bluuebunny/crossref_metadata_2025 huggingface dataset:
Alternating current flashover voltage of a uniform polluted glass flat insulator under parallel electric discharges effect
Authors: Satta Samia , Bayadi Abdelhafid , Boudissa Rabah
Published: May 2022

This work presents a study aiming to analyze the development of parallel discharges on insulating surfaces of different sizes uniformly polluted. Moreover, it is aimed to find an equivalence between multiple point-point and plane-plane configurations. The study is carried out under alternating current (AC) voltage. The effects of many parameters on the electric strength of the considered air gap system configurations are presented. These parameters are the length of the creepage distance, the pollution degree and the contaminated surface width. Glass rectangular surface are used for the tests. Finally, a video camera system is used to support this investigation by laboratory observations of the full process of flashover mechanism. The obtained results show that there is a width limit approximately equal to 8 cm beyond which several arcs, parallel, consecutive and independent can develop. This limit represents the minimal electrical performance of the insulation and we did not observe any effect of the volume conductivity on it.
Fault tolerant nine-level inverter topology for solar water pumping applications
Authors: Rao Mucherla Narasimha , Karthick Nagaraj , Rao Airineni Madhukar
Published: May 2022

Diminished voltage pressure and occasional-general harmonic distortion are the essential causes for such a ways and extensive usage of multi-level inverters (MLIs) in numerous industrial applications. Nonetheless, unwavering quality is one of the significant worries of MLIs as it utilizes countless switches as contrasted to 2-level inverters. Here, a fault tolerant 9-level inverter setup for the use of photovoltaic (PV) system-water pumping applications is suggested. This fault tolerant 9-level inverter is accomplished by combining a 2-level inverter, a 3-level fault tolerant inverter alongside switches with bidirectional ability. The setup is taken care of with four PV fed sources. The arrangement suggested shows the behavior towards switch fault in at least one inverter legs under open circuit conditions. On account of source failure, it could use the better hotspot for introducing continuous power to the water pumping motor. Meanwhile, the suggested fault-tolerant inverter works as seven-level inverter. The activity related to proposed inverter in the course of various failure modes is mentioned and simulated the usage of MATLAB/Simulink.
Palm-sized quadrotor source localization using modified bio-inspired algorithm in obstacle region
Authors: Rausyan Fikri Muhamad , Wibowo Djamari Djati
Published: May 2022

This paper uses a palm-sized quadrotor to demonstrate the gas source localization (GSL) in obstacle regions. The chemical substance performs a dilute dispersion in the air, and it is challenging to locate the source position. The difficulty level of the GSL increases if the location is in the obstacle region. To estimate the gas source position, it indeed uses high computational cost systems. However, it leads to a new problem: a palm-sized quadrotor has a limited size and payload capacity. In this paper, a search based on the silkworm moth is employed. The algorithm works straightforwardly as the detection gas triggers its motion. A repulsion function is integrated into a searching method to compensate the algorithm for working in single surface obstacle regions. The validation data and the palm-sized quadrotor design are provided to convince the search effectiveness performance.
An automated transmitter positioning system for misalignment compensation of capacitive-coupled electric vehicles
Authors: Siddique Md. Nazrul Islam , Ahmed Nadim , Abdullah Saad Mohammad , Khan Md. Ziaur Rahman
Published: May 2022

Misalignments are one of the most unfavorable aspects of the capacitive power transfer (CPT) system, which is inevitable in most of the applications. Misalignments affect the overall resonances in the circuit and decrease the power transfer capability and efficiency. In this paper, an automated electro-mechanical transmitter positioning system is proposed for capacitive wireless charging to withstand both axial and rotational misalignments. The system can align the transmitter based on an adaptive algorithm, with respect to the position of the receiver to mitigate the misalignments. The overall system is designed using SolidWorks and the algorithm is verified using Processing. Then, a hardware prototype is constructed in the laboratory. The accuracy of the proposed system is calculated and compared with the simulation results. The system can achieve an accuracy of 99.5%, in case of axial misalignment and an average accuracy of 98.6%, in case of both axial and rotational misalignments, which validate the simulation results.
A review of multi-agent mobile robot systems applications
Authors: Ameer Rasheed Ammar Abdul , Najm Abdullah Mohammed , Sabah Al-Araji Ahmed
Published: May 2022

