bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal reactive power pricing with transformer variable taps using genetic algorithm
Authors: Kor Ahmad , Zeynal Hossein , Zakaria Zuhaina , Hosseini Seyed Hamid
Published: December 2022

Ancillary services in interconnected networks are crucial to secure real power transfers, maintaining network reliability, improving power quality as well as network stability. Reactive power has always been of special importance as one of the most substantial ancillary services required to control the voltage in power grid. This paper presents a fresh reactive power pricing approach by embedding variable transformer taps into voltage ampere reactive (var) pricing method. To carry out the resultant optimization problem with a set of complicating constraints, the genetic algorithm takes part and guarantees the global optimal solution. The simulation results show that when transformer tap works as a control variable, the total cost of reactive power can be decreased. However, the incorporation of transformer taps in the pricing model can slightly complicates voltage profiles as the sensitivity of bus voltages to the reactive power variations in the system is increased. Further, the findings reiterate that by optimal position of transformer taps, technically, the security index of the system can be enhanced while the var price for end-users (lowest purchase cost for independent system operator) being more reasonable. To simulate the proposed reactive pricing method, the standard IEEE 30-bus system is employed to analyze the proposed method.
Maximum power point tracking controller using Lyapunov theorem of wind turbine under varying wind conditions
Authors: Yahiaoui Maamar , Afif Benameur , Brahmi Brahim , Horch Mohamed , Serraoui Mohamed
Published: December 2022

Due to the instantaneous variation in wind speed, it is necessary to identify the optimal rotational speed that ensures maximum energy efficiency and system stability. We proposed a controller based on the Lyapunov theorem to extract the maximum power from wind speed and to ensure the overall stability of the controlled system under random operating conditions imposed by wind speed and parameter variations. The control of the Tip speed ratio is based on the Lyapunov theorem (TSR_LT), which is a controller based on Lyapunov's theory and the definition of a positive, energetic function, to ensure the stability of the system being controlled, the dynamics of this function must be negative. The viability of this work is demonstrated by MATLAB-based mathematical and simulation models and a comparison with the results obtained using proportional integral (PI) controller-based tip speed ratio control (TSR_PI controller). The simulation results demonstrate the controller's effectiveness.
Enhanced two-terminal impedance-based fault location using sequence values
Authors: Idris Muhd Hafizi , Adzman Mohd Rafi , Mokhlis Hazlie , Awalin Lilik Jamilatul , Tajuddin Mohammad Faridun Naim
Published: December 2022

Fault at transmission line system may lead to major impacts such as power quality problems and cascading failure in the grid system. Thus, it is very important to locate it fast so that suitable solution can be taken to ensure power system stability can be retained. The complexity of the transmission line however makes the fault point identification a challenging task. This paper proposes an enhanced fault detection and location method using positive and negative-sequence values of current and voltage, taken at both local and remote terminals. The fault detection is based on comparison between the total fault current with currents combination during the pre-fault time. While the fault location algorithm was developed using an impedance-based method and the estimated fault location was taken at two cycles after fault detection. Various fault types, fault resistances and fault locations have been tested in order to verify the performance of the proposed method. The developed algorithms have successfully detected all faults within high accuracy. Based on the obtained results, the estimated fault locations are not affected by fault resistance and line charging current. Furthermore, the proposed method able to detect fault location without the needs to know the fault type.
Development of methods for managing energy consumption and energy efficiency in a common system
Authors: Aslanova Nigar R. , Abdullayeva Esmira J. , Beloglazov Aleksandr V.
Published: December 2022

