bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of fractional order systems using newton iteration-based approximation technique
Authors: Shrivastava Nitisha , Baliyan Arjun
Published: October 2022

Fractional differential equations play a major role in expressing mathematically the real-world problems as they help attain good fit to the experimental data. It is also known that fractional order controllers are more flexible than integer order controllers. But when it comes to the numerical approximation of fractional order functions inaccuracies arise if the conversion technique is not chosen properly. So, when a fractional order plant model is approximated to an integer order system, it is required that the approximated model be accurate, as the overall system performance is based on the estimated integer order model. Nitisha-Pragya-Carlson (NPC) is a recent approximation technique proposed in 2018 to derive the rational approximation of fractional order differ-integrators. In this paper, three fractional order plant models having fractional powers 3.1, 1.25 and 1.3 is analyzed in frequency domain in terms of magnitude and phase response. The performance of approximated third and second order NPC based integer model is studied and compared with the integer models developed using other existing technique. The approximation error is calculated by comparing the frequency response of the developed models with the ideal response. It has been found that in all the three examples NPC based models are very much close to the ideal values. Hence proving the efficacy of NPC technique in approximation of fractional order systems.
An approach for radiation dose reduction in computerized tomography
Authors: Narayan Shama Bekal , Mahabaleshwara Savitha Halkare
Published: October 2022

Minimization of radiation dose plays an important role in human wellbeing. Excess of radiation dose leads to cancer. Radiation greatly affects young children less than 10 years of age as their life span is longer. Radiation can be reduced by hardware and/or by software techniques. Hardware methods deal with variation of parameters such as tube voltage, tube current, exposure time, focal distance and filter type. Software techniques include image processing methods. The originally acquired X-ray images may be contaminated with noise due to the fact of instability in the case of sensors, electrical power or X-ray source, that is responsible for the degradation of the image attributes. An enhanced image denoising algorithm has been proposed which decreases Gaussian noise combined with salt and pepper noise that retains most information particulars.
Estimation of bit error rate in 2×2 and 4×4 multi-input multi-output-orthogonal frequency division multiplexing systems
Authors: Ravi Kumar Mokkapati , Lakshmi Prasanna Jagupilla , Alagarsamy Aravindhan , Pravallika Tadiboyina Mouni , Jyothsna Kandimalla , Sindhuja Pushadapu Hima , Santhosh Chella
Published: October 2022

Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-to-noise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the ith transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track time-varying channels rapidly.
A constrained model predictive control for the building thermal management with optimal setting design
Authors: Boutchich Noureddine , Moufid Ayoub , Bennis Najib
Published: October 2022

Today, the building sector is the most important consumer of energy. The main challenge in building management is to obtain the desired performance taking into account many aspects such as comfort requirements, variation of building physical characteristics, system constraints, and energy management. For this purpose, a predictive control approach applied to the building thermal has been designed to achieve desired performances combined with an energy optimization approach based on intrinsic system parameters. The developed approach is applied with an online identification system for effective predictive control to take into account the reel building characteristics and to choose the optimal tuning parameters. The simulation results show good performances in terms of accuracy and robustness face to internal and external disturbances with respect to system constraints.
A deterministic method of distributed generation hosting capacity calculation: case study of underground distribution grid in Morocco
Authors: Naciri Soukaina , Moufid Ismail , Moussaoui Hassan El , Lamhamdi Tijani , Markhi Hassane El
Published: October 2022

Global warming has become a significant concern over the past decades. As a result, governments have shifted their policies toward renewable energy sources and environmentally friendly industries. This approach requires a renewal of the electrical networks to accommodate this new intermittent generation (from solar and wind sources) while remaining stable and reliable. In this vision, the notion of hosting capacity has been introduced to define the amount of new distributed generation that an electrical network can host without affecting its stability and reliability. This study proposes a deterministic method based on the π model of cables to estimate the underground feeder's hosting capacity. This method considers reverse power flow, overvoltage, reconfiguration, overloading, and the physical characteristics of lines. It is applied to the Moroccan medium voltage underground radial feeder. Through DIgSILENT power factory software, the power flow analysis is carried out to validate its effectiveness in overcoming overvoltage and cable overload problems. The results validate the relevance of our method, its reliability, its fluidity of application, and its ability to maintain performance indices within the acceptable range.
Optimal connection of wind turbines to distribution grid to minimize power loss
Authors: Chung Phan Dinh , An Luu Ngoc
Published: October 2022

