Search

The highest rate at which information may be reliably sent via a communication link is known as its capacity. In the case of non-Gaussian noise, the capacity of the channel depends on the specific characteristics of the noise, which can cause severe errors and reduce the reliability of communication systems over a fading channel. The Gaussian mixture impulsive noise model (GMINM), which is a more general and flexible non-Gaussian model for impulsive noise, has been compared in this paper with the Middleton Class-A impulsive noise model (MCAINM) in terms of derived channel capacity normalized by channel bandwidth (C/BW) with and without Rayleigh fading (Rf) channels. It also investigated the trade-off between complexity and accuracy in modeling the impulsive noise using two simplified Middleton Class-A impulsive noise models based on derived C/BW. The derived C/BW of these models under various conditions, such as different signal-to-noise ratios and impulsive noise parameters and models, have been performed and evaluated using two different scenarios: the exact method and the semi-analytical method. When the impulsive noise parameters and A are both near 0 in GMINM and MCAINM, respectively, the capacity of the impulsive noise channel is found to be equivalent to that of the Gaussian channel sustainable, as shown by the findings based on Monte-Carlo simulations. We have shown that when the impulsive noise decreases, the capacity increases in all models; however, the capacity of Gaussian noise is higher than the capacity of non-Gaussian noise, which in turn is higher than the capacity of non-Gaussian noise over the Rf channel overall values of SNR in dB. Moreover, multi-channel configuration introduces spatial diversity and multiplexing gains that have been proposed to sustainably optimize the ergodic capacity for the challenge case when the channel state information (CSI) is unknown at the transmitter in non-Gaussian noise over Rf channel. In today's rapidly evolving world, sustainable communication systems play a crucial role in ensuring efficient and responsible utilization of resources. As the demand for wireless communication continues to rise, it becomes imperative to optimize the capacity of communication channels, especially in scenarios involving non-Gaussian noise models and fading channels.

Multimodal medical image fusion methods, which combine complementary information from many multi-modality medical images, are among the most important and practical approaches in numerous clinical applications. Various conventional image fusion techniques have been developed for multimodality image fusion. Complex procedures for weight map computing, fixed fusion strategy and lack of contextual understanding remain difficult in conventional and machine learning approaches, usually resulting in artefacts that degrade the image quality. This work proposes an efficient hybrid learning model for medical image fusion using pre-trained and non-pre-trained networks i.e. VGG-19 and SNN with stacking ensemble method. The model leveraging the unique capabilities of each architecture, can effectively preserve the detailed information with high visual quality, for numerous combinations of image modalities in image fusion challenges, notably improved contrast, increased resolution, and lower artefacts. Additionally, this ensemble model can be more robust in the fusion of various combinations of source images that are publicly available from Havard-Medical-Image-Fusion Datasets, GitHub. and Kaggle. Our proposed model performance is superior in terms of visual quality and performance metrics to that of the existing fusion methods in literature like PCA+DTCWT, NSCT, DWT, DTCWT+NSCT, GADCT, CNN and VGG-19. [ABSTRACT FROM AUTHOR]

The radar of the Yagi-Uda antenna array offers useful traits for the detection of stealth targets. It employs the Very High Frequency (VHF) band, which has the advantage of counteracting stealth technology, as the shape of the target and the absorbing materials used for absorbing radar signals are less efficient against VHF. Moreover, the meter wavelength enables the radar to detect targets at distances exceeding those accessible to millimeter- and centimeter-wave radars. Nevertheless, the range of detection of this type of radar remains constrained and requires further development. The majority of current research is concentrated on the signal processing aspect, with less focus being placed on the antenna array. The antenna elements represent a crucial component of the radar station. This article presents a novel development of the traditional 16-element Yagi-Uda antenna array, which is commonly used in surveillance radar systems operating at a meter frequency band. The development is achieved through a modification of the previously mentioned antenna, which aims to increase the directivity of the antenna array, with a minimum cost and the same signal processing as the original antenna array platform. An additional two-row Yagi-Uda antenna array is proposed, comprising 14 elements in each row and an associated power unit. This modification is designed to enhance the pattern characteristics of the array. Simulation and measurement results for the pattern of the antenna indicate that the achieved gain for the proposed modification is more than 3 dB in compression compared to that of the traditional 16-element array. [ABSTRACT FROM AUTHOR]

This study aims to reveal the role of one of the artificial intelligence (AI) techniques, "ChatGPT," in improving the educational process by following it as a teaching method for the subject of automatic analysis for students of the Chemistry Department and the subject of computer security for students of the Computer Science Department, from the fourth stage at the College of Education for Pure Science (Ibn Al-Haitham), and its impact on their computational thinking to have a good educational environment. The experimental approach was used, and the research samples were chosen intentionally by the research community. Research tools were prepared, which included a scale for CT that included 12 items and the achievement test in both scientific subjects for departments as the second tool. They reached a lot of conclusions. Accordingly, a set of recommendations were proposed. [ABSTRACT FROM AUTHOR]

