In this paper, we have analyzed the full width-half maximum (FWHM) response of a uniform fiber Bragg grating (FBG) for petroleum sensor applications numerically. The analysis; which is depending on parameters such as the gratings length (Lg) and the amplitude of an induced index profile (Δn) change have successfully investigated using MATHCAD software. From the numerical simulation, there is an effect of the Lg and the Δn on the FWHM response. Results show the Lg value that effect the FWHM response is ≤7 mm. In contrast, results show that there is no effect of temperature on the FWHM response. In light of the increasing demand for a wide FWHM band within the applications of optical sensor technology, the results obtained are very important, especially in the field of petroleum industry.
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.
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.
The last few years Quadrotor became an important topic, many researches have implemented and tested concerning that topic. Quadrotor also called an unmanned Aerial Vehicle (UAV), it's highly used in many applications like security, civil applications, aid, rescue and a lot of other applications. It’s not a conventional helicopter because of small size, low cost and the ability of vertical and takeoff landing (VTOL). The models kept an eye on quadrotors were presented, the advancement of this new kind of air vehicle is hindered for a very long while because of different reasons, for example, mechanical multifaceted nature, enormous size and weight, and challenges in charge particularly. Just as of late a lot of interests and endeavors have been pulled in on it; a quadrotor has even become a progressively discretionary vehicle for useful application. Quadrotor can be used in variable, different , outdoor and indoor missions; these missions should be implemented with high value of accuracy and quality. In this work two scenarios suggested for different two missions. First mission the quadrotor will be used to reach different goals in the simulated city for different places during one flight using path following algorithm. The second mission will be an indoor arrival mission, during that mission quadrotor will avoid obstacles by using only Pure pursuit algorithm (PPA). To show the benefit of using the new strategy it will compare with a victor field histogram algorithm (VFH) which is used widely in robotics for avoiding obstacles, the comparison will be in terms of reaching time and distance of reaching the goal. The Gazebo Simulator (GS) is used to visualize the movement of the quadrotor. The gazebo has another preferred position it helps to show the motion development of the quadrotor without managing the mathematical model of the quadrotor. The Robotic Operating System (ROS) is used to transfer the data between the MATLAB Simulink program and the Gazebo Simulator. The diversion results show that, the proposed mission techniques win to drive the quarter on the perfect route similarly at the limit with regards to the quadrotor to go without hitting any obstacle in the perfect way.
Many technical approaches were implemented in the antenna manufacturing process to maintain the desired miniaturiza- tion of the size of the antenna model which can be employed in various applied systems such as medical communication systems. Furthermore, over the past several years, nanotechnology science has rapidly grown in a wide variety of applications, which has given rise to novel ideas in the design of antennas based on nanoscale merits, leading to the use of antennae as an essential linkage between the human body and the different apparatus of the medical communication system. Some medical applications dealt with different antenna configurations, such as microstrip patch antenna or optical nanoantenna in conjugate with sensing elements, controlling units, and monitoring instruments to maintain a specified healthcare system. This study summarizes and presents a brief review of the recent applications of antennas in different medical communication systems involving highlights, and drawbacks with explores recommended issues related to using antennas in medical treatment.
A Programmable logic controller (PLC) uses the digital logic circuits and their operating concepts in its hardware structure and its programming instructions and algorithms. Therefore, the deep understanding of these two items is staple for the development of control applications using the PLC. This target is only possible through the practical sensing of the various components or instructions of these two items and their applications. In this work, a user-friendly and re-configurable ladder, digital logic learning and application development design and testing platform has been designed and implemented using a Programmable Logic Controller (PLC), Human Machine Interface panel (HMI), four magnetic contactors, one Single-phase power line controller and one Variable Frequency Drive (VFD) unit. The PLC role is to implement the ladder and digital logic functions. The HMI role is to establish the virtual circuit wiring and also to drive and monitor the developed application in real time mode of application. The magnetic contactors are to play the role of industrial field actuators or to link the developed application control circuit to another field actuator like three phase induction motor. The Single -phase power line controller is to support an application like that of the soft starter. The VFD is to support induction motor driven applications like that of cut-to-length process in which steel coils are uncoiled and passed through cutting blade to be cut into required lengths. The proposed platform has been tested through the development of 14 application examples. The test results proved the validity of the proposed platform.
In this paper, a semi-elliptical annular slot loaded trapezoidal dipole antenna with band-notched characteristics for UWB applications is designed. A microstrip feedline consisting of multiple feedline sections is used for improving the impedance matching. The band-notched characteristics for WLAN band are achieved by loading the trapezoidal dipole arms with semi- elliptical annular slots. The designed antenna structure has an operating range from 3.5-12.4 GHz(109%) with band-rejection in the frequency range of 5-6 GHz. Nearly omnidirectional patterns are achieved for the designed antenna structure. The designed antenna structure provided an average peak gain of 2.12 dB over the entire frequency range except in the notched band where it reduced to -2.4 dB. The experimental and simulation results are observed to be in good agreement. An improved bandwidth performance with miniaturized dimensions as compared to earlier reported antenna structures is achieved.
In medium voltage and high-power drive applications, pulse width modulation (PWM) techniques are widely used to achieve effective speed control of AC motors. In real-time, an industrial drive system requires reduced hardware complexity and low computation time. The reliability of the AC drive can be improved with the FPGA (field programmable gate array) hardware equipped with digital controllers. To improve the performance of AC drives, a new FPGA-based Wavect real-time prototype controller (Xilinx ZYNQ-7000 SoC) is used to verify the effectiveness of the controller. These advanced controllers are capable of reducing computation time and enhancing the drive performance in real- time applications. The comparative performance analysis is carried out for the most commonly used voltage source inverter (VSI)-based PWM techniques such as sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) for three-phase, two-level inverters. The comparative study shows the SVPWM technique utilizes DC bus voltage more effectively and produces less harmonic distortion in terms of higher output voltage, flexible control of output frequency, and reduced harmonic distortion at output voltage for motor control applications. The simulation and hardware results are verified and validated by using MATLAB/Simulink software and FPGA-based Wavect real-time controller respectively.
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.
This paper discusses the design and performance of a frequency reconfigurable antenna for Internet of Things (IoT) applications. The antenna is designed to operate on multiple frequency bands and be reconfigurable to adjust to different communication standards and environmental conditions. The antenna design consists of monopole with one PIN diode and 50Ωfeed line. By changing the states of the diode, the antenna can be reconfigured to operate in a dual-band mode and a wideband mode. The performance of the antenna was evaluated through simulation. The antenna demonstrated good impedance matching, acceptable gain, and stable radiation patterns across the different frequency bands. The antenna has compact dimensions of (26×19×1.6) mm3. It covers the frequency range 2.95 GHz -8.2 GHz, while the coverage of the dual- band mode is (2.7-3.8) GHz and (4.57-7.4) GHz. The peak gain is 1.57 dBi for the wideband mode with omnidirectional radiation pattern. On the other hand, the peak gain of the dual-band mode is 0.87 dBi at 3 GHz and 0.47 dBi at 6 GHz with an omnidirectional radiation pattern too.
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.
