TELKOMNIKA (Telecommunication Computing Electronics and Control)
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Performance optimization of MIMO-NOMA systems in Nakagami-m fading environments
Get PDFIn this context, the utilization of multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) technologies is applied to improve wireless communication. This paper is dedicated to the evaluation of the performance in the MIMO-NOMA system under Nakagami-m fading environments, which is a more general scenario for different kinds of fading conditions that occur normally. Our proposed framework is applied to evaluate key performance metrics, including bit error rate (BER), outage probability, spectral efficiency, and ergodic capacity. The results reveal the deep impact of Nakagami-m fading on these key performance metrics, emphasizing an intricate balance between reliability and spectral efficiency that is achieved through power domain multiplexing in conjunction with successive interference cancellation (SIC). Our results are further evidence of the strength and flexibility of MIMO-NOMA, and point to insights and practical guidelines that are new towards the optimization of next-generation wireless networks. This overall analysis not only closes the gap in current literature on the subject but also sets a new benchmark for future research on advanced communication technologies
Integration of image processing with 6-degrees-of-freedom robotic arm for advanced automation
Get PDFThis paper presents the design, construction, and development of a 6-degrees-of-freedom robotic arm, specifically tailored to the conditions at our university. The arm is powered by stepper motors and controlled via a programmable logic controller, while utilizing image processing data from a Raspberry Pi board. The objective of this research is to study automated pick-and-place operations, specifically targeting the handling of fruits such as oranges and apples. The system integrates advanced motion control techniques with vision-based object recognition to enable precise and reliable manipulation of the fruits. The robotic arm is equipped with an end-effector capable of handling objects with varying shapes and sizes, ensuring safe and efficient grasping and placement. Image processing algorithms are employed to identify and localize the fruits in real time, allowing the robotic arm to perform tasks in dynamic environments with minimal human intervention. Calibration, motion planning, and feedback control strategies are optimized to ensure high accuracy and prevent collisions or damage to the fruits. The system’s performance is evaluated through a series of experiments that demonstrate its capability to effectively pick and place oranges and apples, making it a promising solution for applications in agricultural automation and food processing
The use of dolomite to overcome grounding resistance in acidic swamp land
Get PDFThis research addresses the effectiveness of grounding systems in acidic swampland, which poses a challenge in protecting people and electrical equipment from the risk of electric shock. The increasing use of swampland for electrical installations necessitates a solution to reduce the high grounding resistance resulting from poor soil resistivity values. This study proposes using dolomite as an admixture to improve soil conductivity and lower grounding resistance. Experimental methods were conducted by embedding rod electrodes of various materials in dolomite-mixed media with varying compositions. The results showed that adding dolomite significantly decreased the grounding resistance, although there were inconsistencies in the test results; on average, the decrease in resistance reached 25%. Galvanized electrodes proved to be the most effective in this system. These findings provide new insights in the field of grounding systems and offer practical solutions that are environmentally friendly and sustainable. This research is expected to be an important reference for developing more innovative and effective grounding system techniques in the future
Realization of Bernstein-Vazirani quantum algorithm in an interactive educational game
Get PDFQuantum algorithms are celebrated for their computational superiority over classical counterparts, yet they pose significant learning challenges for non-physics audiences. Among these, the Bernstein-Vazirani (BV) algorithm stands out for its quantum speedup by efficiently identifying a secret binary string. However, the accessibility of such algorithms remains constrained by their inherent technical complexity. To address this educational gap, this paper introduces a gamified, web-based tool that innovatively reinterprets the BV algorithm’s complex mathematical settings through an into engaging scenario of identifying broken lamps. Players assume the role of an investigator, utilizing both classical and quantum solvers to identify faulty lamps with minimal queries. By transforming the BV algorithm into an intuitive gameplay experience, the tool helps reducing technical barriers, making quantum concepts much more comprehensible for educators and students than traditional methods that demand rigorous mathematical understanding. Developed using Qiskit, IBM’s Python package for quantum computation, and deployed via Flask, a popular Python microframework for building web applications, the game effectively simplifies complex quantum algorithms while demonstrating the practical applications of quantum speedup. This contribution advances quantum education by merging technical depth with interactive design, fostering a broader understanding of quantum principles and inspiring new innovations in gamified learning
Design and implementation of a power supply unit for a smart airport lighting control system
Get PDFIn this paper, a buck-boost converter is used to design and implement a power supply for intelligent airport lighting system applications. Innovative approaches to power supply design are required to meet the increasing demand for fault detection solutions for lighting systems in vital infrastructure such as airports. The buck-boost converter’s ability to step up or down input voltage levels makes it particularly well suited to this application, ensuring stable operation over a range of load conditions. With a fast-settling time of 26 ms at 6.1 V input and dropping to 6 ms at 22.4 V input, the power supply offers exceptional output stability. The output stabilizes steadily at 5 V with low ripple over a wide input voltage range (5 V to 23 V). The physical prototype, simulations, component selection and circuit design are all carefully tested and supported by experimental results. According to these results, the proposed converter-based power unit operates with stability and reliability, making it ideal for demanding lighting applications. By improving power stability in dynamic environments, this work improves the reliability of aviation infrastructure power systems and lays the groundwork for future advances in intelligent airport technologies
