The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless system involves the building of a wireless sensor network (WSN) for data acquisition and controller area network (CAN) protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID- like Fuzzy Neural Petri Net (FNPN) system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN) controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.
In this paper we present the details of methodology pursued in implementation of an HMI and Demo Temperature Monitoring application for wireless sensor-based distributed control systems. The application of WSN for a temperature monitoring and control is composed of a number of sensor nodes (motes) with a networking capability that can be deployed for monitoring and control purposes. The temperature is measured in the real time by the sensor boards that sample and send the data to the monitoring computer through a base station or gateway. This paper proposes how such monitoring system can be setup emphasizing on the aspects of low cost, energy-efficient, easy ad-hoc installation and easy handling and maintenance. This paper focuses on the overall potential of wireless sensor nodes and networking in industrial applications. A specific case study is given for the measurement of temperature (with thermistor or thermocouple), humidity, light and the health of the WSN. The focus was not on these four types of measurements and analysis but rather on the design of a communication protocol and building of an HMI software for monitoring. So, a set of system design requirements are developed that covered the use of the wireless platforms, the design of sensor network, the capabilities for remote data access and management, the connection between the WSN and an HMI software designed with MATLAB.
Some engineering applications requires constant engine speed such as power generators, production lines ..etc. The current paper focuses on adding a new closed loop based on engine torque. Load cells can be used to measure the torque of load applied , the electrical signal is properly handled to manipulate a special fuel actuator to compensate for the reduction in engine speed. The speed loop still acts as the most outer closed loop. This method leads to rapid speed compensation and lead control action.
Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people's health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people's lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.
In this paper, a fuzzy based controller for boost type AC/DC converter has been presented. Its operation and performance have been investigated through its simulation in the environment of Mat Lab. The system has been tested under various loading conditions. The obtained results showed that this fuzzy based controller can effectively control the power factor and the harmonic contents of the current drawn from the power factor system distribution network.
In this paper the identification and control for the impressed current cathodic protection (ICCP) system are present. Firstly, an identification model using an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was implemented. The identification model consists of four inputs which are the aeration flow rates, the temperature, conductivity, and protection current, and one output that represented by the structure-to-electrolyte potential. The used data taken from an experimental CP system model, type impressed current submerged sample pipe carbon steel. Secondly, two control techniques are used. The first control technique use a conventional Proportional-Integral-Derivative (PID) controller, while the second is the fuzzy controller. The PID controller can be applied to control ICCP system and quite easy to implement. But, it required very fine tuning of its parameters based on the desired value. Furthermore, it needed time response more than fuzzy controller to track reference voltage. So the fuzzy controller has a faster and better response.
This paper presents and discusses a buck DC/DC converter control based on fuzzy logic approach, in which the fuzzy controller has been driven by voltage error signal and a current error signal for which the load current has been taken as a reference one. The validity of the proposed approach has been examined through starting the buck DC/DC converter at different loading and input voltages (to monitor the starting performances), exposing the converter into large load resistance and input voltage step variations (to explore its dynamic performance), in addition to step and smooth variation in the reference voltage (to see its ability in readjusting its operating point to comply with the new setting). The simulation results presented an excellent load & line regulations abilities in addition to a good reference tracking ability. It also showed the possibility of using the buck converter as smooth variable voltage source (under smooth reference voltage variations).
It's not easy to implement the mixed / optimal controller for high order system, since in the conventional mixed / optimal feedback the order of the controller is much than that of the plant. This difficulty had been solved by using the structured specified PID controller. The merit of PID controllers comes from its simple structure, and can meets the industry processes. Also it have some kind of robustness. Even that it's hard to PID to cope the complex control problems such as the uncertainty and the disturbance effects. The present ideas suggests combining some of model control theories with the PID controller to achieve the complicated control problems. One of these ideas is presented in this paper by tuning the PID parameters to achieve the mixed / optimal performance by using Intelligent Genetic Algorithm (IGA). A simple modification is added to IGA in this paper to speed up the optimization search process. Two MIMO example are used during investigation in this paper. Each one of them has different control problem.
This paper presents a new optimization algorithm called corrosion diffusion optimization algorithm (CDOA). The proposed algorithm is based on the diffusion behavior of the pitting corrosion on the metal surface. CDOA utilizes the oxidation and reduction electrochemical reductions as well as the mathematical model of Gibbs free energy in its searching for the optimal solution of a certain problem. Unlike other algorithms, CDOA has the advantage of dispensing any parameter that need to be set for improving the convergence toward the optimal solution. The superiority of the proposed algorithm over the others is highlighted by applying them on some unimodal and multimodal benchmark functions. The results show that CDOA has better performance than the other algorithms in solving the unimodal equations regardless the dimension of the variable. On the other hand, CDOA provides the best multimodal optimization solution for dimensions less than or equal to (5, 10, 15, up to 20) but it fails in solving this type of equations for variable dimensions larger than 20. Moreover, the algorithm is also applied on two engineering application problems, namely the PID controller and the cantilever beam to accentuate its high performance in solving the engineering problems. The proposed algorithm results in minimized values for the settling time, rise time, and overshoot for the PID controller. Where the rise time, settling time, and maximum overshoot are reduced in the second order system to 0.0099, 0.0175 and 0.005 sec., in the fourth order system to 0.0129, 0.0129 and 0 sec, in the fifth order system to 0.2339, 0.7756 and 0, in the fourth system which contains time delays to 1.5683, 2.7102 and 1.80 E-4 sec., and in the simple mass-damper system to 0.403, 0.628 and 0 sec., respectively. In addition, it provides the best fitness function for the cantilever beam problem compared with some other well-known algorithms.
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.
This article presents a novel optimization algorithm inspired by camel traveling behavior that called Camel algorithm (CA). Camel is one of the extraordinary animals with many distinguish characters that allow it to withstand the severer desert environment. The Camel algorithm used to find the optimal solution for several different benchmark test functions. The results of CA and the results of GA and PSO algorithms are experimentally compared. The results indicate that the promising search ability of camel algorithm is useful, produce good results and outperform the others for different test functions.
In coordination of a group of mobile robots in a real environment, the formation is an important task. Multi- mobile robot formations in global knowledge environments are achieved using small robots with small hardware capabilities. To perform formation, localization, orientation, path planning and obstacle and collision avoidance should be accomplished. Finally, several static and dynamic strategies for polygon shape formation are implemented. For these formations minimizing the energy spent by the robots or the time for achieving the task, have been investigated. These strategies have better efficiency in completing the formation, since they use the cluster matching algorithm instead of the triangulation algorithm.
In this study, we propose a compact, tri-band microstrip patch antenna for 5G applications, operating at 28 GHz, 38 GHz, and 60 GHz frequency bands. Starting with a basic rectangular microstrip patch, modifications were made to achieve resonance in the target frequency bands and improve S11 performance, gain, and impedance bandwidth. An inset feed was employed to enhance antenna matching, and a π–shaped slot was incorporated into the radiating patch for better antenna characteristics. The design utilized a Rogers RT/Duroid-5880 substrate with a 0.508 mm thickness, a 2.2 dielectric constant, and a 0.0009 loss tangent. The final dimensions of the antenna are 8 x 8.5 x 0.508 mm3. The maximum S11 values obtained at the resonant frequencies of 27.9 GHz, 38.4 GHz, and 56 GHz are -15.4 dB, -18 dB, and -26.4 dB, respectively. The impedance bandwidths around these frequencies were 1.26 GHz (27.245 - 28.505), 1.08 GHz (37.775 - 38.855), and 12.015 GHz (51.725 - 63.74), respectively. The antenna gains at the resonant frequencies are 7.96 dBi, 6.82 dBi, and 7.93 dBi, respectively. Radiation efficiencies of 88%, 84%, and 90% were achieved at the resonant frequencies. However, it is observed that the radiation is maximum in the broadside direction at 28 GHz, although it peaks at −41o/41o and −30o/30o at 38 GHz and 56 GHz, respectively. Furthermore, the antenna design, simulations, and optimizations were carried out using HFSS, and the results were verified with CST. Both simulators showed a reasonable degree of consistency, confirming the effectiveness and reliability of the proposed antenna design.
In this paper, a new compact coplanar antenna used for Radio frequency identification (FID) applications is presented. This antenna is operated at the resonant frequency of 2.45 GHz. The proposed antenna is designed on an epoxy substrate material type (FR-4) with small size of (40 × 28) mm2 in which the dielectric thickness (h) of 1.6 mm, relative permittivity (er) of 4.3 and tangent loss of 0.025. In this design the return loss is less than −10 dB in the frequency interval (2.12 − 2.84) GHz and the minimum value of return loss is -32 dB at resonant frequency. The maximum gain of the proposed antenna is 1.22 dB and the maximum directivity obtained is 2.27 dB. The patch and the ground plane of the proposed antenna are in the same surface. The proposed antenna has a wide bandwidth and omnidirectional radiation pattern with small size. The overall size of the compact antenna is (40 × 28 × 1.635) mm3. The Computer Simulation Technology (CST) microwave studio software is used for simulation and gets layout design.
A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. Different approaches have used for simulation and modeling of SN (Sensor Network) and WSN. Traditional approaches consist of various simulation tools based on different languages such as C, C++ and Java. In this paper, MATLAB (7.6) Simulink was used to build a complete WSN system. Simulation procedure includes building the hardware architecture of the transmitting nodes, modeling both the communication channel and the receiving master node architecture. Bluetooth was chosen to undertake the physical layer communication with respect to different channel parameters (i.e., Signal to Noise ratio, Attenuation and Interference). The simulation model was examined using different topologies under various conditions and numerous results were collected. This new simulation methodology proves the ability of the Simulink MATLAB to be a useful and flexible approach to study the effect of different physical layer parameters on the performance of wireless sensor networks.
