Volume: 23
Issue: 1
Page: 57-65
Publish at: 2024-11-26

Effects of atmospheric turbulence and reconfigurable intelligent surfaces on near terrestrial optical link for internet of things

10.12928/telkomnika.v23i1.25521
Duong Huu; The University of Danang Ai , Hoang; The University of Danang Huu Duc , Khanh Ty; The University of Danang Luong , Ngoc Dat; The University of Danang Vo
Near terrestrial free-space optical (NT-FSO) communication refers to the use of light to transmit data wirelessly through the atmosphere over relatively short distances, typically within the earth’s atmosphere. It is an alternative to traditional radio frequency (RF) communication and fiber optics, providing high-speed data transmission without the need for physical cables. NT-FSO is an attractive solution for high-speed, short-distance wireless communications where physical infrastructure like fiber is impractical. Reconfigurable intelligent surfaces (RIS) are a cutting-edge technology that enhances wireless communication systems by dynamically controlling how electromagnetic waves propagate through an environment. RIS technology is increasingly relevant in modern communication systems like 5G and beyond, aiming to improve the efficiency, coverage, and energy usage of wireless networks. This study investigates the effects of atmospheric turbulence and RIS on near terrestrial optical link for internet of things. Several numerical outcomes obtained for different link distance and average electrical signal signal-to-noise ratio (SNR) are shown to quantitatively illustrate the average spectral efficiency.
Volume: 23
Issue: 1
Page: 66-72
Publish at: 2024-11-26

Simple RNN-LSTM hybrid deep learning model for Bitcoin and EUR_USD forecasting

10.12928/telkomnika.v23i1.25925
Mohamed; University Sidi Mohamed Ben Abdellah EL Mahjouby , Khalid; University Sidi Mohamed Ben Abdellah El Fahssi , Mohamed; University Sidi Mohamed Ben Abdellah Taj Bennani , Mohamed; University Sidi Mohamed Ben Abdellah Lamrini , Mohamed; University Sidi Mohamed Ben Abdellah El Far
The popularity of deep learning in time series prediction has significantly increased compared to the past. In this article, we utilize deep learning methods, which encompass long short term memory (LSTM) networks, simple recurrent neural network (SimpleRNN) networks, and gated recurrent units (GRU) networks. This research introduces a hybrid foundational model for forecasting future closing prices of EUR_USD in financial time series and Bitcoin, combining SimpleRNN with LSTM, referred to as SimpleRNN-LSTM. To improve the precisions of our hybrid model, we incorporate twenty-one technical indicators into the training data. Then, we compute four measures to evaluate the performance of various prediction models. When predicting currency pairs EUR_USD and Bitcoin, our hybrid foundational model outperforms SimpleRNN, LSTM, and GRU models.
Volume: 23
Issue: 1
Page: 175-184
Publish at: 2024-11-26

PV solar anomaly detection using low-cost data logger and ANN algorithm

10.12928/telkomnika.v23i1.26155
Younes; Hassania School of Public Works (EHTP) Ledmaoui , Adila; Mohammed VI Polytechnic University (UM6P) El Maghraoui , Mohamed; Hassania School of Public Works (EHTP) El Aroussi , Rachid; Hassania School of Public Works (EHTP) Saadane , Abdellah; Royal Military College of Canada Chehri , Ahmed; Mohammed VI Polytechnic University (UM6P) Chebak
This paper presents an innovative edge device architecture that significantly enhances solar energy management systems. By integrating advanced functionalities such as generation prediction, maintenance alerts, and solar anomaly detection, this architecture transforms solar energy management. Through edge computing, it enables real-time analysis and decision-making at the network edge. Leveraging machine learning algorithms and accurate predictive models, these edge devices provide precise energy generation forecasts, facilitating optimal energy utilization and strategic planning for stakeholders. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime, and enabling timely maintenance. This approach ensures uninterrupted energy generation, enhancing the reliability and efficiency of the entire monitoring system. The integration of these features within edge devices improves the performance and reliability of energy monitoring systems. Implementing this cutting-edge architecture empowers stakeholders to achieve superior energy management, substantial cost reductions, and unparalleled system reliability.
Volume: 23
Issue: 1
Page: 258-266
Publish at: 2024-11-26

Designing common-source low noise amplifier utilizing GaN HEMT for sub-6 GHz in 5G wireless applications

