bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplemental Table 6 from Hypophosphatemia Correction Reduces ICANS Incidence and Duration in CAR T-cell Therapy: A Pooled Clinical Trial Analysis
Authors: Tang Jack Pengfei , Lafeuille Penelope , Socolov Alexandru , Diamond Sheila S. , Aptekar Jacob , Moore Theodore B. , Nie Esther H. , Hanudel Mark R. , Nowicki Theodore S.
Published: October 2024

Incidence of hypophosphatemia and AKI following CAR-T cell infusion.
Figure S6 from Hypophosphatemia Correction Reduces ICANS Incidence and Duration in CAR T-cell Therapy: A Pooled Clinical Trial Analysis
Authors: Tang Jack Pengfei , Lafeuille Penelope , Socolov Alexandru , Diamond Sheila S. , Aptekar Jacob , Moore Theodore B. , Nie Esther H. , Hanudel Mark R. , Nowicki Theodore S.
Published: October 2024

Figure S6. Histogram plots of nadir phosphorus values stratified by ICANS status.
A combined control method of supply harmonic current and source harmonic voltage for series hybrid active power filter
Authors: Thuyen Chau Minh , Nguyen Hoai Thuong , Thao Phan Thi Bich
Published: October 2024

The series hybrid active power filter (SHAPF) is known as a very effective harmonic filtering model in power systems. Typically, SHAPF is controlled by a control method based either on the harmonic voltage of the load or on the supply harmonic current. However, the above two methods have the disadvantage of requiring the control coefficient much be large enough, which easily causes system instability. Therefore, this paper presents a new control method for SHAPF. It is a combined control method of the supply harmonic current and the source harmonic voltage. The advantage of the proposed method is the ability to reduce the total harmonic distortion of the supply current and voltage applied to the load with a control coefficient that is not too large. A fuzzy-proportional integral controller is designed for the proposed control method to reduce the compensation error in steady state under variable load conditions. Mathematical models and simulation results have demonstrated the effectiveness of the proposed method in reducing the total harmonic distortion of the supply current, voltage applied to the load and minimize the compensation error at steady state.
Network reconfiguration for improving performance system in ULP Sungguminasa considering nonlinear loads
Authors: Faraby Muhira Dzar , Rahman Yuli Asmi , Sofyan Sofyan , Thaha Sarma , Lukman Musfirah Putri , Amaliah Asma , Mustika Mustika , Sirad Mochammad Apriyadi Hadi , Sonita Anisya
Published: October 2024

Network reconfiguration is a very economical technique that can improve electrical system performance. The development of semiconductor electrical equipment technology to meet human needs and work, known as nonlinear loads, has had a negative impact in the form of the spread of harmonic distortion which can accelerate the aging process and even damage equipment. In this paper, the effect of the optimization results of network reconfiguration techniques on the Sungguminasa 165-bus Executive Unit Service or Unit Layanan Pelanggan (ULP) electrical system is contaminated with harmonic distortion due to the use of nonlinear loads. This technique was optimized using the particle swarm optimization (PSO) method with a multi objective function in the form of minimizing %THDv and total losses with several limitations. Simulation results from the optimization process of several study cases are shown by activating the five tie switches from the network reconfiguration process on the Sungguminasa 165-bus ULP system which is able to improve power quality by reducing the average %THDv by 3.89% and total losses by 8.19%.
Optimal energy management of hybrid battery/supercapacitor storage system for electric vehicle application
Authors: Rezk Hegazy , Al-Dhaifallah Mujahed , Amrr Syed Muhammad , Alharbi Abdullah
Published: October 2024

A proposed approach for efficient energy management for lithium-ion battery and supercapacitor hybrid energy storage system is outlined in this study. The primary aim is to ensure the electric vehicle receives a stable and high-quality supply of electricity. The suggested management strategy focuses on maintaining the bus voltage at a consistent level while meeting the varying load demands with high-quality power across different scenarios. To achieve this, a management controller is employed, which utilizes a metaheuristics technique to define the parameters of the integral sliding mode control. The supercapacitor units are responsible for controlling the direct current (DC) bus, while the lithium-ion battery plays a role in balancing power distribution on the common line. This study evaluates the potential benefits of integrating salp swarm algorithm techniques into the controller's performance. The findings of the study suggest combining metaheuristic optimization methods with integral sliding mode control. Can lead to improvements in power quality. The proposed management algorithm not only efficiently allocates power resources but also safeguards them, ultimately ensuring a consistent and high-quality power supply for the electric vehicle.
Optimal allocation of wind and solar power based distributed generation: case study
Authors: Dodamani Sateesh N. , Magadum Rudresh B.
Published: October 2024

