Showing 218 results for Im
Mohamed Khalaf, Ahmed Fawzi, Ahmed Yahya,
Volume 20, Issue 1 (3-2024)
Abstract
Cognitive radio (CR) is an effective technique for dealing with scarcity in spectrum resources and enhancing overall spectrum utilization. CR attempts to enhance spectrum sensing by detecting the primary user (PU) and allowing the secondary user (SU) to utilize the spectrum holes. The rapid growth of CR technology increases the required standards for Spectrum Sensing (SS) performance, especially in regions with low Signal-to-Noise Ratios (SNRs). In Cognitive Radio Networks (CRN), SS is an essential process for detecting the available spectrum. SS is divided into sensing time and transmission time; the more the sensing time, the higher the detection probability) and the lower the probability of a false alarm). So, this paper proposes a novel two-stage SS optimization model for CR systems. The proposed model consists of two techniques: Interval Dependent De-noising (IDD) and Energy Detection (ED), which achieve optimum sensing time, maximum throughput, lower and higher. The Simulation results demonstrated that the proposed model decreases the, achieves a higher especially at low SNRs ranging, and obtains the optimum sensing time, achieving maximum throughput at different numbers of sensing samples (N) and different SNRs from -10 to -20 dB in the case of N = 1000 to 10000 samples. The proposed model achieves a throughput of 5.418 and 1.98 Bits/Sec/HZ at an optimum sensing time of 0.5ms and 1.5ms respectively, when N increases from 10000 to 100000 samples. The proposed model yields an achievable throughput of 5.37 and 4.58 Bits/Sec/HZ at an optimum sensing time of 1.66ms and 13ms respectively. So, it enhances the SS process than previous related techniques.
Priyanka Handa, Balkrishan Jindal ,
Volume 20, Issue 1 (3-2024)
Abstract
The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios. The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory.
Smita Jolania, Ravi Sindal,
Volume 20, Issue 1 (3-2024)
Abstract
Fifth Generation-New Radio (5G-NR) is an advanced air interface defined to fulfil diverse services with ubiquitous coverage in next generation Wireless networks. The waveform is the crucial part of air interface that must have good spectral confinement and low peak-to-average power ratio (PAPR). Orthogonal Frequency Division Multiplexing (OFDM) is a widely used air interface in Fourth Generation Long Term Evolution (4G-LTE) system. But OFDM suffers from high PAPR, Carrier Frequency offset (CFO), and loss of spectral efficiency due to insertion of cyclic prefix. So, the high dense networks with heterogeneous traffic in the 5G requires new multicarrier waveform. In the proposed work, waveforms based on sub-band filtering are considered due to more flexibility and shorter filter length as compared to the sub-carrier-based filtering waveforms. Two major 5G waveform candidates Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM) and Universal Frequency Division Multiplexing (UFMC) are proposed in the system design. Channel coding is the inherent part of air interface for enhancing the error performance. New error correcting channel codes introduced in NR to support variable information block length and flexible codeword size. The capacity achieving Polar codes is the highlight of this paper adopted for control channels. 5G NR air interface using new modulation waveform along with the polar coding can be an effective way to enhance error performance. This paper presents comparative analysis of comprehensive systems Polar coded F-OFDM (PC-F-OFDM) and Polar coded UFMC (PC-UFMC) in massive MIMO scenario. Simulation results indicate that the proposed PC-F-OFDM systems significantly outperform the PC-UFMC systems in AWGN channel. But in massive MIMO setup BER performance of PC-UFMC is better than PC-F-OFDM system.
Majid Najjarpour, Behrouz Tousi, Shahaboddin Yazdandoust Moghanlou,
Volume 20, Issue 1 (3-2024)
Abstract
In recent decades, because of the rapid population growth of the world, considerable changes in climate, the reduction of fossil fuel sources to consume the traditional power plants and their high depreciation, and the increase in fuel prices. Due to the increased penetration of DG units which have a random nature into the power system, the ordinary equations of power flow must be changed. For the power system to operate in a stable condition estimating future demand and calculating the important and operational indexes such as losses of the power system is an important duty that must be done precisely and rapidly. In this paper, the Improved Taguchi method and phasor measurement unit are used to model the uncertainties of DGs and estimate the error of voltage, respectively. The results show that the magnitude error and the angle error of voltage are decreased using PMU. The applied optimal power flow and state estimations are analyzed and verified using standard IEEE 30-bus and 14-bus test power systems by MATLAB, and MINITAB softwares. The Made Strides Taguchi strategy appears to have modeled the DG units precisely and successfully, and using the PMU, the mistake of the point and greatness estimation is exceptionally moot. The values that were evaluated are very close to the values that were done by the Newton-Raphson stack stream.
