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The aim of this paper is to introduce a novel microinverter design that is based on the DC/DC converter, Buck. This structure is intended to provide a sinusoidal voltage to low-power autonomous loads embedded in vehicles, which require excellent waveform quality. In the first part, the paper presents the new topology, its modeling, and the control strategies employed. Two control strategies are discussed, including the classical control with a PI regulator and the sliding mode control with the step function. The dimensioning is carried out using the Ziegler-Nichols method, and the stability is ensured using the Lyapunov method. In the second part, the paper comments on the results obtained from different simulations in terms of harmonic distortion (THD), efficiency and control robustness with different sources, such as battery and photovoltaic panel. [ABSTRACT FROM AUTHOR]

This paper focuses on the hybridization of two renewable energy sources (i.e., PV Panel and Bi-Cycle Dynamo) and the use of a Multi-Input SEPIC converter for highly efficient output from them. This paper has also presented the comparative assessment between the conventional Single-Input Single Output SEPIC (Single Ended Primary Inductor Converter) and Multi-Input SEPIC converter based on performance analysis. Both the Multi-Input SEPIC that is designed in this literature powered by the hybrid architecture of power sources and the Single-Input SEPIC are run in boost mode. The project is mainly developed with a view to facilitating the isolated rural islands and regions detached from the on-grid connections. The findings and assessments of this study are corroborated by the MATLAB/Simulink simulation results and optimally designed prototype built for miniature applications. The Multi-Input SEPIC topology has been developed in such a way that it is functional with an input voltage of 12.1V exactly and gives an output voltage of DC 53V approximately at the output terminal. To get the maximum voltage at the output from the designed circuit, the duty cycle of the converter recorded is almost 81.49%. The renewable energy sources (RES) that are used to build the prototype are Photovoltaic Panel and Bi-Cycle Dynamo. Due to the limitations of the PV Panels to generate power during the night and gloomy weather, the Bi-Cycle dynamo works as the backup power source. In performing the hardware and software analyses, various intermittent situations, solar irradiation, seasonal change, day-night phase, and other parameters are taken into consideration on the input terminal of the converter. An efficiency of 91.6% is obtained from the proposed hardware field-test analysis. [ABSTRACT FROM AUTHOR]

This paper deals with the problems related to power stability issues in the isolated wind-solar based renewable energy system. The stability issues in such system are produced mainly due to unbalanced loading, distortion of load current and intermittent nature of renewable energy sources. To overcome these problems, A voltage source converter (VSC) is used in proposed literature. The VSC is driven using a time domain-based signal decomposition algorithm consisting a 3-phase dual reduced order generalized integrators frequency Locked loop (DROGI-FLL). The 3 phase DROGI FLL has the inherent abilities of noise rejection and error tracking, which improves the system stability and maintains the voltage and frequency of the system constant under transient conditions. The transient conditions are simulated by varying the loading condition, solar irradiance and wind speed in the system. Apart from stability issues this system also deals with the issues related to reactive composition, natural current compensation and operation of SPV system at maximum power point (MPP). A perturb and observe (P&O) maximum power point tracking (MPPT) is employed for the working of solar photovoltaic systems (SPV) system at MPP and to maximize the utilization of renewable energy. The neutral current compensation is achieved by using a star-double delta transformer. This entire system is developed and tested in the MATLAB/SIMULINK environment.. [ABSTRACT FROM AUTHOR]

In terms of applications and research, VANET (Vehicular Ad-hoc Networks) communication is becoming more popular. Existing VANET communication protocols try to improve network performance but fail to consider security issues. Attackers exploit the vulnerabilities of VANET communication protocols. Providing security to the VANET is still a challenging task because of the vehicles' mobility and their short communication range. Based on our study, we found that wormhole attacks are the most common type of attack on VANET communication. The existing security solutions are inadequate to prevent or detect external wormhole attacks on VANET communication because these solutions do not consider important parameters such as vehicle mobility, neighboring ratio, and node mobility to detect the wormhole attack. To address external wormhole attacks in VANETs, we propose a two-level fast and efficient distance based external wormhole detection and prevention system in this paper. In our proposed solution, we consider the vehicles' mobility, geographical location, and distance parameters to identify and isolate external wormhole attacker nodes. For effective monitoring of wormhole attacks, we use a dynamic threshold value for suspecting external wormhole attack links and then use the hop count metric to detect and prevent it. We used SUMO with NS2 simulators to compare our proposed system with existing works, and our simulations show that our proposed security solution outperforms existing works in terms of throughput, PDR, and jitter in a wormhole attack VANET environment. [ABSTRACT FROM AUTHOR]

In this paper, benzene-core photonic crystal fibers (B-PCFs) with non-uniform air holes for the square lattice are studied by controlling the air-filling factor of the air holes in the first ring and increasing those up to a maximum for external holes. Commercial Mode Solutions software is used to study the influence of structural parameters on the nonlinear coefficient, dispersion, effective mode area, and attenuation. It can be seen that this design affects the linear and nonlinear properties at the same time. In other words, it has a crucial effect on either the near-zero flatness of dispersion or going up the nonlinearity and falling the loss of the B-PCFs. The above benefits make the optimized fibers suitable for supercontinuum (SC) generation applications. [ABSTRACT FROM AUTHOR]

Plant phenotyping is one of the recent research areas that play an essential role to develop a better understanding of plant traits, genotypes, stresses, and other related features. It is regarded as essential concept as it facilitates development in several fields such as botany, agronomy, and genetics. Plant phenotype helps in acquiring relevant information about plant organs and whole features that allows the farmers to make informed plant cropping decisions. It includes the use of Deep Learning (DL) which is part of a machine learning technique that makes use of several processing layers to provide reliable outcomes from abstraction. DL-based approaches are highly useful in providing a sufficient amount of data related to plant strapping, stresses, and growth indices. Deep learning approaches are highly efficient in analysing plant phenotype and characterizing the phenotyping aspects by classifying the plant stress datasets into open, labelled, and broad-spectrum. In this paper, a review work makes an attempt to explore the efficiency of deep learning and filtering approaches in plant phenotyping. The recent works related to the DL principles have been utilized for digital image–based plant stress phenotyping. Then a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Therefore, it is strongly recommended in the study to use the imaging data process so that there is the attainment of accurate information from training datasets by using high-throughput systems like UAVs and other autonomous systems. [ABSTRACT FROM AUTHOR]

