Articles in This Issue
Abstract
In recent years, there has been a lot of interest in the study of P300 potential-based approaches for lie detection. The variations in brain signal activity (EEG-P300 component) that distinguish between lying and starting the truth are investigated. As soon as participants respond to an experiment stimulus for the first time, their brain signals are examined and the P300 signal is extracted. This paper aims to improve the signal-to-noise ratio (SNR) of P300, which leads to an increase in the classification accuracy of lie detection. Ten subjects were randomly assigned to groups of lying and innocent people, and 14 electrodes captured the EEG data for each group. This work proposed to use some denoising techniques like averaging the raw EEG signal, regression-based baseline correction, and independent component analysis (ICA). The suggested approach and other early published methods vary mostly in the regression-based technique used in bassline correction to adaptively indicate the baseline interval (baseline window). Compared to other studies, the suggested technique gives an increase in the mean amount of SNR by up to 20% was obtained.
Abstract
As demand for sustainable energy continues to grow, wind energy especially provided by permanent magnet synchronous generators (PMSG) connected to wind turbines, has become an important research area. This article provides a comprehensive review of various converter topologies used in PMSG-based wind turbines. The transition from asynchronous to synchronous generators reflects the industry’s response to the evolving landscape of energy requirements. The review explores the advantages and disadvantages associated with different power converter topologies. Among these, the ”back-to-back” converter emerges as a common and favored topology due to its superior performance over Doubly Fed Induction Generators (DFIGs). The study delves into the intricate details of these converter topologies, shedding light on their operating intricacies and the impact on overall wind energy conversion efficiency. Furthermore, the analysis demonstrates recent developments and outcomes in power conversion topologies, including resonant converters, matrix converters, and multilevel converters. Tests have shown that the continuously clamped three-phase neutral diode topology (3L NPC-BTB) is superior to the BTB 2L-VSC parallel two-phase converter with DC coupling and multi-level converters. The proposed converter topology improves energy extraction and provides a gainful solution for generator on the side converters of high-power, variable speed PMSG wind turbines. This review provides a comprehensive guide to the power converter topologies of PMSG in wind turbines and contributes to ongoing discussions on advancing wind energy technology. Additionally, this review article is also useful for researchers, engineers, and professionals interested in renewable energy systems.
Abstract
Global agriculture employs central pivot irrigation system(CPIS) as a highly significant method for intelligent irrigation. Cultivating crucial crops like wheat and other strategically important crops that occupy extensive land areas contributes to global food security. The Central Pivot Irrigation System encounters technical issues that result in malfunctions in its automatic control system. These malfunctions occasionally cause damage to the primary pipes and towers that operate the system, resulting in significant material losses for farmers and agricultural crops. Moreover, the repair process is time-consuming. Therefore, to address this issue, this study employed the YOLOv5 models to accurately identify and detect defects in the CPIS machine by determining whether they are in a safe or dangerous state. The dataset that was used in this study was gathered from agricultural areas in Salah al-Din Governorate. The CPIS detection model yielded the following results: the grayscale color system with Yolov5n achieved a 98 % detection rate with accuracy and F1-score values of 0.866. Similarly, Yolov5m achieved a 98 % detection rate with accuracy and F1-score values of 0.804. In the RGB color system, the maximum results achieved with Yolov5n are 97 % for accuracy and 0.812 for F1-score. On the other hand, Yolov5s6 achieves a result of 95 % for accuracy and 0.82 for both F1-score and accuracy. Based on the aforementioned outcome, we can infer that yolov5s6 accurately detects the CPIS in both its safe and dangerous states. Therefore, they can be deployed in a real-time system for CPIS defect monitoring and control systems.
Abstract
This research paper presents an innovative approach to wind nowcasting, addressing specific performance parameters through advanced machine learning techniques. The research aims to overcome inherent challenges in capturing intricate spatiotemporal relationships within wind data. Our novel methodology integrates Conv-LSTM-3D models, emphasizing the prediction of next-frame wind patterns. The Conv-LSTM-3D architecture, combining 3D convolutions and LSTM networks, is specifically tailored to effectively learn temporal dependencies and spatial features in wind data. The introduction outlines the pressing issues associated with traditional wind nowcasting methods, emphasizing the need for improved accuracy and prediction reliability. The primary objectives of this study are to explore the potential of Conv-LSTM-3D models in enhancing wind nowcasting and to assess their performance against traditional methods. Through comprehensive experiments, our approach demonstrates significant improvements in critical performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Specifically, improvements of 0.01, 19.23, 0.11, and 0.64 are observed, highlighting the enhanced accuracy and prediction reliability in the context of next-frame wind nowcasting. Notably, the system achieves these advancements within a reduced time frame, taking only 1149 seconds. This research contributes significantly to the advancement of meteorological prediction techniques, offering a refined short-term wind forecasting tool with potential applications across various fields. The improved clarity and organization of our methodology and findings pave the way for more effective utilization of Conv-LSTM-3D models in enhancing wind nowcasting capabilities.
Abstract
Recently, face recognition technology has become more prevalent in various applications, including mobile devices, access control, and financial transactions. Therefore, it is crucial to address potential vulnerabilities that attackers might exploit. In this study, a method for face presentation attack detection (PAD) is introduced. The method utilizes the diversity of modalities provided by some cameras and sensors to detect face spoofing using convolutional neural networks (CNN) within the context of deep learning. To assess the effectiveness of the proposed approach in real-world scenarios, the wide multi-channel presentation attack (WMCA) dataset is used. The presented method exploits the multi-modal data, including RGB, depth, IR, and thermal channels, to enhance system performance and explore different techniques for combining the results from each modality. Furthermore, this study explores diverse techniques for fusing results from each channel in two fusion scenarios, pre-fusion and post-fusion. In the pre-fusion scenario, data from the four channels is combined, resulting in an ACER value of 0.19%. In the post-fusion scenario, the results of each modality are fused using different fusion techniques, such as majority voting, weighted voting, average pooling, and a stacking classifier. The stacking classifier yields the most favorable outcome with an ACER ratio of 0.03%. This performance is notably superior when compared to state-of-the-art methodologies.
Abstract
The distribution network suffers from low voltage problems, low frequency, and rising power losses greater than transmission systems. Load shedding is one solution to these challenges and is widely regarded as the last choice for avoiding voltage collapse and outages caused by significant disturbances. The conventional approach to load shedding reduces loads without regard for their significance until the voltage of the network is enhanced. Shedding loads without taking priority into account will cause power interruptions in critical facilities. In this paper, PSO-ANN algorithm-based load shedding to improve the voltage and frequency of distribution networks. Furthermore, a multi-objective function is developed that takes into account the linear static voltage stability margin (VSM) and the amount of load reduction. The aim of the work is to obtain the optimal level of voltage stability and remaining load when implementing load shedding while maintaining the load priority of each bus in the distribution network. Using MATLAB software requirements, the proposed technique has been implemented for two scenarios (overload, line disconnection) of the IEEE 33 bus system. The results showed that the proposed technique is the most distinctive compared to the results of the voltage sensitivity method and the conventional approach.