Upkeeping the Battery State-Of-Charge (SoC) and its life are of great significance in Battery Electric Vehicle (BEV) & Hybrid Electric Vehicles (HEV). This is possible by integrating Solar Photovoltaic Panels (PPs) on the Roof-top of the BEVs & HEVs. However, unlike Solar Powered Vehicle Charging stations and other PV applications where the solar panels are installed in such a way to extract the maximum Photon energy incident on the panel, vehicle Roof-top mount Solar PPs face many challenges in extracting maximum Power due to partial shading issues especially under dynamic conditions when passing under trees, high rise buildings and cloud passages. This paper proposes a new strategy called “Super-capacitor Assisted Photovoltaic Array”. In which Photovoltaic Modules are integrated with Super-capacitors to improve the transient performance of the Photovoltaic Array system. The design of proposed Super-capacitor Assisted PV array is validated & its performance is compared with conventional PV array in Matlab/ Simulink environment.
Soft commutation techniques have been of great interest during the last few years in power supply switching applications. The recently developed Zero-Voltage transition (ZVT) and Zero-Current transition (ZCT) pulse width modulation (PWM) technique incorporated soft-switching function into PWM converters, so that the switching losses can be reduced with minimum voltage/current stresses and circulating energy. The ZCT technique can significantly reduce the switch turn-off loss which is usually the dominant switching loss in high-power applications. In this paper the steady state analysis and design of the ZCT PWM boost converter are introduced. Control and drive circuit have been designed to drive a 100 Watt ZCT PWM boost converter to experimentally investigate its features and characteristics.
Vast number of researches deliberated the separation of speech mixtures due to the importance of this field of research . Whereas its applications became widely used in our daily life; such as mobile conversation, video conferences, and other distant communications. These sorts of applications may suffer from what is well known the cocktail party problem. Independent component analysis (ICA) has been extensively used to overcome this problem and many ICA algorithms based on different techniques have been developed in this context. Still coming up with some suitable algorithms to separate speech mixed signals into their original ones is of great importance. Hence, this paper utilizes thirty ICA algorithms for estimating the original speech signals from mixed ones, the estimation process is carried out with the purpose of testing the robustness of the algorithms once against a different number of mixed signals and another against different lengths of mixed signals. Three criteria namely Spearman correlation coefficient, signal to interference ratio, and computational demand have been used for comparing the obtained results. The results of the comparison were sufficient to signify some algorithms which are appropriate for the separation of speech mixtures.
Hand gesture recognition is a quickly developing field with many uses in human-computer interaction, sign language recognition, virtual reality, gaming, and robotics. This paper reviews different ways to model hands, such as vision-based, sensor-based, and data glove-based techniques. It emphasizes the importance of accurate hand modeling and feature extraction for capturing and analyzing gestures. Key features like motion, depth, color, shape, and pixel values and their relevance in gesture recognition are discussed. Challenges faced in hand gesture recognition include lighting variations, complex backgrounds, noise, and real-time performance. Machine learning algorithms are used to classify and recognize gestures based on extracted features. The paper emphasizes the need for further research and advancements to improve hand gesture recognition systems’ robustness, accuracy, and usability. This review offers valuable insights into the current state of hand gesture recognition, its applications, and its potential to revolutionize human-computer interaction and enable natural and intuitive interactions between humans and machines. In simpler terms, hand gesture recognition is a way for computers to understand what people are saying with their hands. It has many potential applications, such as allowing people to control computers without touching them or helping people with disabilities communicate. The paper reviews different ways to develop hand gesture recognition systems and discusses the challenges and opportunities in this area.
In this paper, a compact two-element cylindrical dielectric resonator antenna (CDRA) array with corporate feeding is proposed for X-band applications. The dielectric resonator antenna (DRA) array is excited by a microstrip feeder using an efficient aperture-coupled method. The designed array antenna is analyzed using a CST microwave studio. The fabricated sample of the proposed CDRA antenna array showed bandwidth extending from 10.42GHz to 12.84GHz (20.8%). The achieved array gain has a maximum of 9.29dB i at frequency of 10.7GHz. This is about 2.06dB i enhancement of the gain in comparison with a single pellet CDRA. The size of the whole antenna structure is about 50 50mm 2 .
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.
The multilevel inverter is attracting the specialist in medium and high voltage applications, among its types, the cascade H bridge Multi-Level Inverter (MLI), commonly used for high power and high voltage applications. The main advantage of the conventional cascade (MLI) is generated a large number of output voltage levels but it demands a large number of components that produce complexity in the control circuit, and high cost. Along these lines, this paper presents a brief about the non-conventional cascade multilevel topologies that can produce a high number of output voltage levels with the least components. The non-conventional cascade (MLI) in this paper was built to reduce the number of switches, simplify the circuit configuration, uncomplicated control, and minimize the system cost. Besides, it reduces THD and increases efficiency. Two topologies of non-conventional cascade MLI three phase, the Nine level and Seventeen level are presented. The PWM technique is used to control the switches. The simulation results show a better performance for both topologies. THD, the power loss and the efficiency of the two topologies are calculated and drawn to the different values of the Modulation index (ma).
The use of smart network applications based on the Internet of Things is increasing, which increases the attractiveness of malicious activities, leading to the need to increase the adequate security of these networks. In this paper, the latest recent breakthroughs in blockchain for the Internet of Things are examined in the context of electronic health (e-health), smart cities, smart transportation, and other applications in this article. Research gaps and possible solutions are discussed, such as security, connection, transparency, privacy, and the IoT's blockchain regulatory challenges. In addition, the most important consensus algorithms used in the blockchain have been discussed, including Proof of Work, Proof of Stake, and Proof of Authority, each of which operates within certain rules.
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.
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.
This paper presents a proposed configuration of paralleling scheme PWM DC/DC buck converter. The topological structure and operation principles are presented. A Bode plot diagram technique is used to study the stability of the scheme for different values of controller parameters and with a number of parallel modules. It is found that the results are confidence, and the proposed scheme can be used in high power applications by increasing the number of parallel modules.
A compact and low cost butterfly shaped UWB filtenna with a pair of parasitic elements and a pair of slits is proposed in this work. The filtenna is supposed to be designed on a common and low-cost FR4 substrate with overall dimensions of 26mm*20mm*1.6mm .By inserting a pair of g /2( where g is waveguide wavelength ) D-shaped parasitic elements around the antenna feed line, the radiation of the 5 GHz WLAN applications is canceled to eliminated the interference . Furthermore, the rejection of the X-band satellite downlink is achieved by engraving a pair of g /4 J-shaped slits on the ground plane. The simulation results exhibits the perfect coverage of the proposed filtenna for the UWB frequency band as well as the elimination of the undesired radiation within the filtenna operating band.
Multi-level inverters, as a result of the significant contributions they have made to the fields of high voltage and renewable energy applications, MLI has earned a prestigious place in the field of industrial electronics applications. The use of MLI makes it possible to generate an alternating voltage from a DC voltage or from voltages that are continuously applied thanks to this capability. The quality of the produced wave depends on minimizing the level of total harmonic distortion (THD) in the ensuing output voltage. Increasing the total number of levels is required in order to bring down the THD. The bigger the number of layers, the lower the THD. On the other hand, this necessitates an increase in the number of power switches that are utilized, in addition to an increase in the number of DC sources for certain types. A greater number of levels are achieved in this work with a reduced number of switches, and the DC source necessitates the use of specialized control over the switches as well as the grading of the DC source values. In order to demonstrate that the suggested converter achieves the needed outcomes, the MATLAB simulator is utilized.