Aws Alazawi, Huda Jameel, Mohammed Mohsen,
Volume 20, Issue 2 (6-2024)
Abstract
This study explores the use of distortion product otoacoustic emission (DPOAE) as a hearing screening modality for newborns and adults with hearing impairment. The goal is to improve cochlear response by developing digital filter characteristics to make it consistent for specialists to make accurate diagnoses. To accomplish this, the proposed system consists of a DPOAE ER-10C as stimulation and cochlear response probe, a digital signal processor, an oscilloscope, PC, and audio cables. Real-time distortion product frequency components were extracted using a signal processor of TMS320C6713. To validate the system, a senior medical physicist at Baghdad Medical City in Iraq conducted a study with five hearing-normal volunteers ages 38 and 55 at the center for hearing and communication. The results showed an ability to extract distortion product components in real-time implementation, with the superiority of shape parameters greater than 0.5. In addition, the quantization of filter coefficients was compared for both floating-point arithmetic and fixed-point arithmetic. Noisy environment-based noise reduction techniques have to be investigated by considering the implementation of robust digital signal processing techniques. Finally, the proposed system would contribute to advancements in hearing screening and treatment for those with hearing impairment.
Shivanand Konade, Manoj Dongre,
Volume 20, Issue 2 (6-2024)
Abstract
The proposed research presents a two-port compact Multiple Input Multiple Output (MIMO) antenna for Ultra-Wide Band (UWB) applications. The designed antenna has two identical radiators and has an overall dimension of 20 × 44.1 × 1.6mm3 on a FR4 substrate. The designed antenna is fed by a 50-microstrip line. Extended F-shaped stubs are introduced in the shared ground plane of the proposed antenna to produce high isolation between the MIMO antenna elements. Extended F-shaped stubs are introduced in the ground plane to produce multiple resonance and high isolation between the radiating elements. The antenna offers good impedance matching in the UWB band. The proposed antenna has lower isolation < -25 dB and Envelope Correlation Coefficient (ECC) < 0.015 from 3.1 to 10.6 GHz. Antenna parameters are evaluated in term of return loss, ECC, Diversity Gain (DG), gain, Total active reflection coefficient (TRAC) radiation pattern and isolation. The proposed antenna is tested and fabricated. However, obtained results are good agreement which make suitable for UWB wearable applications.
Pampa Debnath, Diptadip Barai, Rajorshi Mandal, Ayeshee Sinha, Jeet Saha, Arpan Deyasi,
Volume 20, Issue 2 (6-2024)
Abstract
A novel architecture is proposed in the present paper for detection and monitoring of air pollution at real-time condition following industrial standard, embedded with gas sensors which are able to identify both organic as well as inorganic hazardous contents. A vis-à-vis comparative analysis is carried out with existing literature highlighting cons of most referred circuits, both in component, system and power consumption levels, and a generalized drawback is reported citing their inefficacy for real-time data collection and accuracy level. Detailed review is reported based on qualitative assessments also, and henceforth, justifies the significance of the proposed design; where not only higher ranges of detection are possible, however is also associated with lower power consumption (26.41% and 10.71% respectively compared to the two latest circuits) and finer detection of dust particles even at extremely low concentration. The architecture will help to implicate precautionary steps at real-time condition for controlling the harmful effect in Society.
Ali Riyadh Ali , Rakan Khalil Antar, Abdulghani Abdulrazzaq Abdulghafoor ,
Volume 20, Issue 3 (9-2024)
Abstract
Artificial intelligence-based optimization algorithm was used to compute the switching angle values. In order to run the inverter with the lowest possible Total Harmonic Distortion (THD) value, it is suggested in this study to use an algorithm such as the Practical Swarm Algorithm (PSA). The multilevel inverter and optimization algorithm were created and simulated in this study using a MATLAB software. A frequency spectrum analysis was also conducted and found to be consistent with the theoretical analysis of the system. To provide practical results, the FPGA generates PWM signals that are appropriate for the inverter switches. On the Spartan-3E Starter set, the suggested control schemes were developed and put it into practice. Xilinx-ISE 12.1i design software and VHDL hardware description language were used to create the FPGA software. The suggested approaches have a number of benefits over conventional digital PWM techniques, including straightforward hardware implementation, minimum scaling of digital circuits, easy digital design, reconfigurable, and flexibility in adaptability. The outcomes of the experiment and the simulation agreed rather well.
