Iraqi Journal for Electrical And Electronic Engineering

In smart cities, health care, industrial production, and many other fields, the Internet of Things (IoT) have had significant success. Protected agriculture has numerous IoT applications, a highly effective style of modern agriculture development that uses artificial ways to manipulate climatic parameters such as temperature to create ideal circumstances for the growth of animals and plants. Convolutional Neural Networks (CNNs) is a deep learning approach that has made significant progress in image processing. From 2016 to the present, various applications for the automatic diagnosis of agricultural diseases, identifying plant pests, predicting the number of crops, etc., have been developed. This paper involves a presentation of the Internet of Things system in agriculture and its deep learning applications. It summarizes the most essential sensors used and methods of communication between them, in addition to the most important deep learning algorithms devoted to intelligent agriculture.

Low-quality data can be dangerous for the machine learning models, especially in crucial situations. Some large-scale datasets have low-quality data and false labels, also, datasets with images type probably have artifacts and biases from measurement errors. So, automatic algorithms that are able to recognize low-quality data are needed. In this paper, Shapley Value is used, a metric for evaluation of data, to quantify the value of training data to the performance of a classification algorithm in a large ImageNet dataset. We specify the success of data Shapley in recognizing low-quality against precious data for classification. We figure out that model performance is increased when low Shapley values are removed, whilst classification model performance is declined when high Shapley values are removed. Moreover, there were more true labels in high-Shapley value data and more mislabeled samples in low-Shapley value. Results represent that mislabeled or poor-quality images are in low Shapley value and valuable data for classification are in high Shapley value.

Today, the trends are the robotics field since it is used in too many environments that are very important in human life. Covid 19 disease is now the deadliest disease in the world, and most studies are being conducted to find solutions and avoid contracting it. The proposed system senses the presence according to a specific injury to warn of it and transfer it to the specialist doctor. This system is designed to work in service departments such as universities, institutes, and all state departments serving citizens. This system consists of two parts: the first is fixed and placed on the desk and the other is mobile within a special robot that moves to perform the required task. This system was tested at the University of Basrah within the college of engineering, department of electrical Engineering, on teaching staff, students, and staff during the period of final academic exams. The presence of such a device is considered a warning according to a specific condition and isn’t a treatment for it, as the treatment is prescribed by the specialist doctor. It is found that the average number of infected cases is about 3% of the total number of students and the teaching staff and the working staff. The results were documented in special tables that go to the dean of the college with the attendance tables to know the daily health status of the students.

The necessity for an efficient algorithm for resource allocation is highly urgent because of the increased demand for utilizing the available spectrum of wireless communication systems. This paper proposes an Enhanced Bundle-based Particle Collision Algorithm (EB-PCA) to get the optimal or near optimal values. It applied to the Orthogonal Frequency Division Multiple Access (OFDMA) to evaluate allocations for the power and subcarrier. The analyses take into consideration the power, subcarrier allocations constrain, channel and noise distributions, as well as the distance between the user’s equipment and the base station. Four main cases are simulated and analyzed under specific operation scenarios to meet the standard specifications of different advanced communication systems. The sum rate results are compared to that achieved by employing another existing algorithm, Bat Pack Algorithm (BPA). The achieved results show that the proposed EB-PAC for the OFDMA system is an efficient algorithm in terms of estimating the optimal or near optimal values for both subcarrier and power allocation.

One crucial challenge confronting operators worldwide is how to ensure that everything runs smoothly as well as how to monitor the network. The monitoring system should be accurate, easy to use, and quick enough to reflect network performance in a timely way. Passive network monitoring is an excellent tool for this. It could be used to look for issues with a single network device or a large-scale issue affecting the whole LAN or core network. However, passive network monitoring is not limited to issue resolution; it could also be used to generate network statistics and measure network performance. As shown in this review, it is a very strong tool, as seen by the sheer volume of data published on Google Scholar. The main objective of this review is to analyze and comprehend monitoring measurements for quality of service to serve as a resource for future research and application. Essential terms and concepts of network monitoring and their quality of service are presented. Network monitoring measurements (which can be passive, active, or hybrid) and their wireless network monitoring tools (which can be public domain or commercial tools) are also covered in terms of relevance, advantages, and disadvantages. Finally, the review is summarized.

This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller’s design. The effects show a neural local (NARMA-L2) can attribute even higher than PID. The settling time, upward jab time, and most overshoot of the response of NARMA-L2 is a notable deal an awful lot less than the corresponding factors for the accepted PID controller. The conclusion from this paper can be to utilize synthetic neural networks of industrial elements and sturdy manageable to be viewed as a dependable desire to normal modeling, simulation, and manipulation methodologies. The model developed in this paper can be used offline to structure and manufacturing points of conditions monitoring, faults detection, and troubles shooting for wind generation systems.