The robot is a repeated task plant. The control of such a plant under parameter variations and load disturbances is one of the important problems. The aim of this work is to design Genetic-Fuzzy controller suitable for online applications to control single link rigid robot arm plant. The genetic-fuzzy online controller (forward controller) contains two parts, an identifier part and model reference controller part. The identification is based on forward identification technique. The proposed controller it tested in normal and load disturbance conditions.
Preserving privacy and security plays a key role in allowing each component in the healthcare system to access control and gain privileges for services and resources. Over recent years, there have been several role-based access control and authentication schemes, but we noticed some drawbacks in target schemes such as failing to resist well-known attacks, leaking privacy-related information, and operational cost. To defeat the weakness, this paper proposes a secure electronic healthcare record scheme based on Schnorr Signcryption, crypto hash function, and Distributed Global Database (DGDB) for the healthcare system. Based on security theories and the Canetti-Krawczyk model (CK), we notice that the proposed scheme has suitable matrices such as scalability, privacy preservation, and mutual authentication. Furthermore, findings from comparisons with comparable schemes reveal that the suggested approach provides greater privacy and security characteristics than the other schemes and has enough efficiency in computational and communicational aspects.
This article analyzes thoroughly the performance of the Multi-Pulse Diode Rectifiers (MPDRs) regarding the quality of input/output voltage and currents. Two possible arrangements of MPDRs are investigated: series and parallel. The impact of the DC side connection on the performance of the MPDRs regarding the operation parameters and rectifier indices are comprehensively examined. Detailed analytical formulas are advised to identify clearly the key variables that control the operation of MPDRs. Moreover, comprehensive simulation results are presented to quantify the performance and validate the analytical analysis. Test-rig is set up to recognize the promising arrangement of MPDRs. Significant correlation is there between simulation and practical results. The analytical results are presented for aircraft systems (400Hz), and power grid systems (60Hz). This is to study the impact of voltage and frequency levels on the topology type of MPDRs. In general, each topology shows merits and have limitations.
Soft computing control system have been applied in various applications particularly in the fields of robotics controls. The advantage of having a soft computing controls methods is that it enable more flexibility to the control system compared with conventional model based controls system. In this paper, a UAV airship is controlled using fuzzy logic for its propulsion and steering system. The airship is tested on a simulation level before test flight. The prototype airship has on board GPS and compass for telemetry and transmitted to the ground control system via a wireless link.
In this paper, an analysis of performance acceleration of an external laser source (ELS) model based polymer fiber gratings (PFGs) by reducing the turn-on delay time (TDelay) is successfully investigated numerically by optimizing model parameters. In contrast to all previous studies that relied either on approximate or experimental equations, the analysis was based on an exact numerical formula. The analysis is based on the investigation of the effect of diode injected current (Iin j), temperature (T), recombination rate coefficients (i.e. Anr, B, and C), and optical feedback (OFB) level. Results have demonstrated that by optimizing model parameters the Delay can be controlled and reduced effectively.
In this article, a comparison of innovative multilevel inverter topology with standard topologies has been conducted. The proposed single phase five level inverter topology has been used for induction heating system. This suggested design generates five voltage levels with a fewer number of power switches. This reduction in number of switches decreases the switching losses and the number of driving circuits and reduce the complexity of control circuit. It also reduces the cost and size for the filter used. Analysis and comparison has been done among the conventional topologies (neutral clamped and cascade H-bridge multilevel inverters) with the proposed inverter topology. The analysis includes the total harmonic distortion THD, efficiency and overall performance of the inverter systems. The simulation and analysis have been done using MATLAB/ SIMULINK. The results show good performance for the proposed topology in comparison with the conventional topologies.
This work presents a healthcare monitoring system that can be used in an intensive care room. Biological information represented by ECG signals is achieved by ECG acquisition part . AD620 Instrumentation Amplifier selected due to its low current noise. The ECG signals of patients in the intensive care room are measured through wireless nodes. A base node is connected to the nursing room computer via a USB port , and is programmed with a specific firmware. The ECG signals are transferred wirelessly to the base node using nRF24L01+ wireless module. So, the nurse staff has a real time information for each patient available in the intensive care room. A star Wireless Sensor Network is designed for collecting ECG signals . ATmega328 MCU in the Arduino Uno board used for this purpose. Internet for things used For transferring ECG signals to the remote doctor, a Virtual Privet Network is established to connect the nursing room computer and the doctor computer . So, the patients information kept secure. Although the constructed network is tested for ECG monitoring, but it can be used to monitor any other signals. INTRODUCTION For elderly people, or the patient suffering from the cardiac disease it is very vital to perform accurate and quick diagnosis. Putting such person under continuous monitoring is very necessary. (ECG) is one of the critical health indicators that directly bene ¿ t from long-term monitoring. ECG signal is a time-varying signal representing the electrical activity of the heart. It is an effective, non- invasive diagnostic tool for cardiac monitoring[1]. In this medical field, a big improvement has been achieved in last few years. In the past, several remote monitoring systems using wired communications were accessible while nowadays the evolution of wireless communication means enables these systems to operate everywhere in the world by expanding internet benefits, applications, and services [2]. Wireless Sensor Networks (WSNs), as the name suggests consist of a network of wireless nodes that have the capability to sense a parameter of interest like temperature, humidity, vibration etc[3,4]. The health care application of wireless sensory network attracts many researches nowadays[ 5-7] . Among these applications ECG monitoring using smart phones[6,8], wearable Body sensors[9], remote patient mentoring[10],...etc. This paper presents wireless ECG monitoring system for people who are lying at intensive care room. At this room ECG signals for every patient are measured using wireless nodes then these signals are transmitted to the nursing room for remote monitoring. The nursing room computer is then connected to the doctors computer who is available at any location over the word by Virtual Privet Network (VPN) in such that the patients information is kept secure and inaccessible from unauthorized persons. II. M OTE H ARDWARE A RCHITECTURE The proposed mote as shown in Fig.1 consists of two main sections : the digital section which is represented by the Arduino UNO Board and the wireless module and the analog section. The analog section consists of Instrumentation Amplifier AD620 , Bandpass filter and an operational amplifier for gain stage, in addition to Right Leg Drive Circuit. The required power is supplied by an internal 3800MAH Lithium-ion (Li-ion) battery which has 3.7V output voltage.
Chaotic Sine-Cosine Algorithms (CSCAs) are new metaheuristic optimization algorithms. However, Chaotic Sine-Cosine Algorithm (CSCAs) are able to manipulate the problems in the standard Sine-Cosine Algorithm (SCA) like, slow convergence rate and falling into local solutions. This manipulation is done by changing the random parameters in the standard Sine-Cosine Algorithm (SCA) with the chaotic sequences. To verify the ability of the Chaotic Sine-Cosine Algorithms (CSCAs) for solving problems with large scale problems. The behaviors of the Chaotic Sine-Cosine Algorithms (CSCAs) were studied under different dimensions 10, 30, 100, and 200. The results show the high quality solutions and the superiority of all Chaotic Sine-Cosine Algorithms (CSCAs) on the standard SCA algorithm for all selecting dimensions. Additionally, different initial values of the chaotic maps are used to study the sensitivity of Chaotic Sine-Cosine Algorithms (CSCAs). The sensitivity test reveals that the initial value 0.7 is the best option for all Chaotic Sine-Cosine Algorithms (CSCAs).
Self-driving cars are a fundamental research subject in recent years; the ultimate goal is to completely exchange the human driver with automated systems. On the other hand, deep learning techniques have revealed performance and effectiveness in several areas. The strength of self-driving cars has been deeply investigated in many areas including object detection, localization as well, and activity recognition. This paper provides an approach to deep learning; which combines the benefits of both convolutional neural network CNN together with Dense technique. This approach learns based on features extracted from the feature extraction technique which is linear discriminant analysis LDA combined with feature expansion techniques namely: standard deviation, min, max, mod, variance and mean. The presented approach has proven its success in both testing and training data and achieving 100% accuracy in both terms.
Many assistive devices have been developed for visually impaired (VI) person in recent years which solve the problems that face VI person in his/her daily moving. Most of researches try to solve the obstacle avoidance or navigation problem, and others focus on assisting VI person to recognize the objects in his/her surrounding environment. However, a few of them integrate both navigation and recognition capabilities in their system. According to above needs, an assistive device is presented in this paper that achieves both capabilities to aid the VI person to (1) navigate safely from his/her current location (pose) to a desired destination in unknown environment, and (2) recognize his/her surrounding objects. The proposed system consists of the low cost sensors Neato XV-11 LiDAR, ultrasonic sensor, Raspberry pi camera (CameraPi), which are hold on a white cane. Hector SLAM based on 2D LiDAR is used to construct a 2D-map of unfamiliar environment. While A* path planning algorithm generates an optimal path on the given 2D hector map. Moreover, the temporary obstacles in front of VI person are detected by an ultrasonic sensor. The recognition system based on Convolution Neural Networks (CNN) technique is implemented in this work to predict object class besides enhance the navigation system. The interaction between the VI person and an assistive system is done by audio module (speech recognition and speech synthesis). The proposed system performance has been evaluated on various real-time experiments conducted in indoor scenarios, showing the efficiency of the proposed system.
This is a design of an expert system in the focused ion beam optical system by using fuzzy logic technique to build an intelligent agent. Present software has been designed as an interpretation expert system, written in Visual C# for optimizing the calculation of three electrostatic lenses column. By using such rule based engine, the axial potential distributions for electrostatic fields undergo the constraints have been used to find spot size focusing for ions in the image plane which have values are very useful for getting and designing FIB model, over ranges of ion beam angles (5, 10,30,50,75 and 100) mrad.