In light of the advanced technological development in the world, this study aims to use alternative energy for generating electric energy at the lowest cost. Solar cells are devices that have special operating conditions. The most important of these conditions are the temperature of cells and the intensity of solar radiation. Sometimes the climate condition does not match the ideal conditions used for operating solar cells in many regions around the world. Although the Middle East and Iraq in particular, is considered one of the sunniest areas in the world, solar energy cannot be relied upon to generate electric energy effectively because of the problem of high temperature in solar cells in the summer as well as the problem of dust. In this paper, a simple and economical method was designed to overcome the main problems in the operation of solar cells represented by high temperature in solar cells as well as the problem of dust accumulated on the surface of solar cells which impedes the entry of sunlight into those cells. The method is conducted by immersing the solar cells in running water at a distance that should be close to the surface of the water. [ABSTRACT FROM AUTHOR]

After the outbreak of the COVID-19 pandemic, the world turned towards intensifying the implementation of modern educational technologies in general, especially those related to remote teaching and learning. These technologies have proven to save learners considerable time and effort, particularly in emergencies that necessitate a shift from traditional to remote education systems. They offer easy and secure methods for remote teaching and learning. One of the most significant modern technologies in this context is cloud computing, a term that refers to on-demand access to computer resources and systems over the network. Cloud computing can deliver a range of integrated computer services without being constrained by local resources, aiming to facilitate user interaction. The successful use and integration of cloud computing applications in educational institutions are key indicators of achieving outstanding educational quality. This includes attaining the highest possible levels of sustainable development competencies in education. The effectiveness of utilizing these applications is contingent upon the proper management of educational technology applications and their flexible and rapid activation from any location through the Internet. Therefore, this study aimed to assess the current utilization and deployment of cloud computing applications in activating e-learning at King Faisal University and provide insight into realizing the desired outcomes of implementing one of the most crucial e-learning technologies as a means of achieving sustainable development in education. This was accomplished by identifying the problems and challenges facing the use and deployment of this technology in educational institutions in the Kingdom, specifically at King Faisal University. Additionally, the study aimed to propose ways to enhance the utilization of certain applications utilizing this technology in e-learning within educational institutions. [ABSTRACT FROM AUTHOR]

There are several encryption system applications in 5G networks where rapid response is needed, particularly in the military, health sector, traffic, and vehicular movement. This article presents a proposed data security system for 5G networks that fortifies the security of the network through the use of synchronized chaotic systems to produce pseudo-random numbers. The technique by which random numbers are generated during the encryption procedures is closely associated with 5G network security. Many synchronized chaotic systems are used to produce chaotic random models which are used as encryption bases for a wide variety of data. In this study, the encryption was carried out using a variety of data, including two and three-dimensional color images and audio signals of varying lengths, in addition to the use of Fast Fourier Transform (FFT) for encryption of the ingredient energy wave. The results revealed that the algorithm deployed in the process of encryption performed well. Simulations were performed in MATLAB. [ABSTRACT FROM AUTHOR]

The need to constantly and consistently improve the quality and quantity of the educational system is essential. E-learning has emerged from the rapid cycle of change and the expansion of new technologies. Advances in information technology have increased network bandwidth, data access speed, and reduced data storage costs. In recent years, the implementation of cloud computing in educational settings has garnered the interest of major companies, leading to substantial investments in this area. Cloud computing improves engineering education by providing an environment that can be accessed from anywhere and allowing access to educational resources on demand. Cloud computing is a term used to describe the provision of hosting services on the Internet. It is predicted to be the next generation of information technology architecture and offers great potential to enhance productivity and reduce costs. Cloud service providers offer their processing and memory resources to users. By paying for the use of these resources, users can access them for their calculations and processing anytime and anywhere. Cloud computing provides the ability to increase productivity, save information technology resources, and enhance computing power, converting processing power into a tool with constant access capabilities. The use of cloud computing in a system that supports remote education has its own set of characteristics and requires a unique strategy. Students can access a wide variety of instructional engineering materials at any time and from any location, thanks to cloud computing. Additionally, they can share their materials with other community members. The use of cloud computing in e-learning offers several advantages, such as unlimited computing resources, high scalability, and reduced costs associated with e-learning. An improvement in the quality of teaching and learning is achieved through the use of flexible cloud computing, which offers a variety of resources for educators and students. In light of this, the current research presents cloud computing technology as a suitable and superior option for e-learning systems. [ABSTRACT FROM AUTHOR]