Wind energy and its conversion is part and parcel of renewable energy resources as cheaper and cleaner energy today even though the initial cost varies from place to place. Most of the government sector always promotes renewable energy with a provision of subsidies as observed worldwide. Wind energy is an actual solution over costlier conventional energy sources. If it is not properly placed and the selection of turbine design is not up to the mark, then investments may require more time to acquire Net Profit Value called as NPV. This research work is focused on the development of mathematical models to optimize the turbine size and locations considering all constraints such as the distance between the turbines, hub height, and investment in internal road and substation cost. Particle-Swarm-Optimization is an intelligent tool to optimize turbine place and size. The database management system is selected as the appropriate data storage platform for before and after optimization simulation. Various plots and excel outputs of .net programming are addressed for the success of optimization algorithms for the purpose of wind turbine placement and WTG design is suggested to manage wind energy such that power system reliability has been improved and the same is monitored through the reliability indices.

Due to the recent improvements in imaging and computing technologies, a massive quantity of image data is generated every day. For searching image collection, several content-based image retrieval (CBIR) methods have been introduced. However, these methods need more computing and storage resources. Cloud servers can fill this gap by providing huge computational power at a cheap price. However, cloud servers are not fully trusted, thus image owners have legal concerns about the privacy of their private data. In this paper, we proposed and implemented a privacy-preserving CBIR (PP-CBIR) scheme that allows searching and retrieving image databases in a cipher text format. Specifically, we extract aggregated feature vectors to represent the corresponding image collection and employ the asymmetric scalar-product-preserving encryption scheme (ASPE) method to protect these vectors while allowing for similarity computation between these encrypted vectors. To enhance search time, all encrypted features are clustered by the k-means algorithm recursively to construct a tree index. Results show that PP-CBIR has faster indexing and retrieving with good retrieval precision and scalability than previous schemes.

Experts and researchers in the field of information security have placed a high value on the security of image data in the last few years. They have presented several image encryption techniques that are more secure. To increase the security level of image encryption algorithms, this article offers an efficient diffusion approach for image encryption methods based on one-dimensional Logistic, three-dimensional Lorenz, DNA encoding and computing, and SHA-256. The encryption test demonstrates that the method has great security and reliability. This article, also, examines the security of encryption methods, such as secret key space analysis, key sensitivity test, histogram analysis, information entropy process, correlation examination, and differential attack. When the image encryption method described in this article is compared to several previous image encryption techniques, the encryption algorithm has higher information entropy and a lower correlation coefficient.

SARS-COV-2 (severe acute respiratory syndrome coronavirus-2) has caused widespread mortality. Infected individuals had specific radiographic visual features and fever, dry cough, lethargy, dyspnea, and other symptoms. According to the study, the chest X-ray (CXR) is one of the essential non-invasive clinical adjuncts for detecting such visual reactions associated with SARS-COV-2. Manual diagnosis is hindered by a lack of radiologists’ availability to interpret CXR images and by the faint appearance of illness radiographic responses. The paper describes an automatic COVID detection based on the deep learning-based system that applied transfer learning techniques to extract features from CXR images to distinguish. The system has three main components. The first part is extracting CXR features with MobileNetV2. The second part used the extracted features and applied Dimensionality reduction using LDA. The final part is a Classifier, which employed XGBoost to classify dataset images into Normal, Pneumonia, and Covid-19. The proposed system achieved both immediate and high results with an overall accuracy of 0.96%, precision of 0.95%, recall of 0.94%, and F1 score of 0.94%.

Every day, a tremendous amount of image data is generated as a result of recent advances in imaging and computing technology. Several content-based image retrieval (CBIR) approaches have been introduced for searching image collections. These methods, however, involve greater computing and storage resources. Cloud servers can address this issue by offering a large amount of computational power at a low cost. However, cloud servers are not completely trustworthy, and data owners are concerned about the privacy of their personal information. In this research, we propose and implement a secure CBIR (SCBIR) strategy for searching and retrieving cipher text image databases. In the proposed scheme, the extract aggregated feature vectors to represent the related image collection and use a safe Asymmetric Scalar-Product-Preserving Encryption (ASPE) approach to encrypt these vectors while still allowing for similarity computation. To improve search time, all encrypted features are recursively clustered using the k-means method to create a tree index. The results reveal that SCBIR is faster at indexing and retrieving than earlier systems, with superior retrieval precision and scalability. In addition, our paper introduces the watermark to discover any illegal distributions of the images that are received by unlawful data users. Particularly, the cloud server integrates a unique watermark directly into the encrypted images before sending them to the data users. As a result, if an unapproved image copy is revealed, the watermark can be extracted and the unauthorized data users who spread the image can be identified. The performance of the proposed scheme is proved, while its performance is demonstrated through experimental results.