The work aims to analyze and examine renewable energy sources (RES) to develop interconnected energy efficiency and energy consumption management system by integrating the software-defined machine-to-machine (M2M) communication. The article’s objectives include analysis of using RES as alternative raw materials for electricity production, the study of intelligent technologies for integrating RES into monitoring and control systems, research of devices and methods for monitoring energy production and consumption, analysis of sensor application for automation of control systems in the energy sector, a study of data transmission and information processing rates. The study results showed that the data transfer rate was delayed by 6 seconds to process 1,000 MB of information. It has been proven that wind energy can be used most efficiently within a 12-hour daily cycle, in contrast to tidal energy and solar energy. It is shown that due to the cyclical nature of obtaining energy from renewable sources, they do not fully provide energy to a large city, on the basis of which it is necessary to additionally use other energy sources. Three different types of power generation facilities were examined and compared. Wind farms were found to have the highest potential for electricity generation, amounting to 1,600-1,700 kW.
Experimental review of an improving system on wireless power transfer via auto tuning of frequency
Authors: Yamaguchi Kazuya , Okamura Ryusei , Terada Haruto , Iida Kenichi
Published: December 2022

Wireless power transfer for electric vehicles is focused because these vehicles cannot run long distance without frequently charging. If these vehicles are charged from outside wirelessly, for example an alternating current (AC) power supply is embed under road, the problem is going to be solved. However, efficiency of wireless power transfer depends on various factors, therefore many contrivances should be considered to realize optimal transfer. In this paper, we focused on frequency of inverter, and created auto tuning system of it in response to the distance of inductors. On this system, frequency was modified automatically by a microcontroller and sensor at the same time position of a load changed. Finally, we confirmed that voltage of light emitting diode (LED) was improved by utilizing our system compared with non-tuning frequency.
Fabrication and experimental study of transformer 400 V with a simple rectifier circuit design
Authors: Puspasari Fitri , Sismanto Sismanto , Ashari Ahmad
Published: December 2022

The demand for increased voltage in renewable energy sources is relatively high. This study examines the rapid development of technology considering the use of voltage-increasing transformers. Voltage regulator circuits are generally used to stabilize the output voltage of the rectifier according to the amount of input from the transformer. However, components for high-voltage stabilizer circuits are rare, which becomes an obstacle to the stabilization of the rectifier output. This study aimed to determine the performance of the designed rectifier circuit against a non-center tap step-up direct current (DC) 400 V transformer and compare the measurement results to manual calculations. The research method is a direct comparison between the input and output voltage values of the transformer after going through a rectifier circuit. This experiment was conducted using the repeatability method three to five times for each voltage variation on the transformer. The voltage variations successfully created are 0 to 50, 0 to 100, 0 to 200, and 0 to 400 V. The output test results from the DC transformer and rectifier circuit show linear results and an increase in peak-to-peak voltage data between the transformer and rectifier outputs by 3.8%.
Designing smart pulse flow meters using diversion analysis
Authors: Micheev Pavel S. , Muraviev Konstantin A. , Rezchikova Elena V. , Selivanov Kirill V.
Published: December 2022

The operation of modern housing infrastructure is characterized by a constant increase in the cost of the limited resources used. This necessitates the priority implementation in the concept of a smart home of elements aimed at resource saving and their rational management. The study provides an overview of the implementation architectures of the internet of things (IoT) concept in the construction of home automation systems and the requirements they impose on the implementation of smart primary meters of controlled physical quantities. Based on a diversion analysis, a promising smart water meter was developed. The prototype is ergonomic and has a structural form factor convenient for further integration. The designed model of the electronic module of the water flow monitoring system implements, in addition to typical tasks, additional functionality: transfer of recorded indicators and technical information to the cloud storage, warning the user about an emergency situation, accumulation of current data in non-volatile memory. It is possible to use the accumulated statistics for training the predictive analysis module. The proposed architecture option will allow creating energy-efficient elements of home automation systems in the future.
Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language
Authors: Azhari Zul Imran , Setumin Samsul , Noorsal Emilia , Abdullah Mohd Hanapiah
Published: December 2022

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.
Inductively coupled distributed static compensator for power quality analysis of distribution networks
Authors: Yadav Kundala Praveen Kumar , Mangaraj Mrutyunjaya , Sudabattula Suresh Kumar
Published: December 2022