10.12928/telkomnika.v23i1.26324
Samia; Ibn Tofail University Zarrik , Abdelhak; Ibn Tofail University Bendali , Fatehi; Ibn Tofail University ALtalqi , Karima; Ibn Tofail University Benkhadda , Sanae; Ibn Tofail University Habibi , Zahra; Ibn Tofail University Sahel , Mouad; Ibn Tofail University El Kobbi , Abdelkader; Ibn Tofail University Hadjoudja , Mohamed; Ibn Tofail University Habibi
In the domain of gallium nitride based high electron mobility transistors (GaN HEMT), this work refines a class A low noise amplifier (LNA) tailored for fifth generation (5G) wireless applications within the sub-6 GHz band. Employing a common-source topology and leveraging GaN HEMT technology, the amplifier seamlessly achieves operation at 3.5 GHz. Simulations were conducted using Advanced Design System (ADS) software. The GaN HEMT transistor manifests noteworthy intrinsic and extrinsic characteristics, with a Vds of 6 V, Vgs of -1.56 V, and Id of 1024 mA. Through meticulous optimization within the [3.3-3.9] GHz frequency band, the GaN HEMT transistor attains an impressive maximum gain of 16.225 dB, coupled with a minimal low noise figure (NF) of 1.232 dB. Additionally, the amplifier showcases noteworthy power added efficiency (PAE) of approximately 60.527%. These exceptional attributes position the amplifier as highly suitable for sub-6 GHz and millimeter-wave applications across the extensive 5G spectrum. The investigation is centered on precisely situating the LNA as a pivotal catalyst for improving 5G network front-end performance. With a dedicated focus on frequencies below 6 GHz, the research not only addresses challenges but also pioneers’ advancements in 5G application LNA design, ultimately elevating the overall system performance.
Volume: 23
Issue: 1
Page: 48-56
Publish at: 2024-11-26

Predicting big data analytics adoption intention among small and medium enterprises in the Philippines

10.12928/telkomnika.v23i1.26497
Victor; Chung Yuan Christian University James C. Escolano , Wei-Jung; Chung Yuan Christian University Shiang , Alexander; Lyceum of the Philippines University A. Hernandez , Darrel; Bohol Island State University- Bilar Campus A. Cardaña
Big data analytics (BDA) has increasingly become popular both in theory and practice in recent years. Globally, larger businesses have used BDA to collect, study, and evaluate vast volumes of data to identify market trends and insights that lead to sound and intelligent business decisions. However, its adoption in small and medium enterprises (SMEs) is not fully maximized because of a variety of factors, including a lack of expertise and financial repercussions. As such, this paper seeks to delve into the predictors of BDA adoption intention among SMEs in a developing nation by extending the technology acceptance model (TAM). The quantitative surveys obtained from 438 SMEs were analyzed using partial least squares and structural equation modeling (PLS-SEM). The results revealed that perceived benefits, namely system quality, information quality, and predictive analytics accuracy, had positive relationships with perceived ease of use and usefulness, subsequently leading to attitude towards using BDA. Likewise, perceived security significantly influences perceived benefits, perceived ease of use, and attitude towards use of BDA. Further, attitude towards use was the most significant predictor of intention to adopt BDA among SMEs. Generally, the study indicates a positive interest in adopting BDA among Philippine SMEs.
Volume: 23
Issue: 1
Page: 192-200
Publish at: 2024-11-26

Enhanced fuzzy logic control for overcoming intrinsic resistance in inverted pendulum systems

10.12928/telkomnika.v23i1.25998
Tin; Ho Chi Minh City University of Technology (HCMUT) Lu Trung , Hung; Ho Chi Minh City University of Technology (HCMUT) Thoi Ly , Hung; Ho Chi Minh City University of Technology (HCMUT) Duc Nguyen , Minh; Ho Chi Minh City University of Technology (HCMUT) Duc Pham
The paper delves into an in-depth analysis of the intrinsic resistance of the inverted pendulum system which causes the modeling of the system to differ from the actual system. Our primary objective revolves around the implementation and subsequent optimization of fuzzy logic controllers (FLC), drawing inspiration from human perceptual assessments. The processing comprises comprehensive mathematical system modeling, intrinsic resistance examination, and improved fuzzy logic control with detailed membership function and rule design. In addition, we conduct a comparative analysis with the widely recognized linear quadratic regulator (LQR) algorithm, which is considered the conventional control algorithm. The result demonstrates that the improved FLC outperforms the conventional LQR algorithm overshoot mitigation, thereby underscoring its superior efficacy and optimality.
Volume: 23
Issue: 1
Page: 234-245
Publish at: 2024-11-26

Deep learning-based palm tree detection in unmanned aerial vehicle imagery with Mask R-CNN