The main goal of the power system is to congregate the power demand within the power grid while maintaining economical operation, system security and minimal environmental impact. Due to the increasing demand for electrical energy, many problems have arisen with the power systems. These problems include excessive load, uneven system performance, unsatisfactory voltage profile, and an increase in network power losses. To address these issues, more generation sources and improved transmission capacity are required. In order to meet increasing electricity demand, it is more efficient to integrate a sufficient number of smaller generation units. Utilities and consumers can get the significant benefit from installation of distributed generation (DG), which reduces power losses, progress voltage profile, increases power quality and reliability, delays system updates, supports local reactive power, standby generation and peak limiting. This article aims to enrich the performance of the entire network through the best possible placement and penetration of wind energy and solar photovoltaic (PV) dispersed generation.
Performance analysis of wavelet scattering transform-based feature matrix for power system disturbances classification
Authors: Mansour Naema M. , Awaad Ibrahim A. , Abdelsalam Abdelazeem A.
Published: October 2024

Recently, the wavelet scattering transform (WST) was introduced as a powerful feature extraction tool for classification processes. It provides good performance in applications involving audio signals, images, medical data, and quadcopters for structural health diagnosis. It is also employed in several electrical engineering applications, such as the classification of induction motor bearing failures, electrical loads, and industrial robot faults. Despite its development, the performance of the wavelet scattering (WS) network constructed in the MATLAB environment to compute WST coefficients has not been highlighted in the literature so far. In this paper, the properties of the WST feature matrix are examined, and the parameters that have a significant impact on coefficient magnitudes and matrix dimensions are defined. With minimal configuration, a WS network could extract low-variance features from real-valued time series for use in machine learning and deep learning applications. The feature matrix, which contains zero, first, and second-level WST coefficients derived from various power system signal configurations, is constructed to be trained using long short-term memory (LSTM) networks. The simulation results demonstrate the efficacy of the proposed classifier with an accuracy approach of 100%. The MATLAB toolbox has been used to create different signals for the WS and LSTM networks. WST has proven to be a powerful tool for power system disturbance classification.
A comparative analysis of constant impedance and constant power loads in a distribution network
Authors: Ramya Ramya , Joseph Rex
Published: October 2024

Most conventional power systems adopt radial distribution network wherein multiple loads are connected across the distribution transformer. As the number of loads increases, it results in poor voltage profile at the distant receiving end reducing power delivery. This issue worsens with the large-scale influx of electric vehicles and power converter-fed loads, which draw constant power irrespective of supply voltage. Such loads exhibit negative incremental resistance behavior and also have a dynamic response which affects the network in a manner different from constant impedance loads. This paper compares the effects of constant power and constant impedance loads by modeling adjustable converter dynamics for constant power loads. It analyzes line currents, load voltages and power transmitted in a four-load radial test system with optional distributed sources. Results show poorer voltage profile and the effect of power converter dynamics in constant power loads compared to conventional loads. Adding distributed sources improves voltage profile considerably, and transmission losses are reduced. Steady state analysis is then extended to an IEEE 31-bus 23 kV distribution test system with similar results. Transmission losses are computed along different branches, and the influence of loads and sources are analyzed. The outcomes of the analysis can be used in arrival of loss allocation in a system where peer to peer energy sharing is envisaged.
The evolution of smart sprayer system for agricultural sector in Malaysia
Authors: Shamsudin Nur Hazahsha , Noheng Norman Koliah Anak , Chachuli Siti Amaniah Mohd , Selamat Nur Asmiza , Tawai Hrithik , Raof Nurliyana Abdul
Published: October 2024

This study presents the development of a smart sprayer system featuring a microcontroller, ultrasonic sensors, and a Wi-Fi module for agriculture. This system enables 360° movement capabilities and facilitates the activation and deactivation of the sprayer pump remotely. The system offers remote control functionality through smartphone integration, effectively mitigating the need for direct physical contact with hazardous chemicals during the spraying operation. The results demonstrate the efficient operation of the smart sprayer system. The average spraying efficacy is estimated to be 95%, surpassing that of conventional spraying methods, as evidenced by prior research studies. The system is accessible for remote operation via a user-friendly interface, facilitated by the integrated internet of things (IoT) and microcontroller. As anticipated, it successfully executed 360° movements, obstacle detection, water level indication, and remote control of the sprayer pump.
Optical laser-generated electricity for powering tilt-meter sensor
Authors: Nelfyenny Nelfyenny , Bayuwati Dwi , Suryadi Suryadi , Husdi Irwan Rawal , Mulyanto Imam , Prasetio Aditya Dwi , Irawan Dedi , Widiyatmoko Bambang , Setiono Andi
Published: October 2024

