wifi-densepose/rust-port/wifi-densepose-rs/crates/wifi-densepose-desktop/ui/node_modules/json5
rUv 341d9e05a8
ruv-neural: publish 11 crates to crates.io — full implementation, no stubs
* Add temporal graph evolution & RuVector integration research

GOAP Agent 8 output: 1,528-line SOTA research document covering temporal
graph models (TGN, JODIE, DyRep), RuVector graph memory design, mincut
trajectory tracking with Kalman filtering, event detection pipelines,
compressed temporal storage, cross-room transition graphs, and a 5-phase
integration roadmap.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add transformer architectures for graph sensing research

GOAP Agent 4 output: 896-line SOTA document covering Graph Transformers
(Graphormer, SAN, GPS, TokenGT), Temporal Graph Transformers (TGN, TGAT,
DyRep), ViT for RF spectrograms, transformer-based mincut prediction,
positional encoding for RF graphs, foundation models for RF sensing, and
efficient edge deployment with INT8 quantization.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add attention mechanisms for RF sensing research

GOAP Agent 3 output: 1,110-line document covering GAT for RF graphs,
self-attention for CSI sequences, cross-attention multi-link fusion,
attention-weighted differentiable mincut, spatial node attention,
antenna-level subcarrier attention, and efficient attention variants
(linear, sparse, LSH, S4/Mamba). 8 ASCII architecture diagrams.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add sublinear mincut algorithms research

GOAP Agent 5 output: 698-line document covering classical mincut complexity,
sublinear approximation (sampling, sparsifiers), dynamic mincut with lazy
recomputation hybrid, streaming sketch algorithms, Benczur-Karger
sparsification, local partitioning (PageRank-guided cuts), randomized
methods reliability analysis, and Rust implementation with const-generic
RfGraph, zero-alloc Stoer-Wagner, SIMD batch updates.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add CSI edge weight computation research

GOAP Agent 2 output: ~700-line document covering CSI feature extraction,
coherence metrics (cross-correlation, mutual information, phasor coherence),
multipath stability scoring (MUSIC, ESPRIT, ISTA), temporal windowing
(EMA, Welford, Kalman), noise robustness (phase noise, AGC, clock drift),
edge weight normalization, and implementation architecture showing 32KB
memory for 120 edges within ESP32-S3 capability.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add contrastive learning for RF coherence research

GOAP Agent 7 output: 1,226-line document covering SimCLR/MoCo/BYOL for CSI,
AETHER-Topo dual-head extension, coherence boundary detection with multi-scale
analysis, delta-driven updates (2-12x efficiency), self-supervised pre-training
protocol, triplet networks for 5-state edge classification, and MERIDIAN
cross-environment transfer with EWC continual learning.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add resolution and spatial granularity analysis research

GOAP Agent 9 output: 1,383-line document covering Fresnel zone analysis,
node density vs resolution (16-node/5m room → 30-60cm), Cramer-Rao lower
bounds with Fisher Information Matrix, graph cut resolution theory,
multi-frequency enhancement (6cm coherent dual-band limit), RF tomography
comparison, experimental validation protocols, and resolution scaling laws
(8.8cm theoretical limit).

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add RF graph theory and minimum cut foundations research

GOAP Agent 1 output: Graph-theoretic foundations covering max-flow/min-cut
for RF (Ford-Fulkerson, Stoer-Wagner, Karger), RF as dynamic graph with
CSI coherence weights, topological change detection via Fiedler vector and
Cheeger inequality, dynamic graph algorithms, comparison to classical RF
sensing, formal mathematical framework, and 9 open research questions.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ESP32 mesh hardware constraints research

GOAP Agent 6 output: ESP32 CSI capabilities (52/114 subcarriers), 16-node
mesh topology with 120 edges, TDM synchronized sensing (3ms slots),
computational budget (Stoer-Wagner uses 0.07% of one core), channel hopping,
power analysis (0.44W/node), dual-core firmware architecture, and edge vs
server computing with 100x data reduction on-device.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add system architecture and prototype design research

GOAP Agent 10 output: End-to-end architecture with pipeline diagrams,
existing crate integration mapping, new rf_topology module design (DDD
aggregate roots), 100ms latency budget breakdown, 3-phase prototype plan
(4-node POC → 16-node room → 72-node multi-room), benchmark design with
8 metrics, ADR-044 draft, and Rust trait definitions (EdgeWeightComputer,
TopologyGraph, MinCutSolver, BoundaryInterpolator).