This research aims to connect wind turbines to a distribution grid to minimize the power loss and to satisfy the grid’s normal operating condition. The proposed algorithm will determine optimal positions, optimal operation mode and wind turbine type. We must choose the best operation mode from available modes including the constant power factor mode and the constant voltage mode. According to the optimal operation mode, we decide the optimal setting data of wind turbine. This algorithm is coded in MATLAB software and implemented to IEEE 33-buses distribution grid. Noted that in this research, we tested two cases including the original IEEE 33-buses grid and its modification where the power system connected to this grid at multi-position. Results indicated that the proposed algorithm could determine the number of wind turbines, position, optimal operation mode, wind turbine type and the priority order of wind turbine installation to minimize power loss. Moreover, results were also compared to that of other algorithms.
A 27-MHz frequency shift keying wireless system resilient to in-band interference for wireless sensing applications
Authors: Boonrungruedee Thannatorn , Khumsat Phanumas
Published: October 2022

A 27-MHz wireless system with binary frequency shift keying (BFSK) modulation at 400-kHz is reported. The receiver has been designed to handle in-band interference corrupting the BFSK signal with the use of complex filters and amplitude comparison method. The BFSK modulation is carried out with a voltage-controlled oscillator before up-converting with a 27-MHz local oscillator. The bipolar junction transistors (BJT-based) power amplifier with 30% efficiency pumps 220 mW into a spiral antenna. The inductive-degenerated low-noise amplifier with a voltage of more than 30 dB amplifies an incoming signal before feeding into a mixer for complex direct down conversion. With deliberate Gaussian interference injection, the minimum ratios between the signal with interference and the interference only at the distance of 2.5, 10 and 15 m are 3.3, 8.5 and 11.5 dB, respectively at a maximum data rate of 20 kbps. Without any interference included, the system can achieve a data rate of 40 kbps at the maximum transmission distance of 15 m. Conceptually agreed with the presented bit-error-rate (BER) analysis, the BER measurements with Gaussian and single-tone/two-tone in-band interferences also confirm superiority offered by the amplitude comparison method where the signal-to-noise ratio is at 1 dB for BER=10-3 at 10 kbps (10 dB better than the phase detection counterpart).
Characterization of cadmium sulfide light dependent resistors sensors for optical solar trackers
Authors: Boukdir Youssef , Omari Hamid El
Published: October 2022

The aim of this paper is to study the effect of dissimilarity of the intrinsic characteristics of the light dependent resistor (LDRs) on optical sun tracking systems, designed for solar power concentration applications such as parabolic trough collectors, Fresnel mirrors concentrators, and concentrated photovoltaic, a comparative study was done between a sun tracker based on LDRs chosen randomly with and without an initial calibration of the offsets, and a sun tracker based on LDRs selected meticulously thanks to a black box test bench, developed especially for this purpose. By choosing two light dependent resistors randomly, the dissimilarity between them can reach 23.2%, which cause a bad sun tracking even with initial offset calibration, in the other hand, and by the use of selected LDRs using the test bench, the dissimilarity drops to 0.06%, which meets requirements of solar power concentration systems.
Development of a microcontroller based automobile speed limiting device and alarm control system
Authors: Adebisi Oluwaseun Ibrahim , Adejumobi Isaiah Adediji , Durodola Folasade Olayinka , Jim Haastrup Ayobami
Published: October 2022

Road accident due to overspeeding is a common occurrence in a developing nation such as Nigeria. Therefore, the need for a device capable of notifying a vehicle driver when the allowed speed limit of an area is exceeded arises. In this work, a microcontroller based automobile speed limiting device and alarm control system was designed and developed. The core components employed for the system design include Arduino Nano microcontroller, 1602 liquid crystal displays (LCD) module, light-emitting diodes (LEDs), buzzer, 18650 battery, I2C, infrared detectors and push buttons. Data gathering and circuit designs were implemented with microcontroller as focal point using suitable design models. Performance test was carried out on the developed system and the device’s reading error was determined. The developed automobile speed limiting device and alarm control system was functional and performed satisfactorily during testing. The reading error of the device was evaluated as 5.83%. The developed speed limiting device, apart from being suitable and efficient for vehicle speed measurement, could also be deployed for general applications requiring speed measurement.
Feasibility analysis of the conversion of brushed to brushless direct current motor
Authors: Irasari Pudji , Widiyanto Puji , Muqorobin Anwar , Hikmawan Muhammad Fathul
Published: October 2022

