MRL Publishing House Multidisciplinary Journal for Applied Research in Engineering and Technology (MJARET) The Multidisciplinary Journal for Applied Research in Engineering and Technology (MJARET) stands as an internationally recognized beacon of excellence in publishing scholarly, peer-reviewed research that spans a broad spectrum of engineering and technology disciplines. MJARET commits to fostering a confluence of academia, industry, and practical sectors, bridging gaps through a multidisciplinary approach aimed at tackling the complex challenges presented by today's rapidly evolving technological landscape. Scope of MJARET : MJARET covers a broad range of topics within engineering and technology, including but not limited to: Mechanical Engineering Electrical and Electronics Engineering Computer Science and Information Technology Chemical Engineering Civil Engineering Environmental Technologies Biotechnology Aims of the Journal Our foremost goal is to propel the understanding and application of advanced engineering principles in multiple domains, promoting research that elucidates both theoretical frameworks and practical applications. We particularly prize contributions that have the potential to significantly influence both academic research and industrial practices, contributing robust solutions to global challenges. Why Publish with MJARET? MJARET offers a rigorous peer review process ensuring the high standards and impact of your research. Published through an open access model, we guarantee broad visibility of your work, reaching a diverse, interdisciplinary audience promptly. Authors benefit from our commitment to prompt publication timelines, enhancing the timeliness of the discourse your work enters into. Readership The journal caters to a wide audience that includes researchers, educators, and professionals across various engineering disciplines. It also extends its reach to policymakers and public health officials who are keen on integrating the latest technological insights into practical and policy-oriented applications. Submission and Publication Ethics Authors are encouraged to submit original research, review articles, case studies, and technical notes that align with the journal’s scope. Detailed submission guidelines and the review process are available on our website. MJARET adheres to the highest ethical standards in publication, ensuring integrity and transparency at every step. Open Access Policy Committed to accessibility and the dissemination of knowledge, MJARET operates under an open access policy, allowing unrestricted access to its contents, thus fostering a greater global exchange of knowledge. Engagement and Outreach Through strategic partnerships and continuous outreach, MJARET remains at the forefront of technological advancements, promoting the application of research findings in real-world scenarios, thereby contributing to the broader community and impacting real-world technological advancements.
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Optimizing Solar Energy Forecasting Using Deep Learning Techniques: A Study on Predictive Models in Forested Environments
Precise information about the efficiency of harvesting solar energy is an important requirement in the application of the renewable energy on practical projects, especially where climate is characteristically changeable. This paper focuses on the use of deep learning methods namely LSTM to predict the efficiency of solar power in forested and other regions of the land. This seems to be more efficient than conventional neural network like Feedforward Neural Network (FNN) and Convolutional Neural Network (CNN) in terms of accuracy with the MAPE of 22% than FNN and 15% than CNN. Currently, based on the results of the LSTM model the authors got 7.45% and 20.35% improvements in terms of computational time and memory respectively when compared with the CNN models. Based on these results, it can be concluded that deep learning techniques, especially LSTM, are suitable when it comes to the solar power prediction considering accuracy and practicability. The further developments of the work will be the inclusion of other environmental data, as well as enhancing the real time prediction – leading to better sustainable energy solutions.
Solar Energy Forecasting; Deep Learning; Long Short-Term Memory (LSTM); Renewable Energy; Neural Networks; Energy Prediction; Environmental Data; Forest Ecosystems; Computational Efficiency; Machine Learning Models.
Copyright (c) 2025 MJARET https://creativecommons.org/licenses/by-nc/4.0
Neural Network Models for Forecasting Solar Energy Harvesting Efficiency
Accurate efficiency prediction of solar power harvesting methods remains crucial to optimize photovoltaic systems. Conventional forecasting methods struggle when weather conditions are unstable because they lack the ability to detect complex nonlinear and time-related patterns. Personnel implementing this study developed a forecasting system through the combination of Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network architecture. The system adopts adaptive learning features alongside multi-feature encoding to deal with different climate patterns. Standard solar dataset evaluation confirmed that the LSTM model achieved best performance by delivering 0.118 RMSE and surpassing 95% accuracy mark. The conducted analysis shows that neural networks particularly LSTM prove to be exceptionally efficient for energy prediction operations. The proposed system provides an efficient scalable approach for managing solar energy while planning smart grids in variable operating conditions.
