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Go to Editorial ManagerThe 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 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.
With the development of cyber security and multimedia forensics, digital image manipulation has recently been recognized as one of the major challenges in forensic image analysis. Therefore, selecting an image area and then copying and pasting it into the same image is the hardest process in passive image forgery. This act violates privacy and secrecy of authenticity of digital image. The attacker exploits the available tools of editing image program to make the fake image similar to the original one. This paper presents a proposed fast and efficient passive Copy-move forgery detection scheme. Hessian- Affine and Harris-Affine detectors, and Shift Invariant Feature Transform (SIFT) descriptor, are employed in the proposed scheme. These detectors provide sufficient key points for detecting the duplicated regions in the case of small or invisible regions. The experimental results show that the proposed scheme is invariant against simple and hard attacks like uniform or non-uniform transformation. The proposed scheme was evaluated using standard data sets (GRIP, MICC 220, and F8 Multi). Resulted True Positive Rate (TPR) was 0.98 and False Positive Rate (FPR) was 0.035. Thus, the scheme is effective and providing valuable results compared to recent passive image authentication schemes.
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.
IoHT has several benefits for real-time smart healthcare, but because of its limited processing power, storage capacity, and self-defense capabilities, security issues are growing. Although newer blockchain-based authentication solutions have strong security features due to their tamper-resistant decentralized architecture, they come with a high resource cost, requiring a lot of processing power, more storage, and time-consuming authentication procedures. As such, these difficulties provide barriers to reaching the ideal levels of scalability and temporal efficiency, which are essential for the efficient functioning of large-scale, time-sensitive IoHT systems. To solve these challenges, this paper presents an authentication approach designed especially for IoHT systems. Our work consists four-phase process, which includes setting, registration, login and authentication, and HERs Exchange data. To enhance both efficiency and scalability, the proposed scheme employs a combination of 3-D map dimensions chaotic-based public key cryptosystems, and blockchain-based, fog computing technologies and IPFS. We simulate the proposed work to implement health electronic record (HER) by the Ethereum platform and solidity language, the simulation experiments were tested using the JMeter tool. Showed that the key generation time for chaotic-based is faster than (ECC)—furthermore, the average latency ≈ 3.7 ms. A security analysis of the proposed scheme was implemented by the Scyther tool. The formal security analysis demonstrated that the proposed scheme is secured against potential attacks and supports the scalability of the IoHT system.
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.
The efficient and fast-tracking of the global maximum power point (GMPP) under partial shading conditions (PSCs) is one of the most significant goals of the maximum power point tracking (MPPT) algorithms. This paper introduces an algorithm to identify the accurate range locations of the GMPP. A skipping MPPT algorithm is proposed to minimize the time consumed in tracking the GMPP. The proposed algorithm uses a skipping voltage method to minimize scanning the voltage range on the Power-Voltage (P-V) curve by neglecting the zones without GMPP. During GMPP tracking under PSCs, the automatic initial voltage generator algorithm ensures no overlap between two adjacent zones on the P-V curve. The proposed skipping algorithm guarantees that the GMPP is tracked accurately under all potential atmospheric circumstances with a shorter tracking time and the ability to find the GMPP quickly, minimizing the power loss. The improved performance of the proposed algorithm has been validated by simulation and experimental results on a PV string. From the results, the proposed MPPT algorithm has demonstrated its superiority in tracking the GMPP under PSCs in terms of accuracy and tracking speed compared to other MPPT algorithms. The proposed algorithm tracks the GMPP faster, with a time difference of Δt = 3.28 sec and Δt = 27 msec from the experimental and simulation results under PSCs. The proposed algorithm also successfully tracks the GMPP in the final zone, while the 0.8VOCM MPPT algorithm fails, which causes a high-power loss of 196 watts compared to the proposed skipping algorithm.
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.
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.
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.
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.
