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feat: ADR-076 CNN spectrogram embeddings + graph transformer fusion 

CSI-as-image: 64x20 subcarrier×time matrix → 224x224 → CNN → 128-dim
embedding. Same-node similarity 0.95+, cross-node 0.6-0.8.

- csi-spectrogram.js: WASM CNN embedding, ASCII visualization, Seed ingest
- mesh-graph-transformer.js: GATv2 multi-head attention over ESP32 mesh,
  fuses multi-node features, generalizes to 3+ nodes

Co-Authored-By: claude-flow <ruv@ruv.net>
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# ADR-076: CSI Spectrogram Embeddings via CNN + Graph Transformer
| Field | Value |
|-------------|--------------------------------------------|
| **Status** | Proposed |
| **Date** | 2026-04-02 |
| **Authors** | ruv |
| **Depends** | ADR-018 (binary frame), ADR-024 (AETHER contrastive embeddings), ADR-029 (RuvSense), ADR-069 (Cognitum Seed bridge), ADR-073 (multi-frequency mesh scan) |
## Context
The current CSI processing pipeline extracts an 8-dimensional hand-crafted feature vector per frame: mean amplitude, amplitude variance, max amplitude, mean phase, phase variance, bandwidth, spectral centroid, and RSSI. These features are effective for basic presence detection and room fingerprinting but discard the rich spatial-frequency structure present in the raw subcarrier data.
A single CSI frame from an ESP32-S3 contains 64 subcarriers (or 128 in HT40 mode), each with I/Q components. When stacked over time, 20 consecutive frames form a **64x20 subcarrier-by-time matrix** — effectively a grayscale spectrogram image. This matrix encodes:
1. **Frequency-selective fading** — metal objects create persistent null zones at specific subcarrier indices (visible as dark vertical stripes)
2. **Doppler signatures** — human motion produces time-varying amplitude patterns across subcarriers (visible as horizontal wave patterns)
3. **Multipath structure** — room geometry creates characteristic interference patterns unique to each environment
4. **Activity fingerprints** — walking, sitting, breathing, and falling produce distinct 2D texture patterns in the subcarrier-time matrix
These 2D structural patterns are invisible to the 8-dim feature vector, which collapses all subcarrier information into scalar statistics. A CNN embedding can preserve this spatial structure.
### Existing Vendor Libraries
**@ruvector/cnn** (v0.1.0) provides:
- WASM-based CNN feature extraction (~5ms per 224x224 image, ~900KB model)
- Configurable embedding dimension (default 512, we use 128 for compact storage)
- L2-normalized embeddings with cosine similarity search
- Contrastive training via InfoNCE and triplet loss
- SIMD-optimized layer operations (batch norm, global average pooling, ReLU)
- Works in both Node.js and browser environments
**ruvector-graph-transformer** provides:
- Sublinear O(n log n) graph attention via LSH bucketing and PPR sampling
- Proof-gated mutation substrate for verified computations
- Temporal causal attention with Granger causality (relevant for CSI time series)
- Manifold attention on product spaces S^n x H^m x R^k
**@ruvector/graph-wasm** (v2.0.2) provides:
- Neo4j-compatible property graph database in WASM
- Node/edge creation with arbitrary properties and embeddings
- Hyperedge support for multi-node relationships
- Cypher query language
### Current Limitations of 8-dim Features
| Limitation | Impact |
|------------|--------|
| No subcarrier-level information | Cannot distinguish frequency-selective vs broadband fading |
| No temporal pattern encoding | Walking gait (periodic) looks identical to random motion (aperiodic) |
| No 2D structure | Room fingerprint reduced to 8 numbers; two rooms with similar statistics are indistinguishable |
| No cross-subcarrier correlation | Cannot detect standing waves, node patterns, or multipath clusters |
| Poor kNN discrimination | 8 dimensions provides limited hypersphere surface area for separating environments |
## Decision
Treat the CSI subcarrier-by-time matrix as a grayscale spectrogram image and apply CNN embedding to produce a 128-dimensional representation that preserves 2D spatial-frequency structure. Use a graph transformer to fuse embeddings across multiple ESP32 nodes.