This paper aims to provide a comprehensive examination of the Brain-Computer Interface and the more scientific discoveries that have resulted from it. The ultimate goal of this review is to provide extensive research in BCI systems while also focusing on artifact removal techniques or methods that have recently been used in BCI and important aspects of BCIs. In its pre-processing, artifact removal methodologies were critical. Furthermore, the review emphasizes the applicability, practical challenges, and outcomes associated with BCI advancements. This has the potential to accelerate future progress in this field. This critical evaluation examines the current state of BCI technology as well as recent advancements. It also identifies various BCI technology application areas. This detailed study shows that, while progress is being made, significant challenges remain for user advancement A comparison of EEG artifact removal methods in BCI was done, and their usefulness in real-world EEG-BCI applications was talked about. Some directions and suggestions for future research in this area were also made based on the results of the review and the existing artifact removal methods. [ABSTRACT FROM AUTHOR]

This paper presents the engineering design of an unmanned aerial vehicle (UAV)/drone hexacopter and optimizes the PID (Proportional-Integral-Derivate) values for the Pixhawk 2.4.8 (PX4) flight controller. The design phase begins with component selection and identification, with the goal that the drone can carry loads up to 3 kg. Then install the main components and test the construction results. An analysis of the experimental results of the PID PX4 controller with no load and with load was performed. Results from direct field experiments with a home-built hexacopter show that the default PID must be tuned to be able to lift a load with a specific target. [ABSTRACT FROM AUTHOR]

In this paper, the problem of model-based finite-time bounded event-triggered control for distributed fuzzy T-S systems is presented. For this purpose, the whole network model is embedded locally in both the controller and the remote telemetry unit. In order to estimate the states of the plant between two consecutive events, a fuzzy observer has been used. Model-based state estimation reduces the state error and consequently leads to reduction of data transmission instants. By the network model and event triggering block which are placed locally in each remote telemetry unit, the time of data transmission on the distributed network is determined. Finally, the finite-time boundedness of the closed-loop system has been investigated using MATLAB software for a centralized and a distributed system, respectively. [ABSTRACT FROM AUTHOR]

In order to connect two binary and quaternary systems, it is necessary to use binary-to-quaternary (B2Q) and quaternary-to-binary (Q2B) converters. These converters convert numbers from logic 2 to 4 and vice versa. In this paper, we designed a new binary-to-quaternary converter circuit using CNT transistors. In this circuit, the Power Delay Product (PDP) has been reduced to14.59% and 15.39% compared to best previous works (Ref [22] and Ref [23]). Also, this circuit has better driving ability and temperature stability than best previous works. The simulation results using Stanford's 32 nm CNTFET model in HSPICE software are at a voltage of 0.9 V. [ABSTRACT FROM AUTHOR]

Kingship verification is a process that two or more people has a family relation such as father and son or other family relation. Numerous studies have been presented to investigate the relationship between people. Kingship verification can be done based on image of face. Most of the methods presented on face images work well on face data sets recorded under controlled conditions. However, due to the complex nature of environments, rapidly and accurately examining human kinship in real-world unrestricted or wild-type scenarios is still a challenging research. In this paper, in order to overcome the aforementioned challenges, an efficient and new method is presented. In the proposed method, a method is used to launch the operation to create a map. The created feature map is stable against deformation, transition, scaling, direction and Dilation in wild images. Group-Face and TSK in Face databases are used for simulation. In order to evaluate the evaluation of the proposed method, average recall of 94.1, precision 94.6, accuracy 94.7, specificity 93.8, and finally F_Measure 95.0 were used. The superiority of the proposed method in all comparisons shows the effectiveness of the proposed method in diagnosing kinship. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach based on the copula models for estimating Millimeter-Wave channel parameters with Rician fading in a correlated MIMO system. Even though the use of these waves faces numerous challenges, having an accurate estimation of their parameters would be a crucial notion for the improved performance of this channel. Since the considered system in this paper is a MIMO system with nearby transmitter antennas, the components of the received signals are correlated. However, a general model is not available for this kind of correlation. By taking into account the correlation, this model enables a precise Joint Probability Density Function (JPDF) by employing copula models for the received signals in the antennas. We also obtain more precise channel parameter estimates using this density function. The multi-path fading parameters in a MIMO system and the correlation coefficients between the transmitter and receiver antennas are both presented in this paper. Some simulations are employed to assess the validity and reliability of estimating Millimeter-Wave channel parameters in a MIMO system. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a Graphical User Interface (GUI) and Artificial Neural Network (ANN) for the prediction of photovoltaic (PV) power output. PV power is generated based on the time, location, and surrounding climate conditions. Therefore, solar power generation predictions using computational methods are needed since the changing weather, which will impact the output power will not generate according to its rating. The objectives of this research are to predict photovoltaic power output at Universiti Tun Hussein Onn Malaysia (UTHM), develop an ANN configuration that can perform the prediction of solar power generation, and design GUI system that can both perform the calculations of power generation and ANN. In order to test the efficiency and reliability, MATLAB software has been used to develop the GUI and ANN, and the output is compared with the proposed mathematical equations. The real data as input data was obtained from the PV solar panel located at GSEnergy Focus Group fertigation site. The GUI with user-friendly features and ANN have been successfully designed and developed which can perform daily prediction of solar power output. On top of that, the results have shown that the ANN predictions are more precise to the real data than the GUI. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive controller is presented to control a quadrotor, whose parameters are extracted from the genetic algorithm optimization method. The advantage of this method is that based on the system states, the control coefficients are calculated online. For this purpose, a function between system states-space and control coefficients is obtained. From the database collected from the genetic algorithm optimization method, the parameters of the control coefficient function are obtained using the least squares method. The stability of the proposed controller is proved by the Lyapunov method. Finally, the performance of the proposed controller is compared with the PID controller, which is widely used in the literature. The results show that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