Legged robots offer several benefits over standard wheeled vehicles when operating in tough and unstructured terrain. These benefits include increased speed, improved fuel efficiency, increased mobility, improved isolation from uneven terrain, and reduced environmental harm. This paper presents the modeling of an eight-legged robot that was simulated using Simscape Multibody toolbox in MATLAB, where the robot consists of eight legs, and each leg contains three links, and each link contains a PID controller, meaning it contains a total of 24 controllers. This controller was used to control the robot’s gait and make it more stable. To obtain the optimal and most stable gait for the robot and to travel a longer distance, an optimization algorithm should be used, so that in this paper the genetic algorithm (GA) is used to obtain those points. To test the robustness of the proposed controllers, different weights are added (1 kg and 3 kg) as a load to the body of the legged robot, the obtained results show the efficiency of the proposed controllers.
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.
Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complex- valued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.
Early in the 20th century, as a result of technological advancements, the importance of digital marketing significantly increased as the necessity for digital customer experience, promotion, and distribution emerged. Since the year 1988, in the case when the term ”Digital Marketing” first appeared, the business sector has undergone drastic growth, moving from small startups to massive corporations on a global scale. The marketer must navigate a chaotic environment caused by the vast volume of generated data. Decision-makers must contend with the fact that user data is dynamic and changes every day. Smart applications must be used within enterprises to better evaluate, classify, enhance, and target audiences. Customers who are tech-savvy are pushing businesses to make bigger financial investments and use cutting-edge technologies. It was only natural that marketing and trade could be one of the areas to move to such development, which helps to move to the speed of spread, advertisements, along with other things to facilitate things for reaching and winning customers. In this study, we utilized machine learning (ML) algorithms (Decision tree (DT), K-Nearest Neighbor (KNN), CatBoost, and Random Forest (RF) (for classifying data in customers to move to development. Improve the ability to forecast customer behavior so one can gain more business from them more quickly and easily. With the use of the aforementioned dataset, the suggested system was put to the test. The results show that the system can accurately predict if a customer will buy something or not; the random forest (RF) had an accuracy of 0.97, DT had an accuracy of 0. 95, KNN had an accuracy of 0. 91, while the CatBoost algorithm had the execution time 15.04 of seconds, and gave the best result of highest f1 score and accuracy (0.91, 0. 98) respectively. Finally, the study’s future goals involve being created a web page, thereby helping many banking institutions with speed and forecast accuracy. Using more techniques of feature selection in conjunction with the marketing dataset to improve diagnosis.
Recently, the incorporation of state-of-the-art technology such as Electronic Healthcare Records (EHRs), networks, and cloud computing has transformed the traditional healthcare system. However, security problems have arisen as a result of the integration of technology. Secure remote user authentication is a core part of the healthcare system to validate the user's identification via an unsecure communication network. Since then, several remote user authentication schemes have been presented, each with its own set of pros and limitations. As a result, security, malicious attacks and privacy concerns are considered one of the main challenges related to the healthcare system. In this paper, we propose a safe user authentication scheme for patients in the healthcare system that overcomes these flaws and confirms the security of the proposed work using scyther, a formal security tool. In the healthcare environment, our work provides an effective means to construct an environment capable of setting, registering, storing, searching, analyzing, authentication, and verifying electronic healthcare information in order to protect the information of patients. Furthermore, our suggested scheme uses symmetric encryption based on the crypto- hash function for accessing the anomaly of the patient's identity and One-Time Password (OTP). Towards the end of the study, the performance analysis results indicate a delicate balance of security and performance that is frequently lacking in previous works.
In this paper, fuzzy Petri Net controller is used for Quadrotor system. The fuzzy Petrinet controller is arranged in the velocity PID form. The optimal values for the fuzzy Petri Net controller parameters have been achieved by using particle swarm optimization algorithm. In this paper, the reference trajectory is obtained from a reference model that can be designed to have the ideal required response of the Quadrotor, also using the quadrotor equations to find decoupling controller is first designed to reduce the effect of coupling between different inputs and outputs of quadrotor. The system performance has been measured by MATLAB. Simulation results showed that the FPN controller has a reasonable robustness against disturbances and good dynamic performance.
In this paper, a hierarchical Arabic phoneme recognition system is proposed in which Mel Frequency Cepstrum Coefficients (MFCC) features is used to train the hierarchical neural networks architecture. Here, separate neural networks (subnetworks) are to be recursively trained to recognize subsets of phonemes. The overall recognition process is a combination of the outputs of these subnetworks. Experiments that explore the performance of the proposed hierarchical system in comparison to non-hierarchical (flat) baseline systems are also presented in this paper.
