Al-Iraqia Journal for Scientific Engineering Research

Light Fidelity (Li-Fi) is an optical wireless communication technique that delivers high-speed data transmission rates by utilizing light-emitting diodes (LEDs). It is noteworthy that any light source available has the potential to be transformed into a functional Li-Fi node that can offer the possibility of communicating with the remaining sections of the Li-Fi communication network as well as the entire Internet infrastructure. The Li-Fi node utilizes light to provide efficient and secure communication among internet users, additionally allowing access from any location. Simultaneously, the Internet of Things (IoT) represents a collection of innovative and integrated technologies, offering an array of exciting services and solutions that have the potential to revolutionize our daily lives. The development of IoT results in a significant increase in smart devices and sensors. Such devices demand more efficient, faster, and secure data transmission. Li-Fi technology can satisfy the requirements of the forthcoming 6th generation IoT network. It can enhance data rates while consuming minimal energy. Consequently, it can be effectively integrated with the Internet of Things (IoT) in order to develop innovative and intelligent systems.

Due to its inherent characteristics, the Switched Reluctance Motor (SRM) is an up-and-coming contender for further investigation and analysis across diverse industrial domains. One of these domains is the field of electric vehicles(EVs), but this is no exclusive category. The mechanical design of the motor is notable for the inherent simplicity it possesses as well as the cost-effectiveness it offers. This article presents a comprehensive analysis of SRMs, including detailed descriptions, an in-depth review of their advantages and disadvantages, an investigation of their applications, and torque-speed profile and power speed profile graphical representations. The discussion incorporates the most recent findings from research on the methodologies and control strategies used.

This paper suggests a new method to achieve input current ripple (ICR) reduction of the quadratic boost converter (QBC). The planned converter is represented by adding an auxiliary inductor connected in a series with the bootstrapping diode to a QBC. The benefit of this arrangement is to implement the interleaving concept to lessen ICR, while the quadratic concept is to attain the voltage gain within the required level. However, this modification will slightly affect the output voltage; the effect depends on two factors: the duty cycle and the auxiliary inductor value. The performance improvement includes increasing dynamic response speed, reducing reverse recovery and conduction losses in semiconductor devices and less oscillation observed by ICr, leading to better stability than the conventional converter. Therefore, the converter's overall efficiency is improved. A comparison with a typical QBC was presented using the LTspice program to demonstrate the improvement.

Cloud computing has totally changed the way information is put away and gotten to. However, it has moreover raised concerns, approximately the security and astuteness of that information. In arrange to address these concerns and make beyond any doubt that information put away on untrusted cloud servers remains solid and unaltered Remote Data Integrity Verification (RDIV) frameworks have ended up significant. This term paper presents a mapping think about of Inaccessible Data Integrity Confirmation frameworks within the setting of cloud computing. Its objective is to distinguish existing investigate categorize it reveal patterns and shed light on regions for inquire about.

In the digital landscape, the Internet of Things (IoT) has created new opportunities and challenges as everyday objects are becoming interconnected, collecting large volumes of data. We interact with our surroundings and manage data in a whole new way thanks to cloud computing and IoT, which work in symbiotic harmony. Data generated by IoT devices can be harnessed and processed using cloud computing's robust infrastructure. Furthermore, IoT expands the reach of cloud computing to include a myriad of smart devices, sensors, and actuators that are all interconnected.

By conducting a search handle, we have assembled a collection of inquire about papers and scholarly articles that particularly center on RDIV frameworks in cloud computing. These distributions were carefully analyzed utilizing incorporation and prohibition criteria to classify the Remote Data Integrity Verification frameworks based on their fundamental methods cryptographic strategies utilized, confirmation instruments utilized and sending models connected.

By synthesizing the comes about, this paper highlights current investigate trends and gives experiences into potential inquire about crevices within the field of Remote Data Integrity Verification frameworks for cloud computing. We talk about the require for upgraded scalability and execution optimization to bolster large-scale cloud arrangements successfully. Besides, we recognize the require for assist investigation of privacy-preserving Remote Data Integrity Verification procedures to ensure sensitive information from unauthorized introduction.

After the spread of autonomous driving systems in cars, the next step is the development of autonomous drone systems. This will have an application in many military and civil fields, including surveillance and photography instead of satellites, as well as in search and rescue, dissemination, aid and distribution of goods, as it can cover large areas and save effort and human cost.