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add quantum sensing and quantum biomedical research documents

Agent 11: Quantum-level sensors (729 lines) — NV centers, SQUIDs, Rydberg
atoms, quantum illumination, quantum graph theory (walks, spectral, QAOA),
hybrid classical-quantum architecture, quantum ML (VQC, kernels, reservoir
computing), NISQ applications (D-Wave, VQE), hardware roadmap.

Agent 12: Quantum biomedical sensing (827 lines) — whole body biomagnetic
mapping, neural field imaging without electrodes, circulation sensing,
cellular EM signaling, non-contact diagnostics, coherence-based diagnostics
(disease as coherence breakdown), neural interfaces, multimodal observatory,
room-scale ambient health monitoring, graph-based biomedical analysis.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add research index synthesizing all 12 documents (14,322 lines)

Master index for RF Topological Sensing research compendium covering:
graph theory foundations, CSI edge weights, attention mechanisms,
transformers, sublinear algorithms, ESP32 hardware, contrastive learning,
temporal graphs, resolution analysis, system architecture, quantum sensors,
and quantum biomedical sensing. Includes key findings, proposed ADRs
(044, 045), and 5-phase implementation roadmap.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add SOTA neural decoding landscape and 10 application domains research

- Doc 21: Comprehensive SOTA map (2023-2026) of brain sensors, decoders,
  and visualization systems with RuVector/mincut positioning analysis
- Doc 22: Ten application domains for brain state observatory including
  disease detection, BCI, cognitive monitoring, mental health diagnostics,
  neurofeedback, dream reconstruction, cognitive research, HCI, wearables,
  and brain network digital twins with strategic roadmap

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add NV diamond neural magnetometry research document (13/22)

Comprehensive 600+ line document covering NV center physics, neural
magnetic field sources, sensor architecture, SQUID comparison, signal
processing pipeline, RuVector integration, and development roadmap.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural workspace Cargo.toml with 12 crate definitions

Workspace structure for the rUv Neural brain topology analysis system.
12 mix-and-match crates with shared dependencies including RuVector
integration, petgraph, rustfft, and WASM/ESP32 support.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural crate ecosystem — 12 mix-and-match crates (WIP)

Initial implementation of the rUv Neural brain topology analysis system:
- ruv-neural-core: Core types, traits, errors, RVF format (compiles)
- ruv-neural-sensor: NV diamond, OPM, EEG sensor interfaces (in progress)
- ruv-neural-signal: DSP, filtering, spectral, connectivity (in progress)
- ruv-neural-graph: Brain connectivity graph construction (in progress)
- ruv-neural-mincut: Dynamic minimum cut topology analysis (in progress)
- ruv-neural-embed: RuVector graph embeddings (in progress)
- ruv-neural-memory: Persistent neural state memory + HNSW (compiles)
- ruv-neural-decoder: Cognitive state classification + BCI (in progress)
- ruv-neural-esp32: ESP32 edge sensor integration (compiles)
- ruv-neural-wasm: WebAssembly browser bindings (in progress)
- ruv-neural-viz: Visualization + ASCII rendering (in progress)
- ruv-neural-cli: CLI tool (in progress)

Agents still writing remaining modules. Next: fix compilation, tests, push.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Fix ruv-neural crate compilation: all 12 crates build and 1200+ tests pass

- Fix node2vec.rs type inference error (Vec<_> → Vec<Vec<f64>>)
- Fix artifact.rs with full filter-based detection implementations
- Fix signal crate ConnectivityMetric re-export and trait method names
- Fix embed crate EmbeddingGenerator trait implementations
- Complete spectral, topology, and node2vec embedders with tests
- Complete preprocessing pipeline with sequential stage processing
- All workspace crates compile cleanly, 0 test failures

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural-cli README

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* fix: convert desktop icons from RGB to RGBA for Tauri build

Tauri's generate_context!() macro requires RGBA PNG icons. All 5 icon
files (32x32.png, 128x128.png, 128x128@2x.png, icon.icns, icon.ico)
were RGB-only, causing a proc macro panic on Linux builds.

Fixes #200

Co-Authored-By: claude-flow <ruv@ruv.net>

* Add Subcarrier Manifold and Vitals Oracle modules for 3D visualizations

- Implemented Subcarrier Manifold to visualize amplitude data as a 3D surface with height and age attributes.
- Created Vitals Oracle to represent vital signs using toroidal rings and particle trails, incorporating breathing and heart rate dynamics.
- Both modules utilize Three.js for rendering and include custom shaders for visual effects.