Solar Energy Forecasting; Neural Networks; Photovoltaic Efficiency; LSTM; Deep Learning; Renewable Energy; Energy Prediction; Smart Grid; Time Series Analysis; Environmental Data Analytics.
IoT-Based Predictive Maintenance for Solar and Wind Energy Systems Using Machine Learning Models
When the IoT technology is connected with the machine learning models, renewable energy systems, such as solar and wind energy, can benefit from a reliable predictive maintenance. This paper describes an IoT based predictive maintenance system for the smart collection of the real time operation data of solar panels and wind turbines. Raw data contained in the system goes through data cleansing and is processed with the help of sophisticated ML algorithms such as LSTM and Random Forest for failure forecast and proper scheduling of maintenance. It is stated that the proposed system would cut down possible on time by 30%, increase energy effectiveness by 25% and also decrease maintenance consumption by 20%. The general experiment outcomes prove that this technique reaches the desired levels and yields an almost 92% of accuracy towards the expectation of enhancing the dependability and capability of renewable energy structures.
Predictive Maintenance; Internet of Things (IoT); Machine Learning, Solar Energy; Wind Energy; Renewable Energy Systems; Failure Prediction; Smart Monitoring.
Attention-Driven Photo Improvement Architecture for Low-Light Spaceborne Image Assessment
Night-time satellite imagery is very vital in areas such as environmental monitoring, urban planning and disaster management. However, there is a problem in performing remote sensing mainly due to issues of change in illuminance level where structures in images may become blurred and noisy. Most traditional techniques of image enhancement were unable to produce good results uniformly and they also failed to balance the important features of the image under different illuminations. In order to overcome these limitations, the Attention-Driven Photo Improvement Architecture for Low Light Spaceborne Image Assessment is designed. The underlying concept of this framework is based on self-attention mechanisms and multi-scale feature extraction to increase image contrast and visibility. It also applies architectural features such as self-supervised learning, contrastive learning and changes proposed in Retinex-recurrent to enhance the image texture but not the structure. The comprehensive experimental results on several test databases also confirm that the current approach achieve higher PSNR, SSIM, and FSIM values than the deep learning methods. Moreover, through bringing down the computational cost to 30%; it draws path for enhanced, real time analysis of satellite images to open up new frontiers of feasible research in the field of remote sensing.
Low-Light Image Enhancement; Satellite Imagery; Transformer Networks; Deep Learning; Image Restoration; Contrast Enhancement; Remote Sensing; Computer Vision.
Secure Edge Computing Framework for IoT-Based Real-Time Data Processing and Analytics
As IoT and related and connected technologies continue to expand, the need for near real-time analytics and data processing of these IoT produced data at the edge has become important thereby creating the need for secure edge computing architecture. Current centralized architectures of the cloud computing are associated with some difficulties concerning latency, security, and limited resources. To overcome the mentioned drawbacks, this research introduces a novel edge computing paradigm that adopts blockchain to enable access control, homomorphic encryption to perform data analysis, and AI to detect anomalies. The proposed model aims at have low latency processing time, improved data privacy and faster decision making. This protocol has shown up to 35% reduced processing delay and up to 40% enhanced security compared to the existing IoT architectural works. Therefore, these results depict the benefits of EC in enhancing real-time data processing and security in IoT environments.
Secure Edge Computing; IoT Security; Blockchain; Homomorphic Encryption; Real-Time Data Processing; AI-Driven Analytics; Privacy-Preserving Computation.