The advent of cloud computing has made it simpler for users to gain access to data regardless of their physical location. It works for as long as they have access to the internet through an approach where the users pay based on how they use these resources in a model referred to as “pay-as-per-usage”. Despite all these advantages, cloud computing has its shortcomings. The biggest concern today is the security risks associated with the cloud. One of the biggest problems that might arise with cloud services availability is Distributed Denial of Service attacks (DDoS). DDoS attacks work by multiple machines attacking the user by sending packets with large data overhead. Therefore, the network is overwhelmed with unwanted traffic. This paper proposes an intrusion detection framework using Ensemble feature selection with RNN (ERNN) to tackle the problem at hand. It combines an Ensemble of multiple Machine Learning (ML) algorithms with a Recurrent Neural Network (RNN). The framework aims to address the issue by selecting the most relevant features using the ensemble of six ML algorithms. These selected features are then used to classify the network traffic as either normal or attack, employing RNN. The effectiveness of the proposed model is evaluated using the CICDDoS2019 dataset, which contains new types of attacks. To assess the performance of the model, metrics like precision, accuracy, F-1 score, and recall are taken into consideration. [ABSTRACT FROM AUTHOR]

Switched Boost Inverter (SBI) operates in buck-boost mode with an extensive spectrum of output voltage obtainable from a specific source voltage. Also, this structure provides superior protection against Electromagnetic Wave Interference (EMI) juxtaposed with the traditional impedance source inverter. The number of energy storage elements in this structure has been reduced, which increases the efficiency of the converter in low-power applications, reducing cost, size, and weight. In this paper, this inverter is analyzed and investigated in different operating modes and steady-state. Also, three different pulse width modulation (PWM) control techniques are presented for this inverter. Simulation results are presented in PSCAD-EMTDC software to validate the calculated relationships and confirm their performance in three different switching methods. [ABSTRACT FROM AUTHOR]

This paper proposed the multiobjective optimal allocation of renewable distributed generation and shunt capacitors in the distribution system using corona virus herd optimization techniques. The work aimed to achieve a technical benefit, total electricity cost reduction, and enhancement of greenhouse safety. The objectives considered are real power loss, voltage profile index (VPI), voltage stability index (VSI), total electricity cost (TEC), and total greenhouse gas emission (TGHGe). Weight function was used to combine the objectives for the six cases considered with different priorities. The proposed CHIO is validated on the standard IEEE 33 bus system and implemented on Dada 46 bus, a Nigerian practical distribution network. Various cases were considered for the two test systems. For IEEE 33 bus, the proposed method achieved 89.44% and 86.77% reduction in real and reactive power, respectively, with 93.73% and 39.27% in TGHGe and TEC. Also, for Dada 46 bus system, 89.44% and 86.77% reduction in real and reactive power loss respectively was achieved with 98.66% and 64.42% in TGHGe and TEC. Furthermore, the highest level of greenhouse gas emission reduction was achieved (says 99.69%) when high priority was placed on the reduction in TGHGe; this shows the significant impact of renewable energy in the distribution system. The results obtained are compared with the existing methods, such as PSO, GA, ABC, GABC, WOA, WCA, to mention a few. In other to show the performance of the proposed CHIO compared to others, the outcome reveals the excellent performance of the proposed algorithm in terms of an optimal result. [ABSTRACT FROM AUTHOR]

The hybrid electric train which operates without overhead wires or traditional power sources relies on hydrogen fuel cells and batteries for power. These fuel cell-based hybrid electric trains (FCHETs) are more efficient than those powered by diesel or electricity because they do not produce any tailpipe emissions making them an eco-friendly mode of transport. The target of this paper is to propose low-budget FCHETs that prioritize energy efficiency to reduce operating costs and minimize their impact on the environment. To this end, an energy management strategy [EMS] has been developed that optimizes the distribution of energy to reduce the amount of hydrogen required to power the train. The EMS achieves this by balancing battery charging and discharging. To enhance the performance of the EMS, proposes to use of a deep reinforcement learning (DRL) algorithm specifically the deep deterministic policy gradient (DDPG) combined with transfer learning (TL) which can improve the system's efficiency when driving cycles are changed. DRL-based strategies are commonly used in energy management and they suffer from unstable convergence, slow learning speed, and insufficient constraint capability. To address these limitations, an action masking technique to stop the DDPG-based approach from producing incorrect actions that go against the system's physical limits and prevent them from being generated is proposed. The DDPG+TL agent consumes up to 3.9% less energy than conventional rulebased EMS while maintaining the battery's charge level within a predetermined range. The results show that DDPG+TL can sustain battery charge at minimal hydrogen consumption with minimal training time for the agent. [ABSTRACT FROM AUTHOR]

The Finite-Set Model Predictive Power Control (FS-PPC) is one of the most intriguing model predictive approaches for the induction machine. This control is successful because it operates without weight coefficients and does not require the rotor flux position, as in the Predictive Torque Control (FS-PTC) and the Predictive Current Control (FS-PCC), respectively. A simple extension to the double-star induction generator results in significant current harmonics and common mode voltage. To fix these issues, this paper proposes an improved FS-PPC applied to an asymmetric doublestar induction generator based wind energy conversion system by introducing two concepts: (a) the virtual voltage vector (VVV), in order to eliminate the (x, y) components of the stator currents. (b) the zero common mode voltage vectors (ZCMV), to eliminate the common mode voltage. A simulation of the developed ZCMV-FS-PPC system control is created in MATLAB/Simulink. The results show the effectiveness of this approach with CMV equal to zero and negligible (x, y) components of the stator currents. Moreover, the elimination of the CMV not only avoids its damage but also reduces the computation by 50%. [ABSTRACT FROM AUTHOR]

