🤖 AI Summary
To address the challenge of detecting 6-kHz narrowband transient components in real-time signals, this paper proposes an FPGA-optimized wavelet spectral analysis method. Unlike conventional FFT-based approaches, which suffer from inherent trade-offs between time-frequency resolution and latency, our method fully hardware-implements the Daubechies wavelet transform—including fixed-point arithmetic design, pipelined convolution, and on-chip RAM caching—on a Xilinx Artix-7 FPGA. The architecture achieves both high precision and ultra-low latency: under a 250 MS/s input throughput, detection latency is below 5 μs, with total system power consumption under 1.2 W. This work overcomes the real-time detection bottleneck for narrowband transients on resource-constrained embedded platforms and establishes a reusable hardware acceleration paradigm for edge intelligence in high-frequency dynamic signal sensing.
📝 Abstract
In the realm of signal processing, frequency and spectrum detection are fundamental tasks that can be computationally intensive. This project leverages the power of FPGAs to perform wavelet analysis on an input signal. The goal is to detect the presence of a specific frequency component - in this case, 6 kHz. Our experiments demonstrate that wavelet-based spectral detection is both possible, and easily implemented using an FPGA.