Smart Healthcare Monitoring System Using IoT Sensors and Deep Learning A Predictive Maintenance Approach
An exponential rise in IoT healthcare devices combined with rising requirements for immediate patient monitoring needs advanced healthcare management solutions. An innovative patient monitoring platform links IoT sensors to deep learning algorithms in combination with edge computing technology for predictive healthcare diagnostics through predictive maintenance and patient monitoring operations. The system develops a networked security infrastructure that uses blockchain access security and AI anomaly identification to manage fast yet safe health data processing. The system underwent thorough experimental testing which produced patient anomaly detection accuracy at 92% while reaching 88% success in equipment maintenance predictions together with a 40% reduction of critical health event response times. Our system received a 35% increase in data processing speed because of edge computing integration and simultaneously experienced a 40% enhancement in security protocols. The resulting system produced substantial improvements to healthcare operations and presented better data protection methods and maintenance optimization of medical equipment. The healthcare system now achieves processing of real-time health data alongside high security standards because of its significant advancement in technology implementation.
IoT sensors, healthcare monitoring, deep learning, predictive maintenance, edge computing, real-time monitoring, AI-driven analytics, blockchain security, patient care optimization, medical equipment maintenance
IoT-Based Smart Agriculture System Integration of Sensor Networks with AI for Crop Yield Optimization
The application of IoT sensor networks based on AI devices in agriculture enables monitoring of the systems and predictive management to determine optimum usage of resources real-time. This paper introduces a smart agriculture system that covers a system that uses a sensor-based monitoring technique, edge computing, and an AI-based crop yield prediction algorithm for improvement of efficient crop production. The system utilizes a process flow at the system level involving data acquisition from the sensors, artificial intelligence decision-making component, and closed feedback loop for high precision farming. Explorations conclude that there is 92% enhancement in the chances of crop yield prediction, 40% enhancement in the use of resources and they also show 35% reduction in operation costs. Integration of edge computing lowers the processing time to 38% hence the ability to attend to crop health complications instantly. These provide an endorsement of IoT & AI integration in the development of efficient and profitable smart farming innovation within a controlled decision-making framework.
Smart Agriculture, Internet of Things (IoT), Artificial Intelligence, Machine Learning, Crop Yield Optimization, Sensor Networks, Precision Farming, Environmental Monitoring, Predictive Analytics, Sustainable Agriculture
Hybrid CNN-LSTM Architecture for Automated Defect Detection in Industrial Surface Inspection
Automating defect detection in the industrial surface inspection is significant for a product quality and efficiency of the manufacturing process. Inspection techniques that used the conventional methodologies take a long time to complete and are less accurate; hence, the need to use deep learning solutions. This paper presents Hybrid CNN-LSTM Architecture which combined the CNN for spatial feature extraction and LSTM for temporal feature recognition. The employment of CNN and LSTM together improves the recognition capacity of the spatial and temporal features for better defect categories and segmentation. The experiments show that the proposed method improves the defect detection accuracy by 20% and the false positive rates by 30% than the existing method. The framework performs well in detecting the defects on various surface texture type and materials, which makes the framework to be a good solution for the industrial quality control.
Automated Defect Detection; Industrial Surface Inspection; Hybrid CNN-LSTM; Deep Learning; Feature Extraction; Quality Control; Manufacturing Automation.
Deep Learning-Enabled Real-Time Medical Image Segmentation: A Multi-Modal Approach for Diagnostic Accuracy
Deep learning is an innovative tool, which positively impacted medical image segmentation improving the diagnostic processes. In this paper, a novel Real-time medical image segmentation method using deep learning is proposed that uses CNNs with attention mechanism along with methods of self-supervision. As for methods based on the use of tomographic data, we have developed a new method that enhances the accuracy of tissue classification and the identification of anomalies by 20-30%, compared to the individual modality used. As for the validation, percentages presented show that it is possible to achieve up to 15% greater segmentation accuracy compared to traditional approaches. The above study reveals the capability and advantage of DL-based segmentation for performing prompt diagnosis without compromising the system qualities and medical credence.
Smart Attendance Management System Using Convolutional Neural Network
: Businesses and educational institutions rely heavily on attendance records to ensure everything runs smoothly. Logging in and out allows for an accurate record of attendance, but the manual nature of the system raises the possibility of forgeries and proxies. These days, machine learning has been extensively researched for use in computer vision. In order to create reliable and accurate automated attendance systems, we use the idea of machine learning in Face - recognition. For the purpose of taking attendance, this research equips computers with face recognition and face detection algorithms that can quickly and accurately locate and identify human faces in digital photos or video. For this purpose, we employ a camera that sends its data through a Raspberry Pi module. The information is processed by a CNN algorithm, after which an Excel file (.csv) is created, uploaded to the cloud, and sent off to the appropriate authorities.
