This study examines the growing number of satellites, this paper explores the importance of satellite tracking and highlights NOAA-15 for information transmission and weather monitoring. It offers a low-cost configuration for receiving circularly polarized signals, using a Software Defined Radio (SDR) at 137.62 MHz and a V-dipole antenna. The directional characteristics of the V-dipole maximize capture efficiency by improving signal reception during particular satellite orbits. The flexible SDR, in particular the RTL SDR USB dongle, is used to detail signal decoding for Automatic Picture Transmission (APT), allowing for effective processing and tracking. High-quality APT photos are produced by this hardware-software integration, highlighting NOAA's crucial role in international weather forecasting. The study also emphasizes how simple and inexpensive it is to use the V-dipole antenna to receive NOAA weather satellite signals.

This review paper explores the fundamental importance of fractional wavelet filter (FrWF) and discrete wavelet transform (DWT) techniques in signal processing. These techniques are thoroughly examined and serve as the foundation for many different applications, including data compression, telecommunications, and medical diagnostics. DWT’s multi-resolution analysis, facilitated by breaking signals into various scales, plays a vital role in image compression, denoising, and feature extraction. FrWF, designed for memory-constrained environments, notably reduces memory overhead through external memory utilization. The adaptive memory allocation technique enhances memory efficiency, pivotal for real-time applications like sensors and cameras. These techniques, distinct yet impactful, bolster science and technology. This review highlights their vital roles, progressions, and potential in reshaping memory-restricted signal processing, redefining paradigms.

This study explores the efficacy of employing the Fractional Wavelet Filter (FrWF) as a photo compression technique optimized for low-resource platforms, specifically targeting the Raspberry Pi Zero. The investigation involves analyzing the compression of 2n x 2n images using FrWF in conjunction with a 3-level wavelet transform. The FrWF's memory-efficient design renders it highly suitable for systems constrained by limited resources, thereby broadening its applicability across practical real-world scenarios. The paper details the implementation specifics, encompassing the FrWF algorithm, and conducts an evaluation of compression performance, considering memory utilization and image quality, specifically on the Raspberry Pi Zero. Our findings conclusively demonstrate that FrWF exhibits substantial potential as a low-power embedded photo compression technique, showcasing promising outcomes in terms of memory efficiency and preserved image quality.