A multi-agent robot system (MARS) is one of the most important topics nowadays. The basic task of this system is based on distributive and cooperative work among agents (robots). It combines two important systems; multi-agent system (MAS) and multi-robots system (MRS). MARS has been used in many applications such as navigation, path planning detection systems, negotiation protocol, and cooperative control. Despite the wide applicability, many challenges still need to be solved in this system such as the communication links among agents, obstacle detection, power consumption, and collision avoidance. In this paper, a survey of the motivations, contributions, and limitations for the researchers in the MARS field is presented and illustrated. Therefore, this paper aims at introducing new study directions in the field of MARS.
An efficient application of particle swarm optimization in model predictive control of constrained two-tank system
Authors: Kia Kojouri Ahmad , Mashayekhi Fard Javad
Published: May 2022

Despite all the model predictive control (MPC) based solution advantages such as a guarantee of stability, the main disadvantage such as an exponential growth of the number of the polyhedral region by increasing the prediction horizon exists. This causes the increment in computation complexity of control law. In this paper, we present the efficiency of particle swarm optimization (PSO) in optimal control of a two-tank system modeled as piecewise affine. The solution of the constrained final time-optimal control problem (CFTOC) is derived, and then the PSO algorithm is used to reduce the computational complexity of the control law and set the physical parameters of the system to improve performance simultaneously. On the other hand, a new combined algorithm based on PSO is going to be used to reduce the complexity of explicit MPC-based solution CFTOC of the two-tank system; consequently, that the number of polyhedral is minimized, and system performance is more desirable simultaneously. The proposed algorithm is applied in simulation and our desired subjects are reached. The number of control law polyhedral reduces from 42 to 10 and the liquid height in both tanks reaches the desired certain value in 189 seconds. Search time and apply control law in 25 seconds.
A remote-controlled global navigation satellite system based rover for accurate video-assisted cadastral surveys
Authors: Visconti Paolo , Luceri Marzia , Velazquez Ramiro , Roberto De Fazio
Published: May 2022

One of the main tasks of a cadastral surveyor is to accurately determine property boundaries by measuring control points and calculating their coordinates. This paper proposes the development of a remotely-controlled tracking system to perform cadastral measurements. A Bluetooth-controlled rover was developed, including a Raspberry Pi Zero W module that acquires position data from a VBOX 3iSR global navigation satellite system (GNSS) receiver, equipped with a specific modem to download real-time kinematic (RTK) corrections from the internet. Besides, the Raspberry board measures the rover speed with a hall sensor mounted on a track, adjusting the acquisition rate to collect data at a fixed distance. Position and inertial data are shared with a cloud platform, enabling their remote monitoring and storing. Besides, the power supply section was designed to power the different components included in the acquisition section, ensuring 2 hours of energy autonomy. Finally, a mobile application was developed to drive the rover and real-time monitor the travelled path. The tests indicated a good agreement between rover measurements and those obtained by a Trimble R10 GNSS receiver (+0.25% mean error) and proved the superiority of the presented system over a traditional metric wheel.
Personal computer/programmable logic controller based variable frequency drive training platform using WxPython and PyModbus
Authors: Radhi Mahmood Jawad , Salim Ali Ramzy
Published: May 2022

Variable frequency drive (VFD) is one of the key elements in industrial field. It is used to match the three-phase induction motor’s speed and torque to the industrial field process requirements in addition to energy saving and efficiency improvement. This important role of the VFD asks for the development of an efficient training and cost effective platform for the electrical engineering students, technicians, and maintenance personals. This paper introduces a user-friendly platform through which the users can understand and practice the configuration of the various parameters of the VFD unit. This platform uses two computing devices to deal with the VFD; these are the personal computer (PC) and the programmable logic controller (PLC) which is also a computer but designed to operate in wide range of temperature and humidity and can accept digital and analog signals. The PC uses WxPython (cross-platform graphical user interfaces (GUI) toolkit of Python programming language) and PyModbus communication utility to play the role of the human machine interfacing (which allows the user to execute the communication requirements and at the same time provide an oscilloscope like facility to display the platform response in real time mode or history recorded mode). With this platform, the VFD’s parameters configuration is done via the RS-485 communication port using Modbus recommandation temporaire d'utilisation (RTU) communication protocol.
Robust control of aircraft flight in conditions of disturbances
Authors: Karimtaevna Satybaldina Dana , Bekbolatovna Amirzhanova Zinara , Assilkhanovna Mashtayeva Aida
Published: May 2022