The goal of this paper is to discuss using autonomous drones to design a border surveillance system. Where the current surveillance systems were studied to discuss the possibility of using drones as an alternative to long-range cameras, and a design proposal was presented that works on three different levels of surveillance systems. The possibility of controlling the drone through a single-board computer that is installed on the control tower with the base of the charging station for the drone was also discussed.

In order to achieve the required balance between the speed of controlling the drone and the accuracy of object detection and classification, the concept of parallel processing was applied with the fast algorithms for tracking and detection (contour detection) and the high accuracy deep learning model (YOLOv8n) for detection and classification , which was trained on a customized dataset to improve the accuracy of object detection and classification in images taken from different view angles of the drone.

After testing the experimental model of the system, the results showed a good enhancement in the quality of the captured images, as well as a solution to the problem of occlusion or blurring of objects in the image.

Also, the results confirmed the possibility of relying on a single-board computer in processing images and controlling the drone to track moving targets in a way that was not possible by relying on transferring images to remote central processing.

Gold nanoparticles (AuNPs) were synthesized using the pulsed laser ablation technique in liquid. An (Nd: YAG) laser operating at a fixed energy of 600 mJ and a frequency of 4.3 Hz was employed. The absorption spectrum of the synthesized particles was measured using an ultraviolet-visible spectrometer (uv-vis), with peak values observed in the range of (523–530) nm. Scanning electron microscope (SEM) analysis results revealed that the particles exhibited a spherical shape and had an average size ranging from (38 - 92) nm. X-ray diffraction confirmed the crystalline structure of the material, which was determined to be cubic. Transmission electron microscopy (TEM) images demonstrated that the particle diameter was (31 and 21) nm for the respective frequencies, which corresponded to 1200 pulses. Furthermore, the biological activity of the AuNPs was assessed against positive and negative bacteria.

Machine learning has become popular across several disciplines right now. It enables machines to automatically learn from data and make predictions without the need for explicit programming or human intervention. Supervised machine learning is a popular approach to creating artificial intelligence. A computer algorithm is trained on input data that has been labeled for a certain output, making it one of two major areas of machine learning that has seen a lot of successful research. The model is trained until it can identify the underlying correlations and patterns between the input and output labels, enabling it to generate accurate labeling results when confronted with unexplored data. Supervised learning is good at solving classification and regression problems. The problem of regression occurs when the outputs are continuous, while the problem of classification occurs when the outputs are categorical. We will concentrate on the benefits and drawbacks of supervised learning algorithms in this review. Creating a precise model of the distribution of class labels in terms of predictor features is the aim of supervised learning. This work studied the most popular supervised machine learning methods, including Naive Bayes, Decision Trees, Support Vector Machines, Logistic Regression, K-Nearest Neighbors, and Deep Learning, which were discussed in this paper. We also emphasized the algorithms' advantages and disadvantages, and we ultimately talked about the difficulties in developing supervised machine learning algorithms.

SDNs provide flexible and intelligent network capabilities by dividing traditional networks into a centralized control plane and a programmable data plane. Network performance improvement relies on the intelligent control plane, which establishes flow paths for switches. Within the control plane, the controller serves as the core component for all data plane management operations, underscoring the significance of its performance and capabilities. Additionally, it is crucial to utilize accurate and efficient tools for assessing various evaluation parameters. Specifically, based on their capabilities, we identify and categorize 14 controllers, offering a qualitative comparison of their features. Furthermore, we investigate the capabilities of benchmarking tools used to assess controllers for SDN.

In this study, a practical study was conducted to compare the efficient behavior of close loop solar water heater with thermosiphon- riser tubes with dimensions (80 cm width, 120 cm height) with two acrylic covering. both with and without thermal performance enhancer, and to find the optimum mode of convection in the collector. the thermal performance enhancers have been used, helical spring with a core bar, and they were inserted a solar absorber tube The ethanol as a working fluid is used by applying a practical test set to it. The current tests were conducted on September 6, 8 and October 4, 5 , 2022, in Baghdad, Iraq, under identical ambient and load circumstances. All examination were from 08:00 am to 16:00 pm, at tilted angle (45 °) South in sunny days. The experimental tests were carried out at flow rates (1 and 1.5 L/min) and filling ratio (50%). At a flow rate of 1.5 L/min and a filling ratio of 50%, using an inserted helical spring with a core bar yielded the highest daily efficiency of 70%. In the interest of the system.