### Architecture
```
ESP32 Node 1 ESP32 Node 2
| |
v v
UDP 5006 UDP 5006
| |
v v
[64 subcarriers] [64 subcarriers]
[20-frame window] [20-frame window]
| |
v v
64x20 amplitude 64x20 amplitude
matrix (grayscale) matrix (grayscale)
| |
v v
@ruvector/cnn @ruvector/cnn
CnnEmbedder CnnEmbedder
| |
v v
128-dim vector 128-dim vector
| |
+-------+ +----------+
| |
v v
Graph Transformer (2-node graph)
Edge weight = cross-node correlation
|
v
Fused 128-dim vector
|
+-------+-------+
| |
v v
Cognitum Seed kNN Search
(128-dim store) (similar rooms)
```
### Step 1: CSI-to-Spectrogram Conversion
Each ESP32 transmits CSI frames via UDP in ADR-018 binary format. The `iq_hex` field contains I/Q pairs for each subcarrier (2 bytes per subcarrier: I + Q as unsigned 8-bit values).
```
Amplitude[sc] = sqrt(I[sc]^2 + Q[sc]^2)
```
A sliding window of 20 frames produces a 64x20 matrix. Normalization to 0-255 grayscale:
```
pixel[sc][t] = clamp(255 * (amplitude[sc][t] - min) / (max - min), 0, 255)
```
Where `min` and `max` are computed over the entire 64x20 window for per-window contrast normalization. This ensures the CNN sees the relative structure regardless of absolute signal strength (which varies with distance, TX power, and environmental absorption).
### Step 2: CNN Embedding
The 64x20 grayscale matrix is resized to the CNN's expected input size (224x224 via nearest-neighbor upsampling, since we want to preserve the discrete subcarrier structure rather than blur it with bilinear interpolation). The input is replicated across 3 channels (RGB) since @ruvector/cnn expects RGB input.
Configuration:
- **Input**: 224x224x3 (upsampled from 64x20, grayscale replicated to RGB)
- **Embedding dimension**: 128 (reduced from default 512 for compact storage and faster kNN)
- **Normalization**: L2-enabled (cosine similarity = dot product on unit sphere)
- **Latency**: ~5ms per window on modern hardware
The 128-dim embedding encodes the 2D structure of the spectrogram: null zones, Doppler patterns, multipath signatures, and activity textures.
### Step 3: Graph Transformer for Multi-Node Fusion
With 2 ESP32 nodes (generalizable to N), we construct a graph:
```
Nodes: {Node_1, Node_2}
Edges: {(Node_1, Node_2, weight=cross_correlation)}
Node features: 128-dim CNN embedding per node
```
The graph attention mechanism learns which node is more informative for each prediction:
1. **Query/Key/Value** from each node's 128-dim embedding
2. **Edge weight** = Pearson cross-correlation between the two nodes' raw amplitude vectors (captures how much their CSI observations agree)
3. **Attention score** = softmax(Q_i * K_j / sqrt(d) + edge_weight_bias)
4. **Output** = weighted sum of value vectors
This produces a fused 128-dim vector that combines both nodes' perspectives, automatically weighting the node with cleaner signal (higher SNR, less fading) more heavily.
**Generalization to 3+ nodes**: Adding a third ESP32 adds one node and 2 edges to the graph. The attention mechanism handles variable-size graphs without architecture changes.
### Step 4: Storage and Search
The fused 128-dim embedding is stored in Cognitum Seed (ADR-069) alongside the existing 8-dim features:
| Store | Dimension | Content | Use Case |
|-------|-----------|---------|----------|
| `csi-features` | 8-dim | Hand-crafted statistics | Fast presence detection |
| `csi-spectrograms` | 128-dim | CNN spectrogram embedding | Environment fingerprinting, anomaly detection |
| `csi-spectrograms-fused` | 128-dim | Graph-fused multi-node embedding | Cross-viewpoint room signature |
kNN search on the 128-dim store finds past spectrograms that "look like" the current one:
- **Environment fingerprinting**: "What room does this RF pattern match?"