One of the most dangerous parts of the flight is the landing phase, as most accidents occur at this stage. In order to reduce the effect of the low-level wind shear on the longitudinal motion of the aircraft in the glide path landing mode (task) a robust H- control is proposed. Dynamic models of the plane and wind shear are built. H2 and H∞ synthesis methods are investigated for the task of aircraft flight control in a vertical plane during landing under conditions of undefined disturbances. Both control methods allow to reduce height deviation significantly. However, suboptimal control H∞ provides better quality of transition processes both in height and speed than optimal control H2. The results of simulation of the synthesized system confirm the effectiveness of H∞ - control for increasing robust stability to uncertainties caused by wind disturbances.
Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control
Authors: Ayachi Chater El , Housny Halima , El Fadil Hassan
Published: May 2022

When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy uses a two hidden layer deep feedforward network (DFN), where the one-step secant algorithm is chosen for initializing the DFN parameters. All the design steps of the proposed adaptive controller are described. The multidimensional particle swarm optimization (PSO) algorithm is used for tuning the DFN parameters. Then, using two simulation efficiency tests, a comparison between the proposed PSO-based APID-DFN, the (non-optimized) APID-DFN, the feedforward network APID, and the fixed-parameter PID controllers proves much efficiency of the proposed adaptive controller. The results illustrate that the proposed PSO-based APID-DFN controller can ensure good quadrotor system stabilization and achieve minimum overshoot and faster convergence speed for all quadrotor motions. Thus, the proposed control strategy could be considered an additional intelligent method-based adaptive control for unmanned aerial vehicles.
Pose estimation algorithm for mobile augmented reality based on inertial sensor fusion
Authors: Alam Mir Suhail , Morshidi Malik Arman , Gunawan Teddy Surya , Olanrewaju Rashidah Funke , Arifin Fatchul
Published: May 2022

Augmented reality (AR) applications have become increasingly ubiquitous as it integrates virtual information such as images, 3D objects, video, and more to the real world, which further enhances the real environment. Many researchers have investigated the augmentation of the 3D object on the digital screen. However, certain loopholes exist in the existing system while estimating the object’s pose, making it inaccurate for mobile augmented reality (MAR) applications. Objects augmented in the current system have much jitter due to frame illumination changes, affecting the accuracy of vision-based pose estimation. This paper proposes to estimate the pose of an object by blending both vision-based techniques and micro electrical mechanical system (MEMS) sensor (gyroscope) to minimize the jitter problem in MAR. The algorithm used for feature detection and description is oriented FAST rotated BRIEF (ORB), whereas to evaluate the homography for pose estimation, random sample consensus (RANSAC) is used. Furthermore, gyroscope sensor data is incorporated with the vision-based pose estimation. We evaluated the performance of augmenting the 3D object using the techniques, vision-based, and incorporating the sensor data using the video data. After extensive experiments, the validity of the proposed method was superior to the existing vision-based pose estimation algorithms.
Degraded character recognition from old Kannada documents
Authors: Tumkur Narasimhaiah Sridevi , Rangarajan Lalitha
Published: May 2022

This paper addresses preparation of a dataset of Kannada characters which are degraded and robust recognition of such characters. The proposed recognition algorithm extracts the histogram of oriented gradients (HOG) features of block sizes 4x4 and 8x8 followed by principal component analysis (PCA) feature reduction. Various classifiers are experimented with and fine K-nearest neighbor classifier performs best. The performance of proposed model is evaluated using 5-fold cross validation method and receiver operating characteristic curve. The dataset devised is of size 10440 characters having 156 classes (distinct characters). These characters are from 75 pages of not well preserved old books. A comparison of proposed model with other features like Haar wavelet and Geometrical features suggests that proposed model is superior. It is observed that the PCA reduced features followed by fine K-nearest neighbor classifier resulted in the best accuracy with acceptance rate of 98.6% and 97.9% for block sizes of 4x4 and 8x8 respectively. The experimental results show that HOG feature extraction has a high recognition rate and the system is robust even with extensively degraded characters.
A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images
Authors: Musha Ahmmad , Al Mamun Abdullah , Tahabilder Anik , Hossen Md. Jakir , Hossen Busrat , Ranjbari Sima
Published: May 2022