This work presents a wireless communication network (WCN) infrastructure for the smart grid based on the technology of Worldwide Interoperability for Microwave Access (WiMAX) to address the main real-time applications of the smart grid such as Wide Area Monitoring and Control (WAMC), video surveillance, and distributed energy resources (DER) to provide low cost, flexibility, and expansion. Such wireless networks suffer from two significant impairments. On one hand, the data of real-time applications should deliver to the control center under robust conditions in terms of reliability and latency where the packet loss is increased with the increment of the number of industrial clients and transmission frequency rate under the limited capacity of WiMAX base station (BS). This research suggests wireless edge computing using WiMAX servers to address reliability and availability. On the other hand, BSs and servers consume affected energy from the power grid. Therefore, the suggested WCN is enhanced by green self-powered based on solar energy to compensate for the expected consumption of energy. The model of the system is built using an analytical approach and OPNET modeler. The results indicated that the suggested WCN based on green WiMAX BS and green edge computing can handle the latency and data reliability of the smart grid applications successfully and with a self-powered supply. For instance, WCN offered latency below 20 msec and received data reliability up to 99.99% in the case of the heaviest application in terms of data.

According to the growing interest in the soft robotics research field, where various industrial and medical applications have been developed by employing soft robots. Our focus in this paper will be the Pneumatic Muscle Actuator (PMA), which is the heart of the soft robot. Achieving an accurate control method to adjust the actuator length to a predefined set point is a very difficult problem because of the hysteresis and nonlinearity behaviors of the PMA. So the construction and control of a 30 cm soft contractor pneumatic muscle actuator (SCPMA) were done here, and by using different strategies such as the PID controller, Bang-Bang controller, Neural network controller, and Fuzzy controller, to adjust the length of the (SCPMA) between 30 cm and 24 cm by utilizing the amount of air coming from the air compressor. All of these strategies will be theoretically implemented using the MATLAB/Simulink package. Also, the performance of these control systems will be compared with respect to the time-domain characteristics and the root mean square error (RMSE). As a result, the controller performance accuracy and robustness ranged from one controller to another, and we found that the fuzzy logic controller was one of the best strategies used here according to the simplicity of the implementation and the very accurate response obtained from this method.

WiFi-based human activity and gesture recognition explore the interaction between the human hand or body movements and the reflected WiFi signals to identify various activities. This type of recognition has received much attention in recent years since it does not require wearing special sensors or installing cameras. This paper aims to investigate human activity and gesture recognition schemes that use Channel State Information (CSI) provided by WiFi devices. To achieve high accuracy in the measurement, deep learning models such as AlexNet, VGG 19, and SqueezeNet were used for classification and extracting features automatically. Firstly, outliers are removed from the amplitude of each CSI stream during the preprocessing stage by using the Hampel identifier algorithm. Next, the RGB images are created for each activity to feed as input to Deep Convolutional Neural Networks. After that, data augmentation is implemented to reduce the overfitting problems in deep learning models. Finally, the proposed method is evaluated on a publicly available dataset called WiAR, which contains 10 volunteers, each of whom executes 16 activities. The experiment results demonstrate that AlexNet, VGG19, and SqueezeNet all have high recognition accuracy of 99.17 %, 96.25%, and 100 %, respectively.

In this paper, a simulation was utilized to create and test the suggested controller and to investigate the ability of a quadruped robot based on the SimScape-Multibody toolbox, with PID controllers and deep deterministic policy gradient DDPG Reinforcement learning (RL) techniques. A quadruped robot has been simulated using three different scenarios based on two methods to control its movement, namely PID and DDPG. Instead of using two links per leg, the quadruped robot was constructed with three links per leg, to maximize movement versatility. The quadruped robot-built architecture uses twelve servomotors, three per leg, and 12-PID controllers in total for each servomotor. By utilizing the SimScape-Multibody toolbox, the quadruped robot can build without needing to use the mathematical model. By varying the walking robot’s carrying load, the robustness of the developed controller is investigated. Firstly, the walking robot is designed with an open loop system and the result shows that the robot falls at starting of the simulation. Secondly, auto-tuning are used to find the optimal parameter like (KP, KI, and KD) of PID controllers, and resulting shows the robot can walk in a straight line. Finally, DDPG reinforcement learning is proposed to generate and improve the walking motion of the quadruped robot, and the results show that the behaviour of the walking robot has been improved compared with the previous cases, Also, the results produced when RL is employed instead of PID controllers are better.