In recent years, there has been a considerable rise in the applications in which object or image categorization is beneficial for example, analyzing medicinal images, assisting persons to organize their collections of photos, recognizing what is around self-driving vehicles, and many more. These applications necessitate accurately labeled datasets, in their majority involve an extensive diversity in the types of images, from cats or dogs to roads, landscapes, and so forth. The fundamental aim of image categorization is to predict the category or class for the input image by specifying to which it belongs. For human beings, this is not a considerable thing, however, learning computers to perceive represents a hard issue that has become a broad area of research interest, and both computer vision techniques and deep learning algorithms have evolved. Conventional techniques utilize local descriptors for finding likeness between images, however, nowadays; progress in technology has provided the utilization of deep learning algorithms, especially the Convolutional Neural Networks (CNNs) to auto-extract representative image patterns and features for classification The fundamental aim of this paper is to inspect and explain how to utilize the algorithms and technologies of deep learning to accurately classify a dataset of images into their respective categories and keep model structure complication to a minimum. To achieve this aim, must focus precisely and accurately on categorizing the objects or images into their respective categories with excellent results. And, specify the best deep learning-based models in image processing and categorization. The developed CNN-based models have been proposed and a lot of pre-training models such as (VGG19, DenseNet201, ResNet152V2, MobileNetV2, and InceptionV3) have been presented, and all these models are trained on the Caltech-101 and Caltech-256 datasets. Extensive and comparative experiments were conducted on this dataset, and the obtained results demonstrate the effectiveness of the proposed models. The obtained results demonstrate the effectiveness of the proposed models. The accuracy for Caltech-101 and Caltech-256 datasets was (98.06% and 90%) respectively.
Automatic signature verification methods play a significant role in providing a secure and authenticated handwritten signature in many applications, to prevent forgery problems, specifically institutions of finance, and transections of legal papers, etc. There are two types of handwritten signature verification methods: online verification (dynamic) and offline verification (static) methods. Besides, signature verification approaches can be categorized into two styles: writer dependent (WD), and writer independent (WI) styles. Offline signature verification methods demands a high representation features for the signature image. However, lots of studies have been proposed for WI offline signature verification. Yet, there is necessity to improve the overall accuracy measurements. Therefore, a proved solution in this paper is depended on deep learning via convolutional neural network (CNN) for signature verification and optimize the overall accuracy measurements. The introduced model is trained on English signature dataset. For model evaluation, the deployed model is utilized to make predictions on new data of Arabic signature dataset to classify whether the signature is real or forged. The overall obtained accuracy is 95.36% based on validation dataset.
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.
Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV) panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumption in the motors and lifetime limitation of the sun tracker. In this paper, the neuro-fuzzy controller has been designed and implemented using Field Programmable Gate Array (FPGA) board for dual axis sun tracker based on optical sensors to orient the PV panel by two linear actuators. The experimental results reveal that proposed controller is more robust than fuzzy logic controller and proportional- integral (PI) controller since it has been trained offline using Matlab tool box to overcome those disturbances. The proposed controller can track the sun trajectory effectively, where the experimental results reveal that dual axis sun tracker power can collect 50.6% more daily power than fixed angle panel. Whilst one axis sun tracker power can collect 39.4 % more daily power than fixed angle panel. Hence, dual axis sun tracker can collect 8 % more daily power than one axis sun tracker .
In this paper, a new algorithm called table-based matching for multi-robot (node) that used for localization and orientation are suggested. The environment is provided with two distance sensors fixed on two beacons at the bottom corners of the frame. These beacons have the ability to scan the environment and estimate the location and orientation of the visible nodes and save the result in matrices which are used later to construct a visible node table. This table is used for matching with visible-robot table which is constructed from the result of each robot scanning to its neighbors with a distance sensor that rotates at 360⁰; at this point, the location and identity of all visible nodes are known. The localization and orientation of invisible robots rely on the matching of other tables obtained from the information of visible robots. Several simulations implementation are experienced on a different number of nodes to submit the performance of this introduced algorithm.
In this work, the phase lock loop PLL-based controller has been adopted for tracking the resonant frequency to achieve maximum power transfer between the power source and the resonant load. The soft switching approach has been obtained to reduce switching losses and improve the overall efficiency of the induction heating system. The jury’s stability test has been used to evaluate the system’s stability. In this article, a multilevel inverter has been used with a series resonant load for an induction heating system to clarify the effectiveness of using it over the conventional full-bridge inverter used for induction heating purposes. Reduced switches five-level inverter has been implemented to minimize switching losses, the number of drive circuits, and the control circuit’s complexity. A comparison has been made between the conventional induction heating system with full bridge inverter and the induction heating system with five level inverter in terms of overall efficiency and total harmonic distortion THD. MATLAB/ SIMULINK has been used for modeling and analysis. The mathematical analysis associated with simulation results shows that the proposed topology and control system performs well.
Quantum-dot Cellular Automata (QCA) is a new emerging technology for designing electronic circuits in nanoscale. QCA technology comes to overcome the CMOS limitation and to be a good alternative as it can work in ultra-high-speed. QCA brought researchers attention due to many features such as low power consumption, small feature size in addition to high frequency. Designing circuits in QCA technology with minimum costs such as cells count and the area is very important. This paper presents novel structures of D-latch and D-Flip Flop with the lower area and cell count. The proposed Flip-Flop has SET and RESET ability. The proposed latch and Flip-Flop have lower complexity compared with counterparts in terms of cell counts by 32% and 26% respectively. The proposed circuits are designed and simulated in QCADesigner software.
Power transformer protective relay should block the tripping during magnetizing inrush and rapidly operate the tripping during internal faults. Recently, the frequency environment of power system has been made more complicated and the quantity of 2nd frequency component in inrush state has been decreased because of the improvement of core steel. And then, traditional approaches will likely be maloperated in the case of magnetizing inrush with low second harmonic component and internal faults with high second harmonic component. This paper proposes a new relaying algorithm to enhance the fault detection sensitivities of conventional techniques by using a fuzzy logic approach. The proposed fuzzy-based relaying algorithm consists of flux-differential current derivative curve, harmonic restraint, and percentage differential characteristic curve. The proposed relaying was tested with MATLAB simulation software and showed a fast and accurate trip operation.
In the era of modern trends such as cloud computing, social media applications, emails, mobile applications, and URLs that lead to increased risks for defrauding authorized users, and then the attackers try to gain illegal access to accounts of users through a malicious attack. The phishing attack is one of the dangerous attacks caused to access of authorized account illegally way. The finances, business, banking, and other sensitive in states are faces by this type of attacks due to the important information they have. In this paper, we propose a secure verification scheme that can overcome the above-mentioned issues. Additionally, the proposed scheme can resist famous cyberattacks such as impersonate attacks, MITM attacks. Moreover, the proposed scheme has security features like strong verification, forward secrecy, user’s identity anomaly. The security analysis and the experimental results proved the strongest of the proposed scheme compared with other related works. Finally, our proposed scheme balanced between the performance and the security merits.
Due to their vital applications in many real-world situations, researchers are still presenting bunches of methods for better analysis of motor imagery (MI) electroencephalograph (EEG) signals. However, in general, EEG signals are complex because of their nonstationary and high-dimensionality properties. Therefore, high consideration needs to be taken in both feature extraction and classification. In this paper, several hybrid classification models are built and their performance is compared. Three famous wavelet mother functions are used for generating scalograms from the raw signals. The scalograms are used for transfer learning of the well-known VGG-16 deep network. Then, one of six classifiers is used to determine the class of the input signal. The performance of different combinations of mother functions and classifiers are compared on two MI EEG datasets. Several evaluation metrics show that a model of VGG-16 feature extractor with a neural network classifier using the Amor mother wavelet function has outperformed the results of state-of-the-art studies.
Radio frequency identification (RFID) technology is being used widely in the last few years. Its applications classifies into auto identification and data capturing issues. The purpose of this paper is to design and implement RFID active tags and reader using microcontroller ATmega328 and 433 MHz RF links. The paper also includes a proposed mutual authentication protocol between RFID reader and active tags with ownership transfer stage. Our protocol is a mutual authentication protocol with tag’s identifier updating mechanism. The updating mechanism has the purpose of providing forward security which is important in any authentication protocol to prevent the attackers from tracking the past transactions of the compromised tags. The proposed protocol gives the privacy and security against all famous attacks that RFID system subjected for due to the transfer of data through unsecure wireless channel, such as replay, denial of service, tracking and cloning attacks. It also ensures ownership privacy when the ownership of the tag moves to a new owner.
In different modern and future wireless communication networks, a large number of low-power user equipment (UE) devices like Internet of Things, sensor terminals, and smart modules have to be supported over constrained power and bandwidth resources. Therefore, wireless-powered communication (WPC) is considered a promising technology for varied applications in which the energy harvesting (EH) from radio frequency radiations is exploited for data transmission. This requires efficient resource allocation schemes to optimize the performance of WPC and prolong the network lifetime. In this paper, harvest-then-transmit-based WP non-orthogonal multiple access (WP-NOMA) system is designed with time-split (TS) and power control (PC) allocation strategies. To evaluate the network performance, the sum rate and UEs’ rates expressions are derived considering power-domain NOMA with successive interference cancellation detection. For comparison purposes, the rate performance of the conventional WP orthogonal multiple access (WP-OMA) is derived also considering orthogonal frequency-division multiple access and time-division multiple access schemes. Intensive investigations are conducted to obtain the best TS and PC resource parameters that enable maximum EH for higher data transmission rates compared with the reference WP-OMA techniques. The achieved outcomes demonstrate the effectiveness of designed resource allocation approaches in terms of the realized sum rate, UE’s rate, rate region, and fairness without distressing the restricted power of far UEs.