This research investigated the feasibility and efficacy of power over fiber (PoF) transmission systems for geotechnical monitoring applications, addressing challenges associated with traditional power transmission methods. Leveraging fiber optic technology, PoF systems offer advantages such as high reliability, minimal signal loss, and immunity to environmental factors. The study presents a detailed design and implementation of a PoF transmission system, integrating a high-power laser source (HPLS) and photovoltaic technology for efficient power transmission over extended distances. Results demonstrate impressive volt-ampere characteristics and conversion efficiencies, with the optimized system configuration achieving a peak power output of 682 mW. Furthermore, the study evaluated the performance of a surface inclinometer sensor powered by the PoF system, showcasing its effectiveness in monitoring soil movements with remarkable stability and consistent power supply. Future research directions include scalability studies, optimization of system efficiency, and field deployments to broaden the applicability of PoF technology in geotechnical monitoring, ultimately advancing disaster mitigation and infrastructure resilience efforts.
Integration of renewable energy into San Andres Island electrical grid
Authors: Archbold Keyla Newball , Zambrano Alvaro , Rosero Garcia Javier
Published: October 2024

Renewable energy (RE) sources integration in electrical grids is changing the dynamics of planning and operation. Overvoltage, overcurrent, and malfunction of protection schemes are some effects if it is limits are not controlled. This article presents a methodology based on the hosting capacity (HC) concept to estimate performance indexes by considering stochastic methods and systematic simulation taking as study case the grid of San Andres Island. RE is of special interest in islands where diesel generators produce energy with a high footprint and security of supply is low as there is a high dependence on fossil fuels and their transport regime. The simulations are carried out in DigSilent PowerFactory integrated with Python to automate the iterations over different penetration levels. The most limiting factor found is transformer rating. Voltage rise is a factor to be monitored at the end of the circuits. Emissions are reduced with the introduction of renewable energies, but variability needs to be controlled as it could require fast start-up of generators; this modifies monitoring and control schemes to maintain stability. The limit found is higher than the established regulation for non-interconnected zones (NIZ) in Colombia, showing the capability of the grid to integrate RE.
Secure data transmission in power systems using blockchain technology
Authors: Srivatsa Anand , Thammaiah Ananthapadmanabha , Kumar MV Likith , D Rajeshwari , AP Suma
Published: October 2024

Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internet-connected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance.
Internet of things important roles in hybrid photovoltaic and energy storage system: a review
Authors: Ahmed Habiba , Barbulescu Eva-Denisa , Nassereddine Mohamad , Al Khatib Obada
Published: October 2024

Renewable energy systems have become integral components of the electrical grid, offering environmental benefits and cost-effective power generation. Technological advancements have introduced internet of things (IoT) devices with robust data collection and execution capabilities. Solar photovoltaic systems, reliant on unpredictable solar radiation, require hybrid systems incorporating various renewable energy sources and energy storage to ensure system stability. Successful operation necessitates data gathering through IoT devices, which have played a crucial role in enhancing system sustainability. This paper provides a comprehensive review of the role of IoT in photovoltaic systems and energy storage, highlighting its significant contributions to system efficiency, fault detection, output prediction, system stability, and load management. The study underscores the critical dependence of advancements in the renewable energy sector on IoT systems.
Effective ethernet controller protocol architecture verification strategy using system Verilog
Authors: Parameshwara Shubha , Sagar Ganapathi Vithoba , Dasappa Hamsa Rekha Sorekunte , Nagaraj Sheetal
Published: October 2024

The pre-silicon verification is typically more significant than post-silicon verification, which produces an algorithm with the correct functionality and timing parameters. In this paper we propose innovative pre-silicon verification methodology focused on the Ethernet controller architecture as the design under test (DUT). The methodology employs a layered verification architecture implemented using the system Verilog language, aiming to streamline the testing process. A novel test pattern test generator, interfaces and blocks are used to perform the verification. The test patterns are generated based on the operational principles of the ethernet controller block, ensuring comprehensive verification coverage. Additionally, the paper combines different verification parameters with existing approaches to demonstrate the effectiveness of the proposed methodology. It is observed that the performance of the proposed method is better compared to existing methods.
Wideband trans-impedance amplifier with bandwidth tuning for near infra-red spectroscopy bio-medical applications
Authors: Balasubramanian Muthukumaran , Balasubramanian Ramachandran
Published: October 2024