There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
Design and implementation of two-dimensional digital finite impulse response filter using very high speed integrated circuit hardware description language
Authors: Singh Thingujam Churchil , Kumar Manoj
Published: May 2022

The main purpose of this paper is to design a two-dimensional digital finite impulse response (FIR) filter using data broadcast and non-broadcast structure. The implementation of two-dimensional digital FIR filter is done using very high speed integrated circuit hardware description language (VHDL). Rectangular window method is used for calculating 2D digital FIR filter coefficients for data broadcast and non-broadcast structure. The coefficients of the one-dimensional digital FIR filter are obtained using the MATLAB filter design and analysis (FDA) tool for two different cut-off frequencies and are multiplied to get the necessary coefficient for the two-dimensional FIR filter to be designed; the simulation is done on Artix-7 series field programmable gate array (FPGA), target device (xc7a35t-cpg236) using Vivadov.2015.2. The proposed design reduces the area utilization and the power consumption when compared with the existing literature. The experimental result shows that the power consumption is improved by 97% and there is an improvement of 24% in area utilization for the two-dimensional with and without data broadcast one dimensional FIR filter structures.
Real-time eyeglass detection using transfer learning for non-standard facial data
Authors: Jain Ritik , Goyal Aashi , Venkatesan Kalaichelvi
Published: May 2022

The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.
A blind steganography approach for hiding privacy details in images of digital imaging and communications in medicine using QR code
Authors: Rashad Mahmoud , Elhadad Ahmed , El-Saady Kamal
Published: May 2022

This study aims to hide patient’s privacy details of digital imaging and communications in medicine (DICOM) files using the quick response (QR) code images with the same size using steganographic technique. The proposed method is based on the properties of the discrete cosine transform (DCT) of the DICOM images to embed a QR code image. The proposed method includes two parts: data embedding and extraction process. Moreover, the stego DICOM image could be blindly used to produce the embedded QR code image without the existence of the original DICOM image. The performances of proposed method were evaluated using the metrics of the peak signal to noise ratio (PSNR), the structural similarity index (SSIM), the universal quality index (UQI), the correlation coefficient (R) and the bit error rate (BER) values. The experimental results scored a high PSNR after the embedding process by embedding a QR code image into the DICOM image with the same size.
Dominating set based arbitrary oriented bilingual scene text localization
Authors: Mirle Jayanth Roopa , Kapanaiah Mahantesh
Published: May 2022

Localizing and recognizing arbitrarily oriented text in natural scene images is the biggest challenge. It is because scene texts are often erratic in shapes. This paper presents a simple and effective graph representational algorithm for detecting arbitrary-oriented text location to smoothen the text recognition process because of its high impact and simplicity of representation. An arbitrarily oriented text can be horizontal, vertical, perspective, curved (diagonal/off-diagonal), or even a combination. As a pre-processing step, image enhancement is performed in the frequency domain to improve the representation of images that are invariant to intensity. It is necessary to draw bounding boxes for each candidate character in the scene images to extract text regions. This step is carried out by utilizing the advantage of the region-based approach called maximally stable extremal regions. A typical problem with curved text localization is that non-text objects may occur within localized text regions. Our method is the first in the literature that searches for dominating sets to solve this problem. This dominating set method outperforms several traditional methods, including deep learning methods used for arbitrary text localization, on challenging datasets like 13th international conference on document analysis and recognition (ICDAR 2015), multi-script robust reading competition (MRRC), CurvedText 80 (CUTE80), and arbitrary text (ArT).
The effect of Gaussian filter and data preprocessing on the classification of Punakawan puppet images with the convolutional neural network algorithm
Authors: Kusrini Kusrini , Arif Yudianto Muhammad Resa , Al Fatta Hanif
Published: May 2022