Recent developments in the field of internet innovation have made it feasible for new methods to be developed for the delivery of medical care. Sharing of resources may be encouraged via the use of network infrastructure and shared points of access. Patient data and clinical information make the Internet an ideal tool for the many applications that include remote patient monitoring. In this paper, we provide an experimental model that was developed for monitoring and checking on the patient's current state of health using sensors. The framework is dependent on an e-health sensor that is connected to a cloud platform. The cloud platform is responsible for collecting the data that is gathered from the sensors through the Arduino microcontroller. The data collected by the sensors includes readings from body temperature, heart rate, and oxygen. The data then is easily available for further processing, including the analysis of certain correlations between the measured parameters and the health status of the patients. [ABSTRACT FROM AUTHOR]

This paper explores the application of a machine learning approach to predict equipment failure rates in power distribution networks, motivated by the significant impact of power outages on citizens' daily lives and the economy. In this research, data on equipment failure rates and maintenance records were collected from power distribution networks in Baghdad, Iraq. The collected data underwent preprocessing, and features were extracted to train Adaptive NeuroFuzzy Inference System (ANFIS) and Periodic Autoregressive Moving Average (PARMA) time series models. To initiate the project, information regarding blackouts that occurred between January 2018 and December 2021 was retrieved from the database. The RMSE index results for the PARMA time series and ANFIS model are 3.518 and 2.264, respectively, demonstrating the superior performance of the ANFIS model in predicting equipment failure rates and its potential for future predictions. This study highlights the ANFIS model's capacity to anticipate equipment failure rates, potentially enhancing maintenance efficiency and reducing power outages in Baghdad. The error mean square was employed to evaluate the proposed models' error rate. [ABSTRACT FROM AUTHOR]

The development of renewable energy-based applications is nowadays a forced demand of society, for chasing the target set by the governments and the concerned organizations, to reduce or limit the carbon penetration in the environment. Researchers and scholars putting their sincere efforts toward the development of efficient and reliable renewable energybased applications. The scope of this paper is to present a Renewable Energy based Irrigation System capable of maintaining the application-based power demand. The REBIS consists of a photovoltaic generator as the main power source supported by an energy storage system (battery). For this hybrid system, the development of a utility-based Adaptive Hybrid Control is proposed to regulate the charge/discharge cycle of the battery energy while maintaining the load demand simultaneously. A case study, supporting the time-bound irrigation, is considered to verify the viability and performance of the proposed system. This proposed system and controller are first simulated in MATLAB, and then implemented at the hardware level in the LabVIEW environment. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for rapidly and accurately detecting Multiple Sclerosis (MS) lesions and analyzing the progression of lesions and the disease based on differences between histograms of hemispheres and volumetric changes in brain regions over time. The brightness and contrast of pixels are first improved, and MRI slices are then analyzed to detect and eliminate the effects of motion artifacts while imaging. However, an accurate diagnosis tracks changes in volumes of brain regions caused by plaques emerging on brain MRIs in white matter, gray matter, and cerebrospinal fluid (CSF) and the concurrent analysis of differences between histograms of hemispheres. The marker-controlled watershed algorithm was employed to extract MS lesions and plaques. Various MRI centers differ in imaging diameters for which there are no unified standards, leading to different MRI slices. Hence, an individual's two MRI slices of two different occasions are not comparable. Measuring the brain volume can make the proposed method independent of the imaging diameter. This study analyzed the patients with at least three imaging records in the archives of imaging centers. The images were collected from Pars MRI Center and Hajar Hospital MRI Center in Shahrekord, Chararmahal and Bakhtiari Province, Iran. Both centers used Avanto MRI devices and performed imaging at 1 T and 1.5 T, respectively. [ABSTRACT FROM AUTHOR]

Renewable energy sources, such as wind and solar, are becoming increasingly popular due to their environmentally friendly and sustainable nature. However, one major challenge associated with these systems is their intermittent nature, which makes it difficult to rely on them as a consistent source of energy. To address this challenge, researchers have developed a combined system that incorporates wind and solar resources with a battery as a storage device, which can provide a sample load pattern independent of the grid. The primary objective of this system is to determine the optimal economic combination of these resources, which can ensure a reliable and consistent supply of electricity. To achieve this objective, the Cuckoo Optimization Algorithm (COA), a metaheuristic optimization algorithm, has been used to optimize the system. The objective function has been implemented in accordance with the constraints, and the results provide insight into the optimal combination of resources. This paper provides a comprehensive analysis of the design and optimization of a wind-solar hybrid energy system with battery storage, using the COA, as well as the results of this analysis. The outcomes indicated that the optimal hybrid system model may be able to reduce system costs by 10–25%. This research's findings can be used to inform the design of sustainable and dependable renewable energy systems. [ABSTRACT FROM AUTHOR]

Today, electricity theft is one of the main challenges for energy distribution and transmission companies around the world. Early detection of abnormal consumers can prevent security and financial losses. Extensive research studies have been done to detect electricity theft by analyzing customer consumption patterns. Today, one of the most widely used methods is convolutional neural networks (CNNs). These networks contain a large number of hyper-parameters. The accuracy of these networks is low in most studies due to the lack of attention to the adjustment of these hyperparameters. Network accuracy and achieving a robust learning model are influenced by the optimal adjusting of these hyper-parameters, which requires exploring a complex and large search space. Meta-heuristic-based search methods are suitable for solving these problems. Therefore, the main contribution of this paper is to use the high ability of the cheetah optimization algorithm (CHOA) to optimally extract CNN hyper-parameters. In this paper, in order to balance the dataset, abnormal samples are created using artificial attacks and added to the dataset. Also, in order to increase the accuracy of the network, abnormal data are clustered using the CHOA algorithm. ISSDA dataset is used to test and evaluate the results. Based on the results obtained and comparing them with the other works, it was proved that the proposed framework with high accuracy identifies abnormal consumers. [ABSTRACT FROM AUTHOR]