In this research paper, an inductively coupled distributed static compensator (IC-DSTATCOM) for three phase three wire (3P3W) electric power distribution system (EPDS) is proposed. The contraction of power quality (PQ) was marked as a perilous droop mode bump into direct coupled distributed static compensator (DC-DSTATCOM). To regain the PQ, inductive coupling transformer is assisted in conjunction with DC-DSTATCOM. The system equivalent circuit of IC-DSTATCOM is accomplished by take into account of impedance of both transformer and DC-DSTATCOM to reveal the filtering technique. The filtering icos∅ mechanism is performed by following the generalized mathematical approach using MATLAB/Simulink. A case education is reviewed in detail to illustrate the performance of both DC-DSTATCOM and IC-DSTATCOM. The IC-DSTATCOM is amplified healthier as compared to other in terms of harmonics shortening, good power factor, load balancing, and potential regulation. To examine the effectiveness, simulation outputs of the IC-DSTATCOM with different PQ parameter indices are presented by following the benchmark measure of IEEE-2030-7-2017 and IEC-61000-1 system code.
Single-phase transformerless inverter topologies at different levels for a photovoltaic system, with proportional resonant controller
Authors: Abdelhak Lamreoua , Benslimane Anas , Jamal Bouchnaif , Ouariachi Mostafa El
Published: December 2022

In this paper, we have studied the topologies of single-phase transformerless inverters with different levels using a proportional-integral-resonant (PIR) AC controller, and the multi-level cascade inverter topology with sinusoidal pulse with modulation (SPWM) control in an open and closed loop. To ensure that these photovoltaic inverters can inject a defined amount of reactive power into the grid according to international regulations. Therefore, precise monitoring of the mains voltage vector by a phase-locked loop (PLL) system is applied to ensure the proper functioning of this system. For inverter topologies with less than three levels, the simulation results show that the highly efficient and reliable inverter concept (HERIC) topology performance is better than that of H5 and H6. On the other hand, the performance of the topology H6 ameliorate is superior to those of H4, H5, and HERIC in currents of leakage. On the other hand, for the control of cascaded multi-level closed-loop inverters, we notice that there is an improvement in the spectra and the elimination of all frequency harmonics, close to that of the fundamental, and a reduction in the rate of harmonic current distortion.
A novel fuzzy logic control for a zero current switching-based buck converter to mitigate conducted electromagnetic interference
Authors: M'barki Zakaria , Senhaji Rhazi Kaoutar , Mejdoub Youssef
Published: December 2022

This research provides a new control technique for mitigating conducted electromagnetic interference (EMI) in a buck converter designed for solar applications. Indeed, hard-switching direct current to direct current (DC-DC) converters, commonly used in industrial applications, pose a significant risk to the surrounding environment regarding electromagnetic compatibility (EMC). Usually, the fast-switching phase induces abrupt changes in current and voltage, which adds to substantial electromagnetic interference in both conducted and radiated modes and excessive auditory noise. An architecture based on the duality of soft-switching topology and fuzzy logic control technology is developed to address these issues. On the one hand, resonant circuit topologies are used to induce switches to achieve soft switching conditions, which subsequently lessen the effects of EMI. On the other hand, the adoption of fuzzy logic control technology is interesting since it can reduce electrical stresses during switching. Furthermore, the simulation results show that zero current switching (ZCS) soft-switching closed-loop fuzzy logic converters outperform typical open-loop converters and soft-switching closed-loop converters with proportional integral (PI) control in terms of EMC requirements.
A microsystem design for controlling a DC motor by pulse width modulation using MicroBlaze soft-core
Authors: Zemmouri Abdelkarim , Barodi Anass , Dahou Hamad , Alareqi Mohammed , Elgouri Rachid , Hlou Laamari , Benbrahim Mohammed
Published: December 2022

This paper proposes a microsystem based on the field programmable gate arrays (FPGA) electronic board. The preliminary objective is to manipulate a programming language to achieve a control part capable of controlling the speed of electric actuators, such as direct current (DC) motors. The method proposed in this work is to control the speed of the DC motor by a purely embedded architecture within the FPGA in order to reduce the space occupied by the circuit to a minimum and to ensure the reliability of the system. The implementation of this system allows the embedded MicroBlaze processor to be installed side by side with its memory blocks provided by Xilinx very high-speed integrated circuit (VHSIC) hardware description language (VHDL), Embedded C. The control signal of digital pulse-width modulation pulses is generated by an embedded block managed by the same processor. This potential application is demonstrated by experimental simulation on the Vertix5 FPGA chip.
Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm
Authors: Mohammed Reham H. , Aboelmorsy Mohamed E. , Elnaghi Basem E.
Published: December 2022