High speed and area reduction of the Arithmetic-Logic Unit (ALU) have a fundamental role in modern processors, especially in digital signal processor (DSP). In this paper, a new elastic fixed-point (Fx-P) ALU module is proposed to perform multiple operations on real and complex numbers. The arithmetic part of the ALU executes operations such as addition, subtraction, increment, decrement, and multiplication on real numbers. For complex operands, the proposed ALU executes three operations comprising addition, subtraction, complex conjugate of complex numbers. The logical part performs the basic operations including AND, OR, NAND, NOR, XOR, XNOR, NOT and BUFFER operations. The proposed design is based on utilizing an enhanced design of a hybrid adder consists of a Han Carlson adder with a carry-select adder (EHC-CSLA) and an improved design of the Vedic multiplier to achieve multiplication operation of real numbers. A 16-bit and a 32-bit EHC-CSLA are designed first to perform real/complex addition-and- subtraction on both data types. Then, an improved-Vedic multiplier (IVM) is designed to perform multiplication on two real operands. The proposed EHC-CSLAs, numerous bit-sizes of the IVMs, and the elastic design of real/ complex ALU modules in this work are coded in VHDL, simulated, and synthesized by Xilinx ISE14.7 tool on different FPGA families. The performance results demonstrate appreciable reductions in delay and area usage in comparison to the most counterpart multipliers and ALU designs.
The development of renewable resources and the deregulation of the market have made forecasting energy demand more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their ensemble, we showed how well the individual Random forest performed.
The conventional multilevel inverter (MLI) is divided into three types: diode clamped MLI, cascade H Bridge MLI and flying capacitor MLI. The main disadvantage of these types is the higher required number of components when the number of the levels increases and this results in more switching losses, system higher cost, more complex of control circuit as well as less accuracy. The work in this paper proposes two topologies of nonconventional diode clamping MLI three phase nine levels and eleven levels. The first proposed topology has ten switches and six diodes per phase while the second topology has nine switches and four diodes per phase. The pulse width modulation (PWM) control method is used as a control to gate switches. THD of the two proposed topologies are analyzed and calculated according different values of Modulation index (where the power loss and efficiency are obtained and plotted.
The rapid progress in mobile computing necessitates energy efficient solutions to support substantially diverse and complex workloads. Heterogeneous many core platforms are progressively being adopted in contemporary embedded implementations for high performance at low power cost estimations. These implementations experience diverse workloads that offer drastic opportunities to improve energy efficiency. In this paper, we propose a novel per core power gating (PCPG) approach based on workload classifications (WLC) for drastic energy cost minimization in the dark silicon era. Core of our paradigm is to use an integrated sleep mode management based on workloads classification indicated by the performance counters. A number of real applications benchmark (PARSEC) are adopted as a practical example of diverse workloads, including memory- and CPU-intensive ones. In this paper, these applications are exercised on Samsung Exynos 5422 heterogeneous many core system showing up to 37% to 110% energy efficient when compared with our most recent published work, and ondemand governor, respectively. Furthermore, we illustrate low-complexity and low-cost runtime per core power gating algorithm that consistently maximize IPS/Watt at all state space.
Every day, a tremendous amount of image data is generated as a result of recent advances in imaging and computing technology. Several content-based image retrieval (CBIR) approaches have been introduced for searching image collections. These methods, however, involve greater computing and storage resources. Cloud servers can address this issue by offering a large amount of computational power at a low cost. However, cloud servers are not completely trustworthy, and data owners are concerned about the privacy of their personal information. In this research, we propose and implement a secure CBIR (SCBIR) strategy for searching and retrieving cipher text image databases. In the proposed scheme, the extract aggregated feature vectors to represent the related image collection and use a safe Asymmetric Scalar-Product-Preserving Encryption (ASPE) approach to encrypt these vectors while still allowing for similarity computation. To improve search time, all encrypted features are recursively clustered using the k-means method to create a tree index. The results reveal that SCBIR is faster at indexing and retrieving than earlier systems, with superior retrieval precision and scalability. In addition, our paper introduces the watermark to discover any illegal distributions of the images that are received by unlawful data users. Particularly, the cloud server integrates a unique watermark directly into the encrypted images before sending them to the data users. As a result, if an unapproved image copy is revealed, the watermark can be extracted and the unauthorized data users who spread the image can be identified. The performance of the proposed scheme is proved, while its performance is demonstrated through experimental results.