Smart Campus, Machine Learning, Digitalization of attendance, Face Recognition, Convolutional Neural Network (CNN).
Comparative analysis of different machine learning algorithms for diabetes identification
In today's world, diabetes has become one of the deadliest diseases and at the same time the most common disease not only in India but across the globe. Today, people of all ages can develop diabetes, which is linked to lifestyle, genetics, stress, and age-related factors. Whatever the cause of diabetes and diabetes-related diseases, we can identify the disease with different machine-learning algorithms. In the proposed work, we used Support Vector Machine (SVM) and Naive Bayes machine learning algorithms, which are mathematical and statistical concepts that help identify potential opportunities to be affected by diabetes-related diseases. After the data is preprocessed, the features that affect the prediction are selected by performing forward and backward feature selection. By analyzing the dimensionality reduction method of Principal Component Analysis (PCA) after selecting specific features, Naive Bayes achieves a prediction accuracy of 75%, which is significant compared to the 73% accuracy of SVM. This article is about identifying type 1 and type 2 diabetes. Accurate diagnosis and classification of diabetes are essential for effective treatment and management. When the immune system of the body attacks and destroys the cells responsible for producing insulin in the pancreas, it results in an autoimmune disease known as Type 1 diabetes. Type 2 diabetes is a metabolic disorder that occurs when the body becomes insulin resistant or does not produce enough insulin. This review article discusses the various methods and techniques used to identify type 1 and type 2 diabetes, including clinical, biochemical, and genetic markers .
Diabetes, Support vector Machine, Naive Bayes algorithm, Principle Component analysis, accuracy.
High Gain Co-Planar Parasitic Patch Antenna Design for 5G sub-6GHz Applications
A simple coaxial probe fed patch antenna with two co-planar parasitic stepped patches is proposed for the enhancement of gain. The bandwidth is also enhanced by merging the additional resonance due to two symmetrical parasitic patches along with the main radiating patch. The parasitic patch elements affect the field distribution based on the variation in its dimensions, and consequently increasing the gain. The three circular defects on each of the parasitic patches further increase the gain to 10.36dBi at the designed center frequency of 3.5GHz. The 10dB impedance bandwidth obtained is from 3.37GHz to 3.57GHz which falls under the n78 (3.2GHz to 3.67GHz) band used at sub-6GHz range for 5G applications .
circular defects; microstrip patch antenna; parasitic patch antenna; 5G; sub-6GHz
Earlier detection of stroke and stimulating a nerve using EEG in human brain
According to the World Health Organization, stroke is the second leading cause of disability. In 2022, 1 in 6 deaths from cardiovascular disease will be due to stroke. In India, 6.5 million stroke cases have been identified, with half of the strokes occurring between the ages of 46 and 65, and one-third occurring after the age of 65. The main aim of our project is to identify the brain stroke by using non-invasive methods and stimulating the nerves with current stimulation with a voltage range of 90 to 100 volts and a frequency of 3 Hz to 5 Hz. This paper mainly focused on the EEG level generated and when it reaches above threshold level stimulation is given when a brain wave signal is an abnormal condition to generate a small electrical signal for brain nerve stimulation. The main result of our paper is that normal or abnormal conditions of stroke exist. When it reaches an abnormal condition to generate the electric stimulation and prevent stroke.
Streaming Data System for Roadside Sensor Monitoring
Today's civilization confronts substantial transportation-related concerns, including but not limited to traffic, health, and pollution. In today's transportation arrangements, information communication pushes extended increasing ideas and essentiality. Vehicle manufacturers are developing in-vehicle sensors and applications in a variety of areas, including safety, traffic management, and entertainment. Government agencies are putting together roadside equipment, such as sensors and cameras, to collect data on biological and traffic conditions. Their discovering and correspondence capacities may be employed to achieve intense and powerful transportation arrangements by using reliably linking autos and different equipment. We discuss how sensor innovation may be integrated into the transportation infrastructure to create a practical Intelligent Transportation System, as well as how distinct sensors delivered in diverse portions of an ITS can boost prosperity, traffic signal, and infotainment applications.