Nowadays, many algorithms are introduced, and some researchers focused their research on the utilization of convolutional neural network (CNN). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this research suggested that to increase the accuracy of the classification, preprocessing mechanism is another significant factor to be considered too. This study utilized Gaussian filter for preprocessing mechanism and VGG16 for learning architecture. The Gaussian filter was combined with different preprocessing mechanism applied on the selected dataset, and the measurement of the accuracy as the result of the utilization of the VGG16 learning architecture was acquired. The study found that the utilization of using contrast limited adaptive histogram equalization (CLAHE) + red green blue (RGB) + Gaussian filter and thresholding images showed the highest accuracy, 98.75%. Furthermore, another significant finding is that the Gaussian filter was able to increase the accuracy on RGB images, however the accuracy decreased for green channel images. Finally, the use of CLAHE for dataset preprocessing increased the accuracy dealing with the green channel images.
Microwave characterization of pandanus atrocarpus as potential organic-based dielectric substrate
Authors: Aris Mohd Aziz , Miswadi Nurfarahin , Subahir Suhaila , Jaafar Hajar , Mohd Redzwan Fatimah Nur
Published: May 2022

This study investigated the microwave characterization of a potential organic-based substrate pandanus atrocarpus. Pandanus atrocarpus, also known as "pandan mengkuang", is easily found at riverside and beach areas in Penisular Malaysia. The experiment's objective was to measure dielectric constant and tangent loss using the dielectric coaxial probe method by employing vector network analyzer (VNA) and dielectric probe. Dielectric constant and tangent loss are the crucial parameter in the microwave design. Three different samples of pandanus atrocarpus were measured and analyzed. The result showed that the dielectric constant of the pandanus substrate material depended on the leaves' water content. All experimental results obtained were analyzed, presented, and discussed in this paper.
A new compact grounded coplanar waveguide slotted multiband planar antenna for radio frequency identification data applications
Authors: Dakir Rachid , Mouhsen Ahmed
Published: May 2022

This research presents the development and conception of a new compact grounded coplanar waveguide fed slotted rectangular planar antenna with a multi-frequency band for radio frequency identification data (RFID) reader applications which is based on the antenna mono-band frequency to use for a various applications RFID to support a different operating range. The optimized of the final prototype designing operates a multiple frequency bands ranging from 0.7-1.1 GHz, 2.2-2.5 GHz and 5.4-6 GHz for 0.9/2.4 GHz and 5.8 GHz RFID operation bands which is adapted from ultra-high frequency band (0.9 GHz) to microwave frequency band (2.4-5.8 GHz) RFID systems. This antenna is implemented and printed on a FR4 substrate with a size of 30×50×1.6 mm3. The novel prototype includes of a radiator rectangular patch with a symmetrical slot and a U-slot with I-stub on ground plan. The principles parameters of the antenna have been studied optimized and miniaturized by using a two simulators CST Microwave Studio and advanced design system (ADS) to validate the simulation results before the planar antenna realization. The final structure is achieved and validated of the results measurement. Experimental results show that the proposed antenna with a small size has good and stable radiation and thus promising for a various RFID applications.
A novel multi-resonant and wideband fractal antenna for telecommunication applications
Authors: Nejdi Ibrahime Hassan , Rhazi Youssef , Lafkih Mustapha Ait , Bri Seddik , Mohammed Lamsalli
Published: May 2022

This letter presents the design, simulation, and measurement of a novel multiband fractal circular antenna for wireless applications. In the antenna design, we used a circular antenna where we took a ring. Then, in the first iteration, we added a new ring divided into two of the same size. For the second iteration, we added a ring of the same size after dividing it into two halves. In the third iteration, we added the third ring of the same size after dividing it into four. Due to the resonator defection, we were able to reduce the size of the starting antenna from 60×70×2 mm3 to 50×50×1.6 mm3, to get the frequency of 2.48 GHz, and we generated new bandwidths with a high gain that reaches 5.02 dB. The proposed antenna radiation characteristics, such as the impedance matching, the gain, the radiation pattern, and the surface current distribution are presented and discussed. We find that the simulated and measured results are in acceptable agreement and affirm the good performance of the proposed antenna. The results obtained affirm that the proposed fractal antenna is a better candidate for integration into wireless communication circuits.
A novel design of wide and multi-bands 2×2 multiple-input multiple-output antenna for 5G mm-wave applications
Authors: Shakir Muttair Karrar , Zuhair Ghazi Zahid Ali , Ahmed Shareef Oras , Qasim Kamil Ahmed Mohammed , Farhan Mosleh Mahmood
Published: May 2022