Shamil Alnajjar, Prof. Dr. Khalid K. Mohammed,
Volume 20, Issue 3 (9-2024)
Abstract
This work presents an analysis and design of the two barrier-quantum well asymmetric spacer tunnel layer (QW-ASPAT) diodes for implantable rectenna circuits application. The RF and DC characteristic of a 10×10μm2 QW-ASPAT devices based on GaAs and In0.53Ga0.47As platform was simulated and extracted by using SILVACO atlas software. The highest extracted curvature coefficient, kv value of the both QW-ASPAT devices at zero bias was about 33V-1 compared with the standard structure GaAs/InGaAs was about 13V-1. The effects of changing in the thickness of the thin AlAs-barrier, the well width, and the spacer layer are fully investigated on the non-linear relationship between current and voltage of these diodes. A CV simulation was carried out, and it was found that the addition of the quantum-well layer between spacers and barrier reduced the junction capacitance of the QW-ASPAT device when compared with standard devices. The cut-off frequency of the proposed QW-GaAs and QW-InGaAs devices are 26GHz and 46GHz respectively. Finally, we conclude that the QW-ASPAT device is the best structure and can be used for microwave rectifiers in the miniaturized integrated rectenna systems.
Mohamad Almas Prakasa, Mohamad Idam Fuadi, Muhammad Ruswandi Djalal, Imam Robandi, Dimas Fajar Uman Putra,
Volume 20, Issue 3 (9-2024)
Abstract
The unbalanced load distribution in the electrical distribution network caused crucial power losses. This condition occurs in one of the electrical distribution networks, 20 kV Tarahan Substation, Province of Bandar Lampung, Indonesia. This condition can be maintained using optimal reconfiguration with the integration of Distributed Generation (DG) based on Renewable Energy (RE). This study demonstrates the optimal reconfiguration of the 20 kV Tarahan Substation with the integration of the Photovoltaic (PV) and Battery Energy Storage System (BESS). The reconfiguration process is optimized by using the Firefly Algorithm (FA). This process is conducted in the 24-hour simulation with various load profiles. The optimal reconfiguration is investigated in two scenarios based on without and with DG integration. The optimal configuration with more balanced load distribution conducted by FA reduces the power losses by up to 31.39% and 32.38% in without and with DG integration, respectively. Besides that, the DG integration improves the lowest voltage bus in the electrical distribution network from 0.95 p.u to 0.97 p.u.
Nguyen Cong Chinh,
Volume 20, Issue 3 (9-2024)
Abstract
This paper presents an intelligent meta-heuristic algorithm, named improved equilibrium optimizer (IEO), for addressing the optimization problem of multi-objective simultaneous integration of distributed generators at unity and optimal power factor in a distribution system. The main objective of this research is to consider the multi-objective function for minimizing total power loss, improving voltage deviation, and reducing integrated system operating costs with strict technical constraints. An improved equilibrium optimizer is an enhanced version of the equilibrium optimizer that can provide better performance, stability, and convergence characteristics than the original algorithm. For evaluating the effectiveness of the suggested method, the IEEE 69-bus radial distribution system is chosen as a test system, and simulation results from this method are also compared fairly with many previously existing methods for the same targets and constraints. Thanks to its ability to intelligently expand the search space and avoid local traps, the suggested method has become a robust stochastic optimization method in tackling complex optimization tasks.
Eisa Zarepour, Mohammad Reza Mohammadi, Morteza Zakeri-Nasrabadi, Sara Aein, Razieh Sangsari, Leila Taheri, Mojtaba Akbari, Ali Zabihallahpour,
Volume 20, Issue 3 (9-2024)
Abstract
Using mobile phones for medical applications are proliferating due to high-quality embedded sensors. Jaundice, a yellow discoloration of the skin caused by excess bilirubin, is a prevalent physiological problem in newborns. While moderate amounts of bilirubin are safe in healthy newborns, extreme levels are fatal and cause devastating and irreversible brain damage. Accurate tests to measure jaundice require a blood draw or dedicated clinical devices facing difficulty where clinical technology is unavailable. This paper presents a smartphone-based screening tool to detect neonatal hyperbilirubinemia caused by the high bilirubin production rate. A machine learning regression model is trained on a pretty large dataset of images, including 446 samples, taken from newborns' sternum skin in four medical centers in Iran. The learned model is then used to estimate the level of bilirubin. Experimental results show a mean absolute error of 1.807 mg/dl and a correlation of 0.701 between predicted bilirubin by the proposed method and the TSB values as ground truth.