Animating human face presents interesting challenges because of its familiarity as the face is the part utilized to recognize individuals. This paper reviewed the approaches used in facial modeling and animation and described their strengths and weaknesses. Realistic face animation of computer graphic models of human faces can be hard to achieve as a result of the many details that should be approximated in producing realistic facial expressions. Many methods have been researched to create more and more accurate animations that can efficiently represent human faces. We described the techniques that have been utilized to produce realistic facial animation. In this survey, we roughly categorized the facial modeling and animation approach into the following classes: blendshape or shape interpolation, parameterizations, facial action coding system-based approaches, moving pictures experts group-4 facial animation, physics-based muscle modeling, performance driven facial animation, visual speech animation.
This paper deals with the navigation of a mobile robot in unknown environment using artificial potential field method. The aim of this paper is to develop a complete method that allows the mobile robot to reach its goal while avoiding unknown obstacles on its path. An approach proposed is introduced in this paper based on combing the artificial potential field method with fuzzy logic controller to solve drawbacks of artificial potential field method such as local minima problems, make an effective motion planner and improve the quality of the trajectory of mobile robot.
The advancements in modern day computing and architectures focus on harnessing parallelism and achieve high performance computing resulting in generation of massive amounts of data. The information produced needs to be represented and analyzed to address various challenges in technology and business domains. Radical expansion and integration of digital devices, networking, data storage and computation systems are generating more data than ever. Data sets are massive and complex, hence traditional learning methods fail to rescue the researchers and have in turn resulted in adoption of machine learning techniques to provide possible solutions to mine the information hidden in unseen data. Interestingly, deep learning finds its place in big data applications. One of major advantages of deep learning is that it is not human engineered. In this paper, we look at various machine learning algorithms that have already been applied to big data related problems and have shown promising results. We also look at deep learning as a rescue and solution to big data issues that are not efficiently addressed using traditional methods. Deep learning is finding its place in most applications where we come across critical and dominating 5Vs of big data and is expected to perform better.
Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then select important features before passing them to a classification model. Our method is capable of early breast cancer prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic regression and support vector machine) to produce one model that carries the strengths of the models that have been combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a recall of 0.9846, a precision of 1 and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%.
In modern robotic field, many challenges have been appeared, especially in case of a multi-robot system that used to achieve tasks. The challenges are due to the complexity of the multi-robot system, which make the modeling of such system more difficult. The groups of animals in real world are an inspiration for modeling of a multi- individual system such as aggregation of Artemia. Therefore, in this paper, the multi-robot control system based on external stimuli such as light has been proposed, in which the feature of tracking Artemia to the light has been employed for this purpose. The mathematical model of the proposed design is derived and then Simulated by V-rep software. Several experiments are implemented in order to evaluate the proposed design, which is divided into two scenarios. The first scenario includes simulation of the system in situation of attraction of robot to fixed light spot, while the second scenario is the simulation of the system in the situation of the robots tracking of the movable light spot and formed different patterns like a straight-line, circular, and zigzag patterns. The results of experiments appeared that the mobile robot attraction to high-intensity light, in addition, the multi-robot system can be controlled by external stimuli. Finally, the performance of the proposed system has been analyzed.
In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The objective of this work is to achieve better classification with multiple parameters using K-Nearest Neighbor (K-NN) for different movements of a prosthetic arm. The proposed structure is simulated using MATLAB Ver. R2009a, and satisfied results are obtained by comparing with the conventional recognition method using Artificial Neural Network (ANN). Results show the proposed K-NN technique achieved a uniformly good performance with respect to ANN in terms of time, which is important in recognition systems, and better accuracy in recognition when applied to lower Signal-to-Noise Ratio (SNR) signals.
Fast and accurate frequency estimation is essential in various engineering applications, including control systems, communications, and resonance sensing systems. This study investigates the effect of sample size on the interpolation algorithm of frequency estimation. In order to enhance the accuracy of frequency estimation and performance, we describe a novel method that provides a number of approaches for calculating and defending the sample size for of the window function designs, whereas, the correct choice of the type and the size of the window function makes it possible to reduce the error. Computer simulation using Matlab / Simulink environment is performed to investigate the proposed procedure’s performance and feasibility. This study performs the comparison of the interpolation algorithm of frequency estimation strategies that can be applied to improve the accuracy of the frequency estimation. Simulation results shown that the proposed strategy with the Parzen and Flat-top gave remarkable change in the maximum error of frequency estimation. They perform better than the conventional windows at a sample size equal to 64 samples, where the maximum error of frequency estimation is 2.13e-2 , and 2.15e-2 for Parzen and Flat-top windows, respectively. Moreover, the efficiency and performance of the Nuttall window also perform better than other windows, where the maximum error is 7.76×10-5 at a sample size equal to 8192. The analysis of simulation result showed that when using the proposed strategy to improve the accuracy of the frequency estimation, it is first essential to evaluate what is the maximum number of samples that can be obtained, how many spectral lines should be used in the calculations, and only after that choose a suitable window.
The PH regulation of cooling tower plant in southern fertilizers company (SCF) in Iraq is important for industry pipes protection and process continuity. According to the Mitsubishi standard, the PH of cooling water must be around (7.1 to 7.8). The deviation in PH parameter affects the pipes, such as corrosion and scale. Acidic water causes pipes to corrode, and alkaline water causes pipes to scale. The sulfuric acid solution is used for PH neutralization. The problem is that the sulfuric acid is pumped manually in the cooling tower plant every two or three hours for PH regulation. The manual operation of the sulfuric acid pump makes deviations in the PH parameter. It is very difficult to control the PH manually. To solve this problem, a PID controller for PH regulation was used. The reason for using the PID controller is that the PH response is irregular through the neutralization process. The methodology is to calculate the transfer function of the PH loop using the system identification toolbox of MATLAB, to design and implement a PID controller, to optimize the PID controller response using particle swarm optimization PSO algorithm, and to make a comparison among several tuning methods such as Ziegler Nichols (ZN) tuning method, MATLAB tuner method, and PSO algorithm tuning method. The results showed that the PSO-based PID controller tuning gives a better overshoot, less rise time, and an endurable settling time than the other tuning methods. Hence, the PH response became according to the target range. The experimental results showed that the PH regulation improved using the PSO-based PID controller tuning.
Microgrids (ℳ-grids) can be thought of as a small-scale electrical network comprised of a mix of Distributed Generation (DG) resources, storage devices, and a variety of load species. It provides communities with a stable, secure, and renewable energy supply in either off-grid (grid-forming) or on-grid (grid-following) mode. In this work, a control strategy of coordinated power management for a Low Voltage (LV) ℳ-grid with integration of solar Photovoltaic (PV), Battery Energy Storage System (BESS) and three phase loads operated autonomously or connected to the utility grid has been created and analyzed in the Matlab Simulink environment. The main goal expressed here is to achieve the following points: (i) grid following, grid forming modes, and resynchronization mode between them, (ii) Maximum Power Point Tracking (MPPT) from solar PV using fuzzy logic technique, and active power regulator based boost converter using a Proportional Integral (PI) controller is activated when a curtailment operation is required, (iii) ℳ-grid imbalance compensation (negative sequence) due to large single-phase load is activated, and (iv) detection and diagnosis the fault types using Discrete Wavelet Transform (DWT). Under the influence of irradiance fluctuation on solar plant, the proposed control technique demonstrates how the adopted system works in grid- following mode (PQ control), grid- formation, and grid resynchronization to seamlessly connect the ℳ-grid with the main distribution system. In this system, a power curtailment management system is introduced in the event of a significant reduction in load, allowing the control strategy to be switched from MPPT to PQ control, permitting the BESS to absorb excess power. Also, in grid-following mode, the BESS's imbalance compensation mechanism helps to reduce the negative sequence voltage that occurs at the Point of Common Coupling (PCC) bus as a result of an imbalance in the grid's power supply. In addition to the features described above, this system made use of DWT to detect and diagnose various fault conditions.
In this paper, the hybrid-climbing legged robot is designed, implemented, and practically tested. The robot has four legs arranged symmetrically around the body were designed for climbing wire mesh fence. Each leg in robot has 3DOF which makes the motion of the robot is flexible. The robot can climb the walls vertically by using a unique design of gripper device included metal hooks. The mechanism of the movement is a combination of two techniques, the first is the common way for the successive movement like gecko by using four limbs, and the second depending on the method that used by cats for climbing on the trees using claws, for this reason, the robot is named hybrid-climbing legged robot. The movement mechanism of the climbing robot is achieved by emulating the motion behavior of the gecko, which helped to derive the kinematic equations of the robot. The robot was practically implemented by using a microcontroller for the mainboard controller while the structure of the robot body is designed by AutoCAD software. Several experiments performed in order to test the success of climbing on the vertical wire mesh fence.
Wireless Multimedia Sensor Networks (WMSNs) are being extensively utilized in critical applications such as envi- ronmental monitoring, surveillance, and healthcare, where the reliable transmission of packets is indispensable for seamless network operation. To address this requirement, this work presents a pioneering Distributed Dynamic Coop- eration Protocol (DDCP) routing algorithm. The DDCP algorithm aims to enhance packet reliability in WMSNs by prioritizing reliable packet delivery, improving packet delivery rates, minimizing end-to-end delay, and optimizing energy consumption. To evaluate its performance, the proposed algorithm is compared against traditional routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), as well as proactive routing protocols such as Optimized Link State Routing (OLSR). By dynamically adjusting the transmission range and selecting optimal paths through cooperative interactions with neighboring nodes, the DDCP algorithm offers effective solutions. Extensive simulations and experiments conducted on a wireless multimedia sensor node testbed demonstrate the superior performance of the DDCP routing algorithm compared to AODV, DSR, and OLSR, in terms of packet delivery rate, end-to-end delay, and energy efficiency. The comprehensive evaluation of the DDCP algorithm against multiple routing protocols provides valuable insights into its effectiveness and efficiency in improving packet reliability within WMSNs. Furthermore, the scalability and applicability of the proposed DDCP algorithm for large-scale wireless multimedia sensor networks are confirmed. In summary, the DDCP algorithm exhibits significant potential to enhance the performance of WMSNs, making it a suitable choice for a wide range of applications that demand robust and reliable data transmission.