Personal Identity Identification and Authentication Statistics Based on Countermeasures against Spoofing
To make the online banking system secure, a three-step authentication method using the face, iris, and fingerprint is used. To improve security, spoofing detection has also been included. A spoofing attack happens when someone attempts to impersonate someone else and gets unauthorized access. The face and iris are captured by the camera, while the fingerprint is captured by the fingerprint module. Gray Level Co-occurrence Matrix is used to extract a picture of a person's face, iris, and fingerprint (GLCM). A reference module is constructed using the extracted picture so that the input may be compared to it, and if the input matches the module, a one-time password (OTP) is delivered to the user's mobile phone. Only the online transaction may take place if the OTP is entered correctly. Otherwise, the account holder will get an alert message. This method allows for a very secure online transaction
Spoofing detection, face, iris, fingerprint, image extraction, Gray Level C-occurrence Matrix, reference module.
Ultra-Low Latency Hybrid Feature Learning for Accurate Multiple Sclerosis Detection Using Multi-Scale Extraction and Adaptive Attention
Multiple Sclerosis (MS) is a severe neurological disease and requires a fast and precise diagnosis for the optimal treatment outcome. This paper proposes a novel model that combines the hybrid multi-scale feature extraction with an adaptive attention mechanism to achieve ultra-low latency feature learning for MS detection in medical imaging. The proposed deep learning models have significantly improved the detection accuracy and reduced the latency. It reached 95% detection accuracy within 50 ms latency which means a 15% increase over the conventional ones. The adaptive attention mechanisms pay particular attention to key features which further increases the classification accuracy. This advanced detection model can be used in real-time monitoring and interventions which can effectively improve Multiple Sclerosis management.
Multiple Sclerosis (MS); Ultra-Low Latency; Feature Learning; Hybrid Multi-Scale Feature Extraction; Adaptive Attention; Medical Imaging; Real-Time Detection; Machine Learning; Neural Networks
Quantum Key Distribution for Securing 6G Networks: Shaping the Future of Mobile Communication
The roll-out of 6G requires enhanced security of mobile networks because a huge amount of information will be transmitted via mobile networks in a 6G era. This paper seeks to introduce a major method for secure communication in the 6G environment: Quantum Key Distribution (QKD). Based on quantum mechanics, QKD provides a much more secure key exchange method for communication between 6G network nodes. Our result reveals that with the implementation of QKD, at least more than 70% of cyberattacks can be resolved. Data transmission and confidentiality can thus be guaranteed. After that, we also consider the main issues of QKD, such as system complexity and the construction cost of infrastructure. In conclusion, we believe that QKD is regarded as an essential approach to enable the progress of secure mobile communication in the 6G environment.
Quantum Key Distribution (QKD); 6G Security; Mobile Communication; Cybersecurity; Quantum Mechanics; Secure Key Exchange; Data Integrity; Eavesdropping Protection; Telecommunications; Future Networks.
Blockchain-Enabled Secure Communication Framework for 6G: Achieving Ultra-Reliable Low-Latency Connectivity
This paper presents a blockchain-based architecture to make the information security of 6G wireless networks and ultra-reliable low-latency communication (URLLC) more efficient. The architecture makes use of the decentralized organization of blockchain to mitigate potential security vulnerabilities and to facilitate secure and tamper-proof data transactions for next-generation mission-critical communications in various applications such as autonomous vehicles and smart cities. Performance measurements have indicated at least a 50% reduction in data transmission latency and at least 45% enhancements in network security metrics. Moreover, blockchain-based data transmission has reduced incidents of unauthorized access by more than 30% and accelerated the efficiency of data integrity verification by 35%. Findings from real-world measurements demonstrate the potential of blockchain as a keystone technology to build secure and resilient communication infrastructures for the new milestone of 6G.
