فصلنامه کهربا
سال سیزدهم شماره 45 (پاییز 1403)

Software-Defined Networking (SDN) is an architectural approach that optimizes network operations. In this study, we explore the integration of the Data Plane Development Kit (DPDK) and Internet Protocol Security (IPSec) within SDN. Our goal is to design a customized communication protocol that combines high throughput with robust security. DPDK, a set of libraries and drivers, enables fast packet processing and includes advanced memory management features like Huge Pages and Non-Uniform Memory Access (NUMA). The research aims to enhance Packet processing speed significantly. However, packet processing introduces additional overhead due to encryption. Meanwhile, IPSec ensures data security by providing encryption and authentication, creating a secure communication layer in a high-performance SDN framework. Additionally, we investigate packet processing (VPP) as a virtual router or switch and evaluate its potential impact on the proposed solution.

The use of lithium batteries in the automotive industry has led to a great revolution in the production of electric vehicles due to their extraordinary characteristics. This article examines the features and benefits of this technology in depth and examines the essential role of lithium batteries in improving the performance and efficiency of modern cars. The advantages of these batteries include high power and efficiency, long life, short charging time, and lack of greenhouse gas emissions. Considering all these features, it seems that the use of lithium batteries in automobile manufacturing is an important step in the direction of sustainability, economic and environmental, and has set a bright future for the automobile industry.

Many researchers have introduced motivation as the main factor of movement towards the goal. This research has introduced a model to increase the motivation of teachers and students during the outbreak of the Corona virus. This disease has disrupted the normal process of education in the world. For this reason, the need to pay attention to alternative solutions and changes in educational conditions is felt.

This research, while explaining the concept and applications of artificial intelligence, provides a model for learning children with disabilities or autism disorders. Among the tools and techniques of artificial intelligence in teaching children with autism and identifying these children, recognizing and measuring the level of attention and hyperactivity disorder, recognizing and recognizing the differences of children with learning disabilities such as: reading or writing failure, math learning disorder and language is used.

In recent decades, control theory and dynamic systems have been recognized as a key tool in the analysis and design of complex systems in various scientific and industrial fields. This review article focuses on the fundamental and advanced concepts of optimal control of dynamic systems and examines various optimization methods and techniques. First, the concept of optimal control is explained with an emphasis on calculus of variations and fuzzy methods, and it analyzes the challenges in the design and implementation of these systems. Then the article deals with the wide applications of control theory in various fields such as engineering, medicine and economics. In the field of engineering, the results of optimization in automatic systems, robotics and control of industrial processes are investigated.In the field of medicine, how control theory is used to improve diagnostic and therapeutic systems as well as drug management is analyzed. In economics, the role of optimal control in predicting economic patterns and optimizing financial and human resources is discussed. This paper also discusses some recent innovations in the field of integration of artificial intelligence and control of dynamic systems and shows that this combination can help improve the performance of systems. Considering the growing trends in today's world, the importance and application of control theory as an effective factor in scientific and technological advances is clearly visible. The results of this review can serve as a basis for future research in the field of optimal control and its applications in different fields and provide new solutions to solve existing challenges.

Undoubtedly, the brain, as the most sensitive organ of the body, controls the basic and important functions of the human body. A brain tumor is a serious cancer that is caused by the uncontrolled and abnormal division of cells. Because the incorrect classification of brain tumor can lead to bad consequences, the correct selection of tumor type and grade plays an important role in determining the appropriate treatment plan. For this reason, automatic brain tumor classification plays a vital and efficient role in accelerating the treatment process, planning and increasing the survival rate of patients. In order to address this issue, a new approach called convolution network optimized with meta-heuristic algorithm (LO CNN) has been developed. This approach involves preprocessing brain MRI images to reduce false tumor detection rates. Then, using line segments to preserve hidden edge details, a candidate region process is applied to identify the tumor region. Various features are extracted from the segmented region, which is classified using a convolutional neural network (CNN). The proposed LO CNN system is evaluated using pixel accuracy, error rate, precision, specificity and sensitivity criteria. This system achieves 99% accuracy on the Kaggle dataset.