Abstract
Legged robots offer several benefits over standard wheeled vehicles when operating in tough and unstructured terrain. These benefits include increased speed, improved fuel efficiency, increased mobility, improved isolation from uneven terrain, and reduced environmental harm. This paper presents the modeling of an eight-legged robot that was simulated using Simscape Multibody toolbox in MATLAB, where the robot consists of eight legs, and each leg contains three links, and each link contains a PID controller, meaning it contains a total of 24 controllers. This controller was used to control the robot’s gait and make it more stable. To obtain the optimal and most stable gait for the robot and to travel a longer distance, an optimization algorithm should be used, so that in this paper the genetic algorithm (GA) is used to obtain those points. To test the robustness of the proposed controllers, different weights are added (1 kg and 3 kg) as a load to the body of the legged robot, the obtained results show the efficiency of the proposed controllers.
Abstract
Speaker recognition refers to identifying the speaker by his or her voice. People talk in a variety of tones and each speaking voice has features that distinguish one person from another. Speaker verification (SV)involves comparing a set of measures of the speaker’s utterances with a reference for the person whose identification is being asserted to accept or reject the speaker’s identity claim. An identity claim is made during speaker verification which consists of two steps: extraction of feature and matching of feature. In this work, the analysis of correlations of Mel-scale coefficients for the voice of utterance to identify the intended speaker is presented. Short text-dependent word and other text-independent word is represented in this study. The correlation accuracy ranged from 98% to 99% for user1 (same speaker) for text-dependent. whereas 83% and 61% for user1 correlation with other speakers for text-dependent and independent respectively. Furthermore, the MFCC feature extraction approach based on distributed Discrete Cosine Transform (DCT) is provided in this research. SV tests are carried out using the MFCC feature extractions method where close variance for the target speaker and away variance for other speakers is obtained. Additionally, the principle component analysis (PCA) is provided to improve the discriminative system performance. Where the PCA chooses the optimal path between every pair of extremely confusing speakers. The results obtained from PCA were similar to the correlation finding from the Mel-scale results with enhancing the discriminative information and with lowering the dimension of MFCCs data..
Abstract
Lumbar spine stenosis (LSS) is a common reason for low back pain, which refers to anatomical spinal canal stenosis. It often causes pressure on the nerve elements due to the surrounding soft tissue and bone. Due to the huge number of spinal injuries, manual diagnosis of lumbar spine stenosis by radiologists is tedious or time-consuming. Therefore, Deep learning techniques have become a more helpful tool to overcome this problem. For this purpose, this study employed the YOLO-v5 to develop an LSS detection model on a dataset of lumbar spine MRI scans from 153 patients with symptomatic low back pain. The dataset was filtered to include 84 mid-sagittal images using annotation techniques. The detection model is utilized to classify the intervertebral disc (IVD) condition as either bulging or normal. The results obtained showed that the model achieved an accuracy exceeding 88% in detecting and classifying the lumbar spine vertebra. The developed models showed that they are effective for lumbar intervertebral disc classification.
Abstract
The mixture (CdS-CdSe) thin films were fabricated by the thermal evaporation technique under very low pressures with a deposition rate (R) of 0.2 nm/sec and a 400 nm thickness (TH). The photoelectric and thermal properties of these films have been studied at different base layer temperatures. It was found that there is a linear relationship between the base layer (substrate) temperature and photocurrent of these photosensitive films. There has been a very influential parameter on the samples, which is the substrate temperature (Ts), where the optimum Ts was (170 °C) with a high adhesion coefficient. The sample that was deposited at this Ts, has good properties compared to other samples. Also, there is a direct relationship between the surface current and the operating temperature for fabricated films. X-ray diffraction (XRD) tests were taken for fabricated films which have been identified as polycrystalline with hexagonal and cubic-phase structures with different directional roles. The dominant direction of CdS 002 and 111 for CdSe. Analysis for films that were fabricated at (210 oC) and (90oC) shows an excess of (S) and (Cd) respectively. This condition greatly affects the film resistivity. In future work, new and different results can be obtained using different preparation parameters.
Abstract
The efficient and fast-tracking of the global maximum power point (GMPP) under partial shading conditions (PSCs) is one of the most significant goals of the maximum power point tracking (MPPT) algorithms. This paper introduces an algorithm to identify the accurate range locations of the GMPP. A skipping MPPT algorithm is proposed to minimize the time consumed in tracking the GMPP. The proposed algorithm uses a skipping voltage method to minimize scanning the voltage range on the Power-Voltage (P-V) curve by neglecting the zones without GMPP. During GMPP tracking under PSCs, the automatic initial voltage generator algorithm ensures no overlap between two adjacent zones on the P-V curve. The proposed skipping algorithm guarantees that the GMPP is tracked accurately under all potential atmospheric circumstances with a shorter tracking time and the ability to find the GMPP quickly, minimizing the power loss. The improved performance of the proposed algorithm has been validated by simulation and experimental results on a PV string. From the results, the proposed MPPT algorithm has demonstrated its superiority in tracking the GMPP under PSCs in terms of accuracy and tracking speed compared to other MPPT algorithms. The proposed algorithm tracks the GMPP faster, with a time difference of Δt = 3.28 sec and Δt = 27 msec from the experimental and simulation results under PSCs. The proposed algorithm also successfully tracks the GMPP in the final zone, while the 0.8VOCM MPPT algorithm fails, which causes a high-power loss of 196 watts compared to the proposed skipping algorithm.
Abstract
Software Defined Wireless Sensor Networks (SDWSN) has emerged as a contemporary model to achieve dynamic and secure control in the realm of Internet of Things (IoT) applications. By leveraging the benefits of Software Defined Networks (SDN), SDWSN enables ease of management and configuration of wireless networks, thereby overcoming the challenges associated with traditional Wireless Sensor Networks (WSN). However, SDWSN networks are susceptible to emerging network intrusion and threats, particularly Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, which can significantly impact the network’s performance and cause operational losses. This study proposes a machine learning based algorithm for detecting and preventing DoS and DDoS attacks in SDWSN networks. The proposed algorithm uses various features to distinguish between benign traffic and malicious traffic generated by attacks. The results demonstrate that the proposed algorithm can effectively detect and prevent DoS attacks, significantly contributing to the security of SDWSN networks.
Abstract
Wind Energy Conversion Systems (WECSs) have experienced significant growth in recent years.Among various types of generators employed in WECSs,Permanent Magnet Synchronous Generators (PMSGs) are an attractive choice among the wide variety of wind generators due to several advantages.The growing penetration of PMSG-based WEGSs into the worldwide electrical grid raises the concern that the failure of wind turbine generators may potentially result in the collapse of the system.This prompted several countries to adopt the Low-Voltage Ride-Through (LVRT) for wind farms.LVRT is the capability to maintain the connection between the wind farm and the grid during certain periods of voltage sag.This paper presents an efficient LVRT control strategy for a 12.0MW (6*2MW) grid-connected PMSG-based Wind Farm (PMSG-WF).The proposed strategy aims to enhance the power quality and amount of injected power to achieve the grid code requirements by integrating a Braking Chopper (BC) and a Dynamic Voltage Restorer (DVR) with the conventional structure of PMSG-WF. The detailed mathematical models for a wind turbine, PMSG, power converters, DVR system, and grid model are utilized to analyze the dynamic behavior and operation of PMSG-WF.For DVR, a PI controller is used for voltage sag mitigation to inject reactive power during grid faults, while a hysteresis controller-based BC system is utilized to keep DC-link voltage within its permissible limits.The proposed system is exposed to three scenarios of symmetrical and asymmetrical grid fault conditions (single-phase, two-phase, and three-phase faults) at the point of common coupling to evaluate its dynamic response.MATLAB/SIMULINK environment is used to validate the effectiveness of the proposed strategy during the studied scenarios.The results show the superiority of DVR in improving the voltage stability of PMSG-WF and maintaining the uninterrupted operation of the grid during different grid faults.