6G Networks; Blockchain Technology; Ultra-Reliable Low Latency Communication (URLLC); Secure Communication; Decentralized Systems; Data Integrity; Trust Management
A Comprehensive Machine Learning Framework for Securing Mobile Edge Computing Against New Threats
Mobile edge computing (MEC) allows significant reductions in data latency as servers are located closer to end-users, but also raises serious security concerns due to its decentralized design. This paper presents a novel MEC security framework consisting of a layered security architecture scope and applying a machine learning-driven approach to improve the network and physical security of the mobile edge. Through neural network-based models, the framework predicts real-time anomalies, mitigates intrusion attempts, and adapts to dynamic threats. The proposed solution shows a 45% increased fine-grained threat detection rate as well as a 30% reduction in system latency to support real-time applications, that achieve high-security demands. The presented framework combats cyber-attacks to secure the mobile edge and ensures data integrity. It demonstrates a robust security infrastructure to address the rapidly evolving security landscape of MEC.
Mobile Edge Computing (MEC); Machine Learning; Multilayer Security; Anomaly Detection; Cybersecurity; Intrusion Prevention; Threat Detection
Efficient Resource Allocation for D2D Communication in Next-Generation Cellular Networks: Ensuring Security and Performance
This work proposes a novel framework for efficient resource allocation for Device-to-Device (D2D) communication in next-generation cellular networks. A Deep Reinforcement Learning (DRL) combining a novel social-awareness mechanism and significant amount of social interaction data as well as device locations was proposed to achieve secure and efficient resource management. A two-stage algorithm was proposed to improve resource allocation for ensuring low interference and high network throughput. Evaluation through employing a 5G network simulator for different cell densities and QoS constraints proved the DRL mechanism to perform significantly better on resource allocation by increasing 28% in network throughput and 32% in reducing latency in comparison with state-of-the-art models. The framework secures communication by significantly lowering interference, improving QoS, and optimising D2D communication in next-generation cellular systems.
D2D communication; resource allocation; next-generation cellular systems; 5G networks; social-community-aware; deep reinforcement learning; Quality of Service (QoS); interference management
Blockchain-Enhanced Mobile Transactions: Strengthening Privacy and Trust in Peer-to-Peer Networks
Blockchain technology can enable secure and decentralized payment transactions in mobile peer-to-peer (P2P) networks, but a lot of challenges still exist related to privacy (30%), trust (25%), and computational efficiency (20%), which hinders the deployment of such services in mobile networks. This paper introduces a secure blockchain system design that enhances the privacy (40%) and trust (35%) of P2P transactions. The system leverages state-of-the-art cryptology and optimized consensus algorithms to generate secure blocks with confidential and untampered transactions. Various performance simulations and comparative studies show an enhancement in transaction privacy (50%), trust management (45%), and network efficiency (30%), which demonstrates the applicability of the framework for secure and reliable mobile P2P transactions.
Blockchain; Secure Mobile Transactions; Privacy Enhancement; Trust Management; Peer-to-Peer (P2P) Networks; Decentralized Transactions
Design of Parity preserving BCD adder using Reversible Gate Logic
Nanotechnology, optical computing, quantum computing, and lw-power CMOS architecture are all using reversible logic circuits. In binary-based electronics, low-power and high-speed adder cells (such as the BCD adder) are employed. Binary addition is a basic operation in almost all digital circuits. All other arithmetic operations may be synthesized from it. The major problem now is to reduce the power consumption of adder circuits while retaining excellent performance across a variety of circuit configurations. For error detection in digital systems, parity preservation is also an option. BCD adder design that is fault resistant and preserves parity is suggested in this paper. Reversible logic gates such as IG, FRG, and F2G are used in the suggested technique to reduce the circuit's power consumption and quantum cost. It is compared to the present circuit in terms of the number of constant inputs and garbage outputs, the latency, and the quantum cost.
BCD Adder, Reversible Logic, Double Feynman Gate, Islamic Gate (IG).
An extremely efficient picture scaling processor is implemented in FPGA.
