Article
MATLAB FRAMEWORK FOR SIGNAL PROCESSING AND REAL-TIME DATA ANALYSIS
MATLAB has become one of the most widely used computational platforms for signal processing and realtime data analysis due to its powerful mathematical capabilities, extensive toolbox support, and userfriendly programming environment. The growing demand for efficient processing of large volumes of data generated from sensors, communication systems, biomedical devices, and industrial applications has necessitated the development of robust frameworks capable of performing accurate and real-time analysis. This study presents a MATLAB-based framework for signal processing and real-time data analysis designed to acquire, process, visualize, and interpret dynamic data streams effectively. The proposed framework integrates signal acquisition modules, preprocessing techniques, filtering algorithms, feature extraction methods, and real-time visualization tools within a unified environment. Advanced digital signal processing techniques, including Fast Fourier Transform (FFT), wavelet analysis, adaptive filtering, and statistical analysis, are employed to enhance signal quality and extract meaningful information from noisy datasets. The framework supports real-time monitoring and decision-making through automated analysis and graphical user interfaces. Experimental evaluation demonstrates improved processing accuracy, reduced computational latency, and enhanced analytical performance across various signal types. The results indicate that the proposed MATLAB framework provides a flexible, scalable, and efficient solution for signal processing and real-time data analytics in engineering, scientific, and industrial applications.
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