This paper analyzes the feasibility of converting brushed direct current (BDC) to brushless direct current (BLDC) motor by substituting the wound rotor with a permanent magnet. The aim is to overcome the low quality of rewinding of wound rotors. The conversion was performed by redesigning the motor, referring to the old stator dimensions and motor specifications. Some suitable speed control systems were also examined. Technical analysis was executed by simulating motor characteristics, covering torque-speed curve, cogging, and ripple torques. Afterward, an economic analysis was done to determine the cost of making one rotor unit and the speed control and then compare it with the price of a new BDC motor with equivalent power. The results show that the motor produces 3.6 kW, 18.86 V, 176.71 A, and 28.53 Nm at 2,460 rpm with 2 phases configuration. The maximum cogging torque is 2.9 Nm. The cost of making one rotor unit plus speed control is higher than that of a new BDC motor. These results show that it is technically feasible to convert BDC into a BLDC motor but not economically or vice versa.
Optimal allocation of solar and wind distributed generation using particle swarm optimization technique
Authors: Rekha Rekha , Channappa Byalihal Shankaralingappa
Published: October 2022

Power demand in the current days is increasing more and more where the conventional power generation systems are failing to meet these power demands due to less availability of non-renewable resources. Hence, many of the researchers are working on the distributed generation (DG) by using renewable resources like wind and solar. The penetration towards wind, solar DG faced challenging situations during power generation due to uncertainty in the wind speed and solar radiation. Recent studies have predicted that the combination of both solar and wind can lead to better performance. However, the sizing and placement of DG systems is necessary to achieve efficiency otherwise the systems may lead to adverse effects in distribution networks. This paper introduced the solar DG, wind DG and hybrid (solar and wind) DG system. The particle swarm optimization technique is used to size and place the DG because of its parallel search capability. Also, the combination of wind-solar DG gives better DG sizing in the respective DG location. The voltage profile of these DG systems has shown better results for the efficient power system. In comparison to conventional DG systems, the suggested hybrid DG system is capable of minimizing power loss and maintaining voltage profile.
Methodology for determining the parameters of high-temperature superconducting power transformers with current limiting function
Authors: Manusov Vadim Zinovievich , Ivanov Dmitriy Mihailovich , Semenov Arseniy Valerievich , Ivanov Gennady Viktorovich
Published: October 2022

This paper substantiates a new adaptive method for determining parameters of high-temperature superconducting power transformers with current limiting function. The main focus is the design of current-limiting superconducting windings in the light of new restrictions on current density, magnetic induction, critical current and critical temperature. The presented method considers the nature of alternating current (AC) losses in a superconductor under nominal operating conditions, features of the dielectric medium (liquid nitrogen), as well as the reduced values of the short-circuit voltage (0.5 to 1.5%). The main design features of high-temperature superconducting (HTS) transformers are specified, and a prototype of a three-phase HTS transformer of 63 kVA with a short-circuit current limiting function is developed. It is shown that HTS units have some advantages over conventional transformers: a 90 to 95% active losses reduction, short-circuit current limitation function, explosion and fire safety, a 60% reduction in weight and size, and increased efficiency (up to 99.8%). Experimental studies confirm that the short-circuit current limitation function is safe and efficient. It is demonstrated that during the short-circuit current limitation, significant heat flows occur on the windings, which should not exceed the critical value above which the superconductor could not return to the superconducting state by itself.
Paper biological risk detection through deep learning and fuzzy system
Authors: Sanabria Juan Sebastian , Jimenez-Moreno Robinson , Baquero Javier Eduardo Martinez
Published: October 2022