Many industries, including consumer electronics and medical imaging, use image scaling to enhance the quality of images. Many real-time applications rely on scalability, which necessitates a low-cost VLSI solution. This study offers a low-complexity, low-memory need, and high-quality VLSI implementation of an image scaling processor. Using a clamp filter and bilinear interpolation, the proposed picture scaling method uses a sharpening spatial filter. Prior to bilinear interpolation, spatial and clamp prefilters are utilised to reduce the blurriness and aliasing effects. To preserve memory buffers and computing resources, we use a hybrid approach that combines a spatial and a clamp filter. In order to save money on hardware, the reconfigurable unit utilises several adders. Filter latency will be increased as a consequence. To decrease latency, we describe a high-speed parallel prefix-based link adder. The results of the experiments will show that the proposed architecture is better in terms of space and power overhead than earlier techniques to scaling pictures of lesser complexity.
IQ (image quality), RCUs (reconfigurable calculation units), multiplier–adder (MA).
Design of an Instrument to Estimate R, L and C Parameters Under Non-Sinusoidal Conditions using
The design of an FPGA-based instrument for nonsinusoidal parameter estimation of R, L, and C passive components is presented in this paper. Designers in the electrical and electronic industries often employ nonsinusoidal waveforms while creating products. Nonsinusoidal signals stimulate all R, L, and C passive components in these circuits. Resistors, inductors, and capacitors all behave differently in a nonlinear signal environment than they would in a sinusoidal signal environment due to their intrinsic nonlinearity. When nonsinusoidal surroundings are present, a problem develops since these components cannot be reliably approximated. The selection of R, L, and C components is critical to designing and controlling electrical and electronic circuits in hybrid filter design and control and digital protection circuits in power systems. So, using a sequential implementation of output error, a Field Gate Programmable Array-based measurement device may properly identify R, Land C components and continuously recalculate the anticipated values of the considered components. The results of measurements may be updated in realtime, memory resources can be better used, and computational steps can be reduced.
Impedance, equivalent circuits, non-sinusoidal conditions, RLC measurement, real-time estimation.
EFFECTIVE REMOVAL OF IMPULSE NOISE FROM DIGITAL WATERMARKED IMAGES USING FPGA IMPLEMENTATION
unique decision-based strategy has been proposed for the restoration of images that have been substantially affected by impulse noise. In this paper, we provide an efficient de-noising method and its VLSI architecture for reducing impulse noise in watermarked photos. As a result of the goal of low cost, a VLSI design has been proposed. Decision-tree impulse noise detectors and edge-preserving filters are used to find the pixels that are noisy and reconstruct the intensity values of those pixels. Simple memory buffers and little processing complexity are all that is necessary to implement this approach. It's a low-cost hardware option. In comparison to previous VLSI implementations, ours provides better image quality at a lower hardware cost. It is possible for our design's VLSI architecture to attain a processing rate of about
Decision-based filter, image de-noising, impulse noise, FPGA, PSNR
Fingerprint and Lip Images for Multibiometric Recognition and Verification
Nowadays, biometric authentication is a great approach to ensure security. It has now been established that each human has a unique biometric trait such as a fingerprint, iris, lips, palm, face, and voice. Individuals are identified and their identities are verified via biometric recognition. This study employs two biometric modalities, namely lip and fingerprint, to increase identification accuracy. To solve the shadow, beard, and rotation issues in lip identification, five different mouth corners are recognised. Minutiae score matching is employed in fingerprint identification, and fingerprint verification is done by extracting and matching minutiae. In this multi-biometric recognition, a support vector machine is used for classification. When lip biometric is used with fingerprint biometric, the recognition results improve. We demonstrate that our suggested approaches are suitable for a wide range of recognition applications in security and forensics-oriented computer vision, such as biometrics, object identification, and content-based picture retrieval.
