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29,939 Article Results

Security and imperceptibility improving of image steganography using pixel allocation and random function techniques

10.11591/ijece.v12i1.pp694-705
Noor Alhuda F. Abbas , Nida Abdulredha , Raed Khalid Ibrahim , Adnan Hussein Ali
Information security is one of the main aspects of processes and methodologies in the technical age of information and communication. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies that are used, and they include steganography and cryptography. An effective digital image-steganographic method based on odd/even pixel allocation and random function to increase the security and imperceptibility has been improved. This lately developed outline has been verified for increasing the security and imperceptibility to determine the existent problems. Huffman coding has been used to modify secret data prior embedding stage; this modified equivalent secret data that prevent the secret data from attackers to increase the secret data capacities. The main objective of our scheme is to boost the peak-signal-to-noise-ratio (PSNR) of the stego cover and stop against any attack. The size of the secret data also increases. The results confirm good PSNR values in addition of these findings confirmed the proposed method eligibility.
Volume: 12
Issue: 1
Page: 694-705
Publish at: 2022-02-01

Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques

10.11591/ijeecs.v25.i2.pp972-979
Mohammed Berrahal , Mostafa Azizi
The advancements in artificial intelligence research, particularly in computer vision, have led to the development of previously unimaginable applications, such as generating new contents based on text description. In our work we focused on the text-to-image synthesis applications (TIS) field, to transform descriptive sentences into a real image. To tackle this issue, we use unsupervised deep learning networks that can generate high quality images from text descriptions, provided by eyewitnesses to assist law enforcement in their investigations, for the purpose of generating probable human faces. We analyzed a number of existing approaches and chose the best one. Deep fusion generative adversarial networks (DF-GAN) is the network that performs better than its peers, at multiple levels, like the generated image quality or the respect of the giving descriptive text. Our model is trained on the CelebA dataset and text descriptions (generated by our algorithm using existing attributes in the dataset). The obtained results from our implementation show that the learned generative model makes excellent quantitative and visual performances, the model is capable of generating realistic and diverse samples for human faces and create a complete portrait with respect of given text description.
Volume: 25
Issue: 2
Page: 972-979
Publish at: 2022-02-01

A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

10.11591/ijece.v12i1.pp596-604
Salma Firdose , Surendran Swapna Kumar , Ravinda Gayan Narendra Meegama
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
Volume: 12
Issue: 1
Page: 596-604
Publish at: 2022-02-01

High efficiency step-up converter using single switch with coupled inductors

10.11591/ijeecs.v25.i2.pp690-696
Ali Hussein Al-Omari , Saif Al-Zubaidi
Due to high demand for voltage boosting in the field of renewable energy sources, a novel topology of step-up converter with single switch is proposed in this paper. The converter uses single switch with coupled inductors architecture. The proposed topology enables to control the stepping-up gain by adjusting the duty cycle and selecting appropriate turn ratio of the coupled inductors. The two coupled inductors in the circuit can act as a step-up transformer. Moreover, the proposed technique aims to harness the turning off voltage stresses across the switching element and suppress the stress by transferring it to the load. The switching induced voltage was reduced by forwarding the power to charge a capacitor then transfer the power through a diode to the output. Circuit configuration, principles of operation and the gain transfer function of the converter are demonstrated. The proposed circuit is verified by comparing the practical results to the theoretical.
Volume: 25
Issue: 2
Page: 690-696
Publish at: 2022-02-01

Optimizing of the installed capacity of hybrid renewable energy with a modified MPPT model

10.11591/ijece.v12i1.pp73-81
Sukarno Budi Utomo , Iwan Setiawan , Berkah Fajar , Sonny Hady Winoto , Arief Marwanto
The lack of wind speed capacity and the emission of photons from sunlight are the problem in a hybrid system of photovoltaic (PV) panels and wind turbines. To overcome this shortcoming, the incremental conductance (IC) algorithm is applied that could control the converter work cycle and the switching of the buck boost therefore maximum efficiency of maximum power point tracking (MPPT) is reached. The operation of the PV-wind hybrid system, consisting of a 100 W PV array device and a 400 W wind subsystem, 12 V/100 Ah battery energy storage and LED, the PV-wind system requires a hybrid controller for battery charging and usage and load lamp and it’s conducted in experimental setup. The experimental has shown that an average increase in power generated was 38.8% compared to a single system of PV panels or a single wind turbine sub-system. Therefore, the potential opportunities for increasing power production in the tropics wheather could be carried out and applied with this model.
Volume: 12
Issue: 1
Page: 73-81
Publish at: 2022-02-01

Grid reactive voltage regulation and cost optimization for electric vehicle penetration in power network

10.11591/ijeecs.v25.i2.pp741-754
Farrukh Nagi , Aidil Azwin , Navaamsini Boopalan , Agileswari K. Ramasamy , Marayati Marsadek , Syed Khaleel Ahmed
Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.
Volume: 25
Issue: 2
Page: 741-754
Publish at: 2022-02-01

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

10.11591/ijece.v12i1.pp303-310
Nermeen Elmenabawy , Mervat El-Seddek , Hossam El-Din Moustafa , Ahmed Elnakib
A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.
Volume: 12
Issue: 1
Page: 303-310
Publish at: 2022-02-01

Design and fabrication of rotor lateral shifting in the axial-flux permanent-magnet generator