In this paper, we present a new design for a multiple-input multiple-output (MIMO) antenna with four ports operating in wide and multi-millimeter-wave (Mm-Wave) bands for various 5G applications (including the internet of things (IoT), communication devices, and smartphones). The antenna is designed in a rectangular zigzag shape with slots to make the antenna operate at different frequencies. For this, the antenna operates at multiple frequencies from 38 to 62 GHz, so it supports all advanced wireless communication applications. The most important characteristic of the design is its small size and compact structure compared to designs presented by researchers in previous literature so the antenna dimensions for four elements are 29×49 mm2. The antenna performance based on the results obtained from CST Studio Suite is good since the reflection coefficients of the antenna resonate at six main frequencies are 39.128 GHz, 42.992 GHz, 47.384 GHz, 51.536 GHz, 55.472 GHz, and 59.288 GHz. In addition, the isolation value between all antenna elements is ≤30 dB and the diversity gain value for all frequencies is 10 dB. Moreover, a very small value was obtained for the envelope correlation coefficient (ECC) is <4.0576×10−11. Finally, the results indicate a favorable design and potential competitor for all 5G MIMO Mm-Wave applications.
Energy-efficient data-aggregation for optimizing quality of service using mobile agents in wireless sensor network
Authors: Basappa Prapulla S. , Gangadhar Shobha , Thanuja Tiptur Chandrashekar
Published: May 2022

Quality of service (QoS) is essential for carrying out data transmission using resource-constrained sensor nodes in wireless sensor network (WSN). The introduction of mobile agent-based data aggregation is reported to offer energy efficiency; however, it has limitations, especially using a single mobile agent, where QoS optimization is not feasible. A review of existing studies showcases some dedicated attempts to use a mobile agent-based approach and address QoS enhancements. However, they were never combined studied. Therefore, this paper introduces a unique concept of retaining maximum QoS performance during data aggregation using a single mobile agent. The model introduces a unique communication framework, transmission provisioning using exceptional routine management, and simplified energy modeling. The proposed model has aimed for a lower delay and faster data aggregation speed with lower consumption of transmittance energy. The implementation and assessment of the model are carried out considering the challenging environment of WSN with multiple scales of data priority. The proposed model also contributes to evolving out with simplified communication vectors in a highly decentralized method. MATLAB's simulation outcome shows that the proposed system offers better delay performance, optimal energy management, and faster response time than existing schemes.
Performance evaluation of dual backhaul links RF/FSO for small cells of 5G cellular system
Authors: A. Aldhaibani Jaafar , Naser Jurn Yaseen , Ibrahim Abdulkhaleq Nadhir
Published: May 2022

Radio frequency (RF) backhaul links between the wireless stations especially between small cells stations such as relay stations (RSs) are insufficient with high capacities and huge number of users for 5G cellular networks. An alternative solution is using free space optics (FSO) communications, however, there are limitations in this system. In this paper we proposed a mixed of RF link together with FSO link. Using hardselector between them and programmatically controlled according of quality of links to overcome the obstacles faced the transfer of high data. Based on the outage probability of each links an algorithm is proposed to select the optimal link and provide the power consumption with guaranteed high quality of the link. The analytical expressions for the outage probability and ergodic capacity are derived. The numerical results show the effectiveness of proposed model in terms of connectivity and capacity compared to other links.
Analysis of student sentiment during video class with multi-layer deep learning approach
Authors: Salehin Imrus , Moon Nazmun Nessa , Talha Iftakhar Mohammad , Hasan Md. Mehedi , Hasan Farnaz Narin Nur , Hakim Md. Azizul , Al Haque A S M Farhan
Published: May 2022

The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the onlineclass is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
4-total edge product cordial for some star related graphs
Authors: Azaizeh Almothana , Hasni Roslan , Haddad Firas , Alsmadi Mutasem , Alkhasawneh Raed , Hamad Asma
Published: May 2022

Let G = (V (G), E(G)) be a graph, define an edge labeling function ψ from E(G) to {0, 1, . . . , k − 1} where k is an integer, 2 ≤ k ≤ |E(G)|, induces a vertex labeling function ψ∗ from V (G) to {0, 1, . . . , k − 1} such that ψ∗(v) = ψ(e1) × ψ(e2) × . . . × ψ(en) mod k where e1, e2, . . . , en are all edge incident to v. This function ψ is called a k-total edge product cordial (or simply k-TEPC) labeling of G if the absolute difference between number of vertices and edges labeling with i and number of vertices and edges labeling with j no more than 1 for all i, j ∈ {0, 1, . . . , k − 1}. In this paper, 4-total edge product cordial labeling for some star related graphs are determined.
Choosing the best quality of service algorithm using OPNET simulation
Authors: Eltaib Mohamed Osman , Alshammari Hamoud H. , Boukrara Ammar , Gasmi Karim , Hrizi Olfa
Published: May 2022