Given the recent events worldwide due to viral diseases that affect human health, automatic monitoring systems are one of the strong points of research that has gained strength, where the detection of biohazardous waste of a sanitary nature is highlighted related to viral diseases stands out. It is essential in this field to generate developments aimed at saving lives, where robotic systems can operate as assistants in various fields. In this work an artificial intelligence algorithm based on two stages is presented, one is the recognition of paper debris using a ResNet-50, chosen for its object localization capacity, and the other is a fuzzy inference system for the generation of alarm states due to biological risk by such debris, where fuzzy logic helps to establish a model for a non-predictive system as the one exposed. A biohazard detection algorithm for paper waste is described, oriented to operate on an assistive robot in a residential environment. The training parameters of the network, which achieve 100% accuracy with confidence levels between 82% for very small waste and 100% in direct view, are presented. Timing cycles are established for validation of the exposure time of the waste, where through the fuzzy system, risk alarms are generated, which allows establishing a system with an average reliability of 98%.
Linear matrix inequalities tool to design predictive model control for greenhouse climate
Authors: Moufid Ayoub , Boutchich Noureddine , Bennis Najib
Published: October 2022

Modeling and regulating the internal climate of a greenhouse have been a challenge as it is a complex and time variant system. The main goal is to regulate the internal climate considering the difference between nighttime and diurnal phases of the day. To depict the comportment of the greenhouse, a multi model approach based on two multivariable black box models have been proposed representing the diurnal and nighttime phases of the day. The least-squares method is utilized to identify the parameters of these two models based on an experimental collected data. We have shown that these two models are more representative than one model to describe the dynamic behavior of the greenhouse. The second contribution is to control the internal temperature and hygrometry respecting constraints on actuators and controlled variables. For this purpose, a constrained model predictive control scheme based on the multi-modeling approach have been developed. The optimization problem of the control law is transformed to a convex optimization problem includes linear matrix inequalities (LMI). The simulation results show that the adopted control method of indoor climate allows rapid and precise tracking of set points and rejects effectively the external disturbances affecting the greenhouse.
Internet of things based automated monitoring for indoor aeroponic system
Authors: Roffi Teuku Muhammad , Jamhari Charisma Aulia
Published: October 2022

On the ever-rising urgency of global food security, efforts are required to develop a robust farming technique. This includes the capability of farming in non-agricultural land or indoor spaces. Farming in the air medium, i.e., aeroponic, has persistently stepped in as a viable solution. Aeroponic farming allows efficient water usage while preventing soil related diseases and pests. With the assistance of light emitting diodes (LEDs) and precise electronic monitoring and control, aeroponic may become the suitable farming technique of the future. This work presents an aeroponic system capable of automated monitoring and control of farming parameters. The system achieved both robustness in indoor farming and remote access by employing LED as an artificial lighting system and the internet-of-things (IoT) connectivity, respectively. The test result demonstrated that the system successfully maintained the root chamber temperature below 30 °C with a typical average temperature of 28.8 °C. The system managed a humidity level which prevented plants from drying out. It was also evident that the LED assistance significantly improved the growth quality of Ipomea reptans. The system, data, and analysis presented in this work is expected to facilitate further development of a robust food production system in overcoming the global food crisis.
Kinematics modeling of six degrees of freedom humanoid robot arm using improved damped least squares for visual grasping
Authors: Setyawan Muhammad Ramadhan Hadi , Dewanto Raden Sanggar , Marta Bayu Sandi , Binugroho Eko Henfri , Pramadihanto Dadet
Published: October 2022

The robotic arm has functioned as an arm in the humanoid robot and is generally used to perform grasping tasks. Accordingly, kinematics modeling both forward and inverse kinematics is required to calculate the end-effector position in the cartesian space before performing grasping activities. This research presents the kinematics modeling of six degrees of freedom (6-DOF) robotic arm of the T-FLoW humanoid robot for the grasping mechanism of visual grasping systems on the robot operating system (ROS) platform and CoppeliaSim. Kinematic singularity is a common problem in the inverse kinematics model of robots, but. However, other problems are mechanical limitations and computational time. The work uses the homogeneous transformation matrix (HTM) based on the Euler system of the robot for the forward kinematics and demonstrates the capability of an improved damped least squares (I-DLS) method for the inverse kinematics. The I-DLS method was obtained by improving the original DLS method with the joint limits and clamping techniques. The I-DLS performs better than the original DLS during the experiments yet increases the calculation iteration by 10.95%, with a maximum error position between the end-effector and target positions in path planning of 0.1 cm.
Calculating the area of white spots on the lungs of patients with COVID-19 using the Sauvola thresholding method
Authors: Supriyanti Retno , Kurniawan Muhammad Rifqi , Ramadhani Yogi , Widodo Haris Budi
Published: October 2022