Mobile robots are typically depending only on robot kinematics control. However, when high-speed motions and highly loaded transfer are considered, it is necessary to analyze dynamics of the robot to limit tracking error. The goal of this paper is to present a new algorithm, chaotic-billiards optimizer (C-BO) to optimize internal controller parameters of a differential-drive mobile robot (DDMR)-based dynamic model. The C-BO algorithm is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. In addition, a comparison between the performance of C-BO and ant colony optimization (ACO) to determine the optimum controller coefficient that provides superior performance and convergence of the path tracking. The ISE criterion is selected as a fitness function in a simulation-based optimization strategy. For the point of accuracy, the velocity-based dynamic compensation controller was successfully integrated with the motion controller proposed in this study for the robot's kinematics. Control structure of the model was tested using MATLAB/Simulink. The results demonstrate that the suggested C-BO, with steady state error performance of 0.6 percent compared to ACO's 0.8 percent, is the optimum alternative for parameter optimizing the controller for precise path tracking. Also, it offers advantages of quick response, high tracking precision, and outstanding anti-interference capability.
Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis
Authors: Valsala Bhavana , Honnamachanahalli Kariputtaiah Krishnappa
Published: December 2022

In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.
A novel sketch based face recognition in unconstrained video for criminal investigation
Authors: Lakshmi Napa , Arakeri Megha P.
Published: December 2022

Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.
Attention correlated appearance and motion feature followed temporal learning for activity recognition
Authors: Ha Manh-Hung , Pham The-Anh , Thanh Dao Thi , Tran Van Luan
Published: December 2022

Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.
Mathematical modeling and kinematic analysis of 5 degrees of freedom serial link manipulator for online real-time pick and place applications
Authors: Singh Abhilasha , Venkatesan Kalaichelvi , Nagarasan Yuvalakshmi , Ramanujam Karthikeyan , Karuppusamy Kumar
Published: December 2022

Modeling and kinematic analysis are crucial jobs in robotics that entail identifying the position of the robot’s joints in order to accomplish particular tasks. This article uses an algebraic approach to model the kinematics of a serial link, 5 degrees of freedom (DOF) manipulator. The analytical method is compared to an optimization strategy known as sequential least squares programming (SLSQP). Using an Intel RealSense 3D camera, the colored object is picked up and placed using vision-based technology, and the pixel location of the object is translated into robot coordinates. The LOBOT LX15D serial bus servo controller was used to transmit these coordinates to the robotic arm. Python3 programming language was used throughout the entire analysis. The findings demonstrated that both analytical and optimized inverse kinematic solutions correctly identified colored objects and positioned them in their appropriate goal points.
Deep learning based masked face recognition in the era of the COVID-19 pandemic
Authors: Abdulmunem Ashwan A. , Al-Shakarchy Noor D. , Safoq Mais Saad
Published: December 2022

During the coronavirus disease 2019 (COVID-19) pandemic, monitoring for wearing masks obtains a crucial attention due to the effect of wearing masks to prevent the spread of coronavirus. This work introduces two deep learning models, the former based on pre-trained convolutional neural network (CNN) which called MobileNetv2, and the latter is a new CNN architecture. These two models have been used to detect masked face with three classes (correct, not correct, and no mask). The experiments conducted on benchmark dataset which is face mask detection dataset from Kaggle. Moreover, the comparison between two models is driven to evaluate the results of these two proposed models.
User equipment geolocation depended on long-term evolution signal-level measurements and timing advance
Authors: Shakir Zaenab D. , Zec Josko , Kostanic Ivica , Al-Thaedan Abbas , M. Salah Monera Elhashmi
Published: December 2022