The concept of quality of service (QoS) is a new computer technology. Previously, there was a slow internet connection to access the sites and it was slow to send information. But now, it requires speeding up the traffic and increasing the efficiency for audio and video. In this study, we discuss the concepts of QoS provided over the network to achieve these goals. This study aims to compare six algorithms to control the QoS, then, the best algorithm will be selected to improve the traffic. These algorithms are named first in first out (FIFO), priority queuing (PQ), custom queuing (CQ), CQ with low latency queuing (LLQ), weighted fair queuing (WFQ), WFQ with low latency queuing (LLQ), so the behavior of these algorithms can be measured. The results obtained by comparing between them using OPNETsimulation show that the best algorithm is the priority queuing algorithm, followed by CQ, then CQ with LLQ, then WFQ, then WFQ with LLQ and finally FIFO. All these results are plotted in the form of graphs to show the paths of these algorithms for the single state with an operation time of 5 minutes for each algorithm.
Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction
Authors: Singh Neeru , Panda Supriya Priyabadini
Published: May 2022

A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to propose the new streamlined form of a back-propagation algorithm for heterogeneous computing and to examine the coalescence of artificial neural network (ANN) with GPU for predicting the groundwater level. twenty years of data set from 2001-2020 has been taken into consideration for three input parameters namely, temperature, rainfall, and water level for predicting the groundwater level using parallelized backpropagation algorithm on compute unified device architecture (CUDA). This employs the back-propagation algorithm to be best suited to reinforce learning and performance by providing more accurate and fast results for water level predictions on GPUs as compared to sequential ones on central processing units (CPUs) alone.
Virtual reality technology to support the independent living of children with autism
Authors: Almazaydeh Laiali , Al-Mohtadi Reham , Abuhelaleh Mohammed , Al Tawil Arar
Published: May 2022

Many designed systems have shown the potential of virtual reality (VR) to greatly transform autism treatment studies. Indeed, the literature shows that treatment via VR is appropriate for effective and repeatable training, without the intense anxiety, allowing trainees to recognize and modulate errors as they occur. This study evaluates the effectiveness of a new VR-based learning environment designed to safely practice and rehearse the daily activities related to the school world in children affected with autism. A total of nine children with autism actively enrolled in the study to learn and test their street crossing skills and social attention. Incremental change of difficulty levels has been added to the designed environment to generalize real-world situations, this includes overlaid distraction audio and increased vehicles intensity and speed. In order to enhance the learning experience, the real-time feedback is given according to the participant’s behavior, additionally, post processing profile is given for analysis purpose, where the participant’s behavior can be reviewed by parents and therapist to determine whether the participant’s mistakes are in decision making or focusing attention. The Wilcoxon signed-rank test for a single sample was used to test the change in the skills of participants with autism after using the educationally and therapeutically VR technology compared to a baseline. As a result, significant effects were found on the behavioral measures indicating that the VR-based learning environment is promoting a positive and informative learning environment.
Automatic fabric defect detection employing deep learning
Authors: Beljadid Aafaf , Tannouche Adil , Balouki Abdessamad
Published: May 2022

A major issue for fabric quality inspection is in the detection of defaults, it has become an extremely challenging goal for the textile industry to minimize costs in both production and quality inspection. The quality inspection is currently done manually by professionals; hence the need for the implementation of a fast, powerful, robust, and intelligent machine vision system in order to achieve high global quality, uniformity, and consistency of fabrics and to increase productivity. Consequently, the automatic inspection control process can improve productivity and enhance product quality. This article describes the approach used in developing a convolutional neural network for identifying fabric defects from input images of fabric surfaces. The proposed neural network is a pre-trained convolutional model ‘DetectNet’, it was adapted to be more efficient to the fabric image feature extraction. The developed model is capable of successfully distinguishing between defective fabric and non-defective with 93% accuracy for the first model and 96% for the second model.
Privacy protection domain-user integra tag deduplication in cloud data server
Authors: Thottipalayam Andavan Mohanaprakash , Vairaperumal Nirmalrani
Published: May 2022