COVID-19 is a pandemic that has occurred in the world since 2019. Researchers have carried out various ways in dealing with this disease, starting from the screening stage to the stage of treatment and therapy for COVID-19 patients. As the gateway to the COVID-19 problem, screening has an essential role in a diagnosis that leads to appropriate treatment. In this paper, we will focus on the screening stage using digital image processing techniques, namely in calculating the area of white spots in the lungs of COVID-19 patients. The white patches are an early indication of how badly COVID-19 is attacking the patient. We use X-Ray Thorax image objects as research data in this paper. Although the current experimental results show that this method has a successful performance of 71.11%, it is pretty promising for further development due to its simplicity.
An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals
Authors: Kemper Guillermo , Oshita Angel , Parra Ricardo , Herrera Carlos
Published: October 2022

This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.
Self scale estimation of the tracking window merged with adaptive particle filter tracker
Authors: Youssef Azdoud , Aouatif Amine , Bouchra Nassih , Mohammed Ngadi
Published: October 2022

Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.
A comparative analysis of chronic obstructive pulmonary disease using machine learning, and deep learning
Authors: Ramalingam Ramadoss , Chinnaiyan Vimala
Published: October 2022

Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms. 
Investigate the maximum power point of photovoltaic system at different environmental conditions
Authors: Khudhur Khaleel Ali , Akram Samad Nabeel Mohammed , Thiab Hasan Ghanim
Published: October 2022

The main objective of this work is to implement a circuit-based simulation model of a photovoltaic (PV) cell in order to investigate the electrical behavior of the practical cell with respect to some changes in weather parameters. The simulation model consists of three subsystems: photovoltaic cells, DC/DC converter and MPPT controller based logic fuzzy control. The maximum power control function is achieved with the appropriate power control of the power inverter. Fuzzy logic controller has been used to perform MPPT functions to get maximum power from the PV panel. The proposed circuit was implemented in MATLAB/Simulink. The obtained results show that the output sequence is non-linear and almost constant current to the open circuit voltage and the power has maximum motion to voltage for certain environmental conditions.
Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition
Authors: Channegowda Ravikumar Hodikehosahally , Karthik Palani , Srinivasaiah Raghavendra , Shivaraj Mahadev
Published: October 2022

In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015.
Combination of texture feature extraction and forward selection for one-class support vector machine improvement in self-portrait classification
Authors: Rahma Reina Alya , Nugroho Radityo Adi , Kartini Dwi , Faisal Mohammad Reza , Abadi Friska
Published: October 2022

This study aims to validate self-portraits using one-class support vector machine (OCSVM). To validate accurately, we build a model by combining texture feature extraction methods, Haralick and local binary pattern (LBP). We also reduce irrelevant features using forward selection (FS). OCSVM was selected because it can solve the problem caused by the inadequate variation of the negative class population. In OCSVM, we only need to feed the algorithm using the true class data, and the data with pattern that does not match will be classified as false. However, combining the two feature extractions produces many features, leading to the curse of dimensionality. The FS method is used to overcome this problem by selecting the best features. From the experiments carried out, the Haralick+LBP+FS+OCSVM model outperformed other models with an accuracy of 95.25% on validation data and 91.75% on test data.
A genetic algorithm for shortest path with real constraints in computer networks
Authors: Alghamdi Fahad. A. , Younes Hamed Ahmed , Alghamdi Abdullah M. , Salah Abderrazak Ben , Farag Tamer Hashem , Hassan Walaa
Published: October 2022