A new approach is described for investigating the accuracy of positioning active long-term evolution (LTE) users. The explored approach is a network-based method and depends on signal level measurements as well as the coverage of the serving cell. In a two-dimensional coordinate system, the algorithm simultaneously applies LTE measured data with a combination of a basic prediction model to locate the mobile device’s user. Furthermore, we introduce a unique method that combines timing advance (TA) and the measured signal level to narrow the search region and improve accuracy. The developed method is assessed by comparing the predicted results from the proposed algorithm with satellite measurements from the global positioning system (GPS) in various scenarios calculated via the number of cells that user equipment concurrently reports. This work separates seven different cases starting from a single reported cell to five reported cells from up to 3 sites. For analysis, the root mean square error (RMSE) is computed to obtain the validation for the proposed approach. The study case demonstrates location accuracy based on the numbers of registered cells with the mean RMSE improved using TA to approximately 70-191 m for the range of scenarios.
Flexible handover solution for vehicular ad-hoc networks based on software defined networking and fog computing
Authors: Murtadha Muayad Khalil , Mushgil Baydaa Mohammad
Published: December 2022

Vehicular ad-hoc networks (VANET) suffer from dynamic network environment and topological instability that caused by high mobility feature and varying vehicles density. Emerging 5G mobile technologies offer new opportunities to design improved VANET architecture for future intelligent transportation system. However, current software defined networking (SDN) based handover schemes face poor handover performance in VANET environment with notable issues in connection establishment and ongoing communication sessions. These poor connectivity and inflexibility challenges appear at high vehicles speed and high data rate services. Therefore, this paper proposes a flexible handover solution for VANET networks by integrating SDN and fog computing (FC) technologies. The SDN provides global knowledge, programmability and intelligence functions for simplified and efficient network operation and management. FC, on the other hand, alleviates the core network pressure by providing real time computation and transmission functionalities at edge network to maintain the demands of delay sensitive applications. The proposed solution overcomes frequent handover challenges and reduces the processing overhead at core network. Moreover, the simulation evaluation shows significant handover performance improvement of the proposed solution compared to current SDN based schemes, especially in terms of handover latency and packet loss ratio under various simulation environments.
A simple design and fabrication of polarization reconfigurable antenna for industrial scientific and medical-band applications
Authors: Hossain Md. Azad , Rahman Muhammad Asad , Murshed Abu Hena , Nishiyama Eisuke , Toyoda Ichihiko
Published: December 2022

This paper proposes a simple microstrip patch antenna (MPA) that can reconfigure its polarization states from linear to circular polarization in real-time by means of a PIN diode. An antenna is fed by a 50 Ω coaxial cable through the substrate of Teflon with relative permittivity of 2.15. The proposed antenna possesses a simple patch with a one-sided corner truncated to achieve polarization reconfigurability. A PIN diode is loaded to connect the main patch with a truncated corner and further maintain dual polarization states such as linear polarization (LP) and circular polarization (CP). Advanced design system (ADS) was used as a simulator to simulate the antenna, and a good understanding was obtained between simulated and measured results. Measured results showed a good agreement with simulated results at all working frequencies of interest. It shows minimum reflection coefficient gain with -10 dB scattering bandwidth 100 MHz for LP states and 170 MHz for CP states. It also shows an axial ratio of 1.56 dB for CP, and the cross-polarization level is also in a satisfying range.
Channel state information estimation for reconfigurable intelligent surfaces based on received signal analysis
Authors: Almamori Aqiel , Abbas Mohammed Adil
Published: December 2022

Recently, reconfigurable intelligent surfaces have an increasing role to enhance the coverage and quality of mobile networks especially when the received signal level is very weak because of obstacles and random fluctuation. This motivates the researchers to add more contributions to the fields of reconfigurable intelligent surfaces (RIS) in wireless communications. A substantial issue in reconfigurable intelligent surfaces is the huge overhead for channel state information estimation which limits the system’s performance, oppressively. In this work, a newly proposed method is to estimate the angle of arrival and path loss at the RIS side and then send short information to the base station rather than huge overhead as in previous research. The estimated channel state information is used to beamform the downlink waveform toward users accurately. The simulation results indicate that the proposed algorithm calculated the angle of arrival of users, admirably especially at a high signal-to-noise ratio. Moreover, a considerable spectral efficiency enhancement is obtained as compared to the traditional methods.
Secure cluster-based routing using multi objective-trust centric artificial algae algorithm for wireless sensor network
Authors: Habbanakuppe Balachandra Divyashree , Chaluve Gowda Puttamadappa , Kanakapura Shivaprasad Nandini Prasad
Published: December 2022