Precise power sharing considered is necessary for the effective operation of an Autonomous microgrid with droop controller especially when the total loads change periodically. In this paper, reactive power sharing control strategy that employs central controller is proposed to enhance the accuracy of fundamental reactive power sharing in an islanded microgrid. Microgrid central controller is used as external loop requiring communications to facilitate the tuning of the output voltage of the inverter to achieve equal reactive power sharing dependent on reactive power load to control when the mismatch in voltage drops through the feeders. Even if central controller is disrupted the control strategy will still operate with conventional droop control method. additionally, based on the proposed strategy the reactive power sharing accuracy is immune to the time delay in the central controller. The developed of the proposed strategy are validated using simulation with detailed switching models in PSCAD/EMTDC.
Most of routing protocols used for Mobile Ad hoc Network (MANET), such as Ad hoc on demand distance vector (AODV) routing, uses minimum hops as the only metric for choosing a route. This decision might lead to cause some nodes become congested which will degrade the network performance. This paper proposes an improvement of AODV routing algorithm by making routing decisions depend on fuzzy cost based on the delay in conjunction with number of hops in each path. Our simulation was carried out using OMNET++ 4.0 simulator and the evaluation results show that the proposed Fuzzy Multi-Constraint AODV routing performs better than the original AODV in terms of average end-to-end delay and packet delivery.
This work presents aneural and fuzzy based ECG signal recognition system based on wavelet transform. The suitable coefficients that can be used as a feature for each fuzzy network or neural network is found using a proposed best basis technique. Using the proposed best bases reduces the dimension of the input vector and hence reduces the complexity of the classifier. The fuzzy network and the neural network parameters are learned using back propagation algorithm.
Five-phase machine employment in electric drive system is expanding rapidly in many applications due to several advantages that they present when compared with their three-phase complements. Synchronous reluctance machines(SynRM) are considered as a proposed alternative to permanent magnet machine in the automotive industry because the volatilities in the permanent magnet price, and a proposed alternative for induction motor because they have no field excitation windings in the rotor, SyRM rely on high reluctance torque thus no needing for magnetic material in the structure of rotor. This paper presents dynamic simulation of five phase synchronous reluctance motor fed by five phase voltage source inverter based on mathematical modeling. Sinusoidal pulse width modulation (SPWM) technique is used to generate the pulses for inverter. The theory of reference frame has been used to transform five-phase SynRM voltage equations for simplicity and in order to eliminate the angular dependency of the inductances. The torque in terms of phase currents is then attained using the known magnetic co-energy method, then the results obtained are typical.
In this paper, a control strategy for a combination PV-BESS-SC hybrid system in islanded microgrid with a DC load is designed and analyzed using a new topology. Although Battery Energy Storage System (BESS) is employed to keep the DC bus voltage stable; however, it has a high energy density and a low power density. On the other hand, the Supercapacitor (SC) has a low energy density but a high-power density. As a result, combining a BESS and an SC is more efficient for power density and high energy. Integrating the many sources is more complicated. In order to integrate the SC and BESS and deliver continuous power to the load, a control strategy is required. A novel method for controlling the bus voltage and energy management will be proposed in this paper. The main advantage of the proposed system is that throughout the operation, the State of Charging (SOC), BESS current, and SC voltage and current are all kept within predetermined ranges. Additionally, SC balances fast- changing power surges, while BESS balances slow-changing power surges. Therefore, it enhances the life span and minimizes the current strains on BESS. To track the Maximum Power Point (MPP) or restrict power from the PV panel to the load, a unidirectional boost converter is utilized. Two buck converters coupled in parallel with a boost converter are proposed to charge the hybrid BESS-SC. Another two boost converters are used to manage the discharge operation of the BESS-SC storage in order to reduce losses. The simulation results show that the proposed control technique for rapid changes in load demand and PV generation is effective. In addition, the proposed technique control strategy is compared with a traditional control strategy.
In this paper describe to mathematical analysis for a three-phase, two level inverter designs. As we know the power electronic devices (inverter) to convert the DC power to AC power (controller on output voltage and frequency level). In Industrial applications, the inverters are used for adjustable speed (AC Drives). In this paper, the mathematical analyses for inverter design are done by using Software packages C++ Builder and visual C++ Language. For non- linear distortions described by the load power factor in power system networks. The P.F is reverse proportional with the harmonics distortion. Small P.F means much more of harmonic distortion, and lower power quality for consumers. to improve the P.F, and power quality in this paper the small capacitor installed as part of the rectified the load current has power (30 KW with P.F load 0.8), the fluctuations of the rectified voltage must not greater than +/- 10%.The power factor proportion of the load power, with Modulation coefficient p.u approximately unity. The calculation is achieved with different integrations steps with load power 30KW, 0.8 P.F. all results done Based on model and experimental data..
In this work, the collective behavior of Artemia Salina is studied both experimentally and theoretically. Several experiments have been designed to investigate the Artemia motion under different environment conditions. From the results of such experiments, a strategy to control the direction of motion of an Artemia population, by exploiting their sensitivity to light, has been derived and then implemented.
Given the role that pipelines play in transporting crude oil, which is considered the basis of the global economy and across different environments, hundreds of studies revolve around providing the necessary protection for it. Various technologies have been employed in this pursuit, differing in terms of cost, reliability, and efficiency, among other factors. Computer vision has emerged as a prominent technique in this field, albeit requiring a robust image-processing algorithm for spill detection. This study employs image segmentation techniques to enable the computer to interpret visual information and images effectively. The research focuses on detecting spills in oil pipes caused by leakage, utilizing images captured by a drone equipped with a Raspberry Pi and Pi camera. These images, along with their global positioning system (GPS) location, are transmitted to the base station using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, deep learning techniques, specifically Holistically-Nested Edge Detection (HED) and extreme inception (Xception) networks, are employed for image processing to identify contours. The proposed algorithm can detect multiple contours in the images. To pinpoint a contour with a black color, representative of an oil spill, the CIELAB color space (LAB) algorithm effectively removes shadow effects. If a contour is detected, its area and perimeter are calculated to determine whether it exceeds a certain threshold. The effectiveness of the proposed system was tested on Iraqi oil pipeline systems, demonstrating its capability to detect spills of different sizes.
Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.
Vehicular network security had spanned and covered a wide range of security related issues. However solar energy harvesting Road Side Unit (RSU) security was not defined clearly, it is this aspect that is considered in this paper. In this work, we will suggest an RSU security model to protect it against different internal and external threats. The main goal is to protect RSU specific data (needed for its operation) as well as its functionality and accessibility. The suggested RSU security model must responds to many objectives, it should ensure that the administrative information exchanged is correct and undiscoverable (information authenticity and privacy), the source (e.g., VANET server) is who he claims to be (message integrity and source authentication) and the system is robust and available (using Intrusion Detection System (IDS)). In this paper, we suggest many techniques to strength RSU security and they were prototyped using an experimental model based on Ubicom IP2022 network processor development kit .
Load Frequency Control (LFC) is a basic control strategy for proper operation of the power system. It ensures the ability of each generator in regulating its output power in such way to maintain system frequency and tie-line power of the interconnected system at prescribed levels. This article introduces comprehensive comparative study between Chaos Optimization Algorithm (COA) and optimal control approaches, such as Linear Quadratic Regulator (LQR), and Optimal Pole Shifting (OPS) regarding the tuning of LFC controller. The comparison is extended to the control approaches that result in zero steady-state frequency error such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. Ziegler-Nicholas method is widely adopted for tuning such controllers. The article then compares between PI and PID controllers tuned via Ziegler-Nicholas and COA. The optimal control approaches as LQR and OPS have the characteristic of steady-state error. Moreover, they require the access for full state variables. This limits their applicability. Whereas, Ziegler-Nicholas PI and PID controllers have relatively long settling time and high overshoot. The controllers tuned via COA remedy the defects of optimal and zero steady-state controllers. The performance adequacy of the proposed controllers is assessed for different operating scenarios. Matlab and its dynamic platform, Simulink, are used for stimulating the system under concern and the investigated control techniques. The simulation results revealed that COA results in the smallest settling time and overshoot compared with traditional controllers and zero steady-state error controllers. In the overshoot, COA produces around 80% less than LQR and 98.5% less than OPS, while in the settling time, COA produces around 81% less than LQR and 95% less than OPS. Moreover, COA produces the lowest steady-state frequency error. For Ziegler-Nicholas controllers, COA produces around 53% less in the overshoot and 42% less in the settling time.
Mathematical modeling is very effective method to investigate interaction between insulin and glucose. In this paper, a new mathematical model for insulin-glucose regulation system is introduced based on well-known Lokta-Volterra model. Chaos is a common property in complex biological systems in the previous studies. The results here are in accordance with previous ones and indicating that insulin-glucose regulating system has many dynamics in different situations. The overall result of this paper may be helpful for better understanding of diabetes mellitus regulation system including diseases such as hyperinsulinemia and Type1 DM.