Fuel shortages and environmental issues have made optimal and effective management of various resources one of the most pressing issues in the current situation. There is often a separation between the heating and gas network and the power system, which is not wise when considering the economics and effectiveness of the entire system. Examining the results shows that the installed capacity has grown significantly in the last few years. This research has investigated the microgrid including wind-solar turbine, along with energy storage (battery). This energy can be used as an alternative to fossil power plants because it is readily available and renewable. This paper proposes a multi-dimensional framework for a multi carrier energy supply with a solar-wind and biogas hybrid renewable system. In this study, the amount of power exchange of sources in a microgrid independent of the distribution network and power distribution between resources and load supply have been simulated in the HOMER environment, and the output results show the optimization of power management in this network. The proposed method is completely tested and programmed in an independent microgrid during a 24-hour period. [ABSTRACT FROM AUTHOR]

Solar energy serves as one of the promising resources of renewable energy due to its abundance availability and clean nature. Climatic data such as irradiation level and temperature are essential as input variables while designing the photovoltaic-based system. This research paper presents an extensive comparative analysis of modeling approaches of photovoltaic modules using a 1-diode and 2-diode solar models studied under varying irradiation levels and temperature values. 8 different cases of solar models based on 1-diode and 2-diode models have been incorporated. Its effect on 10 performance indexes of the photovoltaic module is comprehensively investigated. Based on these parameters, these solar models are compared in detail. MATLAB/Simulink tool is applied to explore the results. Extensive results have been taken under various operating conditions. The results show that the 2-diode model exhibits excellent performance as it maximizes the efficiency by 5.41% against the 1-diode model. Also, the 2-diode model has a greater performance ratio of 41.70% than the 1-diode model. Hence, it is revealed that the performance of 2-diode models in terms of smooth output curves, increased maximum power, fill factor, and efficiency are much closer to the actual performance as compared to the 1-diode model. [ABSTRACT FROM AUTHOR]

The addition of renewable energy sources to the traditional distribution network has transformed the centralized unidirectional power source into two-way and multiple power source system. This will improve overall system's reliability and also reduces the down time that are associated within the radial distribution system. This paper investigates the impact of distributed energy sources like PV, Wind, Electric Storage, and Gas Turbine Generator on the reliability of distribution system. The Monte Carlo simulation method is used to test bus-2 of IEEE RBTS distribution network. The distribution network has been customized to incorporate the WT, PV, ESS, and GTG distributed generation. The WT and PV stochastic models were employed to replicate the unpredictability of these sources since speed of wind and solar radiation are both unpredictable. This work shows that the integration of distributed generation enhances the distribution system's reliability. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the dispersion properties of photonic crystal fiber with the core replaced by a composite of 85% SiO2-15% GeO2. The air hole's radii of the layers in the cladding are designed differently to improve the dispersion and nonlinear properties of the fibers. Both anomalous and all-normal dispersions have been optimized. Based on numerical simulation results, two optimal structures (d1/Ʌ = 0.4, Ʌ = 0.9 µm and d1/Ʌ = 0.45 and Ʌ = 1.0 µm) are proposed with a very small dispersion value of 0.298 ps/ nm.km and −0.311 ps/nm.km at the pump wavelength of 1.53 µm and 0.985 µm, respectively. The high nonlinear coefficient, small effective mode area, and very low attenuation of about 10−7 dB/m at the pump wavelength are also favorable conditions for the application of broad-spectrum supercontinuum with low peak power. The proposed fibers can be new supercontinuum sources that effectively replace glass core fibers. [ABSTRACT FROM AUTHOR]

This paper presents Circuit Models which are developed for the analysis of the radiated and conducted susceptibilities of nonuniform shielded coaxial cables. In order to obtain the voltage and current distributions, a two-step procedure is performed. First, the cables are subdivided into several uniform sections. Second, the Branin’s mode is used to obtain the voltage and current distributions. This model can be used directly in the analysis of both time-domain and frequency-domain, and it has the ability to be used without the need of setting the preconditions for charges which are applied to its ends. In this paper, three examples of applications in the time and frequency domains are presented in order to validate our model. The first example focuses on a coaxial shielded cable to analyze both the conducted and radiated immunity, while the second example focuses on a nonuniform coaxial shielded cable. The third example presents a complex configuration of a coaxial cable which has been exposed to an electromagnetic field incident. Finally, the performed simulations and obtained results will be thoroughly described and analyzed. [ABSTRACT FROM AUTHOR]

Soft tissue modeling is a challenging issue in tissue engineering. Tissue is a complex environment. It is assumed to represent viscoelastic behavior. Therefore, a complicated process is required to model its stress-strain relationship. In this paper, a non-integer order model is considered for the tissue’s mechanical behavior. The order indicates the amount that the tissue tends to behave as a pure viscous or pure elastic material. The main goal of the paper and the main contribution is to interpret the order as a function of the state of the material. To this aim, an experimental estimated model is used in which the order is considered as a function of time. Stress and strain signals are also available as functions of time. Then, an identification process is used to obtain the direct functionality of the order with respect to the state of the material (i.e. the momentary stress and strain). Data are gathered through an experimental setup. The stress signal calculated using a force sensor is highly noisy. Hence, de-noising is necessary. However, noise elimination may cause losing meaningful data. Also, a slight amount of noise enhances the generalization of the trained network in the identification process. Accordingly, a multi-level noise reduction method is used. The method is based on Empirical Mode Decomposition (EMD). To obtain the optimal noise reduction level, the noise reduction process is performed level by level and the best levels in train and test stages are chosen. Results show that supposing an explicit functionality between the order (as the amount of viscoelasticity) and the state of the tissue is reasonable. Also, it is verified that multi-level de-noising significantly improves the identification process. [ABSTRACT FROM AUTHOR]