Nowadays, wireless sensor network (WSN) is developed as a key technology to observe and track applications over a wide range. However, energy consumption and security are considered as important issues in the WSN. In this paper, the multi objective-trust centric artificial algae algorithm (M-TCAAA) is proposed to accomplish a secure broadcasting over the WSN. The proposed M-TCAAA is used to choose the secure cluster head (SCH) as well as routing path, based on the distinct fitness measures such as trust, communication cost, residual energy, and node degree. Hence, the M-TCAAA is used to ensure a secure data transmission while decreasing the energy consumed by the nodes. The performance of the M-TCAAA is analyzed by means of energy consumption, packet delivery ratio (PDR), throughput, end to end delay (EED), normalized routing load (NRL), and network lifetime. The existing researches namely energy aware trust and opportunity-based routing with mobile nodes (ETOR-MN), grey wolf updated whale optimization (GUWO), secure cluster-based routing protocol (SCBRP), secure routing protocol based on multi-objective ant-colony-optimization (SRPMA) and multi objective trust aware hybrid optimization (MOTAHO) are considered for evaluating the M-TCAAA. The PDR of the M-TCAAA for 100 nodes is 99.87%, which is larger than the ETOR-MN, GUWO, SRPMA and MOTAHO.
Discovering the spatial locations of the radio frequency radiations effects around mobile towers
Authors: Al-Allaq Zaid Jabbar , Dhaam Haidar Zaeer , Al-Khazraji Mohammed Jawad Al Dujaili , Al-Khuzaie Muntadhar Hameed Ismael
Published: December 2022

Nowadays, smart devices have become a major part of human life, and this need has led to an increase in the demand for these devices, prompting major telecommunications companies to compete with each other to acquire the bulk of this market. This competition led to a significant increase in the number of mobile towers, to expand the coverage area. Each communication tower has transmitters and receivers to connect subscribers within the mobile network and other networks. The receivers and transmitters of each mobile tower operate on radio frequency waves. These waves can cause harm to humans if the body tissues absorb the radiation resulting from these waves. Headache, discomfort, and some other diseases are among the effects resulting from the spatial proximity to the mobile towers. In this paper, a model based on geographic information systems (GIS) software is proposed for the purpose of discovering the area of exposure to radio frequency radiation. This model can assists mitigate the opportunities of exposure to these radiations, thus reducing its danger. Real data of the levels of electromagnetic pollution resulting from mobile towers were analyzed during this study and compared with international safety standards.
New low-density-parity-check decoding approach based on the hard and soft decisions algorithms
Authors: El Ouakili Hajar , Touati Hassan , Kadi Abdelilah , Mehdaoui Younes , El Alami Rachid
Published: December 2022

It is proved that hard decision algorithms are more appropriate than a soft decision for low-density parity-check (LDPC) decoding since they are less complex at the decoding level. On the other hand, it is notable that the soft decision algorithm outperforms the hard decision one in terms of the bit error rate (BER) gap. In order to minimize the BER and the gap between these two families of LDPC codes, a new LDPC decoding algorithm is suggested in this paper, which is based on both the normalized min-sum (NMS) and modified-weighted bit-flipping (MWBF). The proposed algorithm is named normalized min sum- modified weighted bit flipping (NMSMWBF). The MWBF is executed after the NMS algorithm. The simulations show that our algorithm outperforms the NMS in terms of BER at 10-8 over the additive white gaussian noise (AWGN) channel by 0.25 dB. Furthermore, the proposed NMSMWBF and the NMS are both at the same level of decoding difficulty.
Automatic modulation classification based deep learning with mixed feature
Authors: Shah Ali H. , Miry Abbas Hussien , Salman Tariq M.
Published: December 2022