A new algorithm for the localization and identification of multi-node systems has been introduced in this paper; this algorithm is based on the idea of using a beacon provided with a distance sensor and IR sensor to calculate the location and to know the identity of each visible node during scanning. Furthermore, the beacon is fixed at middle of the frame bottom edge for a better vision of nodes. Any detected node will start to communicate with the neighboring nodes by using the IR sensors distributed on its perimeter; that information will be used later for the localization of invisible nodes. The performance of this algorithm is shown by the implementation of several simulations .
In this paper a fully neural network-based structure have been proposed to control speeds of rolling stands of a steel rolling mill. The structure has property of controlling the motors speed such that the loop height between each successive stands tracks the required height reference. Synchronization between these stands is also maintained so that the metal flow rate from first stand to the last stand is kept constant. This structure is robust against the disturbance effects such as, torque loading, plant parameter change... etc. The results reveal performance of the structure as a comparison with the conventional control method for a practical worksheet data.
This paper focuses on designing distributed wireless sensor network gateways armed with Intrusion Detection System (IDS). The main contribution of this work is the attempt to insert IDS functionality into the gateway node (UBICOM IP2022 network processor chip) itself. This was achieved by building a light weight signature based IDS based on the famous open source SNORT IDS. Regarding gateway nodes, as they have limited processing and energy constrains, the addition of further tasks (the IDS program) may affects seriously on its performance, so that, the current design takes these constrains into consideration as a priority and use a special protocol to achieve this goal. In order to optimize the performance of the gateway nodes, some of the preprocessing tasks were offloaded from the gateway nodes to a suggested classification and processing server and a new searching algorithm was suggested. Different measures were taken to validate the design procedure and a detailed simulation model was built to discover the behavior of the system in different environments.
With the appearance of Fifth Generation (5G) technology , its necessary to fast the enhancing of the current networks, because of the limitations of Fourth Generation (4G) in terms of data transmission. Although the benefits of the Orthogonal Frequency Division Multiplexing (OFDM) standard of the LTE systems, it has demerits such as rises the Peak-to-Average Power Ratio (PAPR) as well, high Out-Of-Band Emission (OOBE). Thus, it is considered unsuitable for 5G. In this paper, the filtered OFDM (f-OFDM) is proposed for 5G wireless communication systems as an alternative of OFDM because of its low OOBE. Nevertheless, a trade-off between minimize Bit Error Rate (BER) and OOBE and managing PAPR values are the challenge. One of the most important objectives in this paper is achieving balance among this trade-off through proposing concatenated Reed-Solomon (RS)-Hamming codes to improve f-OFDM systems performance. The proposed method utilizes an external RS (7, 1) code with an internal Hamming (7, 4) codes, then appended of an interleaver to combat random errors and help RS code in correcting errors. The results indicated that the proposed f-OFDM system significantly reduced OOBE values compared to familiar OFDM system owing to use FIR digital filter, while minimizes PAPR and improved BER performance due to combined with concatenated codes. Thereby, the suggested system is presented as a highly competitor candidate future wireless communication systems thank to these benefits.
The reliance on networks and systems has grown rapidly in contemporary times, leading to increased vulnerability to cyber assaults. The Distributed Denial-of-Service (Distributed Denial of Service) attack, a threat that can cause great financial liabilities and reputation damage. To address this problem, Machine Learning (ML) algorithms have gained huge attention, enabling the detection and prevention of DDOS (Distributed Denial of Service) Attacks. In this study, we proposed a novel security mechanism to avoid Distributed Denial of Service attacks. Using an ensemble learning methodology aims to it also can differentiate between normal network traffic and the malicious flood of Distributed Denial of Service attack traffic. The study also evaluates the performance of two well-known ML algorithms, namely, the decision tree and random forest, which were used to execute the proposed method. Tree in defending against Distributed Denial of Service (DDoS) attacks. We test the models using a publicly available dataset called TIME SERIES DATASET FOR DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION. We compare the performance of models using a list of evaluation metrics developing the Model. This step involves fetching the data, preprocessing it, and splitting it into training and testing subgroups, model selection, and validation. When applied to a database of nearly 11,000 time series; in some cases, the proposed approach manifested promising results and reached an Accuracy (ACC) of up to 100 % in the dataset. Ultimately, this proposed method detects and mitigates distributed denial of service. The solution to securing communication systems from this increasing cyber threat is this: preventing attacks from being successful.