A wide band trans-impedance amplifier (WBTIA) with tunable bandwidth for near infra-red spectroscopy (NIRS) bio-medical applications is presented in this research article. The first stage of the proposed WBTIA is implemented by a modified inverter-cascode (InvCas) trans-impedance amplifier (TIA) with series and shunt inductive peaking for the bandwidth extension and a common-source amplifier as a second stage for gain boosting. Bandwidth tuning is achieved by a novel tuning mechanism with a fixed capacitor and tunable metal–oxide–semiconductor (MOS) capacitor with a control voltage. The fixed capacitor provides a coarse-grain bandwidth tuning whereas the tunable MOS capacitors are used for fine-grain bandwidth tuning. The WBTIA is designed in 45 nm technology and it achieved a maximum trans-impedance gain (TIG) of 84.91 dBΩ and 354.81 MHz bandwidth. The proposed WBTIA consumes 41.24 µW power from 1 V supply voltage. The input referred current noise at 100 MHz is 169 fA/sqrt (Hz) and the output noise voltage is 69.8 pV/sqrt (Hz).
Performance enhancement of brushless direct current motor under different novel optimization techniques
Authors: Ashok Babu , Kumar Mahesh
Published: October 2024

This research paper presents a novel attempt of speed control for brushless direct current (BLDC) motor in low power/servo motor applications. The performance is measured based on the swiftness for the recovery of desired speed amidst in disturbances, sensitive to supply/motor load fluctuations. The proportional integral (PI) controller is competent only for linear time invariant systems. The state of art technology is, PI controller is used with metaheuristic optimization algorithms viz. Honeybee mating optimization (HBO), artificial immune system (AIS), and frog leaping guided algorithm (FLG), for fine tuning of gain coefficients. Earlier literature survey shows power quality and time domain specifications for separate applications. An innovative approach for the assessment of performance indicators like maximum overshoot (M_p), settling time (t_s), power factor (PF) and total harmonic distortion (THD) simultaneously in the optimized PI controller is suggested. By avoiding local optima trapping, this method gives better dynamic performance for various test conditions. MATLAB/Simulink 2021a software is utilized in the examination of performance in various load and speed scenarios, subsequently validated with hardware where cost effective Arduino controller replaced programmable interface controllers (PIC) microcontroller.
Impact of start-stop systems on motorcycle fuel savings in urban traffic
Authors: Murga-Garcia Kevin , Chacaltana-Silva Rodrigo , Paiva-Peredo Ernesto
Published: October 2024

The start/stop (S/S) system implemented in motorcycles aims primarily at fuel savings. This study was conducted to assess the effectiveness of this system in conditions of heavy traffic and traffic lights in Lima, using a virtual channel identifier (VCI) and a technical schedule. The detailed analysis covered critical aspects of the S/S system, the description of the route taken, and its segmentation to understand the number of stops and mileage. Speed limits, schedules, and measurement equipment were established, including the MICODUS-ORBD2 device and the VCI-Hero. The study included tests conducted with and without the MICODUS-ORBD2 device, recording times, distances, and fuel consumption. Data were collected with the S/S activated and deactivated, concluding the system achieves a 10.1% fuel saving. This finding provides valuable insights into understanding the system's effectiveness in actual traffic conditions and emphasizes the importance of maintaining key vehicle components to optimize S/S performance.
Optimal control of the dynamics of nonlinear oscillating systems using synergetic principles of self-organization
Authors: Xakimovich Siddikov Isomiddin , Maxamadjanovna Umurzakova Dilnoza
Published: October 2024

This paper analyses the evolution of nonlinear oscillation control methods and presents an innovative approach known as analytical design of aggregated oscillation controllers (ADACO). This method is based on the principles of synergetic control theory and focuses on the integration of self-organization and control processes to synthesize energy-efficient control laws for nonlinear oscillating systems. The authors elaborate on the theoretical foundations of ADACO, which extends the previous analytical design of aggregated controllers (ADAC) method by incorporating energy invariants and integrals of motion into the synthesis of control laws. This approach demonstrates significant advantages over traditional methods, offering a versatile framework for the design of energy-efficient control systems for a wide range of nonlinear oscillating systems in various fields such as aerospace, robotics, vibromechanical systems, and objects with chaotic dynamics. The aim of the paper is to establish a unified approach to the control of nonlinear oscillations, solving both the problems of generation of stable oscillations and suppression of unwanted perturbations. The application of synergetic control principles in the framework of ADACO opens prospects for further development of nonlinear control theory.
Optimal efficiency on nuclear reactor secondary cooling process using machine learning model
Authors: Hajar Ibnu , Kassim Murizah , Minhat Mohd Sabri , Azmi Intan Nabina
Published: October 2024