Biometric, Lip recognition, Fingerprint recognition, Multi-biometric, Fusion, SVM, Minutiae
A PRODUCTIVE IBPRE MODEL FOR SECURE DATA SHARING IN BLOCKCHAIN TECHNOLOGY BASED IOT
Intelligent tailored services are necessary to fulfill the expectations of users. In order to provide a highly dependable service, additional information about the user's surroundings is required. In order to capture a vast quantity of data, the Internet of Things (IoT), which links multiple gadgets over the Internet, has been created. Although cloud computing is frequently utilized to effectively manage data, it has the potential to delay the transmission of data as well. In this work, we suggest a proxy re-encryption strategy to secure data sharing in cloud contexts. Data owners may outsource their encrypted data to the cloud using identity-based encryption, while proxy re-encryption construction will enable genuine user’s access to the data. An edge device serves as a proxy server for computationally expensive tasks since IoT devices have limited resources. Using information-centric networking capabilities, we may effectively serve cached material in the proxy, hence enhancing service quality and efficiently using network traffic. We've also built our system on block chain, a revolutionary new technology that makes it possible to centralize the sharing of data. It reduces the load on centralized systems and allows for more precise management of data access. The security study and assessment of our scheme reveal the potential of our technique in protecting data confidentiality, integrity, and security.
Block chain, data security, identity-based proxy re-encryption, information-centric network (ICN), Internet of Things (IoT).
6G IoE Unification: Building AI Zigbee 3.0 Networking: Toward Seamless Mobility
6G and IoE hold the promise of becoming an enabling platform technology for the creation of low-latency, low-cost, and reliable wireless communication networks that deliver seamless mobility to the IoE world. This paper presents a unified approach to harness the unique features of 6G and Zigbee 3.0 (ZB3.0) enhanced by AI, to implement an IoE networking framework and provides a high-level system design. The proposed system is capable of real-time device-to-device (D2D) communication with ultra-low latency and high data throughput enabled by 6G, while the power-efficient ZB3.0-enabled IoE devices use minimum power to establish communication and data delivery. AI algorithms are utilized to optimize network operation, improve traffic management, and enhance security. Simulation results show that the framework can improve network efficiency by 55 % and reduce mobility handover delay by 45%, hence, facilitating a grand leap towards the creation of future smart environments with seamless mobility.
6G Networks; Internet of Everything (IoE); Zigbee 3.0; AI-Driven Networking; Seamless Mobility; Device-to-Device Communication; Handover Delay Reduction
6G Networks for Automated Driving: Building AI-Driven Mobility Platform Towards Cooperative Vehicular Communication
6G networks with AI-enabled platforms will accelerate the development of automated driving by enhancing vehicular communication and safety. In this paper, we propose a framework of cooperative vehicular communication based on a 6G network, which enables timely interactions between vehicles, the roadside infrastructure and the edge devices. Based on the ultra-low latency (50% lower than 5G), high-speed data transmission, and AI decision-making capability, it is able to provide bedrock support for autonomous driving, which is more efficient and reliable. The key features of the proposed platform include inter-vehicle distance control, joint topology formation and edge computing, which enhance the collaborative performance of vehicles, reducing the percentage of traffic congestion by 35% and risks of accidents by 40%. Comparative simulations show that the 6G cooperative vehicular communication platform has a 45% improvement in communication capability in terms of efficiency, over 5G, and a 30% enhancement in terms of safety metrics. This platform will inevitably lead to the intelligent transportation revolution.
6G Networks; AI-Driven Mobility; Automated Driving; Cooperative Vehicular Communication; Edge Computing; Inter-Vehicle Distance Control; Intelligent Transportation Systems
Machine Learning Based Context-Aware Security Management for Mobile IoT: A New Adaptive Approach to Threat Mitigation
As mobile devices are connected to the Internet of Things (IoT), potentially dynamic and distributed environments are difficult to keep safe. Due to an increase in context-specific cyber-attacks, conventional security mechanisms fail to tackle 30% of the threats. This paper proposes a novel context-aware security management framework for self-adaptive and self-healing mobile IoT environments. The framework uses machine learning models to detect normal and anomalous behavior patterns, ensuring real-time alerting and mitigation of threats. The framework uses adaptive machine learning techniques to understand new threats, minimizing false positives (45%) and enhancing overall security performances. With the help of extensive simulations, we find that our framework shows a 40% lower number of security breaches, and 50 more accurate detection of threats, making it suitable for next-generation mobile IoT networks.
Mobile IoT; Machine Learning; Context-Aware Security; Adaptive Threat Mitigation; Anomaly Detection; Real-Time Security; IoT Threat Management