Abstract
The Maximal Power Point Tracking (MPPT) is a method employed to maximize the generated power from an energy source, such as PV (photovoltaic) or PEMFC (Proton Exchange Membrane Fuel Cell). In this study, the Grey Wolf Optimizer algorithm is utilized for the MPPT to regulate the boost converter positioned between the stack cell and the battery. The primary challenge addressed by the MPPT is that the efficiency of PEMFC is influenced by the supplied gases and cell temperature. To maintain optimal performance, the system aims to operate at the efficient power point, and the MPPT assists in achieving this by adjusting the voltage to align with the point where the PEMFC characteristic yields the maximum available power. Consequently, the MPPT’s objective is to identify the Maximum Power Point (MPP) and guide the PEMFC to operate at this specific point. This process is essential to overcome challenges associated with fluctuating inputs and to optimize the system for improved performance in a PEMFC. Typically, the MPPT control algorithm involves modifying the converter duty cycle (denoted as D) to compel the PEMFCs to operate at their MPP, ensuring efficient power production even under varying input conditions
Abstract
This study aims to assimilate distributed generation (DG) unit using a novel hybrid technique to improve the efficiency of electric power distribution networks by minimizing the real power losses (RPL) and enhancing the bus voltages (BV). A hybrid technique has been implemented by combining the features of nature-inspired algorithms namely hunter-prey optimizer (HPO) and ant lion optimizer (ALO) algorithms. The exploitation characteristic of ALO and exploration characteristic of HPO is utilized to optimize single DG in radial distribution power network (DPN). The efficacy of the suggested hybrid optimization technique is validated using MATLAB/Simulink software tool. The proposed hybrid technique was executed to optimize type I and type III DG in a balanced IEEE 69-bus radial DPN. The optimized type I and type III DG placement minimized the real power losses of a test system to 71.23 kW and 20.38 kW, respectively. Additionally, the least bus voltage of the test system improved to 0.9776p.u and 0.9843p.u following type I and type III DG allocation. The optimized allocation of type I DG and type III DG has resulted in 68.34% and 90.94% power loss reduction, respectively and enhanced the minimum bus voltage of the test system by 7.5% and 8.3%, respectively. The efficacy of the proposed hybrid methodology was investigated by relating its simulation outcome with other optimization methodologies present in the literature. The comparative results revealed that the proposed hybrid optimization technique provided better RPL minimization at improved BV than the compared optimization techniques.
Abstract
In this paper, a comparison between different types of rectifier circuits for RF energy harvesting is conducted. Six types of rectifier circuits consisting of half-wave, full-wave, voltage doubler, Villard charge pump, Graetz charge pump, and Dickson charge pump are designed and simulated. These various types of rectifier circuits are designed for different resistance loads, HSMS282X diodes and standard substrate materials that represents the dielectric constant. Firstly, the rectifiers are firstly designed on an FR-4 substrate with a thickness of 1.6 mm and a dielectric constant of 4.3 using a single-stage LC-match method at 2.4 GHz. The studied range of the input power was from 0 dBm to 30 dBm, the resistance load is 3 kΩ, and the HSMS2820 diode is used. The highest recorded output voltage for the Graetz charge pump was around 27 V, while the highest recorded efficiency for the Graetz charge pump was also 27%. Advanced Design System (ADS) software is used to simulate the rectifier circuits..
Abstract
The security of communications in various transmitted information’s forms such as video, audio, image, and even text and preserving them from attackers has become of great importance in the age of the Internet and cellular networks. Perhaps one of the most important media used to transmit information is digital images. They are distinguished from video and audio by their lack of complexity, and at the same time they are distinguished from text by the possibility of containing more information. Due to the necessity of transmitting huge amounts of information via digital images through additive white Gaussian noise (AWGN) channels for various applications. This transmission of images over unsecured channels is vulnerable to many attacks that must be protected by information security tools. In this research, a hybrid chaos-based system was developed to encrypt and secure images and send them via an orthogonal frequency division multiplexing (OFDM) channel, which leads to transferring large amounts of transmitted information in a short time, with very little interference between the data, and maintaining the transfer rate. Two chaotic techniques, Rossler and Modified Chau system, are used together to create a secret encryption key. This combination of chaotic systems provides highly random sensitive keys with amplitude of 10252 that are difficult to predict by the attacker and which makes restoring the original image very difficult in the event of a very small change in the chaotic parameters. Many tests were conducted to determine the strength of the proposed system, including statistical and differential analysis and entropy to verify the strength of the image security approach, in addition to applying some types of attacks to the encrypted image, such as noise and cropping different parts of the image. It is clear that the proposed scheme has strong immunity to these attacks. This was proven by the comparative experimental results. The entropy ratio was very excellent compared to the rest of the results obtained in the rest of the research. This was also the case with the values of (NPCR), (UACI), (NPCR), and Mean Square Error (MSE) was also very good as compared with other researches in the literature. The proposed security approach for OFDM gave a low link and a low bit error rate. And a higher signal-to-noise ratio (PSNR).
Abstract
Federated learning (FL) is one of the newest and most significant fields for developing artificial intelligence applications. This technology trains its models in a distributed way, using data from different clients who work together in the system without sharing their data. The training process is kept local to protect the privacy of the data. Among the many difficulties that have arisen due to the novelty of this technology is the issue of heterogeneous data between typical clients. Client’s data may differ from each other in different respects, for example non identically and independent distribution (non-IID) between clients and the difference in the type of data used in each client. This can lead to inconsistencies in the model’s predictions and other undesirable outcomes. This paper discussed ways to solve this problem where clients with heterogeneous data were dealt with in terms of number and type. Because there are different types of image data through which doctors can diagnose coronavirus, such as x-rays, CT-scan. A hybrid convolution neural network (CNN ) and long short-term memory model (LSTM) has been proposed in a federated learning system to predict the incidence of this disease by using two clients, each with one of these different data. Good results were obtained with an accuracy of more than 99% in one customer and more than 95% in the second client while maintaining the privacy of this data.