The shortest path problem has many different versions. In this manuscript, we proposed a muti-constrained optimization method to find the shortest path in a computer network. In general, a genetic algorithm is one of the common heuristic algorithms. In this paper, we employed the genetic algorithm to find the solution of the shortest path multi-constrained problem. The proposed algorithm finds the best route for network packets with minimum total cost, delay, and hop count constrained with limited bandwidth. The new algorithm was implemented on four different capacity networks with random network parameters, the results showed that the shortest path under constraints can be found in a reasonable time. The experimental results showed that the algorithm always found the shortest path with minimal constraints.
A reconfigurable dual port antenna system for underlay/interweave cognitive radio
Authors: Abdullah Laith Wajeeh , Sallomi Adheed H. , Jassim Ali Khalid
Published: October 2022

An antenna system that is reconfigurable in frequency is presented in this paper as a novel dual port design that serves both undelay and interweave cognitive radio. This 25×40×0.8 mm3 system is composed of two wide slot antennas: the first is designed as an ultra-wideband (UWB) antenna with controllable band rejection capabilities, while the second antenna is reconfigurable for communication purposes. Three slots are etched into the patch of the UWB antenna to obtain band notching in wireless local area network/Xband/International Telecommunication Union bands (WLAN/Xband/ITU) bands which can be controlled by a positive-intrinsic-negative (PIN) diode across each slot. The configuration states of these three diodes are all useable that produces seven band rejection modes plus the UWB operation mode. The second antenna is configured by five PIN diodes to operate either in Cband, WLAN or Xband regions which results in three interweave modes when setting the first antenna for UWB sensing. The design is simulated by computer simulation technology (CST) v.10. S21 results shows good isolation while input reflection coefficient and realized gain results prove system’s scanning, filtering and communication capabilities. This system is new that it gathers the undelay/interweave operation in a single design and when considering its large number of operation modes it looks adequate for many cognitive radio applications.
Few-mode optical fiber surface plasmon resonance sensor with controllable range of measured refractive index
Authors: Abu Shehab Wael , Salah Ahmad , Al-Sawalmeh Wael , Alashaary Haitham
Published: October 2022

A few-mode optical fiber surface plasmon resonance sensor with graphene layer is investigated, firstly, with the aim of studying the behavior of the guided modes and, secondly, with the aim of determining the range of the measured refractive index for some selected few-mode fibers. The results show that as the number of modes propagated in the fiber increases, the maximum sensitivity of a particular mode decreases while the range of the measured refractive index of that mode increases. Also, it is shown that the range can be easily tuned with sensitivity consideration by only adjusting the operating wavelength without any modification of the sensor, which is desirable from practical point of view. In addition, it is shown that the core diameter of the fiber should be chosen according to sensitivity and range needing, where a compromise between them must be found. The study presented in this paper can significantly help in developing new sensing techniques, such as multi-parameter sensing, by monitoring the various responses of the modes. Also, it can be used to customize the sensor for specific sensing applications in various fields, especially to measure refractive indices in subranges of 1.38 to 1.46.
Influence of ambient temperature in the city of Portoviejo, Ecuador on the energy performance of photovoltaic modules
Authors: Epeni-Tombo Genial Belvinel , Rodríguez-Gámez María , Loor-Castillo Guillermo A. , Vázquez Pérez Antonio , Nuñez Alvarez José Ricardo
Published: October 2022

The research analyzes the influence of the average temperature on the performance of specific photovoltaic solar modules under the environmental conditions of the city of Portoviejo, province of Manabí, Ecuador. The research is carried out using the qualitative methodology. Its main objective is to determine the influence of temperature on the energy performance of photovoltaic modules under the region's environmental conditions. Two electrical diagrams with different configurations were designed, which were analyzed and simulated using the MATLAB Simulink software. The results obtained show the direct relationship between the electrical parameters of voltage, current intensity, and power with the temperature value on the surface of the modules. It is concluded that the modules generate a higher value of voltage, electric current intensity, and electric power at a lower temperature, regardless of the level of solar irradiation they receive.
Fitness function X-means for prolonging wireless sensor networks lifetime
Authors: Radwan Abdelrahman , Abdellatif Nasser , Radwan Eyad , Akhozahieh Maryam
Published: October 2022