The emergence of the remarkable phenomenon of information and communication technology (ICT) in the last two decades of the twentieth century, and its integration into the formal education systems of leading countries, has expanded learning opportunities and facilitated easy access to educational resources. Due to the vast amount of information available, there is a growing emphasis on information management. This approach allows students to enhance their learning by utilizing various tools and visual aids. These tools help in teaching and training by engaging students' different senses, making learning more realistic, practical, and enjoyable. The quality of education and the effectiveness of educational systems are among the most important concerns for educational developers, and decision-makers in any country. The areas of education is one of the fields that has undergone fundamental changes with the emergence of information technology. Information technology has been recognized as an effective tool in the learning and teaching process. In this research, we will discuss the role of ICT and its impact on enhancing the quality of education systems. The results demonstrate that ICT plays an effective role in the design, planning, implementation, learning, educational evaluation, and structure of education. This includes aspects such as timing, suitability, accuracy, adequacy, realism, speed of transmission, learning accuracy cost reduction, and educational effectiveness. Based on the aforementioned points, educational institutions must offer a suitable framework for integrating ICT into education through thorough planning. [ABSTRACT FROM AUTHOR]

The fifth generation of cellular networks (5G) is seeing a rapid expansion, and energy efficiency (EE) is a hot topic of discussion. It has been found that both EE and maximum spectral efficiency (SE) are desired. They are conflicting objectives, meaning that maximizing one will decrease the other. To tackle this issue, strategies for spectrum and energy optimization have been proposed, as well as green communication plans that aim to minimize the tradeoff between SE and EE. Research has been conducted on EE-oriented resource allocations to reduce energy usage while ensuring high-quality results. To do this, the Crow Search Optimization Algorithm (CSA) has been used. Simulation results have demonstrated that this proposed method is effective in finding the most suitable solution. [ABSTRACT FROM AUTHOR]

In cloud computing, resources are used to communicate instead of local servers or individual devices. However, sharing resources among several users is a difficult issue in cloud communication. Cryptography and steganography techniques are used for cloud storage to address data security challenges. This paper presents a novel method for securely encrypting image data for transmission and link exchange with a cloud storage service. There are two phases to accomplish the encryption process, the first phase encrypts the image file by XORing it with a random key that is generated by a new hybrid of the chaotic map. The second phase converts the encrypted image format to audio format to add another layer of security and improve secure image data transfer. The random key is generated using a hybrid chaotic map and has the benefit of having more than 10256 key spaces and the necessary level of security. Based on a statistical analysis of the encryption, the quality of the image is evaluated using several criteria, and the results demonstrate the algorithm's ability to accomplish resist encryption. [ABSTRACT FROM AUTHOR]

Watermarking is widely used in multimedia preservation and communication, which comprises, and is therefore not limited to, data security and validation. Security, readability, imperceptibility, and resilience are some of the key advantages of this technology. However, there are still certain issues that need to be addressed, such as the ability to withstand a variety of assaults without substantially affecting the quality and value of embedded data. Based on its operational domain, the watermarking technology may be divided into two groups: spatial and frequency watermarking. An index mapping-based watermarking approach for copyright protection of multi-media color videos is presented in this paper. We offer a hybrid discrete wavelet transform and discrete cosine transform (DCT) watermarking algorithm for digital video watermarking of a color video watermark. Peak signal to noise ratio (PSNR), similarity structure index measure (SSIM), have been used to analyze the distortion produced by watermarking. It is suggested that the proposed video watermarking method provides greater imperceptibility in conjunction with each other with the human visual system and it offers higher robustness against different attacks. [ABSTRACT FROM AUTHOR]

Cyber security is a term utilized for describing a collection of technologies, procedures, and practices that try protecting an online environment of a user or an organization. For medical images among most important and delicate data kinds in computer systems, the medical reasons require that all patient data, including images, be encrypted before being transferred over computer networks by healthcare companies. This paper presents a new direction of the encryption method research by encrypting the image based on the domain of the feature extracted to generate a key for the encryption process. The encryption process is started by applying edges detection. After dividing the bits of the edge image into (3x3) windows, the diffusions on bits are applied to create a key used for encrypting the edge image. Four randomness tests are passed through NIST randomness tests to ensure whether the generated key is accepted as true. This process is reversible in the state of decryption to retrieve the original image. The encryption image that will be gained can be used in any cyber security field such as healthcare organization. The comparative experiments prove that the proposed algorithm improves the encryption efficiency has a good security performance, and the encryption algorithm has a higher information entropy 7.42 as well as a lower correlation coefficient 0.653. [ABSTRACT FROM AUTHOR]

