Research Article
Technological Stages of Neural Network AI Generation of System Program Code Based on Modular Neuro Integration
Evgeny Bryndin*
Issue:
Volume 11, Issue 1, December 2025
Pages:
1-7
Received:
29 August 2025
Accepted:
12 September 2025
Published:
9 October 2025
DOI:
10.11648/j.ajesa.20251001.12
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Abstract: Artificial intelligence-powered code generation is revolutionizing system software development through the use of machine learning. Neural network generation of software modules of the system is carried out according to prompts in natural language. A trend of vibe coding has emerged in the world of technology – code generation by neural networks from natural language. For example, Cursor via ChatGPT 4.1 can generate various software modules based on descriptions of their functions in natural language. Creating large software systems from generated modules requires integration. Neurocomplexation of software modules is the process of integrating or combining various software modules to create complex systems based on neural models or artificial neural networks. This approach is proposed to be used in the field of artificial intelligence and machine learning to build complex systems where individual modules interact and jointly perform tasks. Promising areas of application are, firstly, the creation of cognitive systems and intelligent ensembles of agents and assistants. Secondly, modeling of thinking and brain function for research in neuroscience, and thirdly, the development of complex solutions in the field of automation and robotics. The key features of the neural network integration process are, firstly, the integration of modules with different functions (recognition, data processing, training). Secondly, the use of neural network algorithms for adaptation and self-training. Thirdly, ensuring the flexibility and scalability of the system.
Abstract: Artificial intelligence-powered code generation is revolutionizing system software development through the use of machine learning. Neural network generation of software modules of the system is carried out according to prompts in natural language. A trend of vibe coding has emerged in the world of technology – code generation by neural networks fr...
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Research Article
Inverse Design of Airfoils Using Artificial Neural Network
Chol Myong Ri,
Kwang Hyok Ri,
Myong Chol Ri*
Issue:
Volume 11, Issue 1, December 2025
Pages:
8-15
Received:
12 August 2025
Accepted:
9 September 2025
Published:
26 November 2025
Abstract: With the development of science and technology, application of UAV is increasing in the world. In order to improve the flight performance of UAV, it is important to inversely design the airfoil that generates lift. Inverse design of airfoils has been a challenging task and subject of investigation for a long time, date back to the beginning of airfoil research. In this paper, an inverse design method of airfoil using ANN and profile database is proposed. In preliminary design phase of the UAV, airfoil database and ANN are constructed and validated to design a profile that satisfied the aerodynamic character of the airfoil for a given flight performance and availability are verified. Many types of airfoils are parameterized using the PARSEC method. For the NASA SC, RAE, HQ, and NACA series of airfoil, the aerodynamic characteristics at the specified Reynolds number and angle of attack are calculated and database are created. These aerodynamic characters are trained by ANN to establish the inverse design process of the airfoil. For the airfoil obtained through the reverse design process, the lift and drag values obtained using CFD calculations are in relatively good agreement with the setting target values.
Abstract: With the development of science and technology, application of UAV is increasing in the world. In order to improve the flight performance of UAV, it is important to inversely design the airfoil that generates lift. Inverse design of airfoils has been a challenging task and subject of investigation for a long time, date back to the beginning of airf...
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Research Article
OFDM Variants and FPGA Implementation:
A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization
Issue:
Volume 11, Issue 1, December 2025
Pages:
16-38
Received:
1 October 2025
Accepted:
16 October 2025
Published:
9 December 2025
DOI:
10.11648/j.ajesa.20251101.13
Downloads:
Views:
Abstract: This paper presents a comprehensive comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) variants and their practical implementations across multiple domains to guide future communication system design decisions. This paper investigates algorithm comparison and methodologies for OFDM variants, explore optical wireless communication integration, examine neural network-based OFDM mapping techniques, and demonstrate field-programmable gate array (FPGA) realizations. The primary objectives of this research are threefold, namely: 1). to establish comprehensive performance benchmarks for major OFDM variants across key metrics including spectral efficiency, computational complexity, and implementation feasibility; 2). to investigate the synergistic benefits of integrating OFDM with optical wireless communication systems; and 3). to evaluate the effectiveness of neural network-based signal processing in OFDM applications while demonstrating practical FPGA realizations. Through systematic comparison of conventional OFDM, Filtered-OFDM (F-OFDM), Universal Filtered Multi-Carrier (UFMC), and Generalized Frequency Division Multiplexing (GFDM), we establish performance benchmarks across spectral efficiency, power consumption, and computational complexity metrics. The optical wireless integration study reveals significant improvements in data transmission rates and energy efficiency. Neural network mapping demonstrates enhanced channel estimation and equalization capabilities, while FPGA implementations provide real-time processing solutions with optimized resource utilization. Experimental results show up to 25% improvement in spectral efficiency and 40% reduction in computational complexity compared to traditional implementations. The findings contribute to the advancement of next-generation wireless communication systems and provide practical implementation guidelines for researchers and engineers.
Abstract: This paper presents a comprehensive comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) variants and their practical implementations across multiple domains to guide future communication system design decisions. This paper investigates algorithm comparison and methodologies for OFDM variants, explore optical wireless communica...
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