This review delves into the quest for optimal efficiency in the secondary cooling process of nuclear reactor water plant coolant systems. Modeling secondary cooling nuclear processes is hardly performed. Thus, Neural networks with traditional statistical methodologies are integrated to innovate a hybrid model to revolutionize nuclear reactor cooling systems' performance, reliability, and safety. A total of 63 indexed papers were reviewed in the nuclear field that analyzed critical research gaps, including the need for uncertainty modeling and resilience against external hazards. Insights into sensor technologies, data analytics, and real-time monitoring underscore the importance of continuous optimization and predictive maintenance were reviewed. A descriptive analysis for a month of sampling data was presented for the parameters of temperature for TT003 and TT004 and pressure for PT002 and PT003 of the secondary process. The confidence level of 95.0% is identified for the temperature and pressure parameters. The lowest standard error was recognized at 0.00032 and 0.01691, respectively. The review culminates with a forward-looking perspective, recognizing the pivotal role of hybrid machine learning models in shaping the future of secondary cooling processes for nuclear reactor water coolant plants to improve the efficiency and sustainability of nuclear reactor systems.
Optimal control strategies based on extended Kalman filter in mathematical models of COVID-19
Authors: Suhika Dewi , Saragih Roberd , Handayani Dewi , Apri Mochamad
Published: October 2024

The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is an extremely contagious variant that has garnered global attention due to its potential for rapid spread and its impact on the effectiveness of vaccines and non-pharmacological measures. In this paper, we investigate mathematical models involving vaccinated individuals and control functions to analyze how the spread of coronavirus disease 2019 (COVID-19) infection evolves over time. In the process of constructing a mathematical model for COVID-19, there are many parameters whose values are not yet known with certainty. Therefore, the extended Kalman filter method is used as a tool to estimate these parameters in an effort to better understand the dynamics of the spread and evolution of this disease. This method helps align the mathematical model with existing empirical data, allowing us to make more accurate predictions about the course of the COVID-19 pandemic and plan more precise actions to address the situation. Furthermore, an optimal control design is applied to reduce the number of infected individuals by implementing seven strategies involving a combination of health education, vaccination, and isolation controls. The simulation results we conducted indicate that the use of optimal control strategies can lead to a significant decrease in the number of individuals infected with COVID-19.
Performance analysis of hybrid bio-inspired algorithms for classifying brain tumors in imbalanced magnetic resonance imaging datasets
Authors: Chakre Rahul Ramesh , Vaidya Archana S. , Patil Dipak V.
Published: October 2024

Magnetic resonance imaging (MRI) is a substantial imaging procedure for diagnosing brain tumors. However, brain tumor classification continues challenging due to the unequal distribution of classes within datasets, complicating precise diagnosis and classification. This research focuses on the class imbalance in medical image datasets by proposing a hybrid bio-inspired algorithm for brain tumor classification. A rider optimization and particle rider mutual information-based dendritic-squirrel search algorithm combined with an artificial immune classifier is developed and tested on imbalanced datasets generated from BRATS and SimBRATS. Experimental outcomes are encouraging, For the imbalanced BRATS dataset, the rider optimization- based classifier achieved an accuracy of 94.84%, sensitivity of 92.96%, and specificity of 94.95%. The particle rider mutual information-based classifier outperformed others with 96.25% accuracy, 94.33% sensitivity, and 94.85% specificity. For the imbalanced SimBRATS dataset, the rider optimization-based classifier achieved 94.95% accuracy, 92.05% sensitivity, and 94.04% specificity. The particle rider mutual information-based classifier excelled with 96.35% accuracy, 94.42% sensitivity, and 95.44% specificity. These findings suggest that the proposed algorithm effectively addresses class imbalance in medical image datasets, offering a robust solution for brain tumor classification. The particle rider mutual information-based classifier shows promise for enhancing diagnostic accuracy in clinical settings, demonstrating the efficacy of hybridized bio-inspired algorithms in managing imbalanced datasets.
Performance analysis of cascade spline adaptive filtering based on normalized orthogonal gradient adaptive algorithm
Authors: Wiangtong Theerayod , Sitjongsataporn Suchada
Published: October 2024

In this paper, the cascade architecture of spline adaptive filtering (CSAF) for nonlinear systems is presented with the normalized version of orthogonal gradient adaptive (NOGA) algorithm. Spline adaptive filtering comprises a sandwich of the first linear adaptive filtering (LAF) and nonlinear adaptive look-up table. In this cascading architecture, SAF is connected to the second LAF. NOGA is considered as the fast convergence applied by stochastic gradient-based approach. Convergence properties of the proposed NOGA-CSAF algorithm in terms of instantaneous errors can be derived by using Taylor series expansion. Experimental results demonstrate the effectiveness of proposed NOGA-CSAF algorithm using the mean square error scheme. It clearly outperforms the traditional least mean square algorithm on CSAF model in the nonlinear identification system.
Deep learning for magnetic resonance imaging brain tumor detection: evaluating ResNet, EfficientNet, and VGG-19
Authors: Muftic Fatima , Kadunic Merjem , Musinbegovic Almina , Almisreb Ali Abd , Jaafar Hajar
Published: October 2024