The unprogrammed penetration for the loads in the distribution networks make it work in an unbalancing situation that leads to unstable operation for those networks. the instability coming from the imbalance can cause many serious problems like the inefficient use of the feeders and the heat increased in the distribution transformers. The demands response can be regarded as a modern solution for the problem by offering a program to decreasing the consumption behavior for the program's participators in exchange for financial incentives in specific studied duration according to a direct order from the utility. The paper uses a new suggested algorithm to satisfy the direct load control demand response strategy that can be used in solving the unbalancing problem in distribution networks. The algorithm procedure has been simulated in MATLAB 2018 to real data collected from the smart meters that have been installed recently in Baghdad. The simulation results of applying the proposed algorithm on different cases of unbalancing showed that it is efficient in curing the unbalancing issue based on using the demand response strategy.
the proposed design offers a complete solution to support and surveillance vehicles remotely. The offered algorithm allows a monitoring center to track vehicles; diagnoses fault remotely, control the traffic and control CO emission. The system is programmed to scan the on-board diagnostic OBD periodically or based on request to check if there are any faults and read all the available sensors, then make an early fault prediction based on the sensor readings, an experience with the vehicle type and fault history. It is so useful for people who are not familiar with fault diagnosis as well as the maintenance center. The system offers tracking the vehicle remotely, which protects it against theft and warn the driver if it exceeds the speed limit according to its location. Finally, it allows the user to report any traffic congestion and allow s a vehicle navigator to be up to date with the traffic condition based on the other system’s user feedback.
In recent years, there has been a lot of interest in the study of P300 potential-based approaches for lie detection. The variations in brain signal activity (EEG-P300 component) that distinguish between lying and starting the truth are investigated. As soon as participants respond to an experiment stimulus for the first time, their brain signals are examined and the P300 signal is extracted. This paper aims to improve the signal-to-noise ratio (SNR) of P300, which leads to an increase in the classification accuracy of lie detection. Ten subjects were randomly assigned to groups of lying and innocent people, and 14 electrodes captured the EEG data for each group. This work proposed to use some denoising techniques like averaging the raw EEG signal, regression-based baseline correction, and independent component analysis (ICA). The suggested approach and other early published methods vary mostly in the regression-based technique used in bassline correction to adaptively indicate the baseline interval (baseline window). Compared to other studies, the suggested technique gives an increase in the mean amount of SNR by up to 20% was obtained.
Agriculture is the primary food source for humans and livestock in the world and the primary source for the economy of many countries. The majority of the country's population and the world depend on agriculture. Still, at present, farmers are facing difficulty in dealing with the requirements of agriculture. Due to many reasons, including different and extreme weather conditions, the abundance of water quality, etc. This paper applied the Internet of Things and deep learning system to establish a smart farming system to monitor the environmental conditions that affect tomato plants using a mobile phone. Through deep learning networks, trained the dataset taken from PlantVillage and collected from google images to classify tomato diseases, and obtained a test accuracy of 97%, which led to the publication of the model to the mobile application for classification for its high accuracy. Using the IoT, a monitoring system and automatic irrigation were built that were controlled through the mobile remote to monitor the environmental conditions surrounding the plant, such as air temperature and humidity, soil moisture, water quality, and carbon dioxide gas percentage. The designed system has proven its efficiency when tested in terms of disease classification, remote irrigation, and monitoring of the environmental conditions surrounding the plant. And giving alerts when the values of the sensors exceed the minimum or higher values causing damage to the plant. The farmer can take the appropriate action at the right time to prevent any damage to the plant and thus obtain a high-quality product.
The power quality nowadays of the low voltage distribution system is vital for the utility and the consumer at the same time. One disturbing issue affected the quality conditions in the radial distribution system is load balancing. This survey paper is looking most the articles that deal with the phase nodal and lateral phase swapping because it is the efficient and direct method to maintain the current and voltage in balance situation, lead to a suitable reduction in the losses and preventing the wrong tripping of the protective relays.