M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi,
Volume 20, Issue 4 (11-2024)
Abstract
Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.
Elahe Moradi,
Volume 20, Issue 4 (11-2024)
Abstract
With the intricate interplay between clinical and pathological data in coronary heart disease (CHD) diagnosis, there is a growing interest among researchers and healthcare providers in developing more accurate and reliable predictive methods. In this paper, we propose a new method entitled the robust artificial neural network classifier (RANNC) technique for the prediction of CHD. The dataset CHD in this paper has imbalanced data, and in addition, it has some outlier values. The dataset consists of information related to 4240 samples with 16 attributes. Due to the presence of outliers, a robust method has been used to scale the dataset. On the other hand, due to the imbalance of CHD data, three data balancing methods, including Random Over Sampling (ROS), Synthetic Minority Over Sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) approaches, have been applied to the CHD data set. Also, six artificial intelligence algorithms, including LRC, DTC, RFC, KNNC, SVC, and ANN, have been evaluated on the considered dataset with criteria such as precision, accuracy, recall, F1-score, and MCC. The RANNC, leveraging ADASYN to address data imbalance and outliers, significantly improved CHD diagnostic accuracy and the reliability of healthcare predictive models. It outperformed other artificial intelligence methods, achieving precision, accuracy, recall, F1-score, and MCC scores of 95.57%, 96.90%, 99.70%, 97.59%, and 93.42%, respectively.
𝐒𝐢𝐫𝐚𝐣𝐮𝐬 𝐒𝐚𝐥𝐞𝐡𝐢𝐧, Shakila Rahman, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐍𝐮𝐫, 𝐀𝐡𝐦𝐚𝐝 𝐀𝐬𝐢𝐟, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐁𝐢𝐧 𝐇𝐚𝐫𝐮𝐧, Jia Uddin,
Volume 20, Issue 4 (11-2024)
Abstract
Abnormal activity detection is crucial for video surveillance and security systems, aiming to identify behaviors that deviate from normal patterns and may indicate threats or incidents such as theft, vandalism, accidents, and aggression. Timely recognition of these activities enhances public safety across various environments, including transportation hubs, public spaces, workplaces, and homes. In this study, we focus on detecting violent and non-violent activities of humans using a YOLOv9-based deep learning model considering the above issues. A diverse dataset has been built of 9,341 images from various platforms, and then the dataset has been pre-processed, i.e., augmentation, resizing, and annotating. After pre-processing, the proposed model has been trained which demonstrated strong performance, achieving an F1 score of 95% during training for 150 epochs. It was also trained for 200 epochs, but early stopping was applied at 148 epochs as there was no significant improvement in the results. Finally, the results of the YOLOv9-based model have been analyzed with other baseline models (YOLOv5, YOLOv7, YOLOv8, and YOLOv10) and it performed better compared with others.
Farhad Amiri, Mohammad H. Moradi,
Volume 21, Issue 1 (3-2025)
Abstract
Low inertia is one of the most important challenges for frequency maintenance in islanded microgrids. To address this issue, the innovative concept of Virtual Inertia Control (VIC) has emerged as a promising solution for enhancing frequency stability in such systems. This paper presents an advanced controller, the PD-FOPID, as a highly effective technique for improving the efficiency of VIC in islanded microgrids. By leveraging the Rain Optimization Algorithm (ROA), this approach enables precise fine-tuning of the controller's parameters. A key advantage of the proposed method is its inherent resilience to disruptions and uncertainties caused by parameter fluctuations in islanded microgrids. To evaluate its performance and compare it with alternative control methods, extensive assessments were conducted across various scenarios. The comparison includes VIC based on an H-infinity controller (Controller 1), VIC based on an MPC controller (Controller 2), Adaptive VIC (Controller 3), VIC based on an optimized PI controller (Controller 4), conventional VIC (Controller 5), and systems without VIC (Controller 6). The results demonstrate that the proposed methodology significantly outperforms existing approaches in the field of VIC. The simulations were conducted using MATLAB software.
