{
  "id": "oai:papers.qnfo.org:topological-signal-processing",
  "slug": "topological-signal-processing",
  "title": "Topological Signal Processing",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
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  "abstract": "The inherent limitations of conventional digital signal processing—specifically the latency and energy bottlenecks imposed by analog-to-digital conversion—represent a critical barrier for next-generation systems like 6G networks. This paper introduces a novel physical computing paradigm that overcomes these limitations by systematically translating topological principles from medical imaging into the domain of signal processing. We establish a rigorous mathematical framework, rooted in the Fourier Slice Theorem and the stability of persistent homology, which proves that robust topological invariants can serve as computational primitives. This framework enables the design of reference-free architectures—including Waveform Computing, Compute-on-Frequency, and Compute-on-Network—that perform computations directly on the intrinsic structure of analog signals. Experimental validation of these architectures demonstrates significant performance gains, confirming the viability of topologically protected physical computing.",
  "abstract_markdown": "The inherent limitations of conventional digital signal processing—specifically the latency and energy bottlenecks imposed by analog-to-digital conversion—represent a critical barrier for next-generation systems like 6G networks. This paper introduces a novel physical computing paradigm that overcomes these limitations by systematically translating topological principles from medical imaging into the domain of signal processing. We establish a rigorous mathematical framework, rooted in the Fourier Slice Theorem and the stability of persistent homology, which proves that robust topological invariants can serve as computational primitives. This framework enables the design of reference-free architectures—including Waveform Computing, Compute-on-Frequency, and Compute-on-Network—that perform computations directly on the intrinsic structure of analog signals. Experimental validation of these architectures demonstrates significant performance gains, confirming the viability of topologically protected physical computing.",
  "date": "2025-10-01",
  "first_published": "2025-10-01",
  "last_revised": "2025-10-01T00:00:00Z",
  "modified": "2025-10-01T00:00:00Z",
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