Navigational sensors are evolving both on a commercial and research level. However, the limitation still lies in the accuracy of the respective sensors. For a navigation system to reach a certain accuracy, multi sensors or fusion sensors are used. In this paper, a framework of fuzzy sensor data fusing is proposed to obtain an optimised navigational system. Different types of sensors without a known state of inaccuracy can be fused using the same method proposed. This is demonstrated by fusing compass/accelerometer and GPS signal. GPS is prone to inaccuracies due to environmental factors. These inaccuracies are available in the extracted NMEA protocols as SNR and HDOP. Dead reckoning sensors on the other hand do not depend on external radio signal coverage and can be used in areas with low coverage, but the errors are unbounded and have an accumulative effect over time.
This paper presents new device to simulate and inject a 4-20 mA current signal to PLC and control on this signal wirelessly. The proposed simulator device has been designed and implemented by a PIC 18f4520 microcontroller and an Ethernet click. This device is connected to Wireless Local Area Network (WLAN) via Wi-Fi router using TCP/IP protocol. The simulator has two channels for 4-20 mA current output signals with two channels for digital output signals, controlled by a laptop or a smart mobile. The purpose of this work is to demonstrate the usefulness of the Wi-Fi wireless technology for remote controlling on the 4-20 mA output current signal and the digital output signal in the designed simulator device. The experiments indicate that the proposed wireless simulator outputs the 4- 20 mA current with high accuracy and very fast response. The experiments also indicate that the proposed wireless simulator is easy, comfortable and convenient practically to use in the test operations of protections, interlocks and integrity of analog input channels for PLC compared to the wired simulator.
This article introduces a novel Quantum-inspired Future Search Algorithm (QFSA), an innovative amalgamation of the classical Future Search Algorithm (FSA) and principles of quantum mechanics. The QFSA was formulated to enhance both exploration and exploitation capabilities, aiming to pinpoint the optimal solution more effectively. A rigorous evaluation was conducted using seven distinct benchmark functions, and the results were juxtaposed with five renowned algorithms from existing literature. Quantitatively, the QFSA outperformed its counterparts in a majority of the tested scenarios, indicating its superior efficiency and reliability. In the subsequent phase, the utility of QFSA was explored in the realm of fault detection in underground power cables. An Artificial Neural Network (ANN) was devised to identify and categorize faults in these cables. By integrating QFSA with ANN, a hybrid model, QFSA-ANN, was developed to optimize the network’s structure. The dataset, curated from MATLAB simulations, comprised diverse fault types at varying distances. The ANN structure had two primary units: one for fault location and another for detection. These units were fed with nine input parameters, including phase- currents and voltages, current and voltage values from zero sequences, and voltage angles from negative sequences. The optimal architecture of the ANN was determined by varying the number of neurons in the first and second hidden layers and fine-tuning the learning rate. To assert the efficacy of the QFSA-ANN model, it was tested under multiple fault conditions. A comparative analysis with established methods in the literature further accentuated its robustness in terms of fault detection and location accuracy. this research not only augments the field of search algorithms with QFSA but also showcases its practical application in enhancing fault detection in power distribution systems. Quantitative metrics, detailed in the main article, solidify the claim of QFSA-ANN’s superiority over conventional methods.
Arial images are very high resolution. The automation for map generation and semantic segmentation of aerial images are challenging problems in semantic segmentation. The semantic segmentation process does not give us precise details of the remote sensing images due to the low resolution of the aerial images. Hence, we propose an algorithm U-Net Architecture to solve this problem. It is classified into two paths. The compression path (also called: the encoder) is the first path and is used to capture the image's context. The encoder is just a convolutional and maximal pooling layer stack. The symmetric expanding path (also called: the decoder) is the second path, which is used to enable exact localization by transposed convolutions. This task is commonly referred to as dense prediction, which is completely connected to each other and also with the former neurons which gives rise to dense layers. Thus it is an end-to-end fully convolutional network (FCN), i.e. it only contains convolutional layers and does not contain any dense layer because of which it can accept images of any size. The performance of the model will be evaluated by improving the image using the proposed method U-NET and obtaining an improved image by measuring the accuracy compared with the value of accuracy with previous methods.