In this paper new semi-empirical formulas are developed to evaluate the variation of both real and imaginary parts of soil complex permittivity with depth inside the earth's surface. Computed values using these models show good agreement with published measured values for soils of the same textures and same frequency band. Use of these models may serve to handle more accurate results especially in the ground probing radar (GPR) applications and other applications relating the detection of buried objects inside the earth's surface, where the use of a single average value of the soil complex permittivity had not necessarily led, for most of the times, to accurate results for the electromagnetic fields propagated inside the earth's surface.
A wireless body area network (WBAN) connects separate sensors in many places of the human body, such as clothes, under the skin. WBAN can be used in many domains such as health care, sports, and control system. In this paper, a scheme focused on managing a patient’s health care is presented based on building a WBAN that consists of three components, biometric sensors, mobile applications related to the patient, and a remote server. An excellent scheme is proposed for the patient’s device, such as a mobile phone or a smartwatch, which can classify the signal coming from a biometric sensor into two types, normal and abnormal. In an abnormal signal, the device can carry out appropriate activities for the patient without requiring a doctor as a first case. The patient does not respond to the warning message in a critical case sometimes, and the personal device sends an alert to the patient’s family, including his/her location. The proposed scheme can preserve the privacy of the sensitive data of the patient in a protected way and can support several security features such as mutual authentication, key management, anonymous password, and resistance to malicious attacks. These features have been proven depending on the Automated Validation of Internet Security Protocols and Applications. Moreover, the computation and communication costs are efficient compared with other related schemes.
The Permanent Magnet Synchronous Motor (PMSM) is commonly used as traction motors in the electric traction applications such as in subway train. The subway train is better transport vehicle due to its advantages of security, economic, health and friendly with nature. Braking is defined as removal of the kinetic energy stored in moving parts of machine. The plugging braking is the best braking offered and has the shortest time to stop. The subway train is a heavy machine and has a very high moment of inertia requiring a high braking torque to stop. The plugging braking is an effective method to provide a fast stop to the train. In this paper plugging braking system of the PMSM used in the subway train in normal and fault-tolerant operation is made. The model of the PMSM, three-phase Voltage Source Inverter (VSI) controlled using Space Vector Pulse Width Modulation technique (SVPWM), Field Oriented Control method (FOC) for independent control of two identical PMSMs and fault-tolerant operation is presented. Simulink model of the plugging braking system of PMSM in normal and fault tolerant operation is proposed using Matlab/Simulink software. Simulation results for different cases are given.
The 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 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.
Nowadays, the Wireless Sensor Network (WSN) has materialized its working areas, including environmental engineering, agriculture sector, industrial, business applications, military, intelligent buildings, etc. Sensor networks emerge as an attractive technology with great promise for the future. Indeed, issues remain to be resolved in the areas of coverage and deployment, scalability, service quality, size, energy consumption and security. The purpose of this paper is to present the integration of WSNs for IoT networks with the intention of exchanging information, applying security and configuration. These aspects are the challenges of network construction in which authentication, confidentiality, availability, integrity, network development. This review sheds some light on the potential integration challenges imposed by the integration of WSNs for IoT, which are reflected in the difference in traffic features.
Over the previous decade, significant research has been conducted in the field of healthcare services and their technological advancement. To be more precise, the Internet of Things (IoT) has demonstrated potential for connecting numerous medical devices, sensors, and healthcare professionals in order to deliver high-quality medical services in remote locations. This has resulted in an increase in patient safety, a decrease in healthcare expenses, an increase in the healthcare services' accessibility, and an increase in the industry's healthcare operational efficiency. This paper provides an overview of the possible healthcare uses of Internet of Things (IoT)-based technologies. The evolution of the HIoT application has been discussed in this article in terms of enabling technology, services of healthcare, and applications for resolving different healthcare challenges. Additionally, effort difficulties and drawbacks with the HIoT system are explored. In summary, this study provides a complete source of information on the many applications of HIoT together the purpose is to help future academics who are interested in working in the field and making advances gain knowledge into the issue.
The Fast Fourier Transform (FFT) and Inverse FFT(IFFT) are used in most of the digital signal processing applications. Real time implementation of FFT/IFFT is required in many of these applications. In this paper, an FPGA reconfigurable fixed point implementation of FFT/IFFT is presented. A manually VHDL codes are written to model the proposed FFT/IFFT processor. Two CORDIC-based FFT/IFFT processors based on radix-2and radix-4 architecture are designed. They have one butterfly processing unit. An efficient In-place memory assignment and addressing for the shared memory of FFT/IFFT processors are proposed to reduce the complexity of memory scheme. With "in-place" strategy, the outputs of butterfly operation are stored back to the same memory location of the inputs. Because of using DIF FFT, the output was to be in reverse order. To solve this issue, we have re-use the block RAM that used for storing the input sample as reordering unit to reduce hardware cost of the proposed processor. The Spartan-3E FPGA of 500,000 gates is employed to synthesize and implement the proposed architecture. The CORDIC based processors can save 40% of power consumption as compared with Xilinx logic core architectures of system generator.
In recent years, wireless microrobots have gotten more attention due to their huge potential in the biomedical field, especially drug delivery. Microrobots have several benefits, including small size, low weight, sensitivity, and flexibility. These characteristics have led to microscale improvements in control systems and power delivery with the development of submillimeter-sized robots. Wireless control of individual mobile microrobots has been achieved using a variety of propulsion systems, and improving the actuation and navigation of microrobots will have a significant impact. On the other hand, actuation tools must be integrated and compatible with the human body to drive these untethered microrobots along predefined paths inside biological environments. This study investigated key microrobot components, including medical applications, actuation systems, control systems, and design schemes. The efficiency of a microrobot is impacted by many factors, including the material, structure, and environment of the microrobot. Furthermore, integrating a hybrid actuation system and multimodal imaging can increase the microrobot’s navigation effect, imaging algorithms, and working environment. In addition, taking into account the human body’s moving distance, autonomous actuating technology could be used to deliver microrobots precisely and quickly to a specific position using a combination of quick approaches.
This paper presents a low-cost Brushless DC (BLDC) motor drive system with fewer switches. BLDC motors are widely utilized in variable speed drives and industrial applications due to their high efficiency, high power factor, high torque, low maintenance, and ease of control. The proposed control strategy for robust speed control is dependent on two feedback signals which are speed sensor loop which is regulated by Sliding Mode Controller (SMC) and current sensor loop which is regulated by Proportional-Integral (PI) for boosting the drive system adaptability. In this work, the BLDC motor is driven by a four-switch three-phase inverter emulating a three-phase six switch inverter, to reduce switching losses with a low complex control strategy. In order to reach a robust performance of the proposed control strategy, the Lévy Flight Distribution (LFD) technique is used to tune the gains of PI and SMC parameters. The Integral Time Absolute Error (ITAE) is used as a fitness function. The simulation results show the SMC with LFD technique has superiority over conventional SMC and optimization PI controller in terms of fast-tracking to the desired value, reduction speed error to the zero value, and low overshoot under sudden change conditions.