This paper investigates the application of convolutional neural networks (CNNs) for the early detection of brain tumors to enhance diagnostic accuracy. Brain tumors present a significant global health challenge, and early detection is vital for successful treatments and patient outcomes. The study includes a comprehensive literature review of recent advancements in brain tumor detection techniques. The main focus is on the development and evaluation of CNN models, including EfficientNetB3, residual networks-50 (ResNet50) and visual geometry group-19 (VGG-19), for binary image classification using magnetic resonance imaging (MRI) scans. These models demonstrate promising results in terms of accuracy, precision, and recall metrics. However, challenges related to overfitting and limited dataset size are acknowledged. The study highlights the potential of artificial intelligence (AI) in improving brain tumor detection and emphasizes the need for further research and validation in real-world clinical settings. EfficientNetB3 reached 99.44% training accuracy but showed potential overfitting with validation accuracy dropping to 89.47%. ResNet50 steadily improved to 83.62% training accuracy and 89.47% validation accuracy. VGG19 struggled, with only 62% accuracy.
Performance enhancement of high-speed free space optical transmission link for the implementation of 5G and internet of things
Authors: Ai Duong Huu , Nguyen Van Loi , Luong Khanh Ty , Le Viet Truong
Published: October 2024

Internet of things (IoT) with continuous development allows multiple devices to be connected to each other through the external environment. With that, free space optical (FSO) transmission links provide high data transmission rates, so that enhanced quality of services, that is very suitable for deployment for fifth generation (5G) and IoT. FSO is known as license free, cost effective and line-of-sight green communication technology, which is employed in various circumstances, such as high data rate connections between buildings within campus or city. This study performs enhancement of FSO link for reconfigurable intelligent surfaces (RISs) aided over free space with gamma-gamma distribution channels. The paper analyzes the performance of FSO links affected by misalignment fading, RISs aided, and link distance with the subcarrier quadrature amplitude modulation scheme. Several numerical outcomes obtained for average electrical end-to-end signal-to-noise ratio, misalignment fading displacement standard deviation and link distance are shown to illustrate the average channel capacity of systems.
Enhancement of energy and spectral efficiency for mm-wave based 5G communication network
Authors: Gaikwad Vishakha , Naik Ashwini
Published: October 2024

5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the performance of QoS parameters such as bit error rate (BER), throughput, power consumption and mean square error (MSE). The maximum energy efficiency achieved is 34% and Spectral efficiency is 36%.
Hybrid digital and analog beamforming design using genetic algorithms
Authors: Bahri Sidi Mohammed , Bouacha Abdelhafid
Published: October 2024

Hybrid analog and digital beamforming is gaining attention for its practical application in large-scale antenna systems. It offers significant cost savings, reduced complexity, and lower power consumption compared to entirely digital beamforming, all while maintaining comparable performance. This article proposes a hybrid beamforming architecture aimed at addressing these challenges by using a reduced number of radio frequency (RF) chains while achieving performance comparable to entirely digital schemes. The study demonstrates that matching the number of RF chains to the total number of data streams enables hybrid beamforming to compete effectively with entirely digital beamformers. The adopted approach focuses on computing analog and digital precoders and combiners using the meta- heuristic method of genetic algorithms, in a point-to-point multiple input multiple output (MIMO) system scenario. The objective is to simplify the system and reduce costs by optimizing the number of antennas, RF chains, and data streams, all while maintaining comparable performance to entirely digital beamforming. The study's results show that increasing the number of antennas significantly impacts the quality and capacity of the hybrid massive MIMO beamforming system. Conversely, reducing the number of RF chains has a negligible effect on quality and capacity, but simplifies the design and minimizes costs.
Exploring optimal resource allocation methods for improved efficiency in flying ad-hoc network environments: a survey
Authors: Ahmed Zeinab E. , Hashim Aisha A. , Saeed Rashid A. , Saeed Mamoon Mohammed Ali
Published: October 2024

This survey explores optimal resource allocation methods to enhance the efficiency of flying ad-hoc networks (FANETs). Unmanned aerial vehicles (UAVs), commonly known as drones, are widely deployed in military and civilian applications, necessitating effective coordination and communication to overcome challenges. FANETs facilitate wireless communication among UAVs, improving coordination and information exchange in environments lacking traditional networks. The dynamic mobility of UAVs introduces unique considerations for network design and connectivity, distinguishing FANETs from conventional ad-hoc networks. This survey reviews various optimization techniques, including genetic algorithms, ant colony optimization, and artificial neural networks, which optimize resource allocation by considering mission requirements, network topology, and energy constraints. The paper also discusses the critical role of intelligent algorithms in enhancing network energy management, quality of service (QoS), maximizing resource allocation, and optimizing overall performance. The systematic literature review categorizes resource allocation strategies based on performance optimization criteria and summarizes their strengths, weaknesses, and applications. This survey highlights the potential of FANETs to revolutionize various industries and unlock new opportunities for UAV-based applications.
Design and enhancement of microstrip patch antenna with frequency selective surface backing for vehicle-to-vehicle communication
Authors: Troudi Ikram , Baccouch Chokri , Belgacem Chibani Rhaimi
Published: October 2024