Abstract
Blockchain innovation is gaining attention in fields like monetary exchange, edge computing, medical care, and datasecurity. Consortium chains, using lightweight consensus algorithms like PBFT, offer alternatives to proof-based mechanisms while maintaining decentralization, security, and scalability. However, it also has some limitations and challenges that need to be addressed to improve its performance and scalability. PBFT is a classical algorithm with high complexity due to three-stage broadcasting and arbitrary selection of master nodes. Its communication efficiency is low, and scalability issues arise when nodes are large, causing significant delays and performance degradation in unstable networks. Furthermore, the requirement for every node to bundle, check, and broadcast the exchange list in the pre-prepared, prepared and commit stages diminishes the efficiency of consensus and performance between nodes and comes down on network correspondence. The research proposes a new methodology for the consensus algorithm, focusing on high-trust nodes to protect the network from malicious actors and reducing computational overhead and latency by eliminating Byzantian nodes and grouping the remaining nodes into groups, each of which has a main node selected based on a higher trust score. According to the results, the suggested methodology leads to significant improvements in communication complexity and Byzantine fault tolerance compared to standard PBFT networks and previous works. This indicates a substantial enhancement in network efficiency and scalability, offering promising prospects for blockchain applications in various fields.
Abstract
Deep falsification of multimedia content, especially videos and photos, threatens social cohesion (e.g., rumour propagation, extortion, and truth distortion) and must not be ignored. In some cases, this issue requires effective detection solutions. Most studies suggest that convolutional neural networks (CNNs) may not be able to extract complex features like those used in deepfake production. Thus, hybrid approaches that can capture complex features and act as powerful descriptors for binary classification are needed to separate bogus from true content. In this paper, a hybrid algorithm is developed to combine gated recurrent units (GRU) and CNN. The proposed model aims to improve the extraction of complex features by simultaneously capturing instantaneous and spatial features. This approach permits the extraction of implicit features that are vital to the final classification process, especially when dealing with a sequential series within video content. Finally, a dense neural network is used to classify these features. Practically, two data sets were used to train the proposed model: the FaceForensics++ (FF++) and DeepFake Detection Challenge (DFDC) datasets. The evaluation results of the proposed model on the FF++ dataset for the Area Under the Curve (AUC) and F1-score metrics reached 0.88% and 0.85%, respectively. While DFDC is 0.95% and 0.86% for the same metrics, respectively.
Abstract
A reconfigurable inset-fed Microstrip Patch Antenna (MPA) for dual-band behavior and stable-radiation direction has been demonstrated in this research. The suggested reconfigurable antenna has dimensions of 60×50 mm2 and is printed on an FR4 substrate that is 1.5 mm thick and has a dielectric constant (εr) and loss tangent (tanδ) equal to 4.3 and 0.02, respectively. An inset-fed line with an impedance of 50Ω is used to feed the proposed antenna structure. The parasitic capacitance is efficiently added to the proposed structure by using the slitline approach. The proposed antenna is tested and simulated, where the result shows two resonant frequencies with S11 values less than -10 dB (S11 ≤ −10). The first resonant frequency is found at 2.45 GHz with a value equal to -30.5 dB, while the second resonant is found at 3.54 GHz with a value equal to -32 dB. Moreover, the slits include two PIN diodes. After analyzing the antenna, two reconfigurable bands are obtained for various uses with stable radiation direction. The suggested antenna is constructed and measured, and the outputs of the simulation and the measurements show good agreement.
Abstract
The main objective of this paper project was to create a state-of-the-art face identification technique that can handle the various difficulties caused by changes in illumination, occlusions, and facial emotions. Face detection is a cornerstone of computer vision, facilitating diverse applications ranging from surveillance systems to human-computer interaction. Throughout this paper, the comprehensive exploration of advancing face detection methodologies has been undertaken, culminating in developing and evaluating a novel approach. The challenges posed by variations in facial expressions, lighting conditions, and occlusions necessitated a multifaceted solution. Our proposed method, which consists of interconnected steps, works quite well to overcome these challenges. Using deep learning architectures to increase feature extraction and discrimination was beneficial in the initial stage of fine-tuning Residual Networks (ResNet-50) to serve as the Region-based Convolutional Neural Network (Faster R-CNN) framework classifier. The process of gradually optimizing thresholds, such as batch size, learning rate, and detection threshold, involved using the Gray Wolf optimization technique (GWO). The conversion process was accelerated and improved overall detection process efficiency and accuracy using a clever fusion of machine learning and metaheuristic optimization techniques. A key component of our methodology is the careful data processing, which was necessary to ensure. The suggested method was carefully examined on a particular dataset, and the 94% training accuracy that was attained together with an identical test dataset accuracy highlights the method’s resilience. These findings support the effectiveness of our approach in reducing false positives and negatives, resulting in unmatched recall and precision in the detection system. The discovery has significant significance as it can potentially improve face detection systems’ performance and reliability in various real-world applications, such as human-computer interaction and surveillance. Convolutional neural networks, deep learning architectures, and metaheuristic optimization approaches were synergized to produce a new and reliable solution.
Abstract
Water scarcity, drought, and population growth accompanying climate change are dangerous factors with serious consequences related to potable water file unless appropriate action is taken urgently to deal with these issues, especially with large populations in the major cities as well as the suburban areas. This work presents an enhanced wide area network for efficient management of the freshwater in the major cities. Hence, it adopts Mosul city as a typical case that contains about 100 residential districts that require 100 sites of water monitoring in the different locations in the city where each site owns three different types of water sensors (water flow, water level, and pressure) in addition to the video surveillance application. The water station sites send the data to the control and monitoring center of the water. The collected data is processed and analyzed by public cloud or private cloud for control and monitoring purposes. The suggested communication network addresses the requirements of the water section applications in terms of monitoring in real-time. This work addresses the WiMAX system as a communication network infrastructure to handle the advantages of resilience, low-cost maintenance, and expansion The suggested network offered excellent behavior in terms of latency (maximum latency is less than 57 msec) and data traffic of the adopted applications.
Abstract
Nonlinear stream ciphers have become a viable alternative to traditional cryptosystems in response to the growing need for secure communication. These ciphers generate a keystream via feedback mechanisms and nonlinear functions, which are then utilized for encryption. Geffe generator system is one of the most keystream generators. Also, these systems have many benefits, like being fast, flexible, and able to create unpredictable and non-repeating keystreams, these systems are susceptible to cryptanalysis attacks, which have the potential to compromise their security. This paper presents the first study of applying chicken swarm optimization (CSO) algorithm in the field of cryptanalysis based on cipher only attack. The standard CSO algorithm and an adaptive multi points CSO (AMPCSO) algorithm are proposed to cryptanalysis nonlinear stream cipher based on Geffe keystream generator. Firstly, the traditional CSO is used to reveal the secret initial values of the Geffe generator. Secondly, an adaptive multi points chicken swarm optimization (AMPCSO) has been proposed to enhance the traditional CSO algorithm to attack Geffe generator systems. The AMPCSO is a new idea to advance the CSO search abilities and improve the foraging behavior of hens and chicks by allowing hens to be influenced by other individuals within the same or different groups and affected by the best individual in the population and enable chicks to learn from four reference points rather than learn from their respective mothers only. Lastly, a new criterion is used to estimate the value of fitness by utilizing a multi-objective fitness function (MOFF), which is grounded on Pareto dominance. The experimental results showed that the CSO and AMPCSO are very effective tools in terms of accuracy, information required, and CPU times when applied to the analysis of nonlinear stream cipher. The AMPCSO required a few characters from ciphertext to attack systems with total LFSRs length up to 59 bits with an appropriate CPU time.