Multiplication-accumulation (MAC) operation plays a crucial role in digital signal processing (DSP) applications, such as image convolution and filters, especially when performed on floating-point numbers to achieve high-level of accuracy. The performance of MAC module highly relies upon the performance of the multiplier utilized. This work offers a distinctive and efficient floating-point Vedic multiplier (VM) called adjusted-VM (AVM) to be utilized in MAC module to meet modern DSP demands. The proposed AVM is based on Urdhva-Tiryakbhyam-Sutra (UT-Sutra) approach and utilizes an enhanced design for the Brent-Kung carry-select adder (EBK-CSLA) to generate the final product. A (6*6)-bit AVM is designed first, then, it is extended to design (12*12)-bit AVM which in turns, utilized to design (24*24)-bit AVM. Moreover, the pipelining concept is used to optimize the speed of the offered (24*24)-bit AVM design. The proposed (24*24)-bit AVM can be used to achieve efficient multiplication for mantissa part in binary single-precision (BSP) floating-point MAC module. The proposed AVM architectures are modeled in VHDL, simulated, and synthesized by Xilinx-ISE14.7 tool using several FPGA families. The implementation results demonstrated a noticeable reduction in delay and area occupation by 33.16% and 42.42%, respectively compared with the most recent existing unpipelined design, and a reduction in delay of 44.78% compared with the existing pipelined design.
Gait as a biometric can be used to identify subjects at a distance and thus it receives great attention from the research community for security and surveillance applications. One of the challenges that affects gait recognition performance is view variation. Much work has been done to tackle this challenge. However, the majority of the work assumes that gait silhouettes are captured by affine cameras where only the height of silhouettes changes and the difference in viewing angle of silhouettes in one gait cycle is relatively small. In this paper, we analyze the variation in gait recognition performance when using silhouettes from projective cameras and from affine cameras with different distance from the center of a walking path. This is done by using 3D models of walking people in the gallery set and 2D gait silhouettes from independent (single) cameras in the probe set. Different factors that affect matching 3D human models with 2D gait silhouettes from single cameras for view-independent gait recognition are analyzed. In all experiments, we use 258 multi-view sequences belong to 46 subjects from Multi-View Soton gait dataset. We evaluate the matching performance for 12 different views using Gait Energy Image (GEI) as gait features. Then, we analyze the effect of using different camera configurations for 3D model reconstruction, the GEI from cameras with different settings, the upper and lower body parts for recognition and different GEI resolutions. The results illustrate that low recognition performance is achieved when using gait silhouettes from affine cameras while lower recognition performance is obtained when using gait silhouettes from projective cameras.
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.
In this paper describe to mathematical analysis for a three-phase, two level inverter designs. As we know the power electronic devices (inverter) to convert the DC power to AC power (controller on output voltage and frequency level). In Industrial applications, the inverters are used for adjustable speed (AC Drives). In this paper, the mathematical analyses for inverter design are done by using Software packages C++ Builder and visual C++ Language. For non- linear distortions described by the load power factor in power system networks. The P.F is reverse proportional with the harmonics distortion. Small P.F means much more of harmonic distortion, and lower power quality for consumers. to improve the P.F, and power quality in this paper the small capacitor installed as part of the rectified the load current has power (30 KW with P.F load 0.8), the fluctuations of the rectified voltage must not greater than +/- 10%.The power factor proportion of the load power, with Modulation coefficient p.u approximately unity. The calculation is achieved with different integrations steps with load power 30KW, 0.8 P.F. all results done Based on model and experimental data..
Five-phase machine employment in electric drive system is expanding rapidly in many applications due to several advantages that they present when compared with their three-phase complements. Synchronous reluctance machines(SynRM) are considered as a proposed alternative to permanent magnet machine in the automotive industry because the volatilities in the permanent magnet price, and a proposed alternative for induction motor because they have no field excitation windings in the rotor, SyRM rely on high reluctance torque thus no needing for magnetic material in the structure of rotor. This paper presents dynamic simulation of five phase synchronous reluctance motor fed by five phase voltage source inverter based on mathematical modeling. Sinusoidal pulse width modulation (SPWM) technique is used to generate the pulses for inverter. The theory of reference frame has been used to transform five-phase SynRM voltage equations for simplicity and in order to eliminate the angular dependency of the inductances. The torque in terms of phase currents is then attained using the known magnetic co-energy method, then the results obtained are typical.
As a key type of mobile robot, the two-wheel mobile robot has been developed rapidly for varied domestic, health, and industrial applications due to human-like movement and balancing characteristics based on the inverted pendulum theory. This paper presents a developed Two-Wheel Self-Balanced Robot (TWSBR) model under road disturbance effects and simulated using MATLAB Simscape Multibody. The considered physical-mechanical structure of the proposed TWSBS is connected with a Simulink controller scheme by employing physical signal converters to describe the system dynamics efficiently. Through the Simscape environment, the TWSBR motion is visualized and effectively analyzed without the need for complicated analysis of the associated mathematical model. Besides, 3D visualization of real-time behavior for the implemented TWSBR plant model is displayed by Simulink Mechanics Explorer. Robot balancing and stability are achieved by utilizing Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controllers' approaches considering specific control targets. A comparative study and evaluation of both controllers are conducted to verify the robustness and road disturbance rejection. The realized performance and robustness of developed controllers are observed by varying object-carrying loaded up on mechanical structure layers during robot motion. In particular, the objective weight is loaded on the robot layers (top, middle, and bottom) during disturbance situations. The achieved findings may have the potential to extend the deployment of using TWSBRs in the varied important application.
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.
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.
The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.
CMOS stack circuits find applications in multi-input exclusive-OR gates and barrel-shifters. Specifically, in wide fan-in CMOS NAND/NOR gates, the need arises to connect a relatively large number of NMOS/PMOS transistors in series in the pull-down network (PDN)/pull-up network (PUN). The resulting time delay is relatively high and the power consumption accordingly increases due to the need to deal with the various internal capacitances. The problem gets worse with increasing the number of inputs. In this paper, the performance of conventional static CMOS stack circuits is investigated quantitatively and a figure of merit expressing the performance is defined. The word “performance” includes the following three metrics; the average propagation delay, the power consumption, and the area. The optimum scaling factor corresponding to the best performance is determined. It is found that under the worst-case low-to-high transition at the output (that is, the input combination that results in the longest time delay in case of logic “1” at the output), there is an optimum value for the sizing of the PDN in order to minimize the average propagation delay. The proposed figure of merit is evaluated for different cases with the results discussed. The adopted models and the drawn conclusions are verified by comparison with simulation results adopting the 45 nm CMOS technology.
This paper presents a numerical analysis for the effect of temperature variations on the strain response of polymer optical fiber (POF) Bragg gratings. Results show that the dependence of the Bragg wavelength (λ B ) upon strain and temperature variations for the POF Bragg gratings is lies within the range of 0.462 – 0.470 fm με -1 °C -1 compare with 0.14 – 0.15 fm με -1 °C -1 for the SOFs Bragg gratings. Also, results show that the strain response for the POF Bragg gratings changed on average by 1.034 ± 0.02fm με - important for strain sensor applications especially in the environments where the temperature change.