This research investigates the application of frequency selective surface (FSS) reflectors to enhance vehicle-to-vehicle (V2V) communication performance. A compact antenna measuring 32×24×1.6 mm, derived from an FSS 10×10×1.6 mm unit cell, was studied. Integration of FSS technology with the antenna resulted in significant performance improvements. The gain increased from 3.9 to 5.4 dB at 5.9 GHz, representing a 1.5-fold enhancement. Additionally, the bandwidth extended to 230.94 MHz. These advancements demonstrate the efficacy of FSS technology for antenna gain enhancement in V2V systems. The compact design, coupled with improved performance, makes this approach particularly suitable for vehicular applications where space is limited. This study not only showcases the potential of FSS technology in antenna design but also suggests its broader applicability in enhancing V2V communication systems, potentially contributing to the development of more efficient and safer transportation networks.
Bone fracture classification using convolutional neural network architecture for high-accuracy image classification
Authors: Solikhun Solikhun , Windarto Agus Perdana , Alkhairi Putrama
Published: October 2024

This research introduces an innovative method for fracture classification using convolutional neural networks (CNN) for high-accuracy image classification. The study addresses the need to improve the subjectivity and limited accuracy of traditional methods. By harnessing the capability of CNNs to autonomously extract hierarchical features from medical images, this research surpasses the limitations of manual interpretation and existing automated systems. The goal is to create a robust CNN-based methodology for precise and reliable fracture classification, potentially revolutionizing current diagnostic practices. The dataset for this research is sourced from Kaggle's public medical image repository, ensuring a diverse range of fracture images. This study highlights CNNs' potential to significantly enhance diagnostic precision, leading to more effective treatments and improved patient care in orthopedics. The novelty lies in the unique application of CNN architecture for fracture classification, an area not extensively explored before. Testing results show a significant improvement in classification accuracy, with the proposed model achieving an accuracy rate of 0.9922 compared to ResNet50's 0.9844. The research suggests that adopting CNN-based systems in medical practice can enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes.
The effect of distance learning on student learning achievement: a meta-analysis
Authors: Nusantara Bayuk , Hadi Samsul , Retnawati Heri , Sumaryanto Sumaryanto , Prasojo Lantip Diat , Sotlikova Rimajon , Arlinwibowo Janu
Published: October 2024

Distance learning has been an option during the last few years due to the coronavirus disease 2019 (COVID-19) pandemic. The aim of this research is to identify the effectiveness of distance learning compared to conventional learning in terms of student learning achievement. This research is meta- analysis of random group contrast design (experiment-control) models. The data selection process refers to the inclusion criteria of year, theme, data type and data completeness. Based on these criteria, 10 articles were selected. The analysis process begins with testing the homogeneity assumption using three methods, namely 𝜏2, 𝐼2, and Q which shows heterogeneous data so that random model selection is appropriate, testing freedom of publication bias with Egger's test and funnel plot which shows that the data collected is free from publication bias, identifying the effect size and standard error, as well as conducting moderator variable analysis which considers domain, continent, subject, education level and year variables. The results of this study show that although distance learning has a positive influence on student learning achievement, the difference is not significant when compared with conventional learning. In addition, these results can be moderated by achievement domain variables, type of subject, level of education, and year.
Using modified Chebyshev functions for approximation in 5G technologies
Authors: Yerzhan Assel , Nakisbekova Balausa , Manbetova Zhanat , Boykachev Pavel , Imankul Manat , Dzhanuzakova Raushan , Shedreyeva Indira , Karnakova Gaini
Published: October 2024

This research addresses the critical challenge of broadband matching in radio engineering, focusing on enhancing phase-frequency response (PFC) linearity across wide frequency bands. A novel approach, utilizing modified Chebyshev functions, demonstrates significant potential in reducing phase distortions within 5G technology applications. Unlike traditional Chebyshev functions, this method incorporates strategically placed transmission zeros— complex conjugate pairs on the s-variable complex plane—without increasing the filter circuit's order. This innovation results in a low-order filter circuit characterized by uniform phase response and group delay characteristics (GDT), offering an effective solution for matching circuit design with less phase-frequency distortion and improved group delay uniformity across diverse load conditions. The modified Chebyshev approximation outperforms its classical counterpart in both phase linearity and selectivity within the 1 to 1.2 cutoff frequency range. This enhancement is crucial for the development of low-frequency filters, with broader implications for creating high-frequency, band-pass, and band-stop filters via known frequency transformations. Empirical results validate the proposed method's reliability and effectiveness, marking a significant advancement in the field of radio engineering by addressing broadband matching challenges with increased efficiency and simplified design implementations.
Parallel numerical simulation of the 2D acoustic wave equation
Authors: Altybay Arshyn , Darkenbayev Dauren , Mekebayev Nurbapa
Published: October 2024