10.11591/ijece.v12i1.pp141-149
Nurma Sari , Gatut Yudoyono , Ali Yunus Rohedi , Yono Hadi Pramono
The development of axial-flux permanent-magnet (AFPM) machines has become a mature technology. The single-stator double-rotor (SSDR) AFPM structure has advantages on the compactness and the low up to medium power applications so the microscale size and low-cost applications are reachable to be designed. The research main objectives are designing and manufacturing the lateral shifting from the north poles of the first rotor face the north poles of the second rotor (NN) to the north poles of the first rotor face the south poles of the second rotor (NS) categories as well as finding the best performance of the proposed method and implementing in a low cost and micro-scale AFPMG. The novel lateral shifting on the one of the rotors shows performance at 19.20 has the highest efficiency at 88.39% during lateral shifting from N–N (00) to N–S (360) on rotor2.
Volume: 12
Issue: 1
Page: 141-149
Publish at: 2022-02-01

Performance analysis of real-time and general-purpose operating systems for path planning of the multi-robot systems

10.11591/ijece.v12i1.pp285-292
Seçkin Canbaz , Gökhan Erdemir
In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system istime-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.
Volume: 12
Issue: 1
Page: 285-292
Publish at: 2022-02-01

Efficient energy for one node and multi-nodes of wireless body area network

10.11591/ijece.v12i1.pp914-923
Sondous Sulaiman Wali , Mohammed Najm Abdullah
Compression sensing approaches have been used extensively with the idea of overcoming the limitations of traditional sampling theory and applying the concept of pressure during the sensing procedure. Great efforts have been made to develop methods that would allow data to be sampled in compressed form using a much smaller number of samples. Wireless body area networks (WBANs) have been developed by researchers through the creation of the network and the use of miniature equipment. Small structural factors, low power consumption, scalable data rates from kilobits per second to megabits per second, low cost, simple hardware deployment, and low processing power are needed to hold the wireless sensor through lightweight, implantable, and sharing communication tools wireless body area network. Thus, the proposed system provides a brief idea of the use of WBAN using IEEE 802.15.4 with compression sensing technologies. To build a health system that helps people maintain their health without going to the hospital and get more efficient energy through compression sensing, more efficient energy is obtained and thus helps the sensor battery last longer, and finally, the proposed health system will be more efficient energy, less energy-consuming, less expensive and more throughput.
Volume: 12
Issue: 1
Page: 914-923
Publish at: 2022-02-01

Robust recognition technique for handwritten Kannada character recognition using capsule networks

10.11591/ijece.v12i1.pp383-391
N. Shobha Rani , Manohar N. , Hariprasad M. , Pushpa B. R.
Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize  Kannada handwritten characters.  Capsule network architecture is built of an input layer,  two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer.  For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%.
Volume: 12
Issue: 1
Page: 383-391
Publish at: 2022-02-01

Comparison of cascade P-PI controller tuning methods for PMDC motor based on intelligence techniques

10.11591/ijece.v12i1.pp1-11
Kareem Ghazi Abdulhussein , Naseer Majeed Yasin , Ihsan Jabbar Hasan
In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
Volume: 12
Issue: 1
Page: 1-11
Publish at: 2022-02-01

Solution for intra/inter-cluster event-reporting problem in cluster-based protocols for wireless sensor networks

10.11591/ijece.v12i1.pp868-879
Raed Taleb Al-Zubi , Abdulraheem Ahmed Kreishan , Mohammad Qasem Alawad , Khalid Ahmad Darabkh
In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.
Volume: 12
Issue: 1
Page: 868-879
Publish at: 2022-02-01

An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices

10.11591/ijeecs.v25.i2.pp1140-1150
Idriss Idrissi , Mostafa Azizi , Omar Moussaoui
Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of algorithms for analyzing and clustering unlabeled data; it allows identifying hidden patterns or performing data clustering over provided data without the need for human involvement. There is no prior knowledge of actual abnormalities when using UL methods in anomaly detection (AD); hence, a DL-intrusion detection system (IDS)- based on AD depends intensely on their assumption about the distribution of anomalies. In this paper, we propose a novel unsupervised AD Host-IDS for internet of things (IoT) based on adversarial training architecture using the generative adversarial network (GAN). Our proposed IDS, called “EdgeIDS”, targets mostly IoT devices because of their limited functionality; IoT devices send and receive only specific data, not like traditional devices, such as servers or computers that exchange a wide range of data. We benchmarked our proposed “EdgeIDS” on the message queuing telemetry transport (MQTTset) dataset with five attack types, and our obtained results are promising, up to 0.99 in the ROC-AUC metric, and to just 0.035 in the ROC-EER metric. Our proposed technique could be a solution for detecting cyber abnormalities in the IoT.
Volume: 25
Issue: 2
Page: 1140-1150
Publish at: 2022-02-01

Design and development of DrawBot using image processing

10.11591/ijece.v12i1.pp365-375
Krithika Vaidyanathan , Nandhini Murugan , Subramani Chinnamuthu , Sivashanmugam Shivasubramanian , Surya Raghavendran , Vimala Chinnaiyan
Extracting text from an image and reproducing them can often be a laborious task. We took it upon ourselves to solve the problem. Our work is aimed at designing a robot which can perceive an image shown to it and reproduce it on any given area as directed. It does so by first taking an input image and performing image processing operations on the image to improve its readability. Then the text in the image is recognized by the program. Points for each letter are taken, then inverse kinematics is done for each point with MATLAB/Simulink and the angles in which the servo motors should be moved are found out and stored in the Arduino. Using these angles, the control algorithm is generated in the Arduino and the letters are drawn.
Volume: 12
Issue: 1
Page: 365-375
Publish at: 2022-02-01
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