10.12928/telkomnika.v23i1.26244
Agung; National Research and Innovation Agency (BRIN) Syetiawan , Danang; National Research and Innovation Agency (BRIN) Budi Susetyo , Yustisi; National Research and Innovation Agency (BRIN) Lumban-Gaol , Susilo; National Research and Innovation Agency (BRIN) Susilo , Mohammad; National Research and Innovation Agency (BRIN) Ardha , Yunus; Dr. Soetomo University Susilo , Wahono; University of Muhammadiyah Malang Wahono
Oil palm is highly valuable in tropical regions like Southeast Asia, including Indonesia. Therefore, accurate monitoring of oil palm trees is necessary for operational efficiency and reducing its environmental impact. Geospatial data, such as orthomosaic imagery from the unmanned aerial vehicle (UAV), can facilitate this goal. This research aims to integrate UAV data with deep learning algorithms, specifically Mask region-based convolutional neural network (R-CNN), to detect oil palm trees in Indonesia. We utilized Resnet-50 as the backbone and trained the model using data sampled from the template matching tool in eCognition. Considering factors like cloud shadows and other features, such as other plants, buildings, and road segments, we divided the study area into three containing different feature combinations in each. The Mask R-CNN model achieved an accuracy exceeding 80%, which is sufficient and makes it suitable for large-scale oil palm tree detection using high resolution images from UAV.
Volume: 23
Issue: 1
Page: 156-165
Publish at: 2024-11-26

Comparative analysis of cross-platform development methodologies: a comprehensive study

10.12928/telkomnika.v23i1.26331
Raiymbek; Auezov University Jangassiyev , Zhanat; Auezov University Umarova , Aisaule; Auezov University Ussenova , Zlikha; Auezov University Makhanova , Nurlybek; Auezov University Zhumatayev , Manat; Auezov University Amirov , Gulzhan; Auezov University Koishibekova
In an era marked by the proliferation of devices and operating systems, delivering native-feeling applications across platforms has become indispensable. This paper scrutinizes native development through the lens of cross-platform frameworks, investigating their merits, major contenders such as React Native, Flutter, Xamarin, and the nascent .NET MAUI, and their practical implementations. By dissecting the distinct strengths and considerations of each framework, we provide developers with insights to make judicious decisions commensurate with their requirements and proficiencies. This inquiry underscores how cross-platform frameworks empower developers to broaden their audience reach while upholding native performance standards, thereby shaping the trajectory of app development through sustained innovation and integration with emergent technologies.
Volume: 23
Issue: 1
Page: 108-118
Publish at: 2024-11-26

Swarm intelligence for intrusion detection systems in internet of things environments

10.12928/telkomnika.v23i1.25857
Apri; Universitas Islam Riau Siswanto , Akmar; Universitas Islam Riau Efendi , Jaroji; Politeknik Negeri Bengkalis Jaroji , Fajar; Politeknik Negeri Bengkalis Ratnawati
The rise of the internet of things (IoT) technology has brought new security challenges, necessitating robust intrusion detection systems (IDS). This research applies swarm intelligence (SI) principles, specifically the pigeon inspired optimization (PIO) algorithm, to enhance IDS effectiveness in IoT environments. Drawing on the behavior of social species, SI fosters decentralized control and emergent behavior from simple rules. These principles guide the PIO algorithm, making it apt for optimizing IDS. We utilize two comprehensive IoT datasets – the Canadian Institute for Cybersecurity (CIC) IoT dataset 2023 and the IoT dataset for IDS, aiming to boost the IDS’s capability to detect illicit attacks. By adapting the PIO algorithm, our IDS learns from the environment, adapts to evolving threats, and mitigates false-positive rates. Preliminary tests show that our SI-based IDS outperforms traditional systems’ accuracy, speed, and adaptability. This research advances SI applications in IoT security, contributing to developing more resilient IDS and ultimately enhancing IoT network security against a range of cyber threats.
Volume: 23
Issue: 1
Page: 81-89
Publish at: 2024-11-26

Rainfall prediction using support vector regression in Udupi region Karnataka, India

10.12928/telkomnika.v23i1.26170
Krishnamurthy; Manipal Institute of Technology, Manipal Academy of Higher Education Nayak , Sumukha; Birla Institute of Technology & Science, Pilani Campus K Nayak , Supreetha; Manipal Institute of Technology, Manipal Academy of Higher Education Balavalikar Shivarama
The hydromatereological processes are examined through analysis of temporal rainfall variability. India is an agricultural land and its economy is mainly dependent on timely rains to produce good harvest. The amount of rainfall varies with regional and temporal variation in distribution. The present research has been conducted to predict the temporal variations in rainfall in Udupi district, Karnataka, India using support vector regression (SVR) model and to validate the findings using actual rainfall records. The data has been collected from the statistical department, Udupi district, Government of Karnataka, India. The prediction accuracy of SVR based rainfall prediction model depends on tuning of algorithmic-based parameters. The parameter optimization is performed using grid search to select the optimal values of hyperparameters. The analysis was performed for the year 2018 based on the training dataset from 2000-2017. It is observed that there is a decreasing trend in total annual rainfall in 2018 and it is concluded that the average yearly rainfall has declined during the years 2018 and 2019. The rainfall predicted results were validated with actual records. The SVR based rainfall prediction model will predicts the rainfall accurately for application in agricultural sector.
Volume: 23
Issue: 1
Page: 166-174
Publish at: 2024-11-26