To improve and facilitate transactions between customers and financial institutions, the utilization of Internet banking has been leveraged to deliver a heightened caliber of service characterized by heightened security and efficiency in contrast to traditional banking modalities. It is suggested in this research introduces an innovative security framework, designed to furnish a protected mechanism ensuring secure communication, authentication, confidentiality, and safeguarding of financial transactions between banking institutions and end-users, all without necessitating reliance on a physical card. The fundamental underpinning of this proposed system involves the amalgamation of a Quick Response (QR) code with a hybridized Advanced Encryption Standard-Elliptic Curve Cryptography (AES-ECC) model. Following the successful installation of the security application on the mobile device, and subsequent to an accomplished registration and encryption of data inputs, encoding and decoding processes are facilitated through the intrinsic encoding and decryption keys embedded within this hybrid algorithm. The clientele receives a QR code containing encrypted transaction details, and upon scanning this code via the designated Android application, the pertinent information is promptly displayed. Empirical assessments validate the effectiveness of the suggested approach, demonstrating superior outcomes when juxtaposed with prevailing methodologies.

Edge computing is used with cloud computing as an extension to increase the performance of delay-sensitive applications such as autonomous vehicles, healthcare systems, video surveillance systems, ..etc. The fast increase in the Internet of Things (IoT) devices increases the amount of data transferred in the network. IoT devices are resource-constrained in terms of energy consumption and computation capability. Data processing near IoT devices enabled by edge devices. Hence reduces the transmission power of sending data to the cloud and causes delays due to the cloud being placed far away from the devices. Most real-time applications depend on artificial intelligence (AI) techniques, increasing the computations on IoT-edge devices. Conversely, if this AI workload is executed on the cloud, the delay increase causes degradation in application performance. How to decide where the computation is done in an IoT, edge and cloud network is an important issue. The purpose of optimizing the workload allocation decision is to increase the application performance in terms of Quality of Experience (QoE) and Quality of Service (QoS); hence, the major goal is to reduce the delay time while maintaining the accuracy of the AI systems. As presented in this review, many researchers focus on proposing a workload allocation decision based on AI techniques. In contrast, other research focuses on the AI workload, hence presenting a method for partitioning the AI model to increase the system's accuracy in the resource constraint devices (end device and edge server). Many other researches also used the AI model for resource allocation and provisioning between edge servers and the cloud. In this review, the integration between AI and edge–cloud environment is investigated, the AI workload allocation methods are presented and analyzed, a brief overview of the application of deep learning in edge-cloud computing is also presented, and many challenges that need to be addressed for the AI application are discussed. Many issues and challenges are also presented for optimizing the edge.

Step-up converters find extensive application in various sectors, including powered vehicles, photovoltaic systems, continuous power supplies, and fuel cell systems. This paper describes the process of designing and building a resistive load step-up (boost) converter, which is a widely used industry application for increasing the direct current (DC) input voltage. This study focuses on ascertaining the appropriate values for the inductor and capacitor within the circuit. The design primarily emphasizes continuous mode operation, involving varying voltage inputs of 10V and 20V DC, while employing a switching frequency input of 25 kHz with the IGBT serving as the switching device. The design evaluation of this circuit aims to regulate the input voltage to support a stable output voltage of 30 VDC, with a simulation power conversion efficiency of 96.18 percent for the input voltage,10V, and a simulation power conversion efficiency of 96.08 percent for the input voltage,20V. This examination also incorporates the requirement of a ripple inductor current, which should not exceed 25% of the total inductor current, and an output voltage ripple is less than 1%. The circuit design parameters are determined based on factors such as output voltage, inductor voltage, and inductor current waveform. MATLAB Simulink software will be used to check the circuit design, thus confirming the agreement between simulation results and theoretical predictions. The simulation results provide compelling evidence that the established model proficiently maintains the output voltage under diverse input voltage scenarios. As a result, these parameters are suitable for the construction of a fully operational boost converter circuit. This paper systematically presents all objectives, calculations, experiments, data, and results in a comprehensive manner.