Mathematical simulation has significantly broadened with the advancement of parallel computing, particularly in its capacity to comprehend physical phenomena across extensive temporal and spatial dimensions. High-performance parallel computing finds extensive application across diverse domains of technology and science, including the realm of acoustics. This research investigates the numerical modeling and parallel processing of the two-dimensional acoustic wave equation in both uniform and non-uniform media. Our approach employs implicit difference schemes, with the cyclic reduction algorithm used to obtain an approximate solution. We then adapt the sequential algorithm for parallel execution on a graphics processing unit (GPU). Ultimately, our findings demonstrate the effectiveness of the parallel approach in yielding favorable results.
Processing of real-time surface electromyography signals during knee movements of rehabilitation participants
Authors: Sengchuai Kiattisak , Sittiruk Thantip , Jindapetch Nattha , Phukpattaranont Pornchai , Booranawong Apidet
Published: October 2024

In this work, we present a knee rehabilitation system focusing on the processing of surface electromyography (sEMG) signals measured from the vastus lateralis (VL) and vastus medialis (VM) muscles of rehabilitation participants. A two-channel electromyography (EMG) device and the NI-myRIO embedded device are used to collect real-time sEMG signals in accordance with pre-designed rehabilitation programs. The novelty and contribution of this work is that we develop an sEMG processing function where real-time sEMG data are automatically processed and sEMG results of both VL and VM in terms of root mean square value (RMS), different RMS levels of VL and VM, and maximum RMS for each round of knee movements are provided. The results here indicate how well the rehabilitation users can move their knees during rehabilitation, referring to knee and muscle performances. Experimental results from healthy participants show that we can automatically and efficiently collect and monitor rehabilitation results, allowing rehabilitation participants to know how their knees performed during testing and medical experts to evaluate and design treatment.
Real-time management and processing of RFID events based on a new RFID middleware architecture
Authors: Haibi Achraf , Oufaska Kenza , El Yassini Khalid , Bouazza Hajar , Boulmalf Mohammed , Bouya Mohsine
Published: October 2024

Radio frequency identification (RFID) is a contemporary technology that enables the identification of objects and facilitates the transmission of additional information, making it possible to achieve real-time object tracking in a mobile object network and to report information on the object's current state at each step. RFID devices continuously generate large amounts of data, and collecting, filtering, and consolidating these data are therefore crucial tasks, which characterize RFID data management by a set of challenges. However, one of the greatest challenges in this field is managing large volumes of data in complex applications, where real-time operation is vital, given that the volume and speed of RFID data often exceed the capacity of the existing technological infrastructure. The aim of this study is to propose an RFID middleware that manages both the RFID hardware network and the large amounts of data that are captured, in order to process and transmit the collected data under the right conditions for ultimate use by an information system. This new RFID middleware architecture, named BTMiddleware combines complex event processing (CEP) with a MongoDB database to offer large-volume data streaming, processing, and storage in real time, as well as better interoperability thanks to the use of the JavaScript object notation (JSON) format for data presentation.
Preliminary diagnosis of respiratory diseases: an innovative approach using a web expert system
Authors: Andrade-Arenas Laberiano , Molina-Velarde Pedro , Yactayo-Arias Cesar
Published: October 2024

This study addressed the challenge of accurate and timely diagnosis of respiratory diseases such as influenza, asthma, and pneumonia by developing and evaluating a web-based expert system. The objective was to develop and assess both the usability and diagnostic efficiency of a web- based expert system adaptable to mobile devices. A combined methodological approach was used, using the rapid application development (RAD) model to build the system and the user usability system (SUS) to evaluate the usability with the participation of 15 users and 21 simulated cases with a confusion matrix to determine the precision, accuracy, sensitivity, and specificity of the system in diagnosing respiratory diseases. The results showed that the expert system has a considerable capacity to identify and differentiate these diseases, with a precision of 86%, an accuracy of 76%, a sensitivity of 80%, and a specificity of 67%. Furthermore, the usability evaluation using the SUS method yielded an average of 82, indicating a positive perception and good usability by the users. In conclusion, although the results suggest a promising potential to improve the diagnostic process in clinical and community settings, the need for future studies to validate its performance in real clinical settings is recognized.