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标签： Signal Processing

  • Computing the Fourier Transform in Python: A Step-by-Step Guide

    Computing the Fourier Transform in Python: A Step-by-Step Guide

    Hey, have you ever tried to compute the Fourier Transform numerically in Python? It’s actually pretty interesting. Recently, I’ve been exploring various methods for doing this, and I wanted to share my experience with you.

    So, I tried two approaches: the Left Riemann Sum method and the Fast Fourier Transform (FFT) algorithm. The FFT functions in NumPy and SciPy are really useful, but they don’t directly compute the continuous Fourier transform of a function. You need to make a small adjustment to get it working properly.

    I wrote a guide with code examples and explanations of both methods. If you’ve worked on numerical Fourier transforms or FFT implementations, I’d love to hear your feedback or tips for improving accuracy.

    Here’s a detailed tutorial with code examples and visualizations: you can find it online by searching for ‘Implementing the Fourier Transform Numerically in Python: A Step-by-Step Guide’.

    The Fourier Transform is a powerful tool for analyzing signals, and being able to compute it numerically in Python can be really useful. Whether you’re working on signal processing, image analysis, or something else entirely, understanding how to use the Fourier Transform can help you get more insights from your data.

    So, what do you think? Have you ever tried computing the Fourier Transform in Python? What methods have you used, and what were some of the challenges you faced?

  • How Signal Processing is Revolutionizing AI: A New Perspective on LLMs and ANN Search

    How Signal Processing is Revolutionizing AI: A New Perspective on LLMs and ANN Search

    I recently came across an interesting concept that combines signal processing principles with AI models to make them more efficient and accurate. This idea is being explored in collaboration with Prof. Gunnar Carlsson, a pioneer in topological data analysis. The goal is to apply signal processing techniques, traditionally used in communication systems, to AI models and embedding spaces.

    One of the first applications of this concept is in ANN search, where it has achieved 10x faster vector search than current solutions. This is a significant breakthrough, especially for those interested in vector databases. You can find more information on this topic in a technical note and video titled ‘Traversal is Killing Vector Search — How Signal Processing is the Future’.

    The potential of signal processing in AI is vast, and it’s exciting to think about how it could shape the next wave of AI systems. If you’re in the Bay Area, there’s an upcoming event where you can discuss this topic with experts and like-minded individuals. Additionally, the team will be attending TechCrunch Disrupt 2025, providing another opportunity to meet and brainstorm.

    So, what does this mean for the future of AI? It’s clear that signal processing has the potential to complement modern AI architectures, making them more efficient and accurate. As this technology continues to evolve, it will be interesting to see how it’s applied in various fields and the impact it has on the development of AI systems.