Abstract
With the aim of enhancing the small signal stability of electric power systems, the present paper evaluated and compared some power system stabilizers (PSSs). The dilemma of small signal instability is avoided by equipping the generator’s automatic voltage regulator (AVR) with a backup controller known as a PSS. Conventional PSS operates with acceptable efficiency when designed to suit specific operating conditions, but there are limitations and drawbacks that arise when disturbances lead to fluctuation in system parameters. Strengthening the design methodology for PSS in the face of these limitations is achieved by adopting artificial intelligence. This research presents a fuzzy, neural system-based approach to the development of PSS. The Adaptive Network Based Fuzzy Inference System (ANFIS) is used to design the Fuzzy Neural Power Systems stabilizer (FNPSS) . ANFIS eliminates the disadvantages of using fuzzy logic and neural networks independently in PSS design. The single machine infinite bus (SMIB) power system was used as a case study to evaluate the effectiveness of the proposed methodology. Additionally, the study includes root locus scheme for loop of voltage regulation by utilizing proportional Integral controller, P-I controller, a widely used traditional linear design technique, for comparison. The simulation results confirm the effectiveness of the method, demonstrating the superiority of the ANFIS design method over other PSS designs. MATLAB, along with Control System Toolbox and SIMULINK, is used for simulation and design.
Abstract
With the development of cyber security and multimedia forensics, digital image manipulation has recently been recognized as one of the major challenges in forensic image analysis. Therefore, selecting an image area and then copying and pasting it into the same image is the hardest process in passive image forgery. This act violates privacy and secrecy of authenticity of digital image. The attacker exploits the available tools of editing image program to make the fake image similar to the original one. This paper presents a proposed fast and efficient passive Copy-move forgery detection scheme. Hessian- Affine and Harris-Affine detectors, and Shift Invariant Feature Transform (SIFT) descriptor, are employed in the proposed scheme. These detectors provide sufficient key points for detecting the duplicated regions in the case of small or invisible regions. The experimental results show that the proposed scheme is invariant against simple and hard attacks like uniform or non-uniform transformation. The proposed scheme was evaluated using standard data sets (GRIP, MICC 220, and F8 Multi). Resulted True Positive Rate (TPR) was 0.98 and False Positive Rate (FPR) was 0.035. Thus, the scheme is effective and providing valuable results compared to recent passive image authentication schemes.
Abstract
Engineers are searching for alternatives to conventional energy sources to address the energy crisis as a result of the sharp increase in energy usage. This work describes developing, simulating, and evaluating a three-phase, 13.25 kW solar power system. PV analysis is also performed. An inverter featuring a dual Electricity flow is connected to a solar system consisting of six consecutive strings of four solar power cells connected in series. The output of the phase lock loop (PLL) feedback in the linearization system is used to generate a signal, and the power conversion voltage is synchronized with the signal by using its output as a voltage reference. This hybrid technology, which has two phases that are optimal for the rechargeable recharging process of the batteries, is used to replenish a battery bank in capacity or float arrangement for eight sequences of 12V - 200-Ah rechargeable batteries. Ultimately, a MATLAB computational model has been created for a grid-connected photovoltaic system that uses sinusoidal modulation of pulse width and an inverter as voltage sources.
Abstract
High speed and area reduction of the Arithmetic-Logic Unit (ALU) have a fundamental role in modern processors, especially in digital signal processor (DSP). In this paper, a new elastic fixed-point (Fx-P) ALU module is proposed to perform multiple operations on real and complex numbers. The arithmetic part of the ALU executes operations such as addition, subtraction, increment, decrement, and multiplication on real numbers. For complex operands, the proposed ALU executes three operations comprising addition, subtraction, complex conjugate of complex numbers. The logical part performs the basic operations including AND, OR, NAND, NOR, XOR, XNOR, NOT and BUFFER operations. The proposed design is based on utilizing an enhanced design of a hybrid adder consists of a Han Carlson adder with a carry-select adder (EHC-CSLA) and an improved design of the Vedic multiplier to achieve multiplication operation of real numbers. A 16-bit and a 32-bit EHC-CSLA are designed first to perform real/complex addition-and- subtraction on both data types. Then, an improved-Vedic multiplier (IVM) is designed to perform multiplication on two real operands. The proposed EHC-CSLAs, numerous bit-sizes of the IVMs, and the elastic design of real/ complex ALU modules in this work are coded in VHDL, simulated, and synthesized by Xilinx ISE14.7 tool on different FPGA families. The performance results demonstrate appreciable reductions in delay and area usage in comparison to the most counterpart multipliers and ALU designs.
Abstract
Electrical motors have been engaged in many residential, industrial and commercial applications. The speed of an electric motor is an essential output quantity which is needed in many processing systems. Therefore, estimating the speed of an electrical motor is an integral part in the hierarchy of operational and control process. In this work, a new speed estimation method is proposed which is based on a naturally occurring signal; the mechanical vibrations the body of the motor endure during operation. These vibration signals are measured in multi-axial dimension through accelerometer and gyroscope. Furthermore, the collected data is trained in a machine learning model. The model is used subsequently to estimate the speed of a self-excited direct current (DC) motor. Two approaches (offline and onboard) are followed to evaluate the fitness and the performance of the proposed method. The offline approach is performed using regression learner MATLAB toolbox and many algorithms are tested and results with different performance metrics are presented. The algorithm that yields best performance in terms of minimum Root Mean Square and maximum regression factor (R2) is selected as candidate for offline revolutions per minute (rpm) estimation. Results documents that with Gaussian process regression algorithm, estimations are obtained with a mean square error of 7 rpm and an R2 value of 1 which is considered a very satisfactory performance. The second approach is motor speed estimation in real time using vibration signals with deep learning model implemented on limited resources electronic board which is proposed for the first time to the best of our knowledge. The proposed method has been successfully implemented by low consumption resources from the selected board with 6.5 kb of ram and 91ms latency. Even with the limited resources, a rated speed estimate percentage error of 0.18% was recorded from real time results. Moreover, the proposed method is characterized by its simplicity, low technical requirements and eventually low cost of implementation. The aforementioned features make this method an attractive platform for speed estimation in many industrial applications.
Abstract
Because elliptic curve cryptography offers a promising trade-off between security and computational performance, the field of current cryptographic techniques has taken a particular interest in it. Two basic digital signature algorithms—the Elliptic Curve Diffie-Hellman Algorithm (ECDH) and the Elliptic Curve Digital Signature Algorithm (ECDSA) are the subject of this performance analysis and comparison. These methods are based on elliptic curve cryptography. The analysis takes into consideration realistic application demands as well as factors like key length and security level. The results provide useful information on trade-offs between performance and security. A list of acceptable ECDSA requirements for digital signatures was used for the comparison. These characteristics are sign, sign/s, no PC verify, no PC verify/s, siglen, keygen, keygen/s, verify, and verify/s.