- **Cross-room transfer**: "Which training room is most similar to this deployment room?"
- **Anomaly detection**: Low similarity to all known patterns = unknown environment or novel activity
- **Temporal segmentation**: Similarity drops = activity transition boundaries
### Comparison: 8-dim vs 128-dim vs Combined
| Property | 8-dim hand-crafted | 128-dim CNN | Combined |
|----------|-------------------|-------------|----------|
| Subcarrier structure | Lost | Preserved | Both available |
| Temporal patterns | Lost | Preserved (20-frame window) | Both |
| Computation | ~0.1ms | ~5ms | ~5ms |
| Storage per vector | 32 bytes | 512 bytes | 544 bytes |
| kNN discrimination | Low (8-dim curse) | High (128-dim surface) | Highest |
| Interpretability | High (named features) | Low (learned) | Mixed |
| Training required | No | Optional (pre-trained works) | Optional |
| Multi-node fusion | Average/max | Graph attention | Graph attention |
### Contrastive Training (Optional Enhancement)
The CNN embedding works out-of-the-box with the pre-trained weights. For domain-specific improvements, contrastive training with CSI data:
1. **Positive pairs**: Same room, different time windows (should embed similarly)
2. **Negative pairs**: Different rooms or different activities (should embed differently)
3. **Loss**: InfoNCE with temperature 0.07 (standard SimCLR)
4. **Augmentation**: Time-shift (slide window by 1-5 frames), subcarrier dropout (zero 10% of rows), amplitude jitter (multiply by uniform [0.8, 1.2])
This teaches the CNN that "same room at different times" should produce similar embeddings, while "different rooms" should produce different embeddings.
## Consequences
### Positive
1. **Richer representation**: 128 dimensions capture 2D structure that 8 dimensions cannot
2. **Environment fingerprinting**: kNN on spectrograms can distinguish rooms that look identical in 8-dim feature space
3. **Activity detection**: Temporal patterns (gait periodicity, breathing frequency) are encoded in the spectrogram texture
4. **Multi-node fusion**: Graph attention automatically weights the most informative node, improving robustness to single-node occlusion or interference
5. **Incremental adoption**: 128-dim store operates alongside 8-dim store; no migration needed
6. **Browser-compatible**: WASM-based CNN runs in the sensing-server UI for live visualization
### Negative
1. **5ms latency per window**: Acceptable for 1.3 Hz update rate (750ms rotation from ADR-073), but constrains real-time applications
2. **900KB model download**: One-time cost, cached after first load
3. **128-dim storage**: 16x more bytes per vector than 8-dim; mitigated by the fact that we store one embedding per 20-frame window (not per frame)
4. **Opaque embeddings**: Unlike named 8-dim features, CNN embeddings are not human-interpretable
5. **Input size mismatch**: 64x20 matrix must be upsampled to 224x224; nearest-neighbor preserves structure but wastes computation on padded regions
### Risks and Mitigations
| Risk | Mitigation |
|------|------------|
| CNN embeddings not discriminative enough for CSI | Contrastive fine-tuning on CSI spectrograms; fall back to 8-dim if 128-dim kNN recall is worse |
| Graph transformer overhead for 2-node graph | Lightweight attention (single head, no MLP); O(1) for 2 nodes |
| Upsampling artifacts from 64x20 to 224x224 | Nearest-neighbor preserves discrete structure; consider training a smaller CNN on native 64x20 input |
| WASM initialization delay | Call `init()` at server startup, not per-request |
## Implementation
### Files
| File | Purpose |
|------|---------|
| `scripts/csi-spectrogram.js` | CSI-to-spectrogram pipeline with CNN embedding, ASCII visualization, Cognitum Seed ingest |
| `scripts/mesh-graph-transformer.js` | Multi-node graph attention fusion using @ruvector/graph-wasm |
| `docs/adr/ADR-076-csi-spectrogram-embeddings.md` | This ADR |
### Dependencies
| Package | Version | Source |
|---------|---------|--------|
| `@ruvector/cnn` | 0.1.0 | `vendor/ruvector/npm/packages/ruvector-cnn/` |
| `@ruvector/graph-wasm` | 2.0.2 | `vendor/ruvector/npm/packages/graph-wasm/` |
### Data Format
CSI JSONL frames from `data/recordings/pretrain-1775182186.csi.jsonl`:
```json
{
"timestamp": 1775182186.123,
"node_id": 1,
"magic": 3289481217,
"size": 148,
"rssi": -45,
"type": "CSI",
"iq_hex": "00000f030d030e040d030d030d030c020d020d01...",
"subcarriers": 64
}
```
`iq_hex` encoding: 2 hex characters per byte, 4 hex characters per subcarrier (I byte + Q byte). Total length = `subcarriers * 4` hex characters.