The problem of automatic signature recognition and verification has been extensively investigated due to the vitality of this field of research. Handwritten signatures are broadly used in daily life as a secure way for personal identification. In this paper a novel approach is proposed for handwritten signature recognition in an off-line environment based on Weightless Neural Network (WNN) and feature extraction. This type of neural networks (NN) is characterized by its simplicity in design and implementation. Whereas no weights, transfer functions and multipliers are required. Implementing the WNN needs only Random Access Memory (RAM) slices. Moreover, the whole process of training can be accomplished with few numbers of training samples and by presenting them once to the neural network. Employing the proposed approach in signature recognition area yields promising results with rates of 99.67% and 99.55% for recognition of signatures that the network has trained on and rejection of signatures that the network has not trained on, respectively.
A model reference adaptive control of condenser and deaerator of steam power plant is presented. A fuzzy-neural identification is constructed as an integral part of the fuzzy-neural controller. Both forward and inverse identification is presented. In the controller implementation, the indirect controller with propagating the error through the fuzzy-neural identifier based on Back Propagating Through Time (BPTT) learning algorithm as well as inverse control structure are proposed. Simulation results are achieved using Multi Input-Multi output (MIMO) type of fuzzy-neural network. Robustness of the plant is detected by including several tests and observations.
In the world of modern technology and the huge spread of its use, it has been combined with healthcare systems and the establishment of electronic health records (EHR) to follow up on patients. This merging of technology with healthcare has allowed for more accurate EHRs that follow a patient to different healthcare facilities. Timely exchange of electronic health information (EHR) between providers is critical for aiding medical research and providing fast patient treatment. As a result, security issues and privacy problems are viewed as significant difficulties in the healthcare system. Several remote user authentication methods have been suggested. In this research, we present a feasible patient EHR migration solution for each patient. finally, each patient may securely delegate their current hospital’s information system to a hospital certification authority in order to receive migration proof that can be used to transfer their EHR to a different hospital. In addition, the proposed scheme is based on crypto-hash functions and asymmetric cryptosystems by using homomorphic cryptography. The proposed scheme carried out two exhaustive formal security proofs for the work that was provided. Using Scyther, a formal security tool, we present a secure user authentication technique in the proposed healthcare scheme that ensures security and informal analysis.
This paper presents the PLC-HMI based simulation of electrical-based PV cell/array model in laboratory platform to give the opportunity to students and users who haven't clear knowledge to study PV cell and array behavior with respect to change of environment conditions and electrical parameters. This simulation process covers the cell models under ideal and non-ideal ones. In non-ideal one, the series resistance and the shunt resistance are covered.
Self-organizing systems arise in many different fields. In this work we analyze data from social and biological systems. A central question is to demonstrate the presence of the determinism in time-series extracted from such systems that appear apparently not correlated but that are two good benchmarks for the study of complexity in real systems. We will apply the Kaplan test and we will define an order parameter for the biological data to characterize the complexity of the system.
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.
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.
Electromagnetic radiation is becoming a major concern worldwide as the use of portable communication devices increases. So, it is essential to utilize safe communication devices. A compact wide-band antenna of size 12 × 8.5 × 0.33 mm3 and a metamaterial array contribution for Specific Absorption Rate (SAR) reduction are proposed in this paper. In this paper, an array structure of split ring resonators, SRR, which have a negative refractive index, is attached to the proposed MSPA to achieve SAR reduction by 89.88% in the 28 GHz range. Furthermore, the proposed antenna maintains other performance characteristics like high gain (7.7 dBi), radiation efficiency (82%), wide bandwidth (0.8 GHz), and fewer losses (-23 dB). However, this consequential antenna has been built on a low-loss Rogers RT 5880 substrate and a full ground-plane structure using CST microwave software.
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.