This paper deals with the application of Fuzzy-Neural Networks (FNNs) in multi-machine system control applied on hot steel rolling. The electrical drives that used in rolling system are a set of three-phase induction motors (IM) controlled by indirect field-oriented control (IFO). The fundamental goal of this type of control is to eliminate the coupling influence though the coordinate transformation in order to make the AC motor behaves like a separately excited DC motor. Then use Fuzzy-Neural Network in control the IM speed and the rolling plant. In this work MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results are presented to verify the effectiveness of the proposed control schemes. It is found that the proposed system is robust in that it eliminates the disturbances considerably.
A Programmable logic controller (PLC) uses the digital logic circuits and their operating concepts in its hardware structure and its programming instructions and algorithms. Therefore, the deep understanding of these two items is staple for the development of control applications using the PLC. This target is only possible through the practical sensing of the various components or instructions of these two items and their applications. In this work, a user-friendly and re-configurable ladder, digital logic learning and application development design and testing platform has been designed and implemented using a Programmable Logic Controller (PLC), Human Machine Interface panel (HMI), four magnetic contactors, one Single-phase power line controller and one Variable Frequency Drive (VFD) unit. The PLC role is to implement the ladder and digital logic functions. The HMI role is to establish the virtual circuit wiring and also to drive and monitor the developed application in real time mode of application. The magnetic contactors are to play the role of industrial field actuators or to link the developed application control circuit to another field actuator like three phase induction motor. The Single -phase power line controller is to support an application like that of the soft starter. The VFD is to support induction motor driven applications like that of cut-to-length process in which steel coils are uncoiled and passed through cutting blade to be cut into required lengths. The proposed platform has been tested through the development of 14 application examples. The test results proved the validity of the proposed platform.
Ad-Hoc networks have an adaptive architecture, temporarily configured to provide communication between wireless devices that provide network nodes. Forwarding packets from the source node to the remote destination node may require intermediate cooperative nodes (relay nodes), which may act selfishly because they are power-constrained. The nodes should exhibit cooperation even when faced with occasional selfish or non-cooperative behaviour from other nodes. Several factors affect the behaviour of nodes; those factors are the number of packets required to redirect, power consumption per node, and power constraints per node. Power constraints per node and grade of generosity. This article is based on a dynamic collaboration strategy, specifically the Generous Tit-for-Tat (GTFT), and it aims to represent an Ad-Hoc network operating with the Generous Tit-for-Tat (GTFT) cooperation strategy, measure statistics for the data, and then analyze these statistics using the Taguchi method. The transfer speed and relay node performance both have an impact on the factors that shape the network conditions and are subject to analysis using the Taguchi Method (TM). The analyzed parameters are node throughput, the amount of relay requested packets produced by a node per number of relays requested packets taken by a node, and the amount of accepted relay requested by a node per amount of relay requested by a node. A Taguchi L9 orthogonal array was used to analyze node behaviour, and the results show that the effect parameters were number of packets, power consumption, power constraint of the node, and grade of generosity. The tested parameters influence node cooperation in the following sequence: number of packets required to redirect (N) (effects on behaviour with a percent of 6.8491), power consumption per node (C) (effects on behaviour with a percent of 0.7467), power constraints per node (P) (effects on behaviour with a percent of 0.6831), and grade of generosity (ε) (effects on behaviour with a percent of 0.4530). Taguchi experiments proved that the grade of generosity (GoG) is not the influencing factor where the highest productivity level is, while the number of packets per second required to redirect also has an impact on node behaviour.
COVID-19 is an infectious viral disease that mostly affects the lungs. That quickly spreads across the world. Early detection of the virus boosts the chances of patients recovering quickly worldwide. Many radiographic techniques are used to diagnose an infected person such as X-rays, deep learning technology based on a large amount of chest x-ray images is used to diagnose COVID-19 disease. Because of the scarcity of available COVID-19 X-rays image, the limited COVID-19 Datasets are insufficient for efficient deep learning detection models. Another problem with a limited dataset is that training models suffer from over-fitting, and the predictions are not generalizable to address these problems. In this paper, we developed Conditional Generative Adversarial Networks (CGAN) to produce synthetic images close to real images for the COVID-19 case and traditional augmentation that was used to expand the limited dataset then used to train by Customized deep detection model. The Customized Deep learning model was able to obtain excellent detection accuracy of 97% accurate with only ten epochs. The proposed augmentation outperforms other augmentation techniques. The augmented dataset includes 6988 high-quality and resolution COVID-19 X-rays images. At the same time, the original COVID-19 X-rays images are only 587.
The Permanent Magnet Synchronous Motor (PMSM) is commonly used as traction motors in the electric traction applications such as in subway train. The subway train is better transport vehicle due to its advantages of security, economic, health and friendly with nature. Braking is defined as removal of the kinetic energy stored in moving parts of machine. The plugging braking is the best braking offered and has the shortest time to stop. The subway train is a heavy machine and has a very high moment of inertia requiring a high braking torque to stop. The plugging braking is an effective method to provide a fast stop to the train. In this paper plugging braking system of the PMSM used in the subway train in normal and fault-tolerant operation is made. The model of the PMSM, three-phase Voltage Source Inverter (VSI) controlled using Space Vector Pulse Width Modulation technique (SVPWM), Field Oriented Control method (FOC) for independent control of two identical PMSMs and fault-tolerant operation is presented. Simulink model of the plugging braking system of PMSM in normal and fault tolerant operation is proposed using Matlab/Simulink software. Simulation results for different cases are given.
The identification of system parameters plays an essential role in system modeling and control. This paper presents a parameter estimation for a permanent magnetic DC motor using the simulink design optimization method. The parameter estimation may be represented as an optimization problem. Firstly, the initial values of the DC motor parameters are extracted using the dynamic model through measuring the values of voltage, current, and speed of the motor. Then, these values are used as an initial value for simulink design optimization. The experimentally input- output data can be collected using a suggested microcontroller based circuit that will be used later for estimating the DC motor parameters by building a simulink model. Two optimization algorithms are used, the pattern search and the nonlinear least square. The results show that the nonlinear least square algorithm gives a more accurate result that almost approaches to the actual measured speed response of the motor. )
A wireless body area network (WBAN) connects separate sensors in many places of the human body, such as clothes, under the skin. WBAN can be used in many domains such as health care, sports, and control system. In this paper, a scheme focused on managing a patient’s health care is presented based on building a WBAN that consists of three components, biometric sensors, mobile applications related to the patient, and a remote server. An excellent scheme is proposed for the patient’s device, such as a mobile phone or a smartwatch, which can classify the signal coming from a biometric sensor into two types, normal and abnormal. In an abnormal signal, the device can carry out appropriate activities for the patient without requiring a doctor as a first case. The patient does not respond to the warning message in a critical case sometimes, and the personal device sends an alert to the patient’s family, including his/her location. The proposed scheme can preserve the privacy of the sensitive data of the patient in a protected way and can support several security features such as mutual authentication, key management, anonymous password, and resistance to malicious attacks. These features have been proven depending on the Automated Validation of Internet Security Protocols and Applications. Moreover, the computation and communication costs are efficient compared with other related schemes.
In this study, Dielectric Barrier Discharge plasma irradiation (DBD) is applied to treatment and improve the properties of the ZnO thin film deposited on the glass substrate as a sensor for glucose detection. The ZnO is prepared via a sol-gel method in this work. ZnO is irradiated by the DBD high voltage plasma to improve of its sensitivity. The optical properties, roughness and surface morphology of the waveguide coated ZnO thin films before and after DBD plasma irradiation are studied in this work. The results showed a significant improvement in the performance of the sensor in the detection of concentrations of glucose solution after plasma irradiation. Where the largest value in sensitivity was equal to 62.7 when the distance between electrodes was 5 cm compared to the sensitivity before irradiation, which was equal to 92. The high response showed in results demonstrating that the fabricated waveguide coated ZnO after plasma irradiation has the excellent potential application as a sensor to detect small concentration of glucose solution.
Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to- Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.
In order to reduce the impact of watermark embedding on the perceptual fidelity of the marked signal, watermarking systems process the generated watermark to match it to the local properties of the underlying host signal prior to embedding. However, this adaptation process could distort the watermark, affecting its robustness and information content. In this paper, a new watermark coding technique is proposed, that enables the application of some mark- nondistorting host-adaptation processing, where the intensity of the watermark could be redistributed according to the local properties of the underlying host without changing the way of interpreting the watermark to be embedded. This completely eliminates the need to equalize adaptation distortions prior to decoding, and hence, to pass any side information about the adaptation processing to the decoder, too.
The multilevel inverter is attracting the specialist in medium and high voltage applications, among its types, the cascade H bridge Multi-Level Inverter (MLI), commonly used for high power and high voltage applications. The main advantage of the conventional cascade (MLI) is generated a large number of output voltage levels but it demands a large number of components that produce complexity in the control circuit, and high cost. Along these lines, this paper presents a brief about the non-conventional cascade multilevel topologies that can produce a high number of output voltage levels with the least components. The non-conventional cascade (MLI) in this paper was built to reduce the number of switches, simplify the circuit configuration, uncomplicated control, and minimize the system cost. Besides, it reduces THD and increases efficiency. Two topologies of non-conventional cascade MLI three phase, the Nine level and Seventeen level are presented. The PWM technique is used to control the switches. The simulation results show a better performance for both topologies. THD, the power loss and the efficiency of the two topologies are calculated and drawn to the different values of the Modulation index (ma).
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.
In this paper, a new algorithm called the virtual circle tangents is introduced for mobile robot navigation in an environment with polygonal shape obstacles. The algorithm relies on representing the polygonal shape obstacles by virtual circles, and then all the possible trajectories from source to target is constructed by computing the visible tangents between the robot and the virtual circle obstacles. A new method for searching the shortest path from source to target is suggested. Two states of the simulation are suggested, the first one is the off-line state and the other is the on-line state. The introduced method is compared with two other algorithms to study its performance.