Trung Kien Do, Thanh Long Duong,
Volume 21, Issue 1 (3-2025)
Abstract
Frequency instability is one of the causes of severe disturbances in the power system, including load shedding and widespread blackouts. Especially in modern power systems, frequency instability has even more serious consequences due to the propagation occurring in interconnected regions. Load frequency control (LFC) is a powerful tool in power system operation to ensure that the frequency is always within the allowable limits. The control parameters of LFC must be optimally adjusted for stable system operation. However, researchers are currently unable to find a suitable and robust method for optimal tuning of LFC control parameters. The paper proposes the Puma Optimizer (PO) algorithm to optimize the parameters of PID, FOPID, and FOPTID+1 controllers for solving the LFC problem. The proposed PO algorithm is evaluated through two models of single-area and two-area power systems with different power sources, including thermal power, hydropower, and gas power. The simulation results show that the integral time absolute error (ITAE) value of the proposed PO method is smaller by 5.25%, 18.16%, 28.35%, and 59.92% compared to Particle Swarm Optimization (PSO), Crested Porcupine Optimization (CPO), Newton-Raphson-based optimization (NRBO), and Global Neighborhood Algorithm (GNA), respectively. The results obtained demonstrate that the PO algorithm is a reliable and efficient tool for finding solutions to the LFC problem.
Zahra Mobini-Serajy, Mehdi Radmehr, Alireza Ghorbani,
Volume 21, Issue 1 (3-2025)
Abstract
Microgrids harness the benefits of non-inverter and inverter-based Distributed Energy Resources (DER) in grid-connected and island environments. Adoption of them with the various types of electric loads in modern MGs has led to stability and power quality issues. In this paper, a two-level control approach is proposed to overcome these problems. A state-space dynamic model is performed for Micro-Grids, for this goal, the state-space equations for generation, network, and load components are separately developed in a local DQ reference frame, and after linearization around the set point, then combining them into a common DQ reference frame. In the first level, the control of inverter-based DERs and some types of loads with fast response are activated, and in the second level, the control of synchronous diesel generator resources with slower response is used. In order to validate and evaluate the effectiveness of the proposed control approach, numerical studies have been established on a standard test MG under normal and symmetrical three-phase fault conditions. Finally, the simulation results are summarized.
Mon Prakash Upadhyay, Arjun Deo, Ajitanshu Vedratnam ,
Volume 21, Issue 1 (3-2025)
Abstract
This paper provides an overview of the current innovations in Building Integrated Photovoltaic Thermal Systems. This paper briefly describes varying performance evaluation techniques, optimisation techniques, and the environmental impact and cost implication of Building Integrated Photovoltaic Thermal systems. The results reveal high energy-pin efficiency with Building Integrated Photovoltaic Thermal systems of over 50% and more efficient than when the two systems are incorporated separately. Exergy analysis is a more insightful means of analyzing system effectiveness than energy analysis. The paper covers the current algorithms for various optimisation algorithms such as Genetic Algorithms and Particle Swarm Optimisation that provide enhanced utilization improvements. An evaluation of the environmental impact of Building Integrated Photovoltaic Thermal in terms of carbon dioxide emission reduction and building energy optimisation is made. The results of the life cycle cost studies show that, even though the initial cost is higher than conventional solutions, the overall economic profit is more significant in the future. Some of the challenges described in the paper include increased initial costs and sophisticated integration procedures. In contrast, possible future developments include new materials, Building Integrated Photovoltaic Thermal system standardization, and integration in smart grids. This review is intended to be a state-of-the-art source of information for researchers, engineers, architects, and policymakers involved in enhancing sustainable building technologies using building-integrated photovoltaic thermal systems.
Sajal Debbarma, Dipu Sarkar,
Volume 21, Issue 1 (3-2025)
Abstract
Transmission line congestion is more severe and persistent in deregulated power systems than it is in traditionally controlled power systems. In a deregulated power market (DPM) scenario, transmission line congestion is one of the most critical problems. To guarantee the electricity system framework runs consistently and securely, the independent system operator (ISO) controls congestion. Congestion management (CM), which takes into account the inherent uncertainties of the restructured power system, is essential to the functioning and security of DPM. This article demonstrates how to control congestion with generation rescheduling. The system is designed in such a way that it helps the traders to compete and trade using the bid prices. Network security is maintained by keeping all constraints within the allowed limits via the Newton-Raphson load flow. An innovative Cheetah Optimizer is employed to handle the congestion management challenge. The weighted sum approach is used instead of multiobjective optimization to simplify the problem as a single-objective optimization, solve the issue for multiple instances of congestion, and be tested in an IEEE 30 bus system. The MATLAB software serves as a tool for modeling the full process, and the results acquired with the Cheetah optimizer give better results than the conventional optimization technique.