X-means and k-means are clustering algorithms proposed as a solution for prolonging wireless sensor networks (WSN) lifetime. In general, X-means overcomes k-means limitations such as predetermined number of clusters. The main concept of X-means is to create a network with basic clusters called parents and then generate (j) number of children clusters by parents splitting. X-means did not provide any criteria for splitting parent’s clusters, nor does it provide a method to determine the acceptable number of children. This article proposes fitness function X-means (FFX-means) as an enhancement of X-means; FFX-means has a new method that determines if the parent clusters are worth splitting or not based on predefined network criteria, and later on it determines the number of children. Furthermore, FFX-means proposes a new cluster-heads selection method, where the cluster-head is selected based on the remaining energy of the node and the intra-cluster distance. The simulation results show that FFX-means extend network lifetime by 11.5% over X-means and 75.34% over k-means. Furthermore, the results show that FFX-means balance the node’s energy consumption, and nearly all nodes depleted their energy within an acceptable range of simulation rounds. 
The impact of M-ary rates on various quadrature amplitude modulation detection
Authors: Algriree Waleed , Sulaiman Nasri , Isa Maryam , Sahbudin Ratna Kalos Zakia , Hassan Siti Lailatul Mohd , Salman Emad Hmood
Published: October 2022

The 5G system-based cognitive radio network is promised to meet the requirements of huge data applications with spectrum. However, the M-ary effect on the detection has not been thoroughly investigated. In this paper, an M-ary of quadrature amplitude modulation detection system is studied. Many rates are used in this study 4, 16, 64, and 256 constellation points. The detection system is applied to cooperative spectrum sensing to enhance the performance of detection for various rates of M-ary with low signal-to-noise ratio (SNR). Further, three kinds of signals based 5G system are sensed: filtered-orthogonal frequency division multiplexing (F-OFDM), filter bank multi-carrier (FBMC), and universal filtered multi-carrier (UFMC). The best detection performance is obtained when the M-ary=4 and number of SUs=50 user, whereas the worst detection performance is obtained when the M-ary=256 and number of SUs=10 user, as revealed in the simulation results. In addition, the detection performance for the F-OFDM signal is better than that of UFMC and FBMC signals for SNR <0 dB.
Ameliorate the performance using soft computing approaches in wireless networks
Authors: Dadhirao Chandrika , Sangam Ravi Sankar
Published: October 2022

Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.
An intelligent humidity control system for mushroom growing house by using beam-switching antennas with artificial neural networks
Authors: Leekul Prapan , Limpiti Thunyawat , Chaisaeng Pornpimon
Published: October 2022

An automatic humidity control system for mushroom growing house based on the free-space technique is presented. The novelty of this work is the modified free-space technique by measuring the amplitude only of transmission coefficient |S21| that reflected from mushroom by using beam-switching antenna with artificial neural networks (ANNs) as a humidity sensor to control quantity and time of water misting nozzle. In the proposed system, the antenna is designed to act as the transmitting antenna at the frequency of 2.45 GHz. Its radiation patterns can be switched to 4 directions covering all corners of mushroom growing house. The measured |S21| from each direction are converted to direct current (DC) voltage by a radio frequency (RF) detector; then are trained with ANNs in the humidity range of 60-85%. The optimized ANNs structure consists of 4 input nodes, two layers of 5 hidden nodes, and 3 output nodes. To verify the proposed system, experiments were set up in controlled humidity mushroom growing house at the humidity level of 75-80% for 120 hours. The results showed that there was slightly average standard deviation (S.D.) of humidity level 1.36. Consequently, the performance of sensor system assures that it is able to apply for humidity control in large growing house.
Machine learning for prediction models to mitigate the voltage deviation in photovoltaic-rich distributed network
Authors: Baniyounis Mohammed , Salah Samer Z. , Ghaeb Jasim A.
Published: October 2022