Abstract
Recent advancements in communication and wireless technologies have greatly increased the number of internet users. These users often share personal information online, making it vulnerable to attackers. Phishing, a common type of online fraud, involves tricking people into giving their personal information through spam or other deceptive methods. Even though this threat has been around for a long time, it is still very active and successful. Attackers have improved their methods over the years to make their attacks more convincing and effective. Therefore, it is important to carefully study this type of attack to raise awareness among both users and cybersecurity researchers. This review paper explains the basics, types, and methods of phishing and presents a unified attack lifecycle framework to provide users and researchers with a clear understanding of phishing. Additionally, anti-phishing methods are thoroughly analyzed to determine their strengths and weaknesses. Researchers use different strategies to develop anti-phishing solutions, including blacklisting, whitelisting, heuristics, machine learning, and deep learning techniques. To help readers choose the best anti-phishing solution, research studies using these strategies are categorized, evaluated, and compared using specific criteria to show their strengths and weaknesses. Furthermore, the datasets used to develop anti-phishing models are discussed and reviewed. Finally, this paper provides a detailed overview of current phishing challenges and suggests future research directions in this area.
Abstract
In recent times, artificial intelligence has become an essential part of our lives, particularly in tasks involving object recognition. This paper explores the use of convolutional neural networks (CNNs) for enhancing underwater search and rescue operations by classifying images of humans, fish, and plants. Leveraging the OpenCV library for preprocessing and the Keras library with a TensorFlow backend for recognition, this study utilizes a dataset captured through field experiments. The methodology involved preprocessing the images for segmentation, followed by training a CNN model to classify these images with high accuracy. The CNN model demonstrated a remarkable classification accuracy of 99.6 %, significantly outperforming other modern machine-learning methods. This work suggests that CNNs can greatly improve the speed and effectiveness of underwater search and rescue operations by accurately identifying and locating submerged persons, which is critical for timely rescue missions.
Abstract
IoHT has several benefits for real-time smart healthcare, but because of its limited processing power, storage capacity, and self-defense capabilities, security issues are growing. Although newer blockchain-based authentication solutions have strong security features due to their tamper-resistant decentralized architecture, they come with a high resource cost, requiring a lot of processing power, more storage, and time-consuming authentication procedures. As such, these difficulties provide barriers to reaching the ideal levels of scalability and temporal efficiency, which are essential for the efficient functioning of large-scale, time-sensitive IoHT systems. To solve these challenges, this paper presents an authentication approach designed especially for IoHT systems. Our work consists four-phase process, which includes setting, registration, login and authentication, and HERs Exchange data. To enhance both efficiency and scalability, the proposed scheme employs a combination of 3-D map dimensions chaotic-based public key cryptosystems, and blockchain-based, fog computing technologies and IPFS. We simulate the proposed work to implement health electronic record (HER) by the Ethereum platform and solidity language, the simulation experiments were tested using the JMeter tool. Showed that the key generation time for chaotic-based is faster than (ECC)—furthermore, the average latency ≈ 3.7 ms. A security analysis of the proposed scheme was implemented by the Scyther tool. The formal security analysis demonstrated that the proposed scheme is secured against potential attacks and supports the scalability of the IoHT system.
Abstract
Autonomous mobile robots (AMRs) are becoming increasingly important in different domains such as healthcare, warehouse automation and household duties, but still encounter problems when it comes to moving around unfamiliar and dynamic environments. This study proposes an advanced robotic navigation system which combines the Soft Actor-Critic (SAC) approach and Vector Field Histogram (VFH) for path planning and avoidance obstacles in completely unknown environments. This system leverages the strengths of deep reinforcement learning and real-time obstacle detection to achieve robust and efficient navigation in certain scenarios. The SAC strategy optimizes robot navigation using policy networks and Q-networks, while the VFH method addresses obstacle avoidance by sensor data processing and dynamically adjusting the robot’s angular velocity to avoid collision. For testing and implementing this system, Gazebo simulation and Robot Operating System (ROS) are used. Experimental results demonstrated that the proposed method outperformed the standard technique and achieved a high success rate in path planning and obstacles avoidance.
Abstract
Utilizing Heating PID control systems is common across numerous industries to attain the desired output. Nevertheless, the development in the status of Fractional Order Proportional Integral Derivative Controllers (FOPID) has led to improved control performance and increased degrees of freedom in industrial applications. The paper proposed real-time microwave heating systems which exhibit several challenging characteristics and are complex enough to effectively demonstrate the robustness advantage of fractional (FOPID) over traditional PID controllers. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was modeled using real-time data to assess the effectiveness of conventional PID and FOPID controllers. The results of the study demonstrated that FOPID controllers outperform conventional PID controllers in terms of performance, robustness, stability, flexibility, and faster response. Additionally, the study utilized MATLAB and LabVIEW software to model the Fractional PID controller, the traditional PID controller, and the ANFIS model. The outcomes illustrate that the FOPID controller demonstrates faster rise times (3.8 seconds vs. 6.0 seconds for PID), lower overshoot (1.0oC vs. 2.5oC, and shorter settling times (10 seconds vs. 17 seconds). During setpoint drops, FOPID exhibits reduced undershoot (1.40C compared to 3.2oC) and quicker recovery (5.5 seconds vs. 8.5 seconds). In the final tracking phase, FOPID maintains a lower residual error ( 0.20C vs. 0.7oC) and achieves a steady-state error of 0.1oC, compared to 0.5oC for PID.
Abstract
This paper presents a novel linear variable structure secondary controller for islanded Microgrid driven by voltage source inverters. The main control stretchy depends on a low pass filter based frequency restoration. The proposed control strategy solves the problem of trade of between accurate frequency restoration and active power sharing accuracy by using variable structure controller. A bank of low pass filters with different parameter values are used instead of single fixed parameter controller. An efficient algorithm is designed to switch between the compensators in the bank to achieve the two objectives, namely accurate frequency restoration and fast power sharing. The switching algorithm uses event driven protocol to trigger its activation until reaching steady state and then staying stand by for the next event where the event is active power change. Simulation results shows an excellent result.
Abstract
An essential component of every RF system’s reception chain is the Low-Noise Amplifier(LNA). The sensitivity and performance of subsequent stages in the receiver chain are significantly influenced by the LNA, which is the initial step. Creating an LNA requires carefully balancing trade-offs in order to have the best possible performance in terms of gain and noise characteristics. Achieving optimal functioning and efficiency in the radio frequency system requires finding the correct balance. This article presents the design of an LNA circuit at the lowest cost without adding components such as inductors, active components, or several stages, which increase the complexity of the circuit, consume power, and add additional noise, by controlling the lengths of the microstrip line, LNA circuit was created by ADS software, and add a matching circuit. At the operating frequency of 2.4 GHz, the suggested design achieved good results with a gain of 17.48dB, NF of 0.7dB, stability factor of 1.5dB, and S11-S22 (-41dB, -25dB) in that order.