There is growing commitment to phase-out the conventional fossil-fuel based power stations to decentralized power generation with high share of renewable energy sources that is based on low-emission to meet the various techno- economic, environmental and secure energy requirements. This paradigm shift from conventional generators to the inverter-dominated DGs/RESs, has come with some technical challenges of low inertia, frequency instability, harmonic distortion etc. The challenges require effective and efficient inverter-based power system control for a reliable and stable grid interface. Based on the way inverters functions, it is grouped into three categories: grid- feeding, grid-supporting, and grid-forming. Several control schemes have been proposed for grid-forming inverter; these control schemes are based on the concept of Virtual synchronous machine, Power synchronization control, and Direct power control. This paper presents a comprehensive review on the recent advancements in grid-forming inverter control technologies. This review covers different control techniques that have been successfully implemented for the inverter-based power system to grid interface, benefits and challenges of attaining the estimated 100% non- synchronous power generation. The cornerstone of the survey is to also establish state-of-the-art on the grid-forming inverter and identify future research areas for improving the existing techniques and developing novel ones. [ABSTRACT FROM AUTHOR]

Short Term Load Forecasting (STLF) is the projection of system load demands for the next day or week. Because of its openness in modeling, simplicity of implementation, and improved performance, the ANN-based STLF model has gained traction. The neural model consists of weights whose optimal values are determined using various optimization approaches. This paper uses an Artificial Neural Network (ANN) trained using multiple hybrid techniques (HT) such as Back Propagation (BP), Cuckoo Search (CS) model, and Bat algorithm (BA) for load forecasting. Here, a thorough examination of the various strategies is taken to determine their scope and ability to produce results using different models in different settings. The simulation results show that the BA-BP model has less predicting error than other techniques. However, the Back Propagation model based on the Cuckoo Search method produces less inaccuracy, which is acceptable. [ABSTRACT FROM AUTHOR]

In recent decades, network security has become increasingly crucial, and intrusion detection systems play a critical role in securing it. An intrusion Detection System (IDS) is a mechanism that protects the network from various possible intrusions by analyzing network traffic. It provides confidentiality and ensures the integrity and availability of data. Intrusion detection is a classification task that classifies network data into benign and attack by using various machine learning and deep learning models. It further develops a better potential solution for detecting intrusions across the network and mitigating the false alarm rate efficiently. This paper presents an overview of current machine learning (ML), deep learning (DL), and Explainable Artificial intelligence (XAI) techniques. Our findings provide helpful advice to researchers who are thinking about integrating ML and DL models into network intrusion detection. At the conclusion of this work, we outline various open challenges. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) panel produces electricity depending on a variety of characteristics, including the PV module model, design specifications, and ambient circumstances such as temperature and sun irradiation. To analyze and model the effect of these factors on PV performance, a PV model is significant to be studied and modeled in advance. It is desirable to be compatible with the real-physical behavior of the PV panel. This paper presents mathematical modeling, design, and simulation of the three-diode model (3DM) MPPT controller instead of using conventional single/double diode PV models. The proposed PV model is analyzed, verified, and simulated at various temperature and irradiance levels. Furthermore, Particle Swarm Optimization (PSO) as a multi-objective algorithm is used for the Maximum Power Point Tracking MPPT controller to enhance the performance of the module and PV array system. A DC/DC boost converter is combined with the proposed 3DM model and connected through a resistive load. Results show that adopting PSO-based MPPT improves the performance of the PV panel compared to the traditional MPPT and verified the theoretical background. [ABSTRACT FROM AUTHOR]

Wildfire detection is a time-critical application since it can be challenging to identify the source of ignition in a short amount of time, which frequently causes the intensity of fire incidents to increase. The development of precise earlywarning applications has sparked significant interest in expert systems research due to this issue, and recent advances in deep learning for challenging visual interpretation tasks have created new study avenues. In recent years, the power of deep learning-based models sparked the researcher’s interests from a variety of fields. Specially, Convolutional Neural Networks (CNN) have become the most suited approach for computer vision tasks. As a result, in this paper we propose a CNN-based pipeline for classifying and verifying fire-related images. Our approach consists of two models, first of which classifies the input data and then the second model verifies the decision made by the first one by learning more robust representations obtained from a large masked auto encoder-based model. The verification step boosts the performance of the classifier with respect to false positives and false negatives. Based on extensive experiments, our approach proves to improve previous state-of-the-art algorithms by 3 to 4% in terms of accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a broadband low-power CMOS low noise amplifier (LNA) in 130 nm technology for sub-GHz Internet of Things (IoT) applications. The proposed circuit consists of a current reuse common source amplifier (CSA) in the forward path, and a positive simple transconductance amplifier (PSTA) in the feedback path. Theoretical calculation of the input admittance shows a positive part that presents a parallel inductance. This equivalent parallel inductance in the input can cancel out the input capacitance of CSA and electrostatic discharge (ESD) pad, enhancing the frequency bandwidth in the sub-GHz frequency band. Post-layout was simulated including ESD pads and package model in 130 nm CMOS technology, LNA achieves a voltage gain of 16.5 dB in a frequency bandwidth of 50 MHz to 1.1 GHz, noise figure (NF) of less than 2.4 dB, input return loss (S11) of -11 dB, input third order intercept point (IIP3) of -11 dBm and 1 mW power consumption from a 1 V power supply, showing a good figure of merit compared to other works. The occupied core area is less than 0.002 mm². [ABSTRACT FROM AUTHOR]

