This study introduces a novel approach to error detection and correction within Very Large-Scale Integration (VLSI) systems, specifically tailored for space applications. The core of this research is the development and implementation of a sophisticated 2-dimensional error correction code designed to significantly enhance memory reliability in the harsh conditions of outer space. Traditional error correction methods, while effective to a certain extent, fall short in addressing the complex phenomenon of burst errors—errors that occur in multiple bits simultaneously as a result of a single disruptive event, such as cosmic radiation. The proposed error correction scheme innovatively employs extended XOR operations, covering larger blocks of data, thus offering a more comprehensive solution for detecting and correcting burst errors. Moreover, the integration of Cyclic Redundancy Check (CRC) techniques further bolsters the error detection and correction capabilities of the system. Through a detailed comparison with existing methods, our study demonstrates that the proposed 2-dimensional code not only addresses the limitations of current error correction techniques but also contributes to the advancement of memory system reliability in space engineering. The implementation of this method is poised to provide better performance in environments where burst errors are prevalent, marking a significant step forward in the domain of space system design and reliability.
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Human Activity Recognition (HAR) plays a significant role in several fields by automatically identifying and monitoring human activities using advanced techniques. It enhances safety, improves healthcare services, optimizes fitness routines, and enables context-aware applications in various fields. HAR contributes to a more efficient and intelligent interaction between humans and technology. It has emerged as an essential research domain with applications in healthcare, smart environments, and human-computer interaction. This study aims to provide a comprehensive survey of the evolving landscape of HAR, including key methodologies, techniques, and trends in existing research. The study discusses various applications of HAR and their significance in modern smart environments. The survey also highlights different types of HAR and data collection techniques. Additionally, it explores various methods for analyzing the collected data and provides a comprehensive analysis of existing human activity classification datasets. It offers valuable insights into understanding the strengths and limitations of various HAR techniques. The study also discusses various challenges and future directions for HAR.
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Medical image fusion technology and its collective diagnosis are becoming crucial day by day.This task confers the latest algorithm for image fusion of medical images to many diagnostic complications. Firstly, transform is employed on input source images. The result of the application of transform is the decomposition of source images into various subbands. Eminent features are extracted from these subbands by using resnet50. These features are fused by phase congruency and guided filtering fusion rules. Finally, inverse transform gives the original image. The experiment results of this algorithm are compared with different methods by taking some pairs of medical images. Subjective and objective outcomes prove that the proposed algorithm exceeds the current methods by giving optimal performance measures in the area of medical diagnosis. Thus, it is revealed that the suggested multimodal image fusion model provides elevated performance over existing models via diverse diseases using MRI-SPECT and MRI-PET.
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Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of
evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including
intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however,
they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature
orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing
protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent
Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial
Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results
displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio,
reduced loss, and so on over compared to the conventional approaches.