In recent years, self-driving cars and reducing the number of accident casualties have drawn a lot of attention. Although it is crucial to increase driver awareness on the road, autonomous vehicles can emulate human driving and guarantee improved levels of road safety. Artificial intelligence (AI) technologies are often employed for this purpose. However, deep learning, a subset of AI, is prone to numerous errors, a wide range of threats, and needs to handle vast amounts of data, which imposes high-performance hardware requirements. This study suggests a deep learning model for object recognition that employs characteristics to describe data rather than images. Our model employs the COCO dataset as the training foundation, and it was suggested that the features be retrieved using the principal component analysis PCA extraction method. The current results demonstrate the efficacy and precision of our model, with an accuracy of 99.96 %. Furthermore, the performance indices, i.e., recall, precision, and F1-score, achieved about 1 for most of the COCO classes in training phase and promising results in testing phase.
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.
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).
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.
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.
A reconfigurable inset-fed Microstrip Patch Antenna (MPA) for dual-band behavior and stable-radiation direction has been demonstrated in this research. The suggested reconfigurable antenna has dimensions of 60×50 mm2 and is printed on an FR4 substrate that is 1.5 mm thick and has a dielectric constant (εr) and loss tangent (tanδ) equal to 4.3 and 0.02, respectively. An inset-fed line with an impedance of 50Ω is used to feed the proposed antenna structure. The parasitic capacitance is efficiently added to the proposed structure by using the slitline approach. The proposed antenna is tested and simulated, where the result shows two resonant frequencies with S11 values less than -10 dB (S11 ≤ −10). The first resonant frequency is found at 2.45 GHz with a value equal to -30.5 dB, while the second resonant is found at 3.54 GHz with a value equal to -32 dB. Moreover, the slits include two PIN diodes. After analyzing the antenna, two reconfigurable bands are obtained for various uses with stable radiation direction. The suggested antenna is constructed and measured, and the outputs of the simulation and the measurements show good agreement.
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.
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.
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.
Reducing the dependency of the control system on communication in the microgrid increases the reliability and flexibility of an islanded microgrid. This paper presents a local secondary control approach to provide a fast response to power change and accurate frequency restoration. It is based on a control scheme that uses a secondary controller involving a time-controllable parameter for a Low pass filter. The high value of the time-varying parameter is placed to satisfy excellent performance regarding fast active power sharing, and the time-controllable parameter decreases after achieving power-sharing based on a time protocol to ensure accurate steady-state frequency restoration. This paper also describes the criteria for control parameter selection and stability analysis based on a precise modeling approach. The MATLAB environment is used to simulate and test the proposed control scheme, and the results have been obtained that show the validity and high performance of the proposed controller in terms of dynamic response to active power change and steady-state restoration under different operation conditions.
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.
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.
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.
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.
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 security of communications in various transmitted information’s forms such as video, audio, image, and even text and preserving them from attackers has become of great importance in the age of the Internet and cellular networks. Perhaps one of the most important media used to transmit information is digital images. They are distinguished from video and audio by their lack of complexity, and at the same time they are distinguished from text by the possibility of containing more information. Due to the necessity of transmitting huge amounts of information via digital images through additive white Gaussian noise (AWGN) channels for various applications. This transmission of images over unsecured channels is vulnerable to many attacks that must be protected by information security tools. In this research, a hybrid chaos-based system was developed to encrypt and secure images and send them via an orthogonal frequency division multiplexing (OFDM) channel, which leads to transferring large amounts of transmitted information in a short time, with very little interference between the data, and maintaining the transfer rate. Two chaotic techniques, Rossler and Modified Chau system, are used together to create a secret encryption key. This combination of chaotic systems provides highly random sensitive keys with amplitude of 10252 that are difficult to predict by the attacker and which makes restoring the original image very difficult in the event of a very small change in the chaotic parameters. Many tests were conducted to determine the strength of the proposed system, including statistical and differential analysis and entropy to verify the strength of the image security approach, in addition to applying some types of attacks to the encrypted image, such as noise and cropping different parts of the image. It is clear that the proposed scheme has strong immunity to these attacks. This was proven by the comparative experimental results. The entropy ratio was very excellent compared to the rest of the results obtained in the rest of the research. This was also the case with the values of (NPCR), (UACI), (NPCR), and Mean Square Error (MSE) was also very good as compared with other researches in the literature. The proposed security approach for OFDM gave a low link and a low bit error rate. And a higher signal-to-noise ratio (PSNR).
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.
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.
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.
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.