Journal of Novel Engineering Science and Technology
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Physical Characteristics Analysis on Intelligent Reflecting Surface for High Speed Telecommunication Networks
Get PDFThe paper mainly focuses on the physical characteristics analysis of an intelligent reflecting surface for high-speed telecommunication networks. The research problem in this study are (i) To overcome the bottleneck, a novel transmission scheme, named hybrid reflection modulation (HRM) must be considered, exploiting both active and passive reflecting elements at the RIS and their combinations, which enables to convey information without using any radio frequency (RF) chains, (ii) In the HRM scheme, the active reflecting elements using additional power amplifiers can be able to amplify and reflect the incoming signal, while the remaining passive elements can reflect the signals with appropriate phase shifts, (iii) Based on this novel transmission model, we will observe an upper bound for the average bit error probability (ABEP), and derive achievable rate of the system using an information theoretic approach, and (iv) Moreover, comprehensive computer simulations could be performed to prove the superiority of the proposed HRM scheme over existing fully passive, fully active and reflection modulation (RM) systems. The research directions are as follows: (i) Implementing the Intelligent Reflecting Surfaces (IRS) and Hybrid Reflection Modulation Technologies for 6G Wireless Communication, (ii) Implementing the Intelligent Reflecting Surfaces (IRS) and Hybrid Reflection Modulation Technologies with physical layer security techniques, and (iii) Modelling the mathematical equation for optimization design of IRS system. There are two portions in this study. The first is designing the signal model in the IRS surface with specific physical parameters. The second one is an analysis of the capacity of point-to-point MIMO channels. The analyses are conducted using by MATLAB language. The results confirm the performance specification of the IRS system for high-speed telecommunication applications
MATLAB-Assisted Dynamic Analysis and Balancing Optimization in Railway Wheel Sets
Get PDFThis study presents a MATLAB-assisted approach for dynamic analysis and optimization of balancing in railway wheel sets. By simulating multi-plane dynamic balancing equations, critical sources of imbalance were identified and mitigated. The methodology reduced counterbalancing weight consumption by 95.9%, achieving significant cost savings and operational efficiency in production trials. Factors such as wheel eccentricity and machine calibration were addressed comprehensively. This approach validates MATLAB as a cost-effective tool for industrial applications, with the provided code enabling adaptation in similar fields
LLMs Solution to Fake News, Disinformation, and Hoaxes: Llama 3 [70B]-based Hoax Detection and Counteraction System
Get PDFIn the digital age, hoaxes or false information are a significant challenge, as they can harm public comprehension, form inaccurate opinions, and endanger the health and safety of individuals. Artificial intelligence technology, particularly large language models (LLMs) like Llama 3, provides an innovative solution to these challenges. A sophisticated generative model with superior natural language processing capabilities, Llama 3 enables the effective detection and clarification of hoaxes. A dataset that is seven times larger than its antecedent, Llama 2, is utilized to train this model. The dataset has a token capacity of up to 128K and a context length of up to 8 K. By utilizing these capabilities, Llama 3 is capable of comprehending context, offering responses that are grounded in scientific data, and reducing response errors. Educational chatbots, interactive web platforms, and mobile applications that are based on Llama 3 can be implemented. This model effectively identifies and clarifies false information regarding cosmic rays that are purportedly hazardous through the presentation of pertinent scientific facts, as demonstrated by case studies. Llama 3's capabilities encompass its capacity to modify parameters to generate valid and pertinent responses. This renders it a critical instrument for bolstering community resilience to the dissemination of falsehoods, as well as digital literacy and awareness. Llama 3, which is open source, facilitates global collaboration in the development of a more secure and trustworthy information ecosystem
Integration of ECDHE Curve25519, RSASSA-PSS, and AES-256 for Enhanced PrivateDH Key Exchange Protocol in End-to-End Communication
Get PDFThe growing demand for secure digital communication calls for cryptographic protocols that are not only efficient but also capable of ensuring message confidentiality, integrity, and authenticity. PrivateDH is one such protocol that combines Diffie-Hellman, RSA, and AES; however, it still exhibits key weaknesses, including the absence of user authentication and reliance on classical Diffie-Hellman algorithms, which are computationally intensive and do not support forward secrecy. This study proposes an enhanced version of the PrivateDH protocol by integrating ECDHE Curve25519 as a replacement for classic DH, and RSASSA-PSS as a robust digital signature mechanism for user authentication. The methodology involves implementing and testing the proposed protocol within a peer-to-peer communication scenario, with performance evaluations based on handshake duration, CPU and memory usage, as well as security assessments including digital signature validation and forward secrecy. The results demonstrate that the enhanced protocol effectively accelerates key exchange, maintains resource efficiency, and provides reliable user authentication. In conclusion, this protocol contributes meaningfully to the advancement of more secure and efficient end-to-end communication systems, aligning with the demands of modern digital environments
Design and Implementation of an IoT-Based Solar Power Measurement System for Charging Stations of Electric Vehicles
Get PDFBecause of its potential to improve monitoring and management skills, integrating Internet of Things (IoT) technology with renewable energy systems has attracted much interest recently. This study describes an experiment that uses an IoT-based solar power monitoring system to measure the output power of a solar panel in real-time. A 50-watt solar panel is used in this paper to demonstrate the design and execution of an Internet of Things-based solar power monitoring system. The use of the IoT can significantly improve solar energy surveillance by better monitoring solar panel performance. The system comprises a 50-watt solar panel, a microcontroller unit, various sensors for measuring power, voltage, current, and temperature, and a wireless communication module for transmitting data. The collected data is sent to a cloud-based platform, where it is processed and analyzed. The results indicate that the Internet of Things (IoT)- based solar power measuring system provides a scalable solution for broader applications in renewable energy management and improves the reliability and precision of power-producing data. Real-time solar power generation monitoring is the goal of the proposed system, which would allow for effective energy management and optimization