The automatic modulation classification (AMC) plays an important and necessary role in the truncated wireless signal, which is used in modern communications. The proposed convolution neural network (CNN) for AMC is based on a method of feature expansion by integrating I/Q (time form) with r/Ɵ (polar form) in order to take advantage of two things: first, feature expansion helps to increase features; the second is that converting to polar form helps to increase classification accuracy for higher order modulation due to diversity in polar form. CNN consists of six blocks. Each block contains symmetric and asymmetric filters, as well as max and average pooling filters. This paper uses DeepSig: RadioML which is a dataset of 24 modulation classes. The proposed network has outperformed many recent papers in terms of classification accuracy for 24 modulation types, with a classification accuracy of up to 96.06 at an SNR=20 dB.
Energy-efficient non-orthogonal multiple access for wireless communication system
Authors: Darus Muhamad Firdaus , Idris Fakrulradzi , Hashim Norlezah
Published: December 2022

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed.
The effectiveness of methods and algorithms for detecting and isolating factors that negatively affect the growth of crops
Authors: Yessenova Moldir , Abdikerimova Gulzira , Ayazbaev Talgatbek , Murzabekova Gulden , Ismailova Aisulu , Beldeubayeva Zhanar , Ainagulova Aliya , Mukhanova Ayagoz
Published: December 2022

This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth’s surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture.
 
Investigating the relationship between knowledge management practices and organizational learning practices in the universities’ environment
Authors: Al-Sulami Zainab Amin , Hashim Hayder Salah , Ali Nor’ashikin , Abduljabbar Zaid Ameen
Published: December 2022

The concept of knowledge management (KM) and organizational learning (OL) has been embraced by organizations to complement each other. Higher education institutions have embraced KM and OL as a means to improve organizational efficiency. This research explores the link between KM and OL. The target population included all the 432 academicians and administrators from 35 public universities in Iraq. The sampling was selected using a stratified random sampling technique. The correlation among the components of KM and OL was tested as well as the effect of KM components on OL. The findings were derived using smart partial least square. The findings showed that there is significant correlation between components of KM and components of OL. The regression analysis showed also that the effect of KM and its components; knowledge creation, knowledge sharing, knowledge storage, knowledge application and knowledge acquisition on OL are significant. These findings provide insights to universities management on strategies to implement KM practices that can align with OL practices to assure dynamic lifelong mechanisms for the basic daily activities such as teaching, learning, researching, and supervision.
Comparing machine learning and deep learning classifiers for enhancing agricultural productivity: case study in Larache Province, Northern Morocco
Authors: Belattar Sara , Abdoun Otman , El Khatir Haimoudi
Published: December 2022

The agriculture sector in the Tangier-Tetouan-Al-Hoceima-Region (Northern Morocco) contributes a significant percentage to the national revenue. The Larache Province is at the regional forefront in agriculture terms due to its large irrigated areas. Golden-Gogi is a biological farm located in the Larache Province, and its objective is to produce organic crops. Besides climate change, this farm suffers from biotic factors such as snails and insects. These problems cause diseases in plants, resulting in massive crop production losses. Early detection of disease and biotic factors in plants is a difficult task for farmers, but it is now possible thanks to artificial intelligence. For that reason, we aim to contribute to this Province by comparing the well-known models in machine learning (ML) and deep learning (DL) used in early plant disease detection to specify the best-classifier in terms of detecting mint plant diseases. Mint plant is a major crop on the Golden-Gogi farm, and its dataset was collected from there. As per findings, DL classifiers outperform ML classifiers in disease detection. The best-classifier is DenseNet201, with high accuracy of 94.12%. Hence, the system using DenseNet201 offers a solution for farmers of this Province in making urgent decisions to avoid mint yield losses.
Effects of different geometric patterns on free form gridshell structures
Authors: Goodarzi Marjan , Azimi Mahshad , Mohades Ali , Forghani-elahabad Majid
Published: December 2022

