Iraqi Journal for Electrical and Electronic Engineering
College of Engineering, University of Basrah
Innovative DNA Clustering-Based Hierarchy Tree Model to Cryptanalyze Shift Register Based-Stream Cipher
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.
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Excitation Control Enhancement for the Synchronous Generator Using Effective Control Methodology
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.
A Reliable Value Selection of Excitation Capacitance for a Self-Excited Induction Generator Operating Under Different Fault and Excitation Conditions
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.
Accurate Capacitance Value. Wind Turbine.
Enhancing American Sign Language Recognition Through Transfer Learning Techniques
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.
Concatenated RS/Hamming Code in f-OFDM System
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.
Alzheimer’s Disease Diagnose using Deep Learning for Brain MRI Images: A Comparative Analysis
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.
Enhancing Communication Technologies with Advanced Optimization of 5 GHz Low Noise Amplifiers
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.
Design Capacitive Pressure Sensor (MEMS) with High Sensitivity and Linearity for Biomedical Application
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.
SAR Reduction Using Array of SRR MTM Based Antenna for 5G Applications
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.
Slantlet Transform-Based ECG Classification System with Efficient Design and Implementation
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 %.
An Attention-Based Graph Neural Network Method for Drug-Drug Interaction Prediction
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.
A LabVIEW Based Fractional-ANFIS PID Controller For Real-Time Microwave Heating System
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.
Optimizing Microstrip Length for Enhanced S-Parameters in 2.4GHz Low Noise Amplifier
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.
A Multilevel Inverter with Fewer Switches Using Boost Converter for Solar PV Applications
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.
Secondary Controller for Parallel Operated Inverters in Islanded Microgrid Using Variable Structure Control Strategy
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.
Local Frequency Restoration-Based Communication-Free Approach in Islanded Microgrids
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.
Performance Analysis of Elliptic Curve Cryptography Digital Signature Algorithm
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.
Securing Healthcare Data Exchange Based on Fog Computing and Blockchain Technologies
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.
Unraveling Phishing Attacks and Countermeasures: A Comprehensive Review
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.
A Hybrid Approach Using Vector Field Histogram and Deep Reinforcement Learning for Dynamic Path Planning
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.
Enhancing Underwater Search and Rescue Operations: A CNN Approach for Human, Fish, and Plant Classification
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.
A Robust Hybrid Multi-Scale Approach to Detect Copy-Move Forgery in Digital Image
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.
Hybrid Learning Algorithm for Power System Stability: Fuzzy-Neural Control Approach
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.
Adaptive Neuro Fuzzy Inference System (ANFIS)
Speed Estimation of a Direct Current Motor Based on a Convolution Neural Network
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.
Elastic Fixed-Point Arithmetic-Logic Unit Based on Hybrid Adder And Vedic Mathematic
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.
Simulation and Analysis of High Performance of On-Grid Solar Panel System Based on Intelligent Controller
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.
Designing Face Detection Systems with GrayWolf Optimization
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.
Fusing Spatial and Temporal Features Extracted Using Convolutional Neural Networks and Gated Recurrent Units for Improved Deepfake Detection
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.
Proposed Design of a Wireless Communication Network ForWater Management Applications in Mosul City
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.
Adaptive Multi Objective Chicken Swarm Optimization for Solving Nonlinear Stream Cryptosystem
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.