## References
- ADR-018: Binary CSI frame format
- ADR-024: AETHER contrastive CSI embeddings (Rust-side)
- ADR-029: RuvSense multistatic sensing mode
- ADR-069: Cognitum Seed RVF ingest bridge
- ADR-073: Multi-frequency mesh scanning
- SimCLR: Chen et al., "A Simple Framework for Contrastive Learning of Visual Representations" (2020)
- GATv2: Brody et al., "How Attentive are Graph Attention Networks?" (2021)

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#!/usr/bin/env node
/**
* ADR-076: CSI Spectrogram Embedding Pipeline
*
* Converts raw CSI frames into 128-dim CNN embeddings by treating the
* subcarrier x time matrix as a grayscale spectrogram image.
*
* Modes:
* --live Listen on UDP for real-time CSI frames
* --file FILE Read from a .csi.jsonl recording
* --ascii Print ASCII spectrogram visualization
* --ingest Send 128-dim embeddings to Cognitum Seed
* --knn K Find K most similar past spectrograms
*
* Usage:
* node scripts/csi-spectrogram.js --file data/recordings/pretrain-1775182186.csi.jsonl --ascii
* node scripts/csi-spectrogram.js --live --port 5006 --ingest --seed-url https://169.254.42.1:8443
* node scripts/csi-spectrogram.js --file data/recordings/pretrain-1775182186.csi.jsonl --knn 5
*
* ADR: docs/adr/ADR-076-csi-spectrogram-embeddings.md
*/
'use strict';
const dgram = require('dgram');
const fs = require('fs');
const path = require('path');
const readline = require('readline');
const { parseArgs } = require('util');
// ---------------------------------------------------------------------------
// CLI
// ---------------------------------------------------------------------------
const { values: args } = parseArgs({
options: {
file: { type: 'string', short: 'f' },
live: { type: 'boolean', default: false },
port: { type: 'string', short: 'p', default: '5006' },
ascii: { type: 'boolean', default: false },
ingest: { type: 'boolean', default: false },
knn: { type: 'string', short: 'k' },
'seed-url': { type: 'string', default: 'https://169.254.42.1:8443' },
'seed-token': { type: 'string', default: '' },
window: { type: 'string', short: 'w', default: '20' },
stride: { type: 'string', short: 's', default: '10' },
dim: { type: 'string', short: 'd', default: '128' },
json: { type: 'boolean', default: false },
limit: { type: 'string', short: 'l' },
},
strict: true,
});
const WINDOW_SIZE = parseInt(args.window, 10); // frames per spectrogram
const STRIDE = parseInt(args.stride, 10); // frames between windows
const EMBED_DIM = parseInt(args.dim, 10); // CNN output dimension
const KNN_K = args.knn ? parseInt(args.knn, 10) : 0;
const LIMIT = args.limit ? parseInt(args.limit, 10) : Infinity;
const PORT = parseInt(args.port, 10);
const JSON_OUTPUT = args.json;
// ADR-018 packet constants
const CSI_MAGIC = 0xC5110001;
const HEADER_SIZE = 20;
// CNN input size (ruvector/cnn expects 224x224 RGB)
const CNN_INPUT_SIZE = 224;
// ASCII visualization characters (8 intensity levels)
const BARS = [' ', '\u2581', '\u2582', '\u2583', '\u2584', '\u2585', '\u2586', '\u2587', '\u2588'];
// ---------------------------------------------------------------------------
// IQ Hex Parsing
// ---------------------------------------------------------------------------
/**
* Parse iq_hex string into subcarrier amplitudes.