The voltage deviation is one of the most crucial power quality issues that occur in electrical power systems. Renewable energy plays a vital role in electrical distribution networks due to the high economic returns. However, the presence of photovoltaic systems changes the nature of the energy flow in the grid and causes many problems such as voltage deviation. In this work, several predictive models are examined for voltage regulation in the Jordanian Sabha distribution network equipped with photovoltaic farms. The augmented grey wolf optimizer is used to train the different predictive models. To evaluate the performance of models, a value of one for regression factor and a low value for root mean square error, mean square error, and mean absolute error are used as standards. In addition, a comparison between nineteen predictive models has been made. The results have proved the capability of linear regression and the gaussian process to restore the bus voltages in the distribution network accurately and quickly and to solve the shortening in the voltage dynamic response caused by the iterative nature of the heuristic algorithm.  
Response time optimization for vulnerability management system by combining the benchmarking and scenario planning models
Authors: Basuki Arif , Adriansyah Andi
Published: October 2022

The growth of information and communication technology has made the internet network have many users. On the other side, this increases cybercrime and its risks. One of the main attack targets is network weakness. Therefore, cyber security is required, which first does a network scan to stop the attack. Points of vulnerability on the network can be discovered using scanning techniques. Furthermore, mitigation or recovery measures can be implemented. However, it needs a short response time and high accuracy while scanning to reduce the level of damage caused by cyber-attacks. In this paper, the proposed method improves the performance of a vulnerability management system based on network and port scanning by combining the benchmarking and scenario planning models. On a network scanning to discover open ports on a subnet, Masscan can achieve response times of less than 2 seconds, and on scenario planning for detection on a single host by Nmap can reach less than 4 seconds. It was combining both models obtained an adequate optimization response time. The total response time is less than 6 seconds.
Frequency reconfigurable rectangular patch antenna for cognitive radio applications
Authors: Kurugodu Hanumanthappa Manjuanatha , Mehta Shilpa
Published: October 2022

A frequency reconfigurable microstrip transformed rectangular patch antenna consisting of two slots able to radiate in S-band and C-band is proposed. Spectrum occupancy is first analyzed using the data from literature and internet sources and hence spectrum holes are identified. A rectangular radiating patch is then designed for 5.8 GHz resonant frequency. A coaxial feed is used in the bottom by a suitable feed point. Two slots at an angle of +45 degree are made at the two corners. The electrical length of the patch is changed by using two varactor diodes in the slots. The varactors enable frequency reconfiguration in the band of frequencies that are unused or the spectral occupancy is very less. The return loss, voltage standing wave ratio (VSWR), and 2D-radiation patterns are analyzed for various values of the capacitances. high-frequency structure simulator (HFSS) is used for simulation. FR4 substrate which is economical, is used with height, h=1.6 mm, width W=25.33 mm, and length L=21.34 mm. On the substrate the rectangular patch is of width 15.73 mm and length 11.74 mm. The return loss and radiation patterns for different values of capacitances is presented. The tunability ratio obtained is 1.93. The results obtained agree with the standards.
Empirical analysis of polarization division multiplexing-dense wavelength division multiplexing hybrid multiplexing techniques for channel capacity enhancement
Authors: Nahar Sabiqun , Arnob Md. Redowan Mahmud , Uddin Mohammad Nasir
Published: October 2022

This paper exemplifies dense wavelength division multiplexing combined with polarization division multiplexing with C-band frequency range-based single-mode fiber. In the proposed link, 32 independent channels with 16 individual wavelengths are multiplexed with two different angles of polarization. Each carrying 130 Gbps dual-polarization data with 200 GHz channel spacing claiming a net transmission rate of 4.16 Tbits/s with spectral efficiency of 69% with 20% side-mode-suppression-ratio (SMSR) and optical signal to noise ratio (OSNR) 40.7. The performance of the proposed techniques has been analyzed using optimized system parameters securing a minimum bit error rate (BER) 10-9 at a transmission distance up to 50 km.
An effective beamformer for interference suppression without knowing the direction
Authors: Tong Luyen , Nguyen Cuong
Published: October 2022

This paper proposes an effective beamformer for uniform linear arrays of half-wave dipole antennas based on binary bat algorithm (BBA) by controlling complex weights (both amplitudes and phases) excited at elements in an array. The proposed beamformer can impose adaptive nulls at interferences without knowing directions in the sidelobe region by minimizing the total output power of an array, whereas the main lobe and sidelobe levels are maintained. To demonstrate this capability, the proposal will be evaluated in several scenarios, compared to a beamformer based on binary particle swarm optimization (BPSO).