In a variety of clinical applications, image fusion is critical for merging data from multiple sources into a single, more understandable outcome. The use of medical image fusion technologies to assist the physician in executing combination procedures can be advantageous. The diagnostic process includes preoperative planning, intra operative supervision, an interventional treatment. In this thesis, a technique for image fusion was suggested that used a combination model of PCA and CNN. A method of real-time image fusion that employs pre-trained neural networks to synthesize a single image from several sources in real-time. A innovative technique for merging the images is created based on deep neural network feature maps and a convolution network. Picture fusion has become increasingly popular as a result of the large variety of capturing techniques available. The proposed design is implemented using deep learning technique. The accuracy of the proposed design is around 15% higher than the existing design. The proposed fusion algorithm is verified through a simulation experiment on different multimodality images. Experimental results are evaluated by the number of well-known performance evaluation metrics. [ABSTRACT FROM AUTHOR]

This research is aimed at planning the manner in which the secure 5G communication can be controlled based on traffic floods. To accomplish excellent administration execution of organization observing and 5G directing calculation is inescapable, dynamic steering in actual organization equipment isn't reasonable for practical plans of action. Accordingly, to bring down functional and capital consumption, we propose a strategy called versatile steering made do with the help of 5G Quality of Service and MANET (Mobile specially appointed Network). A practical 5G steering model which will help with fulfilling the needs of the customers and simultaneously gives huge investment funds in item cost and convenience was presented in this paper. To perform versatile 5G steering, we planned geography in a virtualized climate. We customized CHAOS for network traffic flood the board which goes about as a consistent northward API in 5G directing. Network traffic is infused dynamically from 1MB up to 10MB (connect b/w) in a progressive way for similar organization traffic with 10 organization occurrences. In conclusion, we performed versatile 5G steering applying the control hypothetical method with the assistance of CHAOS and MANET calculations. By looking at two directing situations, we noticed a prominent distinction in throughput for controlling organization traffic applications in MANETs. Network traffic flood data is refreshed after clockwork. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a serious consequence of diabetes that seriously impact on the eyes and is a leading cause of blindness. If the lesions in DR arise in the central portion of the fundus, they may result in significant vision loss, which we refer to as Diabetic Macular Edema (DME). Deep learning (DL) techniques are commonly used utilized in ophthalmology for discriminative tasks such as diabetic retinopathy or age-related macular degeneration (AMD) diagnosis. Deep learning techniques typically need huge picture data sets for deep convolutional neural networks (DCNNs) training, it should be graded by human specialists. According to international protocol, it is classified into five severity categories. However, improving a grading model for high generality needs a significant quantity of balanced training data, which is challenging to obtain, especially at high levels of severity. Typical techniques for data augmentation, in many applications of deep learning in the retinal image processing domain, the difficulty of access to huge annotated datasets and legal concerns about patient privacy are limiting issues. As a result, the concept of creating synthetic retinal pictures that are indistinguishable from actual data has garnered more attention. GANs have been certain to be an effective framework for creating synthetic databases of anatomically accurate retinal fundus pictures. GANs, in particular, have garnered increasing attention in ophthalmology. in this article, we present a loss-less generative adversarial network (DR-LL GAN) to generate good resolution fundus pictures that May be adjusted to include random grading and information about the lesion. As a result, large-scale generated data may be used to train a DR grading and lesion segmentation model with more appropriate augmentation. Our model experiments evaluated on IDRID and MESSIDOR datasets, it's obtained a discrimination loss of 0.69374 and a generation loss of 1.10438, as well as a segmentation accuracy of 0.9840 in our tests. This might support in the optimization techniques of the neural network design and in computer-aided screening of medical picture, thus increasing diagnostic reliability for clinical assessment in the future of sophisticated technological healthcare. [ABSTRACT FROM AUTHOR]

Due to the raising number of using Internet of Things (I O T) for personal and commercial devices and applications, as well as the continues needs for improving the quality of the performance for the internet -- connected devices enhance the researchers to investigate solutions for managing the data through the network Because of the fact that the internet-connected devices needs addresses for each sensor node uses the IP-network and a large number of electronic modules is fabricated to be compatible with IPv4 only like Node Mcu, ESP8266, Arduino WIFI, and ESP32 with knowing that the IPv4 has a limited addresses number that will not be enough for the needs of IOT developments, Many researchers turned to IPv6. In the proposed system, another solution is suggested instead of the IPV6. The proposed idea is based on Non-IP Wireless Sensor Network (WSN) by connecting many sensing nodes to the sink-node that has an IP to forward the data to the server to be visualized in a web-portal. By this method, it is able to connect more than one node to the internet over only one IP, there for the data rate that are needed will be decreased. In addition, by using the non-IP network, the data rate and the power consuming by the sensor nodes have been reduced, the practical results are discussed for connecting four nodes over one IP4 and how it will reduce the data rate and the power consuming. In this work, the esp33 has been used as a Sink-node, and the wireless transceiver module ( NRF24) has been utilized to transmit the data from nodes to esp32. [ABSTRACT FROM AUTHOR]