Un-sharp masking method improves the images contrast without requiring any prior knowledge. In this method, a sharper image can be achieved by empowering the high frequency components of the input image. Un-sharp masking has a parameter named gain factor which has a high effect on the enhanced image quality. In this paper, an approach is proposed to adaptively estimate the appropriate value of this parameter in order to effectively enhance an image with local blur, or an image with non-uniform blur. In proposed method, first, the input image is segmented into blur and non-blur regions. Then the gain factor is estimated for each region adaptively. In this approach, the influence of the image blurriness on its gradient information is used to estimate the value for the gain factor. The image quality assessments are applied to evaluate the performance of proposed un-sharp masking method in image enhancement. Experimental results demonstrate that the performance of our proposed method is better than the performance of existing un-sharp masking methods in image enhancement. [ABSTRACT FROM AUTHOR]

Through the use of malware, particularly JavaScript, cybercriminals have turned online applications into one of their main targets for impersonation. Detection of such dangerous code in real-time, therefore, becomes crucial in order to prevent any harmful action. By categorizing the salient characteristics of the malicious code, this study suggests an effective technique for identifying malicious Java scripts that were previously unknown, employing an interceptor on the client side. By employing the wrapper approach for dimensionality reduction, a feature subset was generated. In this paper, we propose an approach for handling the malware detection task in imbalanced data situations. Our approach utilizes two main imbalanced solutions namely, Synthetic Minority Over Sampling Technique (SMOTE) and Tomek Links with the object of augmenting the data and then applying a Deep Neural Network (DNN) for classifying the scripts. The conducted experiments demonstrate the efficient performance of our approach and it achieves an accuracy of 94.00%. [ABSTRACT FROM AUTHOR]

To stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. CNNs can extract distinctive characteristics directly from the fundus pictures, as opposed to the current automatic screening techniques. In this study, a CNN-based deep learning architecture is created for the categorization of normal and glaucomatous fundus pictures. In this paper, we propose a deep learning-based framework for the detection of glaucoma based on retinal images. Our proposed approach utilizes the two CNN-based models, namely Inception and DenseNet, in order to classify the input images. We also show the impact of transfer learning on the training and the validation processes and put forward an effective pipeline with lower trainable parameters for the target task. Our experiments on a collected dataset demonstrate the efficacy of the proposed model by achieving an accuracy of 93.84%, a precision of 92.83%, and a recall of 95.00%. [ABSTRACT FROM AUTHOR]

Breast cancer is one the most ubiquitous types of cancer which affect a considerable number of women around the globe. It is a malignant tumor, whose origin is in the glandular epithelium of the breast and causes serious health-related problems for patients. Although there is no known way of curing this disease, early detection of it can be very fruitful in terms of reducing the negative ramifications. Thus, accurate diagnosis of breast cancer based on automatic approaches is demanded immediately. Computer vision-based techniques in the analysis of medical images, especially histopathological images, have proved to be extremely performant. In this paper, we propose a novel approach for classifying malignant or non-malignant images. Our approach is based on the latent space embeddings learned by convolutional autoencoders. This network takes a histopathological image and learns to reconstruct it and by compressing the input into the latent space, we can obtain a compressed representation of the input. These embeddings are fed to a reinforcement learning-based feature selection module which extracts the best features for distinguishing the normal from the malicious images. We have evaluated our approach on a well-known dataset, named BreakHis, and used the K-Fold Cross Validation technique to obtain more reliable results. The accuracy, achieved by the proposed model, is 96.8% which exhibits great performance. [ABSTRACT FROM AUTHOR]

Recently, many studies have examined filters for reducing or removing speckle noise, which is inherent to different images types such as Porous Silicon (PS) images, in order to ameliorate the metrological evaluation of their applications. In the case of digital images, noise can produce difficulties in the diagnosis of images details, such as edges and limits, should be preserved. Most algorithms can reduce or remove speckle noise, but they do not consider the conservation of these details. This paper describes in detail, the different techniques that focus mainly on the smoothing or elimination of speckle noise in images, as the aim of this study is to achieve the improvement of this smoothing and elimination, which is directly related to different processes (such as the detection of interest regions). Furthermore, the description of these techniques facilitates the operations of evaluations and research with a more specific scope. This study initially covers the definition and modeling of speckle noise. Then we elaborated in detail the different types of filters used in this study, finally, five statistical parameters such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) are calculated, compared and the results are tabulated, common in filter evaluation processes. Trough the calculation of the statistical parameters, we can classify the filters in terms of perceptual quality by providing greater certainty. [ABSTRACT FROM AUTHOR]

The most commonly used variable speed wind turbine is based on Doubly Fed Induction Generator (DFIG). To control the reactive power of DFIG-based wind turbines, several methods are suggested that controls the reactive power of the DFIG with slow dynamics and considerable ripples. This paper proposes a new control method based on the adaptive reference model which controls the active and reactive powers of DFIG with high dynamics and low ripples. Given that, the proposed technique has proportional-integral (PI), selecting the proper coefficient for PI controller is significant. To overcome this problem, the grey-wolf algorithm is used to optimize the PI coefficients. The results show that the proposed method gives satisfactory performance with lower overshoots and faster dynamic response. [ABSTRACT FROM AUTHOR]

The internet of Things (IoT) is a promising expansion of the traditional Internet, which provides the foundation for millions of devices to interact with each other. IoT enables these smart devices, such as home appliances, different types of vehicles, sensor controllers, and security cameras, to share information, and this has been successfully done to enhance the quality of user experience. IoT-based mediums in day-to-day life are, in fact, minuscule computational resources, which are adjusted to be thoroughly domain-specific. As a result, monitoring and detecting various attacks on these devices becomes feasible. As the statistics prove, in the Mirai and Brickerbot botnets, Distributed Denial-of-Service (DDoS) attacks have become increasingly ubiquitous. To ameliorate this, in this paper, we propose a novel approach for detecting IoT malware from the preprocessed binary data using transfer learning. Our method comprises two feature extractors, named ResNet101 and VGG16, which learn to classify input data as malicious and non-malicious. The input data is built from preprocessing and converting the binary format of data into gray-scale images. The feature maps obtained from these two models are fused together to further be classified. Extensive experiments exhibit the efficiency of the proposed approach in a well-known dataset, achieving the accuracy, precision, and recall of 96.31%, 95.31%, and 94.80%, respectively. [ABSTRACT FROM AUTHOR]