Abstract
Reducing the dependency of the control system on communication in the microgrid increases the reliability and flexibility of an islanded microgrid. This paper presents a local secondary control approach to provide a fast response to power change and accurate frequency restoration. It is based on a control scheme that uses a secondary controller involving a time-controllable parameter for a Low pass filter. The high value of the time-varying parameter is placed to satisfy excellent performance regarding fast active power sharing, and the time-controllable parameter decreases after achieving power-sharing based on a time protocol to ensure accurate steady-state frequency restoration. This paper also describes the criteria for control parameter selection and stability analysis based on a precise modeling approach. The MATLAB environment is used to simulate and test the proposed control scheme, and the results have been obtained that show the validity and high performance of the proposed controller in terms of dynamic response to active power change and steady-state restoration under different operation conditions.
Abstract
To support medium-voltage and high-power applications in flexible power systems, multilevel inverters, which are commonly referred to as MLIs, are currently being developed. The conventional configuration of a multilevel inverter, which aims to accommodate a wide range of applications, necessitates the use of additional switches and sources and is subject to certain constraints. Through the built-in control of the boost converter and the PWM for each level, this research aims to discover a new method that uses a boost converter to obtain an MLI with a minimum number of switches, maintaining this number constant as the number of levels increases. The research results clearly demonstrate the reduction of THD to small values through the use of the boost converter in the proposed method. MLI is usually used in renewable energy applications to obtain certain voltages, for example, from solar cells, therefore, simulations were conducted within the framework of photovoltaic (PV) cells as an input source. When MLI configuration integration is added to a PV system, a lower number of switching components are used for a defined number of voltage output levels. This is in contrast to typical multilevel inverter topologies, which require a larger number of switching components to manage the gating pulse of PV-based MLI. The MATLAB/SIMULINK program assisted in carrying out this work.
Abstract
Drug-drug interactions (DDIs) stand at the forefront of challenges in modern pharmacology, necessitating precise prediction methods to ensure patient safety. This study presents a pioneering approach that synergizes attributed heterogeneous graph embedding with deep learning to forecast DDIs and their specific classifications. Our methodology is delineated into two pivotal stages. The preliminary phase revolves around data assimilation, leading to the creation of specialized feature matrices such as Chemical Composition, Interaction Targets, Enzymatic Reactions, and Biological Pathways. These matrices culminate in a comprehensive drug network where drugs are symbolized as nodes. Upon rigorous data refinement, these matrices serve as attribute markers for each node. Capitalizing on the robustness of the attributed heterogeneous network, we amalgamate diverse drug attributes, thereby amplifying the depth of drug interaction assessments. The subsequent phase sees these drug embedding vectors undergo strategic concatenation, resulting in detailed feature vectors for drug pairings. The final step involves a dense neural network, tasked with decoding intricate drug interaction nuances. The introduction of an attention-driven embedding process further accentuates the model’s capability by emphasizing pivotal interactions. The promising results, coupled with an innovative methodology, sets the stage for future explorations, potentially revolutionizing DDI predictions.
Abstract
Since cardiac conditions are among the most fatal illnesses in the medical community, ECG classification systems are crucial for understanding and diagnosing patients’ health conditions. Numerous techniques for ECG feature extraction and classification algorithms are developed by researchers. This paper presents a method for accurately classifying ECG illnesses based on the 3-scale Slantlet transform (SLT) and artificial neural network (ANN). The ability of the SLT filters to decompose the ECG signal at various resolutions led to excellent classification. As a new realization, all coefficients of the modified designed SLT filters are expressed by the sum-of-power-of-two (SOPOT) approach to reduce the complexity. It is noteworthy that the average and maximum deviation error values between the responses of original and modified filters are very small. Hardwarely, the new realization leads to a less complex implementation for the designed SLT filters on FPGA kit using the Xilinx System Generator for DSP with very small errors between output resposes of the original and modified filters. FPGA results show that the system is designed using a best-selected wordlength method. The proposed classification system is capable of distinguishing the ECG normal case and other four different diseases with a high overall accuracy of 98.50 %.
Abstract
Electromagnetic radiation is becoming a major concern worldwide as the use of portable communication devices increases. So, it is essential to utilize safe communication devices. A compact wide-band antenna of size 12 × 8.5 × 0.33 mm3 and a metamaterial array contribution for Specific Absorption Rate (SAR) reduction are proposed in this paper. In this paper, an array structure of split ring resonators, SRR, which have a negative refractive index, is attached to the proposed MSPA to achieve SAR reduction by 89.88% in the 28 GHz range. Furthermore, the proposed antenna maintains other performance characteristics like high gain (7.7 dBi), radiation efficiency (82%), wide bandwidth (0.8 GHz), and fewer losses (-23 dB). However, this consequential antenna has been built on a low-loss Rogers RT 5880 substrate and a full ground-plane structure using CST microwave software.
Abstract
Alzheimer’s disease (AD), the most common form of dementia, affects over 55 million people worldwide. The most form of dementia progresses into three distinct stages: mild, moderate, and very mild compared to Cognitively Normal (CN). Early detection is crucial to prevent brain damage before the late stages. Convolutional Neural Networks (CNNs), a subfield of deep learning, have recently found remarkable applications in medical image processing and computer-aided diagnosis (CAD). To this end, this paper presents a new efficient multi-classification AlzCNN-Net model to enhance the accuracy and efficacy of MRI image classification for various Alzheimer’s disease conditions. Initially, the training process involves utilizing open-source Alzheimer’s disease datasets from the Kaggle database to classify the brain MRI into its corresponding category. To verify the model’s efficacy, a comparative analysis with three pre-trained models, namely VGG16, Incep-tionV3, and MobileNetV2, has been investigated via transfer learning applied to the same dataset. As a result, the findings reveal that the AlzCNN-Net model exhibits an optimal performance, attaining the best accuracy in training with 99.67%, validation with 98.24%, and testing with 98.9% accuracy at epoch 100 with batch size 32 compared to the existing pre-trained approaches.
Abstract
Aiming to enhance the accuracy of sign classification in sign language (SL), this research presents an innovative approach that combines hand-engineered characteristics with deep learning (DL) algorithms. The focus is on American Sign Language (ASL), a critical communication tool for the deaf and hard-of-hearing community. The goal is to bridge the existing communication chasm between SL users and the general public by designing a real-time SL recognition system that allows non- SL users to converse with the hearing-impaired individuals. The application and assessment of various machine learning (ML) models, such as VGG19, DenseNet, ResNet50, MobileNet, and NASNetMobile, yielded promising outcomes with superior evalu- ation metrics. These models exhibit utility in the classification of ASL signs as they can differentiate between diverse hand gestures with high accuracy (ACC). The paper highlights the potential of these models across an array of ASL recognition applica- tions, considering factors like computational resources, model dimension, and real-time functionality. The findings endorse the application of ML techniques in SL interpretation, promoting inclusive communication for those with hearing impairment.