With the substantial growth of mobile applications and the emergence of cloud computing concepts, therefore mobile Cloud Computing (MCC) has been introduced as a potential mobile service technology. Mobile has limited resources, battery life, network bandwidth, storage, and processor, avoid mobile limitations by sending heavy computation to the cloud to get better performance in a short time, the operation of sending data, and get the result of computation call offloading. In this paper, a survey about offloading types is discussed that takes care of many issues such as offloading algorithms, platforms, metrics (that are used with this algorithm and its equations), mobile cloud architecture, and the advantages of using the mobile cloud. The trade-off between local execution of tasks on end-devices and remote execution on the cloud server for minimizing delay time and energy saving. In the form of a multi-objective optimization problem with a focus on reducing overall system power consumption and task execution latency, meta-heuristic algorithms are required to solve this problem which is considered as NP-hardness when the number of tasks is high. To get minimum cost (time and energy) apply partial offloading on specific jobs containing a number of tasks represented in sequences of zeros and ones for example (100111010), when each bit represents a task. The zeros mean the task will be executed in the cloud and the ones mean the task will be executed locally. The decision of processing tasks locally or remotely is important to balance resource utilization. The calculation of task completion time and energy consumption for each task determines which task from the whole job will be executed remotely (been offloaded) and which task will be executed locally. Calculate the total cost (time and energy) for the whole job and determine the minimum total cost. An optimization method based on metaheuristic methods is required to find the best solution. The genetic algorithm is suggested as a metaheuristic Algorithm for future work.
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.
Video prediction theories have quickly progressed especially after a great revolution of deep learning methods. The prediction architectures based on pixel generation produced a blurry forecast, but it is preferred in many applications because this model is applied on frames only and does not need other support information like segmentation or flow mapping information making getting a suitable dataset very difficult. In this approach, we presented a novel end-to-end video forecasting framework to predict the dynamic relationship between pixels in time and space. The 3D CNN encoder is used for estimating the dynamic motion, while the decoder part is used to reconstruct the next frame based on adding 3DCNN CONVLSTM2D in skip connection. This novel representation of skip connection plays an important role in reducing the blur predicted and preserved the spatial and dynamic information. This leads to an increase in the accuracy of the whole model. The KITTI and Cityscapes are used in training and Caltech is applied in inference. The proposed framework has achieved a better quality in PSNR=33.14, MES=0.00101, SSIM=0.924, and a small number of parameters (2.3 M).
Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) for classification purpose. The results obtained from the different groups are then fused using Naïve Bayes classifier to make the final decision regards the emotion class. Different tests were performed using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the achieved results showed that the system gives the desired accuracy (100%) when fusion decisions of the facial groups. The achieved result outperforms state-of-the-art results on the same database.
Audio encryption has gained popularity in a variety of fields including education, banking over the phone, military, and private audio conferences. Data encryption algorithms are necessary for processing and sending sensitive information in the context of secure speech conversations. In recent years, the importance of security in any communications system has increased. To transfer data securely, a variety of methods have been used. Chaotic system-based encryption is one of the most significant encryption methods used in the field of security. Chaos-based communication is a promising application of chaos theory and nonlinear dynamics. In this research, a chaotic algorithm for the new chaotic chameleon system was proposed, studied, and implemented. The chameleon chaotic system has been preferred to be employed because it has the property of changing from self-excited (SA) to hidden-attractor (HA) which increases the complexity of the system dynamics and gives strength to the encryption algorithm. A chaotic chameleon system is one in which, depending on the parameter values, the chaotic attractor alternates between being a hidden attractor and a self-excited attractor. This is an important feature, so it is preferable to use it in cryptography compared to other types of chaotic systems. This model was first implemented using a Field Programmable Gate Array (FPGA), which is the first time it has been implemented in practical applications. The chameleon system model was implemented using MATLAB Simulink and the Xilinx System Generator model. Self-excited, hidden, and coexisting attractors are shown in the proposed system. Vivado software was used to validate the designs, and Xilinx ZedBoard Zynq-7000 FPGA was used to implement them. The dynamic behavior of the proposed chaotic system was also studied and analysis methods, including phase portrait, bifurcation diagrams, and Lyapunov exponents. Assessing the quality of the suggested method by doing analyses of many quality measures, including correlation, differential signal-to-noise ratio (SNR), entropy, histogram analysis, and spectral density plot. The numerical analyses and simulation results demonstrate how well the suggested method performs in terms of security against different types of cryptographic assaults.
Cross-Site Scripting (XSS) is one of the most common and dangerous attacks. The user is the target of an XSS attack, but the attacker gains access to the user by exploiting an XSS vulnerability in a web application as Bridge. There are three types of XSS attacks: Reflected, Stored, and Dom-based. This paper focuses on the Stored-XSS attack, which is the most dangerous of the three. In Stored-XSS, the attacker injects a malicious script into the web application and saves it in the website repository. The proposed method in this paper has been suggested to detect and prevent the Stored-XSS. The prevent Stored-XSS Server (PSS) was proposed as a server to test and sanitize the input to web applications before saving it in the database. Any user input must be checked to see if it contains a malicious script, and if so, the input must be sanitized and saved in the database instead of the harmful input. The PSS is tested using a vulnerable open-source web application and succeeds in detection by determining the harmful script within the input and prevent the attack by sterilized the input with an average time of 0.3 seconds.
WiMAX (worldwide interoperability for microwave access) is one of the wireless broadband access technologies which supplies broadband services to clients, but it surpasses other technologies by its coverage area, where one base station can cover a small city. In this paper, WiMAX technology is studied by exploring its basic concepts, applications, and advantages / disadvantages. Also a MATLAB simulator is used to verify the operation of the WiMAX system under various channel impairments and for variety of modulation schemes. From the simulation results, we found that WiMAX system works well in both AWGN and multipath fading channels, but under certain constraints that are addressed in this paper.
This work deals with the simulation model of multi-machines system as cold rolling mill is considered as application. Drivers of rolling system are a set of DC motors, which have extend applications in factories as aluminum rolling. Interconnection of multi DC motors in such a way that they are synchronized in their rotational speed. In cold rolling, the accuracy of the strip exit thickness is a very important factors. To realize accuracy in the strip exit thickness, Automatic Gauge Control system is used. In this paper MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results were presented to verify proposed model of cold rolling mill.
Wavelet-based algorithms are increasingly used in the source coding of remote sensing, satellite and other geospatial imagery. At the same time, wavelet-based coding applications are also increased in robust communication and network transmission of images. Although wireless multimedia sensors are widely used to deliver multimedia content due to the availability of inexpensive CMOS cameras, their computational and memory resources are still typically very limited. It is known that allowing a low-cost camera sensor node with limited RAM size to perform a multi-level wavelet transform, will in return limit the size of the acquired image. Recently, fractional wavelet filter technique became an interesting solution to reduce communication energy and wireless bandwidth, for resource-constrained devices (e.g. digital cameras). The reduction in the required memory in these fractional wavelet transforms is achieved at the expense of the image quality. In this paper, an adaptive fractional artifacts reduction approach is proposed for efficient filtering operations according to the desired compromise between the effectiveness of artifact reduction and algorithm simplicity using some local image features to reduce boundaries artifacts caused by fractional wavelet. Applying such technique on different types of images with different sizes using CDF 9/7 wavelet filters results in a good performance.