* feat: complete ruv-neural implementation — physics models, security, witness verification

Replace all stubs/mocks with production physics-based signal models:
- NV Diamond: ODMR Lorentzian dip, 1/f pink noise (Voss-McCartney), brain oscillations
- OPM: SERF-mode, 50/60Hz powerline harmonics, full cross-talk compensation
  via Gaussian elimination with partial pivoting
- EEG: 5 frequency bands, eye blink artifacts (Fp1/Fp2), muscle artifacts,
  impedance-based thermal noise floor
- ESP32 ADC: ring-buffer reader with calibration signal generator, i16 clamp

Security hardening (SEC-001 through SEC-005):
- RVF bounded allocation (16MB metadata, 256MB payload)
- sample_rate validation (>0, finite)
- Signal NaN/Inf rejection
- ADC resolution_bits overflow clamp
- HNSW HashSet visited tracking + bounds checks

Performance optimizations (PERF-001 through PERF-005):
- 67x fewer FFTs via pre-computed analytic signals
- VecDeque O(1) eviction in memory store
- Thread-local FFT planner caching
- BrainGraph::validate() for edge/weight integrity
- Eigenvalue convergence early termination

Ed25519 witness verification system:
- 41 capability attestations across all 12 crates
- SHA-256 digest + Ed25519 signature
- CLI commands: `witness --output` and `witness --verify`

README: ethics warning, hardware parts list (AliExpress), assembly instructions

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: add crates.io badges and install instructions to ruv-neural README

Add version badges linking to each published crate on crates.io,
cargo add instructions, and crate search link in the Crate Map table.

Co-Authored-By: claude-flow <ruv@ruv.net>

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-03-09 10:52:24 -04:00
..
LICENSE.md ruv-neural: publish 11 crates to crates.io — full implementation, no stubs 2026-03-09 10:52:24 -04:00
README.md ruv-neural: publish 11 crates to crates.io — full implementation, no stubs 2026-03-09 10:52:24 -04:00
package.json ruv-neural: publish 11 crates to crates.io — full implementation, no stubs 2026-03-09 10:52:24 -04:00

README.md

JSON5 JSON for Humans

Build Status CoverageStatus

JSON5 is an extension to the popular JSON file format that aims to be easier to write and maintain by hand (e.g. for config files). It is not intended to be used for machine-to-machine communication. (Keep using JSON or other file formats for that. 🙂)

JSON5 was started in 2012, and as of 2022, now gets >65M downloads/week, ranks in the top 0.1% of the most depended-upon packages on npm, and has been adopted by major projects like Chromium, Next.js, Babel, Retool, WebStorm, and more. It's also natively supported on Apple platforms like MacOS and iOS.

Formally, the JSON5 Data Interchange Format is a superset of JSON (so valid JSON files will always be valid JSON5 files) that expands its syntax to include some productions from ECMAScript 5.1 (ES5). It's also a strict subset of ES5, so valid JSON5 files will always be valid ES5.

This JavaScript library is a reference implementation for JSON5 parsing and serialization, and is directly used in many of the popular projects mentioned above (where e.g. extreme performance isn't necessary), but others have created many other libraries across many other platforms.

Summary of Features

The following ECMAScript 5.1 features, which are not supported in JSON, have been extended to JSON5.

Objects

  • Object keys may be an ECMAScript 5.1 IdentifierName.
  • Objects may have a single trailing comma.

Arrays

  • Arrays may have a single trailing comma.

Strings

  • Strings may be single quoted.
  • Strings may span multiple lines by escaping new line characters.
  • Strings may include character escapes.

Numbers

  • Numbers may be hexadecimal.
  • Numbers may have a leading or trailing decimal point.
  • Numbers may be IEEE 754 positive infinity, negative infinity, and NaN.
  • Numbers may begin with an explicit plus sign.

Comments

  • Single and multi-line comments are allowed.

White Space

  • Additional white space characters are allowed.

Example

Kitchen-sink example:

{
  // comments
  unquoted: 'and you can quote me on that',
  singleQuotes: 'I can use "double quotes" here',
  lineBreaks: "Look, Mom! \
No \\n's!",
  hexadecimal: 0xdecaf,
  leadingDecimalPoint: .8675309, andTrailing: 8675309.,
  positiveSign: +1,
  trailingComma: 'in objects', andIn: ['arrays',],
  "backwardsCompatible": "with JSON",
}

A more real-world example is this config file from the Chromium/Blink project.