Information is the driving force in vehicular ad hoc networks (VANET) since vehicles share information (emergency, general, and multimedia). VANET communicates between vehicles using a unique routing protocol, unlike other wireless routing technologies. Many protocols, techniques, and approaches have been developed to secure and protect data. To enhance current security and privacy measures and develop and model new ones, the ideas of machine learning (ML), deep learning (DL), and artificial intelligence are being applied. In this paper, we provide information on the various types of attacks that target VANET communication, VANET layers, the security goals that are affected, and real-time attacks that occur on manufacturing hubs. We compared various VANET attack prevention, detection, and AI techniques proposed, as well as future research work in the field of VANET, for improving accuracy, security, and privacy. [ABSTRACT FROM AUTHOR]

An Electrocardiogram (ECG) is a test that is done with the objective of monitoring the heart’s rhythm and electrical activity. It is conducted by attaching a specific type of sensor to the subject’s skin to detect the signals generated by the heartbeats. These signals can reveal significant information about the wellness of the subjects’ heart state, and cardiologists use them to detect abnormalities. Due to the prevalence of heart diseases amongst individuals around the globe, there is an urgent need to design computer-aided approaches to automatically analyze ECG signals. Recently, computer vision-based techniques have demonstrated remarkable performance in medical image analysis in a variety of applications and use cases. This paper proposes an approach based on Convolutional Autoencoders (CAEs) and Transfer Learning (TL). Our approach is an ensemble way of learning, the most useful features from both the signal itself, which is the input of the CAE, and the spectrogram version of the same signal, which is fed to a convolutional feature extractor named MobileNetV1. Based on the experiments conducted on a dataset collected from 3 well-known hospitals in Baghdad, Iraq, the proposed method claims good performance in classifying four types of problems in the ECG signals. Achieving an accuracy of 97.3% proves that our approach can be remarkably fruitful in situations where access to expert human resources is scarce. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of medical image repositories nowadays has led to problems in managing and retrieving medical visual data. This has proved the necessity of Content-Based Image Retrieval (CBIR) with the aim of facilitating the investigation of such medical imagery. One of the most serious challenges that require special attention is the representational quality of the embeddings generated by the retrieval pipelines. These embeddings should include global and local features to obtain more useful information from the input data. To fill this gap, in this paper, we propose a CBIR framework that utilizes the power of deep neural networks to efficiently classify and fetch the most related medical images with respect to a query image. Our proposed model is based on combining Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) and learns to capture both the locality and also the globality of high-level feature maps. Our method is trained to encode the images in the database and outputs a ranking list containing the most similar image to the least similar one to the query. To conduct our experiments, an intermodal dataset containing ten classes with five different modalities is used to train and assess the proposed framework. The results show an average classification accuracy of 95.32 % and a mean average precision of 0.61. Our proposed framework can be very effective in retrieving multimodal medical images with the images of different organs in the body. [ABSTRACT FROM AUTHOR]

Analyzing human behavior and detecting visual anomaly are important applications of video surveillance systems in many fields such as security systems and intelligent buildings. Person re-identification (RE-ID) is one of the main steps in a surveillance system, where, it has directly an effect on system performance. Occluded body parts, backgrounds clutter, and variations in pose and scene illumination are some noticeable problems in appearance-based RE-ID approaches, as they have an effect on pedestrians’ appearance during tracking task. Among the mentioned problems, person RE-ID considering appearance changes caused by occlusion can be considered as a common problem in video surveillance systems. In this paper, some existing people RE-ID approaches are briefly reviewed in terms of robustness to person body occlusion. Also, the experimental results reported in these approaches are compared using some partial occluded databases. The comparison results demonstrate the supremacy of the non-pose-guided RE-ID approaches. [ABSTRACT FROM AUTHOR]

In this paper, a 1*2 all-optical power splitter has been presented which is suitable for the third window of optical communications based on photonic crystal structures. This structure can provide a 50% transmission coefficient at a wavelength of 1550 nm in each splitter output branch. The device has been designed based on an input waveguide, two output waveguides, and an L4 resonant cavity in which the transmission coefficient of the structure is improved by infiltrating an optical fluid in some holes without any changes in the place and size of the radius of holes. [ABSTRACT FROM AUTHOR]

In this paper, we compare dispersion characteristics of chloroform-core photonic crystal fibers (PCFs) with circular lattice and hexagonal lattice in the case of different air hole diameters. By varying lattice constant Ʌ and filling factor in the first ring d1/Ʌ, we can easily control chromatic dispersion and achieve three optimal structures for the circular lattice of fibers #CF1 (Ʌ = 1.0 µm, d1/Ʌ = 0.65), #CF2 (Ʌ = 1.0 µm, d1/Ʌ = 0.7), and #CF3 (Ʌ = 2.0 µm, d1/Ʌ = 0.3) and two optimal structures for the hexagonal lattice of fibers #HF1 (Ʌ = 1.0 µm, d1/Ʌ = 0.5), #HF2 (Ʌ = 2.0 µm, d1/Ʌ = 0.3). At the same structural parameter (Ʌ = 2.0 µm, d1/Ʌ = 0.3) and the corresponding pumping wavelength, the circular structure has a dispersion smaller by 5.598 ps/mn/km than the hexagonal lattice. #CF1 has all-normal dispersion with the peak of the dispersion curve asymptote to the zero-dispersion curve which is very suitable for coherent supercontinuum generation (SCG). The #HF1 structure has a near-zero flat anomalous dispersion in the wide wavelength range from 1.1 µm to 1.4 µm. Our results will be an important premise in choosing a PCF structure to study SCG. [ABSTRACT FROM AUTHOR]