Abstract
Radio frequency integrated circuits (RFICs) are widely used in wireless technology systems. Low-noise amplifiers, especially in the 5 GHz frequency range, are vital parts of contemporary wireless communication systems. Research on 5 GHz low-noise amplifiers aims to improve the performance of these amplifiers by addressing issues related to noise, gain, and power efficiency. Low-noise amplifiers are used in many different applications and are essential for developing more effective, efficient, and balanced wireless communication systems. The paper presents a wideband low-noise amplifier (LNA) implemented in a 5 GHz (Low-Noise Amplifier) for 5G Wi-Fi applications. It is driven by a 1.8 V supply. To increase the voltage gain and reduce the power consumption, the circuit has a common source layout and is optimized to reduce the noise figure. Single-stage common source decomposition and inductive source decomposition techniques are also used to match the circuit with the source impedance. Genetic algorithm is also used to optimize the circuit operation. The genetic algorithm has been shown to significantly reduce the noise in the low-noise amplifier circuit, which greatly improves the signal quality. The algorithm has increased the gain of the circuit, making it more sensitive to signals and enhancing its ability to process diverse signals. The proposed LNA showed a total current of 2 mA and a minimum noise figure of 1.107 dB with a high voltage gain of 21.86 dB and a power consumption of 3.6 mW. I expect the proposed LNA to be suitable for 5G Wi-Fi applications in the GHz band.
Abstract
With the appearance of Fifth Generation (5G) technology , its necessary to fast the enhancing of the current networks, because of the limitations of Fourth Generation (4G) in terms of data transmission. Although the benefits of the Orthogonal Frequency Division Multiplexing (OFDM) standard of the LTE systems, it has demerits such as rises the Peak-to-Average Power Ratio (PAPR) as well, high Out-Of-Band Emission (OOBE). Thus, it is considered unsuitable for 5G. In this paper, the filtered OFDM (f-OFDM) is proposed for 5G wireless communication systems as an alternative of OFDM because of its low OOBE. Nevertheless, a trade-off between minimize Bit Error Rate (BER) and OOBE and managing PAPR values are the challenge. One of the most important objectives in this paper is achieving balance among this trade-off through proposing concatenated Reed-Solomon (RS)-Hamming codes to improve f-OFDM systems performance. The proposed method utilizes an external RS (7, 1) code with an internal Hamming (7, 4) codes, then appended of an interleaver to combat random errors and help RS code in correcting errors. The results indicated that the proposed f-OFDM system significantly reduced OOBE values compared to familiar OFDM system owing to use FIR digital filter, while minimizes PAPR and improved BER performance due to combined with concatenated codes. Thereby, the suggested system is presented as a highly competitor candidate future wireless communication systems thank to these benefits.
Abstract
The advancement of pressure sensors customized for purposes marks notable progress, in healthcare diagnostics and patient supervision. This article delves into creating and assessing of a capacitive pressure sensor designed to measure physiological pressures with utmost accuracy and sensitivity. The sensor’s structure integrates materials compatible with the body to ensure safety and dependability when interacting with bodily tissues. Thorough simulations and validations showcase the sensors performance emphasizing its responsiveness across various pressures in medical settings. The assessment encompasses an analysis of the sensor’s sensitivity at (12.4 fF/mmHg) exceptional linearity within a nonlinearity range of ±0.015% with a small diaphragm diameter (0.5 mm) and long-term reliability. The results indicate that the suggested capacitive pressure sensor exhibits promising possibilities for use in fields like blood pressure monitoring, intracranial pressure measurement and other crucial areas of biomedicine, providing a nonintrusive and cost-efficient method, for real-time health monitoring and diagnostic purposes.
Abstract
In electrical power plants, the excitation control system is an important part of controlling the output voltage of the synchronous generators. The purpose of this paper is to utilize various methods of excitation control, such as Proportional-Integral-Derivative (PID), Simulated Annealing (SA), and Neural Network (NN) controllers. Each method is examined in terms of its effectiveness in enhancing system stability, reliability, and adaptability to varying operational conditions. The study simulates and optimizes a 2 MVA/400 V synchronous generator driven by a three-phase diesel engine with mechanical coupling and an exciter system. MATLAB 2021 is used to implement the Simulink model. The dynamic responses of field voltage and field current to load changes were analyzed for each control technique. Additionally, the performance of three-phase voltage and current for synchronous generator were examined over a 10-second timeframe. Our findings indicate that the PID controller offers straightforward implementation and reliable performance under varying conditions. The NN controller implementation is more similar to the PID response, and the SA controller demonstrates superior adaptability. The research underscores the potential of integrating these advanced control techniques in synchronous generators, paving the way for enhanced stability and reliability in modern electric power systems, with further implications for renewable energy integration.
Abstract
Recently, researchers have focused their efforts to generate electricity on renewable energy sources. Wind power systems are considered good alternative sources of clean energy. Induction generators are the best choice for generating this energy due to their simplicity, robustness, and low maintenance requirements. However, their main drawback is their need for leading reactive power to build the terminal voltage and generate electrical power. This drawback can be overcome using a terminal capacitor across the generator terminals to generate this leading reactive power. This research focuses on: 1-Provides a methodology for selecting an accurate and reliable value of the excitation capacitance required for self-excited induction generators (SEIG), which can be used in pumps operating as turbines (PATs + SEIG). When operating at different speeds and loads. For these systems, the choice of capacitance for the SEIG is of utmost importance. 2- A simplified and understandable method derived from nodal analysis is presented for calculating the exact excitation capacitance of a self-excited induction generator (SEIG) under various conditions. 3-A new analysis and model of (SEIG) is presented. The proposed model consists of an induction generator, a self-excited capacitor, and a RL load. It is used to study the performance of SEIG under different faults and excitation (sudden short circuit, unbalanced excitation, sudden load surge, sudden disconnection of excitation capacitance, and load disturbance). Simulations are created using MATLAB-SIMULINK to validate the proposed model.
Abstract
The paper presents a novel approach that merges the abilities of the biological environment with the concept of hierarchical trees to attack a specific stream cipher. The model being presented introduces a systematic method that targets a group of stream ciphers, such as the GCM family, these devices are composed of components that are suitable for the proposed method. A restricted set of binaries for the final key sequence is required to implement this technique as an input. The attacked algorithm comprises feedback shift registers, memories, delays, and so on. The stream ciphers are widely used in modern encryption to secure communication devices, so any attempt to analyze or attack it is of the utmost importance. The results of this method have been confirmed to lead to the destruction of the cipher’s security. Many novelties and contributions of the present work can be summarized as follows: firstly, the key generator’s components are attacked individually, disrupting the cohesion between them. This was not possible previously except in rare cases and under difficult conditions. Secondly, the method of verifying the correct initial values is unrelated to the generator’s operation. Thirdly, the technique applies biological concepts and processes to laboratory test tubes for genetic engineering, it can be said that the prepared model targets a broad class of stream key generators, rather than a single algorithm. The proposed technique requires a specific and deterministic number of final key sequence bits, which are easy to provide. The proposed technique creates a search E -tree in the style of hierarchical clusters, in which the first level containsE nodes. Then each successive level contains the square of E nodes of the number of nodes in the previous level, and the root is composed of the total solution space of the stream key generator and produces the nodes of each level from the intersection of the cluster contents in the test tubes for all clusters in the level above it. The contribution and novelty of the present work is cryptanalyzing and attacking shift register-based stream key generators involves fragmentation. The attacking principle entails disassembling generator components from registers and individually attacking them. DNA logic clustering aids in this process, as the strength of these generators relies on component cohesion. Because the components are cryptanalyzed individually, the time complexity of the attack is equal to O(C2N) , where N is the length of the largest component, and C is a constant.