Specification

For a detailed explanation of the JSON5 format, please read the official specification.

Installation and Usage

Node.js

npm install json5

CommonJS

const JSON5 = require('json5')

Modules

import JSON5 from 'json5'

Browsers

UMD

<!-- This will create a global `JSON5` variable. -->
<script src="https://unpkg.com/json5@2/dist/index.min.js"></script>

Modules

<script type="module">
  import JSON5 from 'https://unpkg.com/json5@2/dist/index.min.mjs'
</script>

API

The JSON5 API is compatible with the JSON API.

JSON5.parse()

Parses a JSON5 string, constructing the JavaScript value or object described by the string. An optional reviver function can be provided to perform a transformation on the resulting object before it is returned.

Syntax

JSON5.parse(text[, reviver])

Parameters

  • text: The string to parse as JSON5.
  • reviver: If a function, this prescribes how the value originally produced by parsing is transformed, before being returned.

Return value

The object corresponding to the given JSON5 text.

JSON5.stringify()

Converts a JavaScript value to a JSON5 string, optionally replacing values if a replacer function is specified, or optionally including only the specified properties if a replacer array is specified.

Syntax

JSON5.stringify(value[, replacer[, space]])
JSON5.stringify(value[, options])

Parameters

  • value: The value to convert to a JSON5 string.
  • replacer: A function that alters the behavior of the stringification process, or an array of String and Number objects that serve as a whitelist for selecting/filtering the properties of the value object to be included in the JSON5 string. If this value is null or not provided, all properties of the object are included in the resulting JSON5 string.
  • space: A String or Number object that's used to insert white space into the output JSON5 string for readability purposes. If this is a Number, it indicates the number of space characters to use as white space; this number is capped at 10 (if it is greater, the value is just 10). Values less than 1 indicate that no space should be used. If this is a String, the string (or the first 10 characters of the string, if it's longer than that) is used as white space. If this parameter is not provided (or is null), no white space is used. If white space is used, trailing commas will be used in objects and arrays.
  • options: An object with the following properties:
    • replacer: Same as the replacer parameter.
    • space: Same as the space parameter.
    • quote: A String representing the quote character to use when serializing strings.

Return value

A JSON5 string representing the value.

Node.js require() JSON5 files

When using Node.js, you can require() JSON5 files by adding the following statement.

require('json5/lib/register')

Then you can load a JSON5 file with a Node.js require() statement. For example:

const config = require('./config.json5')

CLI

Since JSON is more widely used than JSON5, this package includes a CLI for converting JSON5 to JSON and for validating the syntax of JSON5 documents.

Installation

npm install --global json5

Usage

json5 [options] <file>

If <file> is not provided, then STDIN is used.

Options:

  • -s, --space: The number of spaces to indent or t for tabs
  • -o, --out-file [file]: Output to the specified file, otherwise STDOUT
  • -v, --validate: Validate JSON5 but do not output JSON
  • -V, --version: Output the version number
  • -h, --help: Output usage information

Contributing

Development

git clone https://github.com/json5/json5
cd json5
npm install

When contributing code, please write relevant tests and run npm test and npm run lint before submitting pull requests. Please use an editor that supports EditorConfig.

Issues

To report bugs or request features regarding the JSON5 data format, please submit an issue to the official specification repository.

Note that we will never add any features that make JSON5 incompatible with ES5; that compatibility is a fundamental premise of JSON5.

To report bugs or request features regarding this JavaScript implementation of JSON5, please submit an issue to this repository.

Security Vulnerabilities and Disclosures

To report a security vulnerability, please follow the follow the guidelines described in our security policy.

License

MIT. See LICENSE.md for details.

Credits

Aseem Kishore founded this project. He wrote a blog post about the journey and lessons learned 10 years in.

Michael Bolin independently arrived at and published some of these same ideas with awesome explanations and detail. Recommended reading: Suggested Improvements to JSON

Douglas Crockford of course designed and built JSON, but his state machine diagrams on the JSON website, as cheesy as it may sound, gave us motivation and confidence that building a new parser to implement these ideas was within reach! The original implementation of JSON5 was also modeled directly off of Dougs open-source json_parse.js parser. Were grateful for that clean and well-documented code.

Max Nanasy has been an early and prolific supporter, contributing multiple patches and ideas.

Andrew Eisenberg contributed the original stringify method.

Jordan Tucker has aligned JSON5 more closely with ES5, wrote the official JSON5 specification, completely rewrote the codebase from the ground up, and is actively maintaining this project.