Searchable symmetric encryption (SSE) is a robust cryptographic method that allows users to store and retrieve encrypted data on a remote server, such as a cloud server, while maintaining the privacy of the user’s data. The technique employs symmetric encryption, which utilizes a single secret key for both data encryption and decryption. However, extensive research in this field has revealed that SSE encounters performance issues when dealing with large databases. Upon further investigation, it has become apparent that the issue is due to poor locality, necessitating that the cloud server access multiple memory locations for a single query. Additionally, prior endeavors in this domain centered on locality optimization have often led to expanded storage requirements (the stored encrypted index should not be substantially larger than the original index) or diminished data retrieval efficiency (only required data should be retrieved).we present a simple, secure, searchable, and cost-effective scheme, which addresses the aforementioned problems while achieving a significant improvement in information retrieval performance through site optimization by changing the encrypted inverted index storage mechanism. The proposed scheme has the optimal locality O(1) and the best read efficiency O(1)with no significant negative impact on the storage space, which often increases due to the improvement of the locality. Using real-world data, we demonstrate that our scheme is secure, practical, and highly accurate. Furthermore, our proposed work can resist well-known attacks such as keyword guessing attacks and frequency analysis attacks.
The Intelligent Control of Vibration Energy Harvesting system is presented in this paper. The harvesting systems use a me- chanical vibration to generate electrical energy in a suitable form for use. Proportional-Integrated-derivative controller and Fuzzy Logic controller have been suggested; their parameters are optimized using a new heuristic algorithm, the Camel Trav- eling Algorithm(CTA). The proposed circuit Simulink model was constructed in Matlab facilities, and the model was tested under various operating conditions. The results of the simulation using the CTA was compared with two other methods.
Vast number of researches deliberated the separation of speech mixtures due to the importance of this field of research . Whereas its applications became widely used in our daily life; such as mobile conversation, video conferences, and other distant communications. These sorts of applications may suffer from what is well known the cocktail party problem. Independent component analysis (ICA) has been extensively used to overcome this problem and many ICA algorithms based on different techniques have been developed in this context. Still coming up with some suitable algorithms to separate speech mixed signals into their original ones is of great importance. Hence, this paper utilizes thirty ICA algorithms for estimating the original speech signals from mixed ones, the estimation process is carried out with the purpose of testing the robustness of the algorithms once against a different number of mixed signals and another against different lengths of mixed signals. Three criteria namely Spearman correlation coefficient, signal to interference ratio, and computational demand have been used for comparing the obtained results. The results of the comparison were sufficient to signify some algorithms which are appropriate for the separation of speech mixtures.
Content-Based Image Retrieval (CBIR) is an automatic process of retrieving images that are the most similar to a query image based on their visual content such as colour and texture features. However, CBIR faces the technical challenge known as the semantic gap between high level conceptual meaning and the low-level image based features. This paper presents a new method that addresses the semantic gap issue by exploiting cluster shapes. The method first extracts local colours and textures using Discrete Cosine Transform (DCT) coefficients. The Expectation-Maximization Gaussian Mixture Model (EM/GMM) clustering algorithm is then applied to the local feature vectors to obtain clusters of various shapes. To compare dissimilarity between two images, the method uses a dissimilarity measure based on the principle of Kullback-Leibler divergence to compare pair-wise dissimilarity of cluster shapes. The paper further investigates two respective scenarios when the number of clusters is fixed and adaptively determined according to cluster quality. Experiments are conducted on publicly available WANG and Caltech6 databases. The results demonstrate that the proposed retrieval mechanism based on cluster shapes increases the image discrimination, and when the number of clusters is fixed to a large number, the precision of image retrieval is better than that when the relatively small number of clusters is adaptively determined.
Nowadays, the trend has become to utilize Artificial Intelligence techniques to replace the human's mind in problem solving. Vehicle License Plate Recognition (VLPR) is one of these problems in which the computer outperforms the human being in terms of processing speed and accuracy of results. The emergence of deep learning techniques enhances and simplifies this task. This work emphasis on detecting the Iraqi License Plates based on SSD Deep Learning Algorithm. Then Segmenting the plate using horizontal and vertical shredding. Finally, the K-Nearest Neighbors (KNN) algorithm utilized to specify the type of car. The proposed system evaluated by using a group of 500 different Iraqi Vehicles. The successful results show that 98% regarding the plate detection, and 96% for segmenting operation.
It can be said that the system of sensing the tilt angle and speed of a multi-rotor copter come in the first rank among all the other sensors on the multi-rotor copters and all other planes due to its important roles for stabilization. The MPU6050 sensor is one of the most popular sensors in this field. It has an embedded 3-axis accelerometer and a 3-axis gyroscope. It is a simple sensor in dealing with it and extracting accurate data. Everything changes when this sensor is placed on the plane. It becomes very complicated to deal with it due to vibration of the motors on the multirotor copter. In this study, two main problems were diagnosed was solved that appear in most sensors when they are applied to a high-frequency vibrating environment. The first problem is how to get a precise angle of the sensor despite the presence of vibration. The second problem is how to overcome the errors that appear when the multirotor copter revolves around its vertical axis during the tilting in either direction x or y or both. The first problem was solved in two steps. The first step involves mixing data of the gyroscope sensor with the data of auxetometer sensor by a mathematical equation based on optimized complementary filter using gray wolf optimization algorithm GWO. The second step involves designing a suitable FIR filter for data. The second problem was solved by finding a non-linear mathematical relationship between the angles of the copter in both X and Y directions, and the rotation around the vertical axis of multirotor copter frame.
This Paper presents a novel hardware design methodology of digital control systems. For this, instead of synthesizing the control system using Very high speed integration circuit Hardware Description Language (VHDL), LabVIEW FPGA module from National Instrument (NI) is used to design the whole system that include analog capture circuit to take out the analog signals (set point and process variable) from the real world, PID controller module, and PWM signal generator module to drive the motor. The physical implementation of the digital system is based on Spartan-3E FPGA from Xilinx. Simulation studies of speed control of a D.C. motor are conducted and the effect of a sudden change in reference speed and load are also included.
This work addresses the critical need for secure and patient-controlled Electronic Health Records (EHR) migration among healthcare hospitals’ cloud servers (HHS). The relevant approaches often lack robust access control and leave data vulnerable during transfer. Our proposed scheme empowers patients to delegate EHR migration to a trusted Third-Party Hospital (TTPH); which is the Certification Authority (CA) while enforcing access control. The system leverages asymmetric encryption utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA), EEC and ECDSA added robust security and lightness EHR sharing. Patient and user privacy is managed due to anonymity through cryptographic hashing for data protection and utilizes mutual authentication for secure communication. Formal security analysis using the Scyther tool and informal analysis was conducted to validate the system’s robustness. The proposed scheme achieved EHR integrity due to the verification of the communicated HHS and ensuring the integrity of the HHS digital certificate during EHR migration. Ultimately, the result achieved in the proposed work demonstrated the scheme’s high balance between data security and accuracy of communication, where the best result obtained represented 7.7/ ms as computational cost and 1248 /bits as communication cost compared with the relevant approaches.
The occurrence of Sub-Synchronous Resonance (SSR) phenomena can be attributed to the interaction that takes place between wind turbine generators and series-compensated transmission lines. The Doubly-Fed Induction Generator (DFIG) is widely recognized as a prevalent generator form employed in wind energy conversion systems. The present paper commences with an extensive exposition on modal analysis techniques employed in a series of compensated wind farms featuring Doubly Fed Induction Generators (DFIGs). The system model encompasses various components, including the aerodynamics of a wind turbine, an induction generator characterized by a sixth-order model, a second- order two-mass shaft system, a series compensated transmission line described by a fourth-order model, controllers for the Rotor-Side Converter (RSC) and the Grid-Side Converter (GSC) represented by an eighth-order model, and a first-order DC-link model. The technique of eigenvalue-based SSR analysis is extensively utilized in various academic and research domains. The eigenvalue technique depends on the initial conditions of state variables to yield an accurate outcome. The non-iterative approach, previously employed for the computation of initial values of the state variables, has exhibited issues with convergence, lack of accuracy, and excessive computational time. The comparative study evaluates the time-domain simulation outcomes under different wind speeds and compensation levels, along side the eigenvalue analysis conducted using both the suggested and non-iterative methods. This comparative analysis is conducted to illustrate the proposed approach efficacy and precision. The results indicate that the eigenvalue analysis conducted using the proposed technique exhibits more accuracy, as it aligns with the findings of the simulations across all of the investigated instances. The process of validation is executed with the MATLAB program. Within the context of the investigation, it has been found that increasing compensation levels while simultaneously decreasing wind speed leads to system instability. Therefore, modifying the compensation level by the current wind speed is advisable.
In today's chemical, refinery, and petrochemical sectors, separation tanks are one of the most significant separating processes. One or more separation tanks must operate consistently and reliably for multiple facilities' safe and efficient operation. Therefore, in this paper, a PI controller unit has been designed to improve the performance of the tank level controller of the industrial process in Basrah Refinery Station. The overall system mathematical model has been derived and simulated by MATLAB to evaluate the performance. Further, to improve the performance of the tank level controller, optimal PI parameters should be calculated, which Closed-Loop PID Autotuner has been used for this task. Several experiments have been conducted to evaluate the performance of the proposed system. The results indicated that the PI controller based on the Autotuner Method is superior to the conventional PI controller in terms of ease to implement and configuration also less time to get optimal PI gains.
