Modeling and Reconstruction of Data in Signal Processing and Electronics

This project looks at how to model and rebuild missing or damaged data in signal processing and electronic systems. In today’s communication and sensor networks, data loss and signal degradation happen often because of channel noise, hardware limits, and environmental interference. To tackle these problems, we study robust interpolation, curve fitting, and statistical reconstruction methods that can restore degraded signals with high accuracy. We focus on writing algorithms that can interpolate multi‑dimensional data points, turning discrete, irregularly sampled, or incomplete data into smooth, continuous waveforms. By comparing the trade‑offs between how much computation is needed and how accurate the reconstruction is, we create a clear framework for testing different interpolation

models under various noise levels. The implementation we propose gives useful guidance for building real‑time signal‑restoration blocks in embedded systems and digital signal processors. In the end, this work helps engineering students see how numerical methods are applied in practice to recover signals, laying a solid base for designing noise‑resilient electronic communication systems.

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