Implementation of Knowledge-Based Graph Neural Networks for Reasoning and Ranking Medical Entities from CORD-19 Texts
Get PDFThe rapid growth of biomedical literature, espe- cially during the COVID-19 pandemic, has introduced new challenges in retrieving clinically relevant information using conventional search methods. This study proposes a novel, interpretable framework for biomedical information retrieval that integrates Named Entity Recognition (NER), knowledge graph construction, and Graph Neural Networks (GNNs) to support semantic reasoning and entity-level ranking. Unlike prior biomedical retrieval systems that operate at document level or perform link prediction over KGs, our framework introduces a novel task formulation contextual entity-level ranking powered by graph-based semantic reasoning. Leveraging the CORD-19 dataset, the system filters abstracts based on user queries, extracts domain-specific entities using SciSpacy, and constructs a semantic graph that captures co-occurrence relationships among medical concepts. A Graph Convolutional Network (GCN) is then employed to prop- agate relevance signals across the graph, enabling context- aware entity ranking. Experimental evaluations using queries such as ”pneumonia” and ”cough” demonstrate superior performance over traditional IR baselines like TF-IDF and BM25, achieving a Mean Average Precision (MAP) of 0.95 and Precision@3 of 1.00. The results confirm the system’s effectiveness in identifying semantically meaningful biomed- ical entities while offering enhanced transparency through graph-based visualizations. This work contributes a scalable and extensible approach to biomedical search and lays the foundation for intelligent literature exploration in medical research and clinical decision support
Performance Optimization of Brain Tumor Detection and Classification Based MRI by Using Batch Normalization Algorithms in Deep Convolution Neural Network
Get PDFBrain tumor is represented as an essential part of critical cancers around the world. These cells multiply and accumulate uncontrolled, forming a mass or lump that can interfere with normal brain function. Primer detection systems not only took too must time in analyzing and setting error, but also extended more datasets to become overfitting, more computation time, and lack accuracy. Supervised ML and traditional CNN are not convenient for estimating the vita feature engineering in larger datasets and they need to be modified using normalization techniques in deep convolutional Neural Networks (CNNs) architectures. The proposed of the research MRI image datasets were evaluated and combined with two popular benchmark data sets, Kaggle, and BRATS. This main objective is to reduce the computational cost avoid overfitting and underfitting and then improve the classification accuracy. In addition, this paper follows the concept of the CNN model and evaluates the modified DCNN with six normalization layers benefits acceptable results with batch normalization techniques and the average number of epochs in a limited time. In this regard, we exploited to extend inside the layer DCNN for the problem of brain tumor classification. This model achieved the best result for the enhanced dataset, with a training accuracy of 99.9%, 98.9% in validation accuracy, 0.0074 in training loss, and a validation loss of 0.0566 in validation loss
Implementation of the Process for Contamination in Electromyography (EMG) Signal by Using Noise Removal Techniques
Get PDFThe paper describes the analysis of electromyography (EMG) signals using noise removal techniques. The problem in this study is to consider a noise removal technique for basic EMG signal processing by the Band Pass Filter method. A research approach to designing simulation codes for observing EMG signal modeling and noise removal techniques through mathematical methods from signals and systems concepts. The results confirm that it can provide high-performance target monitoring of the EMG signal in real-world applications
Analysis of Third-Generation Solar Cell Design with Physics of Semiconductor
Get PDFThe paper presents the Analysis of Generation Solar Cell Design with Physics of Semiconductor. The research problem in this study is how to design a high-performance solar cell with novel semiconductor compounds that are fabricated in the laboratory based on the physical parameters. The approach to solving the proposed research problem is based on experimental studies through theoretical research in recent works. The first one is to develop the effective structure for solar cell design and the other is to develop the energy band structure of III-V compound-based third-generation solar cell. The simulation analyses were carried out with the help of MATLAB language. There are many steps to designing high-performance semiconductor devices for real-world applications. The results confirm that the numerical analyses of these two developments could be supported to estimate the outcomes of experimental studies without using real equipment in the laboratory.
Analysis of Physics of Novel Light-Emitting Diode for Holographic Imaging
Get PDFThe paper focuses on the analysis of the physics of a novel light-emitting diode for holographic imaging. The research problems in this study are based on the specific challenges that were discussed in the introduction. The solution for this research problem is accomplished with two approaches such as (i) device modeling, physics of LEDs, and characteristics of LEDs under the theoretical analysis and (ii) fabrication of LEDs, PL and FTIR measurement, performance check, and performance comparison under the experimental studies. The specific objectives of this research are to advance the LED design for optical transmission in 5G communication systems, to recognize the semiconductor material for LED structure, and to approach theoretical and numerical calculations for LED. In these analyses, there are two main approaches for solving the performance of the novel light-emitting diodes. The first one is to develop the effective structure for light emitting diode and the other is to develop the energy diagram design of III-V and II-VI compound-based LED. The simulation analyses were carried out with the help of MATLAB language. The results confirm that the numerical analyses of these two developments could be supported to estimate the outcomes of experimental studies without using real equipment in the laboratory