Advancements in internet accessibility and the affordability of digital picture sensors have led to the proliferation of extensive image databases utilized across a multitude of applications. Addressing the semantic gap between low- level attributes and human visual perception has become pivotal in refining Content Based Image Retrieval (CBIR) methodologies, especially within this context. As this field is intensely researched, numerous efficient algorithms for CBIR systems have surfaced, precipitating significant progress in the artificial intelligence field. In this study, we propose employing a hard voting ensemble approach on features derived from three robust deep learning architectures: Inception, Exception, and Mobilenet. This is aimed at bridging the divide between low-level image features and human visual perception. The Euclidean method is adopted to determine the similarity metric between the query image and the features database. The outcome was a noticeable improvement in image retrieval accuracy. We applied our approach to a practical dataset named CBIR 50, which encompasses categories such as mobile phones, cars, cameras, and cats. The effectiveness of our method was thereby validated. Our approach outshone existing CBIR algorithms with superior accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S), proving to be a noteworthy addition to the field of CBIR. Our proposed methodology could be potentially extended to various other sectors, including medical imaging and surveillance systems, where image retrieval accuracy is of paramount importance.
The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless system involves the building of a wireless sensor network (WSN) for data acquisition and controller area network (CAN) protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID- like Fuzzy Neural Petri Net (FNPN) system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN) controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.
In today’s world, the data generated by many applications are increasing drastically, and finding an optimal subset of features from the data has become a crucial task. The main objective of this review is to analyze and comprehend different stochastic local search algorithms to find an optimal feature subset. Simulated annealing, tabu search, genetic programming, genetic algorithm, particle swarm optimization, artificial bee colony, grey wolf optimization, and bat algorithm, which have been used in feature selection, are discussed. This review also highlights the filter and wrapper approaches for feature selection. Furthermore, this review highlights the main components of stochastic local search algorithms, categorizes these algorithms in accordance with the type, and discusses the promising research directions for such algorithms in future research of feature selection.
According to the growing interest in the soft robotics research field, where various industrial and medical applications have been developed by employing soft robots. Our focus in this paper will be the Pneumatic Muscle Actuator (PMA), which is the heart of the soft robot. Achieving an accurate control method to adjust the actuator length to a predefined set point is a very difficult problem because of the hysteresis and nonlinearity behaviors of the PMA. So the construction and control of a 30 cm soft contractor pneumatic muscle actuator (SCPMA) were done here, and by using different strategies such as the PID controller, Bang-Bang controller, Neural network controller, and Fuzzy controller, to adjust the length of the (SCPMA) between 30 cm and 24 cm by utilizing the amount of air coming from the air compressor. All of these strategies will be theoretically implemented using the MATLAB/Simulink package. Also, the performance of these control systems will be compared with respect to the time-domain characteristics and the root mean square error (RMSE). As a result, the controller performance accuracy and robustness ranged from one controller to another, and we found that the fuzzy logic controller was one of the best strategies used here according to the simplicity of the implementation and the very accurate response obtained from this method.
Use of multilevel inverters is becoming popular in the recent years for high power applications. The important feature of these inverters is of having low harmonics content in the output voltage. The switching angles in a multilevel inverter are computed so as to produce an ac output voltage with minimum harmonics. A new control circuit is designed to achieve these angles. This control circuit has the ability to control the RMS output voltage using sinusoidal pulse width modulation (SPWM). The results presented in this work prove the ability of the designed control circuit to gain the required ac output voltage with minimum distortion.
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.
PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability.
Real-time detection and recognition systems for vehicle license plates present a significant design and implementation challenge, arising from factors such as low image resolution, data noise, and various weather and lighting conditions.This study presents an efficient automated system for the identification and classification of vehicle license plates, utilizing deep learning techniques. The system is specifically designed for Iraqi vehicle license plates, adapting to various backgrounds, different font sizes, and non-standard formats. The proposed system has been designed to be integrated into an automated entrance gate security system. The system’s framework encompasses two primary phases: license plate detection (LPD) and character recognition (CR). The utilization of the advanced deep learning technique YOLOv4 has been implemented for both phases owing to its adeptness in real-time data processing and its remarkable precision in identifying diminutive entities like characters on license plates. In the LPD phase, the focal point is on the identification and isolation of license plates from images, whereas the CR phase is dedicated to the identification and extraction of characters from the identified license plates. A substantial dataset comprising Iraqi vehicle images captured under various lighting and weather circumstances has been amassed for the intention of both training and testing. The system attained a noteworthy accuracy level of 95.07%, coupled with an average processing time of 118.63 milliseconds for complete end-to-end operations on a specified dataset, thus highlighting its suitability for real-time applications. The results suggest that the proposed system has the capability to significantly enhance the efficiency and reliability of vehicle license plate recognition in various environmental conditions, thus making it suitable for implementation in security and traffic management contexts.
In order to provide an efficient, low cost, and small size radiating structure that passes a certain frequency band with negligible amount of interference, the combination of filters and antennas is proposed to form a single element called filtenna. This paper presents a filtenna element with compact size that can radiates in the 5G mid-band frequency range (3.6-3.8 GHz) and perfectly rejects all the frequencies outside this range. The filtenna is composed of a printed circuit antenna that is terminated with a crescent shaped stub that is coupled electromagnetically with a miniaturized sharp band-pass filter. The simulation results show a filtenna reflection coefficient with a reduced value within the intended 5G band and with high values along the other unwanted frequencies. Moreover, the structure has an omnidirectional pattern with reasonable gain value within the band of interest, and this makes the antenna very suitable for portable 5G devices.
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.
Kinship (Familial relationships) detection is crucial in many fields and has applications in biometric security, adoption, forensic investigations, and more. It is also essential during wars and natural disasters like earthquakes since it may aid in reunion, missing person searches, establishing emergency contacts, and providing psychological support. The most common method of determining kinship is DNA analysis which is highly accurate. Another approach, which is noninvasive, uses facial photos with computer vision and machine learning algorithms for kinship estimation. Each part of the Human -body has its own embedded information that can be extracted and adopted for identification, verification, or classification of that person. Kinship recognition is based on finding traits that are shared by every family. We investigate the use of hand geometry for kinship detection, which is a new approach. Because of the available hand image Datasets do not contain kinship ground truth; therefore, we created our own dataset. This paper describes the tools, methodology, and details of the collected MKH, which stands for the Mosul Kinship Hand, images dataset. The images of MKH dataset were collected using a mobile phone camera with a suitable setup and consisted of 648 images for 81 individuals from 14 families (8 hand situations per person). This paper also presents the use of this dataset in kinship prediction using machine learning. Google MdiaPipe was used for hand detection, segmentation, and geometrical key points finding. Handcraft feature extraction was used to extract 43 distinctive geometrical features from each image. A neural network classifier was designed and trained to predict kinship, yielding about 93% prediction accuracy. The results of this novel approach demonstrated that the hand possesses biometric characteristics that may be used to establish kinship, and that the suggested method is a promising way as a kinship indicator.