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

10.11591/ijres.v13.i3.pp528-541
Abdul Lateef Haroon Phulara Shaik , Sowmya Madhavan , Parameshachari Bidare Divakarachari , Rocío Pérez de Prado , Paramesh Siddappa Parameshwarappa , Kavitha Malali Vishveshwarappa Gowda
Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.
Volume: 13
Issue: 3
Page: 528-541
Publish at: 2024-11-01

Comparing feature usage in IMU-based gesture control for omnidirectional robot via wearable glove

10.11591/ijres.v13.i3.pp542-551
Dahnial Syauqy , Eko Setiawan , Edita Rosana Widasari
To improve the intuitiveness of maneuver control on omniwheeled mobile robot, many hand gesture-based robot controls have been developed. The focus of this research is to develop a wearable system for data acquisition from inertial measurement unit (IMU) sensors and compare its features to be used as gesture recognition using the random forest algorithm. With the need of resource constrained device for wearable system based on microcontrollers, we compared the use of Euler and quaternion-based orientation data as input features. As additional comparison, dimension reduction was also carried out using the principal component analysis (PCA) method. Hand gestures are recognized using data obtained by the IMU sensor embedded in the wearable glove. This study compared the accuracy and size of library files embedded in microcontrollers in several feature usage scenarios. The test evaluation results of all scenarios show that the use of all features provides a balance between high accuracy but small file sizes, respectively 99% and 9.2 KB. However, the use of other fewer features, such as by only using 3 Euler data, 4 quaternion data, or by using PCA algorithm (PC=3) can also be used since the accuracy is still above 90%, with a relatively larger file size.
Volume: 13
Issue: 3
Page: 542-551
Publish at: 2024-11-01

Embedded systems as programmable square wave generator in wireless power transfer

10.11591/ijres.v13.i3.pp568-576
Sabriansyah Rizqika Akbar , Eko Setiawan , Achmad Basuki
This study focuses on the design and development of programmable frequency generator using embedded devices that are able to produce square wave signals in the wireless power transfer (WPT) transmitter. We validate the accuracy of the output signal by measuring distance error. We validate that our system can change and sweep the frequency and produce high power by measuring the absorbed power in the load. We conduct the frequency sweep analysis to find optimal frequency and the frequency splitting phenomenon. The experiments show that the system can produce and sweep the square wave signals with less than 1% error. We also find that the frequency splitting occurred when distance among two coils in the range 0.5-6.5 cm and the splitting disappeared when the distance is above 7.5 cm. The frequency splitting shows that the measured optimum frequency differs from the calculation. The difference confirms that the programmable frequency generator is needed to adjust the frequency that can transfer maximum power to the load.
Volume: 13
Issue: 3
Page: 568-576
Publish at: 2024-11-01

Energy-efficient clustering and routing using fuzzy k-medoids and adaptive ranking-based wireless sensor network

10.11591/ijres.v13.i3.pp774-785
Haritha K. Sivaraman , Rangaiah Leburu
The wireless sensor network (WSN) is a vital component of infrastructure that is seeing tremendous demand and quick expansion in a variety of industries, including forestry, airports, healthcare, and the military. Increasing network lifetime and reducing power consumption (PC) are now major goals in WSN research. This research proposes a unique energy-efficient cross-layer WSN design that aims to maximize network lifetime while maintaining quality of service (QoS) criteria to address these challenges. The research initially utilizes the fuzzy k-medoids (FKMeds) clustering technique to group sensor nodes (SN) to improve resilience, scalability, and minimize network traffic. Following that, the hybrid improved grey wolf and ant colony (HIGWAC) optimization approach is applied to choose cluster heads (CH), minimizing distances, reducing latency, and optimizing energy stability. Finally, data is transmitted through the shortest pathways using the adaptive ranking-based energy-efficient opportunistic routing (ARanEOR) protocol, which ensures effective and energy-conserving routing in WSN while dynamically lowering network overhead. Compared to existing approaches, the proposed method in this study outperforms them in terms of energy efficiency, latency, and network longevity.
Volume: 13
Issue: 3
Page: 774-785
Publish at: 2024-11-01
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