Gridshells are commonly known as structures with the shape and rigidity of a double curvature shell consisting of a grid, not a continuous surface. In recent decades, these structures have attracted significant attention. The impact of various geometric patterns on free form gridshell structures is investigated here to demonstrate the necessity of collaboration between structural and architectural characteristics in enhancing structure efficiency. To that goal, a framework is proposed where three shells are first designed, and then six geometric patterns are formed on them. The main factors for evaluation of gridshells are decreasing the steel weight as an economic index and decreasing the displacement as a structural index, also, finite element method is used for structurally analyzing the gridshells, and the generated gridshells are compared to each other based on the mentioned indices. For the optimization process, an approach is suggested to find the most optimum gridshell, then numerical results show the efficiency of the proposed alternative approach.
Advanced approach for encryption using advanced encryption standard with chaotic map
Authors: Alemami Yahia , Mohamed Mohamad Afendee , Atiewi Saleh
Published: December 2022

At present, security is significant for individuals and organizations. All information need security to prevent theft, leakage, alteration. Security must be guaranteed by applying some or combining cryptography algorithms to the information. Encipherment is the method that changes plaintext to a secure form called cipherment. Encipherment includes diverse types, such as symmetric and asymmetric encipherment. This study proposes an improved version of the advanced encryption standard (AES) algorithm called optimized advanced encryption standard (OAES). The OAES algorithm utilizes sine map and random number to generate a new key to enhance the complexity of the generated key. Thereafter, multiplication operation was performed on the original text, thereby creating a random matrix (4×4) before the five stages of the coding cycles. A random substitution-box (S-Box) was utilized instead of a fixed S-Box. Finally, we utilized the eXclusive OR (XOR) operation with digit 255, also with the key that was generated last. This research compared the features of the AES and OAES algorithms, particularly the extent of complexity, key size, and number of rounds. The OAES algorithm can enhance complexity of encryption and decryption by using random values, random S-Box, and chaotic maps, thereby resulting in difficulty guessing the original text.
Machine learning based augmented reality for improved learning application through object detection algorithms
Authors: Hanafi Anasse , Elaachak Lotfi , Bouhorma Mohammed
Published: December 2022

Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-of-the-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.
Design a cryptosystem using elliptic curves cryptography and Vigenère symmetry key
Authors: Trung Mai Manh , Do Le Phe , Tuan Do Trung , Tanh Nguyen Van , Tri Ngo Quang
Published: December 2022

In this paper describes the basic idea of elliptic curve cryptography (ECC) as well as Vigenère symmetry key. Elliptic curve arithmetic can be used to develop elliptic curve coding schemes, including key exchange, encryption, and digital signature. The main attraction of elliptic curve cryptography compared to Rivest, Shamir, Adleman (RSA) is that it provides equivalent security for a smaller key size, which reduces processing costs. From the theorical basic, we proposed a cryptosystem using elliptic curves and Vigenère cryptography. We proposed and implemented our encryption algorithm in an integrated development environment named visual studio 2019 to design a safe, secure, and effective cryptosystem.
A new four-dimensional hyper-chaotic system for image encryption
Authors: Shakir Huda R. , Mehdi Sadiq A. , Hattab Anwar A.
Published: December 2022

Currently, images are very important with the rapid growth of communication networks. Therefore, image encryption is a process to provide security for private information and prevent unwanted access to sensitive data by unauthorized individuals. Chaos systems provide an important role for key generation, with high randomization properties and accurate performance. In this study, a new four-dimensional hyper-chaotic system has been suggested that is used in the keys generation, which are utilized in the image encryption process to achieve permutation and substitution operations. Firstly, color bands are permuted using the index of the chaotic sequences to remove the high correlation among neighboring pixels. Secondly, dynamic S-boxes achieve the principle of substitution, which are utilized to diffuse the pixel values of the color image. The efficiency of the proposed method is tested by the key space, histogram, and so on. Security analysis shows that the proposed method for encrypting images is secure and resistant to different attacks. It contains a big key space of (2627) and a high sensitivity to a slight change in the secret key, a fairly uniform histogram, and entropy values nearby to the best value of 8. Moreover, it consumes a very short time for encryption and decryption.