This work focuses on using two-dimensional finite element method to calculate apparent and incremental inductances for 40W fluorescent lamp ballast with different loading conditions based on its design documents. Seven inductance calculation techniques are adopted by calculating ballast stored magnetic energy and flux linkage using ANSYS software. The calculated results for apparent inductances show a good agreement with the design and average measured values, while calculated incremental inductances have been verified by noting the behavior of core hysteresis loop experimentally. These seven techniques establish a good base for researchers and designers to obtain an accurate inductance profile for any iron-core inductor.
Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.
This article presents a power-efficient low noise amplifier (LNA) with high gain and low noise figure (NF) dedicated to satellite communications at a frequency of 435 MHz. LNAs’ gain and NF play a significant role in the designs for satellite ground terminals seeking high amplification and maintaining a high signal-to-noise ratio (SNR). The proposed design utilized the transistor (BFP840ESD) to achieve a low NF of 0.459 dB and a high-power gain of 26.149 dB. The study carries out the LNA design procedure, from biasing the transistor, testing its stability at the operation frequency, and finally terminating the appropriate matching networks. In addition to the achieved high gain and low NF, the proposed LNA consumes as low power as only 2 mW.
The Leader detecting and following are one of the main challenges in designing a leader-follower multi-robot system, in addition to the challenge of achieving the formation between the robots, while tracking the leader. The biological system is one of the main sources of inspiration for understanding and designing such multi-robot systems, especially, the aggregations that follow an external stimulus such as light. In this paper, a multi-robot system in which the robots are following a spotlight is designed based on the behavior of the Artemia aggregations. Three models are designed: kinematic and two dynamic models. The kinematic model reveals the light attraction behavior of the Artemia aggregations. The dynamic model will be derived based on the newton equation of forces and its parameters are evaluated by two methods: first, a direct method based on the physical structure of the robot and, second, the Least Square Parameter Estimation method. Several experiments are implemented in order to check the success of the three proposed systems and compare their performance. The experiments are divided into three scenarios of simulation according to three paths: the straight line, circle, zigzag path. The V-Rep software has been used for the simulation and the results appeared the success of the proposed system and the high performance of tracking the spotlight and achieving the flock formation, especially the dynamic models.
The ability to harvest energy from the environment represents an important technology area that promises to eliminate wires and battery maintenance for many important applications and permits deploying self powered devices. This paper suggests the use of a solar energy harvester to charge mobile phone devices. In the beginning, a comprehensive overview to the energy harvesting concept and technologies is presented. Then the design procedure of our energy harvester was detailed. Our prototype solar energy harvester proves its efficiency to charge the aimed batteries under sunlight or an indoor artificial light.
In this paper new semi-empirical formulas are developed to evaluate the variation of both real and imaginary parts of soil complex permittivity with depth inside the earth's surface. Computed values using these models show good agreement with published measured values for soils of the same textures and same frequency band. Use of these models may serve to handle more accurate results especially in the ground probing radar (GPR) applications and other applications relating the detection of buried objects inside the earth's surface, where the use of a single average value of the soil complex permittivity had not necessarily led, for most of the times, to accurate results for the electromagnetic fields propagated inside the earth's surface.
The presented research introduces a control strategy for a three-phase grid-tied LCL-filtered quasi-Z-source inverter (qZSI) using a Lyapunov-function-based method and cascaded proportional-resonant (PR) controllers. The suggested control strategy ensures the overall stability of the closed-loop system and eliminates any steady-state inaccuracy in the grid current. The inverter current and capacitor voltage reference values of qZSI are created by the utilization of cascaded coupled proportional-resonant (PR) controllers. By utilizing synchronous reference frame and Lyapunov function- based control, the requirement to perform derivative operations and anticipate inductance and capacitance are avoided, resulting in achieving the goal of zero steady-state error in the grid current. The qZSI can accomplish shoot-through control by utilizing a simple boost control method. Computer simulations demonstrate that the suggested control strategy effectively achieves the desired control objectives, both in terms of steady-state and dynamic performance.
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.
Vehicle Ad-hoc Network (VANET) is a type of wireless network that enables communication between vehicles and Road Side Units (RSUs) to improve road safety, traffic efficiency, and service delivery. However, the widespread use of vehicular networks raises serious concerns about users’ privacy and security. Privacy in VANET refers to the protection of personal information and data exchanged between vehicles, RSUs, and other entities. Privacy issues in VANET include unauthorized access to location and speed information, driver and passenger identification, and vehicle tracking. To ensure privacy in VANET, various technologies such as pseudonymization, message authentication, and encryption are employed. When vehicles frequently change their identity to avoid tracking, message authentication ensures messages are received from trusted sources, and encryption is used to prevent unauthorized access to messages. Therefore, researchers have presented various schemes to improve and enhance the privacy efficiency of vehicle networks. This survey article provides an overview of privacy issues as well as an in-depth review of the current state-of-the-art pseudonym-changing tactics and methodologies proposed.
Non-Orthogonal Multiple Access (NOMA) has been promised for fifth generation (5G) cellular wireless network that can serve multiple users at same radio resources time, frequency, and code domains with different power levels. In this paper, we present a new simulation compression between a random location of multiple users for Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) that depend on Successive Interference Cancellation (SIC) and generalized the suggested joint user pairing for NOMA and beyond cellular networks. Cell throughput and Energy Efficiency (EE) are gained are developed for all active NOMA user in suggested model. Simulation results clarify the cell throughput for NOMA gained 7 Mpbs over OMA system in two different scenarios deployed users (3 and 4). We gain an attains Energy Efficiency (EE) among the weak power users and the stronger power users.
In the city of Basrah, there is an urgent need to use the water for irrigation process more efficiently for many reasons: one of them, the high temperature in long summer season and the other is the lack of sources fresh water sources. In this work, a smart irrigation system based wireless sensor networks (WSNs) is implemented. This system consists of the main unit that represented by an Arduino Uno board which include an ATmega328 microcontroller, different sensors as moisture sensors, temperature sensors, humidity sensors, XBee modules and solenoid valve. Zigbee technology is used in this project for implementing wireless technology. This system has two modes one manual mode, the other is a smart mode. The set points must be changed manually according to the specified season to satisfy the given conditions for the property irrigation, and the smart operation of the system will be according to these set points.
The incredible growth of FPGA capabilities in recent years and the new included features have made them more and more attractive for numerous embedded systems. There is however an important shortcoming concerning security of data and design. Data security implies the protection of the FPGA application in the sense that the data inside the circuit and the data transferred to/from the peripheral circuits during the communication are protected. This paper suggests a new method to support the security of any FPGA platform using network processor technology. Low cost IP2022 UBICOM network processor was used as a security shield in front of any FPGA device. It was supplied with the necessary security methods such as AES ciphering engine, SHA-1, HMAC and an embedded firewall to provide confidentiality, integrity, authenticity, and packets filtering features.
The power theft is one of the main problems facing the electric energy sector in Iraq, where a large amount of electrical energy is lost due to theft. It is required to design a system capable of detecting and locating energy theft without any human interaction. This paper presents an effective solution with low cost to solve power theft issue in distribution lines. Master meter is designed to measures the power of all meters of the homes connected to it. All the measured values are transmitted to the server via GPRS. The values of power for all energy meters within the grid are also transmitted. The comparison between the power of the master meter and all the other meters are transmitted to the server. If there is a difference between the energy meters, then a theft is happened and the server will send a signal via GSM to the overrun meter to switch off the power supply. Raspberry pi is used as a server and equipped and programmed to detect the power theft.
The power quality problems can be defined as the difference between the quality of power supplied and the quality of power required. Recently a large interest has been focused on a power quality domain due to: disturbances caused by non-linear loads and Increase in number of electronic devices. Power quality measures the fitness of the electric power transmitted from generation to industrial, domestic and commercial consumers. At least 50% of power quality problems are of voltage quality type. Voltage sag is the serious power quality issues for the electric power industry and leads to the damage of sensitive equipments like, computers, programmable logic controller (PLC), adjustable speed drives (ADS). The prime goal of this paper is to investigate the performance of the Fuzzy Logic controller based DVR in reduction the power disturbances to restore the load voltage to the nominal value and reduce the THD to a permissible value which is 5% for the system less than 69Kv. The modeling and simulation of a power distribution system have been achieved using MATLABL/Simulink. Different faults conditions and power disturbances with linear and non-linear loads are created with the proposed system, which are initiated at a duration of 0.8sec and kept till 0.95sec.
In this paper, we evaluate the performance of UMTS (Universal Mobile Telecommunication System) downlink system in vicinity of UWB system. The study is achieved via simulating a scenario of a building which is located within UMTS cell borders and utilizes from both UMTS and UWB appliances. The simulation results show that the UMTS system is considerably affected by the UWB interference. However, in order to battle this interference and achieve reasonable BER (Bit Error Rate) of 10 -4 , we found that it is very necessary to carefully raise the UMTS base station transmitted power against that of UWB interferer. So, the minimum requirements for UMTS system to overcome UWB interference are stated in this work.
In this article, a robust control technique for 2-DOF helicopter system is presented. The 2-DOF helicopter system is 2 inputs and 2 outputs system that is suffering from the high nonlinearity and strong coupling. This paper focuses on design a simple, robust, and optimal controller for the helicopter system. Moreover, The proposed control method takes into account effects of the measurement noise in the closed loop system that effect on the performance of controller as well as the external disturbance. The proposed controller combines low pass filter with robust PID controller to ensure good tracking performance with high robustness. A low pass filter and PID controller are designed based H∞weighted mixed sensitivity. Nonlinear dynamic model of 2-DOF helicopter system linearized and then decoupled into pitch and yaw models. Finally, proposed controller applied for each model. Matlab program is used to check effectiveness the proposed control method. Simulation results show that the proposed controllers has best tracking performance with no overshot and the smallest settling time with respect to standard H∞and optimized PID controller.