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.
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.
The goal for collaborative robots has always driven advancements in robotic technology, especially in the manufacturing sector. However, this is not the case in service sectors, especially in the health sector. Thus, this lack of focus has now opened more room for the design and development of service robots that can be used in the health sector to help patients with ailments, cognitive problems, and disabilities. There is currently a global effort towards the development of new products and the use of robotic medical devices and computer-assisted systems. However, the major problem has been the lack of a thorough and systematic review of robotic research into disease and epidemiology, especially from a technology perspective. Also, medical robots are increasingly being used in healthcare to perform a variety of functions that improve patient care. This scoping review is aimed at discovering the types of robots used in healthcare and where they are deployed. Moreover, the current study is an overview of various forms of robotic technology and its uses the healthcare industry. The considered technologies are the products of a partnership between the healthcare sector and academia. They demonstrate the research and testing that are necessary for the service of robot development before they can be employed in practical applications and service scenarios. The discussion also focused on the upcoming research areas in robotic systems as well as some important technologies necessary for human-robot collaboration, such as wireless sensor networks, big data, and artificial intelligence.
Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%.
The continuous growing developments in the traffic of mobile data limits the data throughput and capacity of cellular networks. “Heterogeneous Networks (HetNets)” are efficient solution to realize such demands. However, in HetNets, the congestion on the overloaded cellular network can be increased when the traffic of data is pushed from a cellular network to the Wi-Fi. In practice, offloading the cellular data traffic to a Wireless Local Area Network (WLAN) depending on the signal quality is a broadly deployed method to solve such problem. The use of Device to Device (D2D) communication further enhances the traffic offloading in WLAN systems and helps to obtain better throughput, end-to-end delay and network load. However, the critical offloading potential and its impacts on the whole performance is not totally understood. In this paper, the offloading of Long Term Evolution (LTE) traffic is presented using a WLAN for voice and video applications. A comparison is performed among two WLAN mecha- nisms; Distributed coordination function (DCF) and Point Coordination Function (PCF). As well, the effect of add- ing a D2D technology to the PCF is discussed. The WLAN effectively offloaded nodes at their Signal to Interference and Noise Ratio (SINR) becomes more than a specific threshold. Results presented that the PCF mechanism outper- forms the DCF one in terms of packet loss ratio, throughput and the maximum load of the entire network. In addi- tion, the use of a D2D serviced in the PCF helps in further reduction in the network load.
Growing interests in nature-inspired computing and bio-inspired optimization techniques have led to powerful tools for solving learning problems and analyzing large datasets. Several methods have been utilized to create superior performance-based optimization algorithms. However, certain applications, like nonlinear real-time, are difficult to explain using accurate mathematical models. Such large-scale combination and highly nonlinear modeling problems are solved by usage of soft computing techniques. So, in this paper, the researchers have tried to incorporate one of the most advanced plant algorithms known as Venus Flytrap Plant algorithm(VFO) along with soft-computing techniques and, to be specific, the ANFIS inverse model-Adaptive Neural Fuzzy Inference System for controlling the real-time temperature of a microwave cavity that heats oil. The MATLAB was integrated successfully with the LabVIEW platform. Wide ranges of input and output variables were experimented with. Problems were encountered due to heating system conditions like reflected power, variations in oil temperature, and oil inlet absorption and cavity temperatures affecting the oil temperature, besides the temperature’s effect on viscosity. The LabVIEW design followed and the results figure in the performance of the VFO- Inverse ANFIS controller.
Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to- Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.
Rehabilitation robots have become one of the main technical instruments that Treat disorder patients in the biomedical engineering field. The robotic glove for the rehabilitation is basically made of specialized materials which can be designed to help the post-stroke patients. In this paper, a review of the different types of robotic glove for Rehabilitation have been discussed and summarized. This study reviews a different mechanical system of robotic gloves in previous years. The selected studies have been classified into four types according to the Mechanical Design: The first type is a tendon-driven robotic glove. The second type of robotic glove works with a soft actuator as a pneumatic which is operated by air pressure that passes through a plastic pipe, pressure valves, and air compressor. The third type is the exoskeleton robotic gloves this type consists of a wearable mechanical design that can used a finger-based sensor to measure grip strength or is used in interactive video applications. And the fourth type is the robotic glove with a liner actuator this type consists of a tape placed on the fingers and connected to linear actuators to open and close the fingers during the rehabilitation process.
Path-planning is a crucial part of robotics, helping robots move through challenging places all by themselves. In this paper, we introduce an innovative approach to robot path-planning, a crucial aspect of robotics. This technique combines the power of Genetic Algorithm (GA) and Probabilistic Roadmap (PRM) to enhance efficiency and reliability. Our method takes into account challenges caused by moving obstacles, making it skilled at navigating complex environments. Through merging GA’s exploration abilities with PRM’s global planning strengths, our GA-PRM algorithm improves computational efficiency and finds optimal paths. To validate our approach, we conducted rigorous evaluations against well-known algorithms including A*, RRT, Genetic Algorithm, and PRM in simulated environments. The results were remarkable, with our GA-PRM algorithm outperforming existing methods, achieving an average path length of 25.6235 units and an average computational time of 0.6881 seconds, demonstrating its speed and effectiveness. Additionally, the paths generated were notably smoother, with an average value of 0.3133. These findings highlight the potential of the GA-PRM algorithm in real-world applications, especially in crucial sectors like healthcare, where efficient path-planning is essential. This research contributes significantly to the field of path-planning and offers valuable insights for the future design of autonomous robotic systems.
Soft robots, which are often considered safer than rigid robots when interacting with humans due to the reduced risk of injury, have found utility in various medical and industrial fields. Pneumatic artificial muscles (PAMs), one of the most widely used soft actuators, have proven their efficiency in numerous applications, including prosthetic and rehabilitation robots. PAMs are lightweight, responsive, precise, and capable of delivering a high force-to-weight ratio. Their structure comprises a flexible, inflatable membrane reinforced with fibrous twine and fitted with gas-sealing fittings. For the optimal design and integration of these into control systems, it is crucial to develop mathematical models that accurately represent their functioning mechanisms. This paper introduces a general concept of PAM’s construction, its various types, and operational mechanisms, along with its key benefits and drawbacks, and also reviews the most common modeling techniques for PAM representation. Most models are grounded in PAM architecture, aiming to calculate the actuator’s force across its full axis by correlating pressure, length, and other parameters that influence actuator strength.
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.
The problem of outlier detection is one of the most important issues in the field of analysis due to its applicability in several famous problem domains, including intrusion detection, security, banks, fraud detection, and discovery of criminal activities in electronic commerce. Anomaly detection comprises two main approaches: supervised and unsupervised approach. The supervised approach requires pre-defined information, which is defined as the type of outliers, and is difficult to be defined in some applications. Meanwhile, the second approach determines the outliers without human interaction. A review of the unsupervised approach, which shows the main advantages and the limitations considering the studies performed in the supervised approach, is introduced in this paper. This study indicated that the unsupervised approach suffers from determining local and global outlier objects simultaneously as the main problem related to algorithm parameterization. Moreover, most algorithms do not rank or identify the degree of being an outlier or normal objects and required different parameter settings by the research. Examples of such parameters are the radius of neighborhood, number of neighbors within the radius, and number of clusters. A comprehensive and structured overview of a large set of interesting outlier algorithms, which emphasized the outlier detection limitation in the unsupervised approach, can be used as a guideline for researchers who are interested in this field.