Advanced pneumonia classification using transfer learning on chest X-ray data with EfficientNet and ResNet
Get PDFPneumonia is a serious lung infection that demands accurate and timely diagnosis to reduce mortality. This study explores the use of deep learning and transfer learning for classifying chest X-ray images into two categories: normal and pneumonia. A total of 5,632 labeled images were used to train and evaluate six pre-trained convolutional neural network (CNN) architectures: EfficientNetB1, B3, B5, B7, ResNet50, and ResNet101. The models were tested across three training scenarios by varying learning rates (LR), batch sizes, and epochs. Among all models, EfficientNetB3 achieved the highest performance, with accuracy of 99.04%, precision of 99.76%, recall of 99.23%, and F1-score of 99.34%. These results indicate that EfficientNetB3 offers a robust and efficient solution for pneumonia detection. This research contributes to the development of intelligent diagnostic tools in the medical field and provides practical guidance for selecting effective deep learning models in clinical imaging applications
Leveraging technology to improve tuberculosis patient adherence: a comprehensive review
Get PDFTuberculosis (TB) is a chronic disease that requires long-term treatment, generally for at least 6 to 9 months. Patients should follow the recommended treatment scheme regularly and completely. Poor adherence to treatment can cause patients to remain a source of infection for others. Patients with TB need additional support during treatment, in terms of information, motivation, and emotional support. Compliance monitoring helps ensure that patients take drugs according to a predetermined schedule. Comprehensive approach review method, careful selection of relevant data from various sources. This aims to provide overview of modern technology used to optimize the success of TB treatment. This paper aims to provide various methods that have existed in conventional and technology-enhanced approaches to monitoring and evaluating the treatment of TB patients. The existing studies only focus on making tools as a reminder to take medication but do not evaluate whether the drug is consumed. In addition, this paper describes prospective ideas involving advanced technology by using the internet of things (IoT)-based smart medicine bottle to accommodate the problem and become an effective communication solution in TB medication
Energy-efficient certificateless signcryption for secure data transfer in wireless sensor networks
Get PDFWireless sensor networks (WSN) have gained high popularity in the realm of technological innovation and have a prime responsibility of transferring the data safely to the sink despite the vulnerable situation presiding around the network. There needs to be a compromise made between energy consumption and the intricate network security system because they are inversely correlated. To create a safe and effective data transfer between the communicating nodes, a new censored regressive jaccard indexed certificateless signcryption (CEJICS) technique is suggested. Initially, the Gaussian likelihood censored regression is applied to identify the energy efficient node which supports enhancing the network lifetime. The security is implemented using the rabin cryptographic jaccard indexive certificateless signcryption (RCJICS). To create a signcryption system with the characteristics of digital signature and ciphertext authenticity, the proposed work focuses on certificateless cryptography. The NS-2 simulator is used for the simulation, and performance metrics are used to evaluate it. According to the observed quantitative results, the suggested CEJICS method outperforms the other conventional methods by improving the packet delivery ratio (PDR) by 93%, achieving a minimum drop of 7%, reducing delay by 22%, reducing overhead to 17%, minimizing energy consumption by 15%, and ultimately extending the network lifetime by 10%
Fuel consumption prediction of civil air crafts using deep learning: a comparative study
Get PDFAccurate fuel consumption prediction is critical for minimizing the adverse impact of fuel emissions on the environment, conserving fuel, and reducing flight costs. Additionally, precise fuel forecasting enhances trajectory prediction and supports effective air traffic management. This study evaluates the predictive performance of two deep learning techniques in predicting the fuel consumption of a civil aircraft belonging to Airbus A320NEO. Based on the analysis, the findings show that the deep neural network (DNN) model has better score of indicators and than the recurrent neural network (RNN) including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R2). By integrating an automated feature selection approach with an optimized deep learning framework, this research contributes to the development of a robust and efficient predictive system for fuel consumption. The findings have practical implications for improving fuel management strategies in aviation, leading to cost savings and reduced emissions. One limitation of this study is its reliance on specific environmental variables, which may limit the model’s generalizability across different flight conditions, aircraft types, and operational scenarios
Metamaterial-enhanced four-port MIMO antenna for 5G communications at 28/38 GHz
Get PDFThis work presents a novel compact four-port multiple-in multiple-out (MIMO) antenna enhanced with metamaterial unit cells for 5G millimeter-wave (mmWave) applications at 28 and 38 GHz. Compact MIMO antennas at mmWave bands often suffer from high mutual coupling, which degrades isolation and diversity performance. To address this, the proposed design integrates metamaterial loading around each radiating element to effectively suppress coupling, enhance isolation, and improve overall efficiency. The antenna, measuring 27×27×0.8 mm³, is implemented on a flexible FR4_epoxy substrate (εr=4.4), enabling compatibility with portable and embedded devices. Full-wave simulations performed in both ANSYS high-frequency structure simulator (HFSS) and computer simulation technology (CST) studio suite confirm the effectiveness of the approach, achieving an exceptionally low envelope correlation coefficient (ECC) (0.0001), a fivefold reduction in channel capacity loss (CCL), and a wide impedance bandwidth of 25.90–34.93 GHz with |S11| below −10 dB in both operating bands. The design also exhibits stable directional gain and low sidelobes. Compared with recent compact MIMO antennas reported in the literature, the proposed configuration offers significantly improved isolation, bandwidth, and mechanical flexibility. These features make it a strong candidate for integration into high-capacity 5G modules, portable terminals, and compact internet of things (IoT) communication systems