Abstract We propose a new modification to the A* algorithm named AA* that significantly improves space and time complexities. In AA*’s forward pass, the node sets (open and closed) are not used, and only the local node neighborhood is saved to take the next move decision. AA* needs a backward pass to bridge and correct gaps and bad decisions made in the forward pass. The work of the backward pass is far less than that of the forward pass, as most of the task has been done. It is shown via empirical experimental work that our proposed AA* algorithm is superior to the classical A* algorithm in the typical three metrics: running time, number of probed nodes, and length of path. Furthermore, our experimental work showed that AA* is suboptimal in terms of length of path compared to the original Dijkstra’s algorithm with an accuracy of 96.95%.

Abstract The beamforming approach has been emerging as a very important concept for next generation networks. In addition to the improved channel capacity, spectral efficiency, energy efficiency, secrecy rate and secrecy outage probability, the upcoming fifth generation network mainly aims at enhancing the parameters of the channel for secure communication. In this paper, we have implied the allocation of resource blocks adaptively using HMM with a beamforming approach in an intruded network. A system model for secure communication in an intruded network has been discussed using a beamforming approach with the main motive being to provide a security scenario to the data which is transmitted over an unsecured channel in a network. In addition to this we have used the approach of HMM for allocating the resource blocks to the users which have been demanded and applied in order to avoid the intrusion and wastage of resource blocks.

Abstract Currently, the pressing issue of spectrum limitation, driven by the increasing demand for wireless communication services, has led to the challenge of co-channel interference (CCI) due to the reuse of frequencies in wireless networks. To address this, non-orthogonal multiple access (NOMA) has emerged as a solution. This report conducts a thorough evaluation of NOMA’s system performance over independent and non-identical Rayleigh fading channels in device-to-device (D2D) communications networks, where CCI is significant. The analysis includes examining the probability density function (PDF) and cumulative distribution function (CDF) of the upper SINR threshold. Communication channels are defined as independent and non-identical Rayleigh fading channels, and probability expressions are formulated to assess system failure likelihood for two users. Additionally, Monte-Carlo simulations are conducted to validate the proposed theoretical mathematical expressions.

Abstract The detection of leaf diseases using modern technology has significant importance in agriculture and artificial intelligence. Deep learning, specifically, plays a crucial role in this field, as it enables accurate and efficient disease classification. Early detection of leaf diseases is vital to implementing timely treatments and preventing widespread damage to leaves. Leaf diseases can be caused by various factors, including bacteria, fungi, viruses, and other pathogens. Among them, bacteria and viruses are the most invasive and can lead to substantial yield losses if not identified and treated promptly. Bacterial and viral infections are common in agricultural settings, affecting leaves of all types and ages. Our research aims to propose a transfer learning-based model for predicting leaf diseases using a dataset of leaf images. The images will be classified into healthy or diseased leaves based on extracted features. The proposed model, named Leaf Disease Transfer Learning Algorithm (LDTLA), demonstrates promising results with an average accuracy of 97.37% on the dataset. Utilizing convolutional neural networks (CNN) and deep learning techniques, our LDTLA model outperforms previous quantitative and qualitative research studies in leaf disease detection. This advanced approach to leaf disease identification holds the potential to revolutionize agriculture by enabling farmers to make informed decisions, implement targeted treatments, and minimize leaf losses caused by diseases.

Abstract This review paper presents a comprehensive study of commonly used power amplifier (PA) structures. In recent years, with the development of modern wireless telecommunications and their dramatic challenges, new requirements are needed. In addition, some applications, like cell phones and tablets, for example, need new considerations, especially in terms of power consumption. Also, linearity is another major factor in designing a PA. Furthermore, fabrication technologies such as complementary metal-oxide semiconductors (CMOS), silicon on insulators (SOI), gallium nitride (GaN), gallium arsenide (GaAs), etc. play a crucial role in terms of power consumption. Therefore, it is necessary for PAs to meet these considerations. This paper reviews design considerations, fabrication technologies, and common PA structures, including envelope tracking (ET), envelope elimination and restoration (EER), Doherty, linear amplification with nonlinear components (LINC), feedback, and feedforward linearization techniques with their pros and cons. This review focuses on the significant achievements, techniques, structures, and characteristics of each. Also, this review focuses on the significant achievements, techniques, structures, and characteristics of each. Also, this paper tries to provide a brief overview of the various methods with the advantages and disadvantages of each. This review paper tries to make readers familiar with common structures so that readers know the advantages and disadvantages of each and choose the desired structure based on their priorities.