This paper proposes a new design of compact coplanar waveguide (CPW) fed -super ultra-wideband (S-UWB) MIMO antenna with a bandwidth of 3.6 to 40 GHz. The proposed antenna is composed of two orthogonal sector-shape monopoles (SSM) antenna elements to perform polarization diversity. In addition, a matched L-shaped common ground element is attached for more efficient coupling. The FR-4 substrate of the structure with a size of 23 × 45 × 1.6 mm3 and a dielectric constant of 4.3 is considered. The proposed design is simulated by using CST Microwave Studio commercial software. The simulation shows that the antenna has low mutual coupling (|S21| < -20 dB) with |S11|<−10 dB, ranging from 3.6 to 40 GHz. Envelope correlation coefficient (ECC) is less than 0.008, diversity gain (DG) is more than 9.99, mean effective gain (MEG) is below - 3 dB and total active reflection coefficient (TARC) is less than -6 dB over the whole response band is reported. The proposed MIMO antenna is expected efficiently cover the broadest range of frequencies for contemporary communications applications.
The ability to harvest energy from the environment represents an important technology area that promises to eliminate wires and battery maintenance for many important applications and permits deploying self powered devices. This paper suggests the use of a solar energy harvester to charge mobile phone devices. In the beginning, a comprehensive overview to the energy harvesting concept and technologies is presented. Then the design procedure of our energy harvester was detailed. Our prototype solar energy harvester proves its efficiency to charge the aimed batteries under sunlight or an indoor artificial light.
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.
Although solar cell parameters are generally measured at 20-30 C°, flat plate modules normally operate at 40-50 C° under terrestrial conditions and even higher temperatures are used for some concentrator cell applications. Therefore it is interesting to calculate the dependence of cell parameters on temperature. In this paper a simple formulation has been derived for obtaining the temperature dependence of open circuit voltage Voc, short circuit current density Jsc, fill factor FF, and conversation efficiency η, for c-Si and a-Si solar cells.
This paper suggests the use of the traditional proportional-integral-derivative (PID) controller to control the speed of multi Permanent Magnet Synchronous Motors (PMSMs). The PMSMs are commonly used in industrial applications due to their high steady state torque, high power, high efficiency, low inertia and simple control of their drives compared to the other motors drives. In the present study a mathematical model of three phase four poles PMSM is given and simulated. The closed loop speed control for this type of motors with voltage source inverter and abc to dq blocks are designed. The multi (Master/Slaves approach) method is proposed for PMSMs. Mathwork's Matlab/Simulink software package is selected to implement this model. The simulation results have illustrated that this control method can control the multi PMSMs successfully and give better performance.
Traditional friction brakes can generate problems such as high braking temperature and pressure, cracking, and wear, leading to braking failure and user damage. Eddy current brake systems (contactless magnetic brakes) are one method used in motion applications. They are wear-free, less temperature-sensitive, quick, easy, and less susceptible to wheel lock, resulting in less brake failure due to the absence of physical contact between the magnet and disc. Important factors that can affect the performance of the braking system are the type of materials manufactured for the permanent magnets. This paper examines the performance of the permanent magnetic eddy current braking (PMECB) system. Different kinds of permanent magnets are proposed in this system to create eddy currents, which provide braking for the braking system is simulated using FEA software to demonstrate the efficiency of braking in terms of force production, energy dissipation, and overall performance findings demonstrated that permanent magnets consisting of neodymium, iron, and boron consistently provided the maximum braking effectiveness. The lowest efficiency is found in ferrite, which has the second-lowest efficiency behind samarium cobalt. This is because ferrite has a weaker magnetic field. Because of this, the PMECBS based on NdFeB magnets has higher power dissipation values, particularly at higher speeds.
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.
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.
Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mobile vehicles. Several studies have tackled the task offloading problem in the VFC field. However, recent studies have not carefully addressed the transmission path to the destination node and did not consider the energy consumption of vehicles. This paper aims to optimize the task offloading process in the VFC system in terms of latency and energy objectives under deadline constraint by adopting a Multi-Objective Evolutionary Algorithm (MOEA). Road Side Units (RSUs) x-Vehicles Mutli- Objective Computation offloading method (RxV-MOC) is proposed, where an elite of vehicles are utilized as fog nodes for tasks execution and all vehicles in the system are utilized for tasks transmission. The well-known Dijkstra's algorithm is adopted to find the minimum path between each two nodes. The simulation results show that the RxV-MOC has reduced significantly the energy consumption and latency for the VFC system in comparison with First-Fit algorithm, Best-Fit algorithm, and the MOC method.
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.
This paper concerns with exploitation of renewable energy sources for meeting energy requirements of remote locations. It presents an investigation which is based on a practical project that was executed in collaboration between academia and industry. It involves design and installation of a prototype integrated renewable energy system which consists of two 15 kW wind turbines, electrolyser, fuel cell system (FCS) and the associated control equipment. It was installed at the furthest island of Shetland, North of Scotland, U.K. The philosophy used in designing this system is summarised as follows: During times of high wind, the electricity generated by wind turbines is normally greater than that required by site electrical load. The excessive amount of generated electricity is stored into Hydrogen by utilising an electrolyser which is then used to generate the deficient electric power by the FCS at times of low wind.
This paper presents a new design to obtain wide dual-band operation from a coplanar probe feed antenna loaded with two shorted walls. The lower band of proposed antenna has a 10 dB bandwidth of 611 MHz (24.18%) around the center frequency 2527MHz, and the upper band has a bandwidth of 1255 MHz (27.88%) around the center frequency 4501MHz. The obtained bandwidths cover WLANs operations on all bands. The bandwidth of the first operating frequency covers ISM band (2400- 2483.5) MHz, which is required by IEEE 802.11b, g and Bluetooth standards, and the bandwidth of the second operating frequency covers U-NII1 (5150-5350) MHz band, which is required by IEEE 802.11a and HiperLAN2 standards, and also covers U-NII2 (5470-5725) MHz and U-NII3/ISM (5725-5825) MHz bands, which are required by IEEE 802.11a standard. A three dimensional finite-difference time-domain (3-D FDTD) method is employed to analyze the proposed structure and find its performance. The simulated results are compared with the experimental results.
Soft-switching technique can substantially improve the performance of power converters, mainly due to the increase of switching frequency, that result in better modulation quality. This is more concerned particularly in the high power applications, where devices [gate turn off (GTO) or something else similar) can not operate over a few hundreds of hertz in conventional hard switching converter structures. In this paper, design and analysis of moderate power ZCT three-phase PWM inverter has been presented. Also, the designed inverter and its novel control circuit is implemented experimentally to investigate its characteristics with this new zero-current transition ZCT technique.