The cloud with strong storage management has recently developed in the big data world which can confirm the data integrity and keep just a single data duplicate. Many cloud auditing storage techniques have been developed to overcome the data deduplication (DD) problem, but they are vulnerable and can't resist brute force attacks (BFA). There is some privacy leakage problem that occurred in the present method. In this article, an original strategy called domain-user integra tag (DUIT) has been presented which comprises inter and intra deduplication with file tag and symmetric encryption key. The DUIT has two phases, the first one is random tag generation for Intra deduplication and the other is random ciphertext (CT) generation for encryption. The benefit of the DUIT is the security of individual user’s files would not reveal to people in general, hence we proved that the DUIT is protected from the BFA. Finally, an experiment has conducted in Linux processor and C program software. The outcome of DUIT demonstrates that our method has reduced the computation cost (CC) by 27% and 35% and searching complexity (SC) by 10% and 26% related with the previous methods. It is decided that the DUIT achieves the low CC and SC.
A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy
Authors: Trong Le Nghia , Trieu Phung Tan , Huy Quyen Anh. , Phung Nguyen Bao Long , Ngoc Nguyen Au
Published: May 2022

This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique.
Fuzzy optimization strategy of the maximum power point tracking for a variable wind speed system
Authors: Belkacem Belkacem , Bouhamri Noureddine , Koridak Lahouari Abdelhakem , Allali Ahmed
Published: May 2022

Wind power systems are gaining more and more interests; in order to diminish dependence on fossil fuels. In this paper, we present a variable speed-wind energy global system based on a synchronous generator with permanent magnetic (PMSG). The major goal of this study is to track the maximum power that is present in the turbine. An examination of control methods to extract the MPPT point, from a wind energy conversion system (WECS) under variable speed situations is presented. An intelligent controller based on the fuzzy logic control (FLC) is proposed for regulating permanent magnetic synchronous generator (PMSG) output power, in order to improve tracking performance. The principle of this maximum power point tracking (MPPT) algorithm consists in looking for an optimal operating relation of the maximum power, then tracking this last. We simulated our system with MATLAB-Simulink software. The found results will be debated to elucidate performance of the global system.
Cuckoo algorithm with great deluge local-search for feature selection problems
Authors: Khalil Alsmadi Mutasem , Alzaqebah Malek , Jawarneh Sana , Brini Sami , Al-Marashdeh Ibrahim , Briki Khaoula , Alrefai Nashat , Ali Alghamdi Fahad , Al-Rashdan Maen T.
Published: May 2022

Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.
Fuzzy logic based authentication in cognitive radio networks
Authors: Nasir Abdulhussien Israa , Abdulridha Abduljaleel Safa
Published: May 2022

Security is a critical issue in cognitive radio networks because the cognitive node enters and variably leaves the spectrum, so it is difficult to process communication secretly. We suggested a fuzzy logic-based implicit authentication mechanism to be a solution for the confusion if there were any cognitive node doubts it to be unauthentic, and to improve user privacy in cognitive radio networks. Using a fuzzy logic technique, the proposed scheme computed certification based on proposed feedback. When a cognitive node needs to join the network, it is verified by using fuzzy logic if the node was authenticated or not. Our proposed fuzzy logic's results implicit authentication proved that it was a very successful and applicable scheme on cognitive radio networks, and it will be able to make an effective final decision in the context of incompleteness, ambiguity, and heterogeneity
Application of optimization algorithms for classification problem
Authors: Eleyan Alaa , Salman Mohammad Shukri , Al-Sheikh Bahaa
Published: May 2022

The work presented in this paper investigates the use of metaheuristic optimization algorithms for the face recognition problem. In the first setup, a face recognition system is implemented using particle swarm optimization (PSO) and firefly optimization algorithms, separately. PSO and firefly are used for forming the feature vectors in the feature selection stage. These feature vectors serve as the new representation for the face images that will be fed to the classifier. In the second setup, selected features from both PSO and firefly algorithms are fused to form one single feature vector for each face image before the classification stage. Extensive simulations are conducted using Poznan University of Technology (PUT) and face recognition technology (FERET) face databases. Optimal values for population size and maximum iterations number were selected before conducting the experiments. The effect of using different numbers of selected features on the performance is investigated for feature selection using PSO, firefly, and feature fusion of both.