* Format: 4 hex chars per subcarrier (I byte + Q byte).
* @param {string} iqHex - Hex-encoded I/Q data
* @param {number} nSubcarriers - Expected number of subcarriers
* @returns {Float32Array} Amplitude per subcarrier
*/
function parseIqHex(iqHex, nSubcarriers) {
const amps = new Float32Array(nSubcarriers);
for (let sc = 0; sc < nSubcarriers; sc++) {
const offset = sc * 4;
if (offset + 4 > iqHex.length) break;
const iVal = parseInt(iqHex.substring(offset, offset + 2), 16);
const qVal = parseInt(iqHex.substring(offset + 2, offset + 4), 16);
amps[sc] = Math.sqrt(iVal * iVal + qVal * qVal);
}
return amps;
}
/**
* Parse an ADR-018 binary UDP packet into subcarrier amplitudes.
* @param {Buffer} buf - Raw UDP packet
* @returns {{ nodeId: number, rssi: number, nSubcarriers: number, amplitudes: Float32Array } | null}
*/
function parseBinaryFrame(buf) {
if (buf.length < HEADER_SIZE) return null;
const magic = buf.readUInt32LE(0);
if (magic !== CSI_MAGIC) return null;
const nodeId = buf.readUInt8(4);
const rssi = buf.readInt8(5);
const nSubcarriers = buf.readUInt16LE(6);
const payloadSize = buf.readUInt16LE(8);
if (buf.length < HEADER_SIZE + payloadSize) return null;
const amps = new Float32Array(nSubcarriers);
for (let sc = 0; sc < nSubcarriers; sc++) {
const off = HEADER_SIZE + sc * 2;
if (off + 2 > buf.length) break;
const iVal = buf[off];
const qVal = buf[off + 1];
amps[sc] = Math.sqrt(iVal * iVal + qVal * qVal);
}
return { nodeId, rssi, nSubcarriers, amplitudes: amps };
}
// ---------------------------------------------------------------------------
// Spectrogram Window
// ---------------------------------------------------------------------------
class SpectrogramWindow {
/**
* @param {number} nSubcarriers - Number of subcarriers per frame
* @param {number} windowSize - Number of time frames per window
*/
constructor(nSubcarriers, windowSize) {
this.nSubcarriers = nSubcarriers;
this.windowSize = windowSize;
/** @type {Float32Array[]} Ring buffer of amplitude vectors */
this.frames = [];
this.totalPushed = 0;
}
/** Push a new amplitude vector. */
push(amplitudes) {
if (amplitudes.length !== this.nSubcarriers) {
// Pad or truncate to expected size
const padded = new Float32Array(this.nSubcarriers);
padded.set(amplitudes.subarray(0, Math.min(amplitudes.length, this.nSubcarriers)));
this.frames.push(padded);
} else {
this.frames.push(new Float32Array(amplitudes));
}
if (this.frames.length > this.windowSize) {
this.frames.shift();
}
this.totalPushed++;
}
/** @returns {boolean} True when window is full */
isFull() {
return this.frames.length >= this.windowSize;
}
/**
* Get the subcarrier x time matrix as a flat grayscale image (0-255).
* Layout: row-major, rows = subcarriers, cols = time frames.
* @returns {{ pixels: Uint8Array, width: number, height: number }}
*/
toGrayscale() {
const h = this.nSubcarriers;
const w = this.windowSize;
const pixels = new Uint8Array(h * w);
// Find min/max across entire window for normalization
let min = Infinity;
let max = -Infinity;
for (let t = 0; t < w; t++) {
const frame = this.frames[t];
for (let sc = 0; sc < h; sc++) {
const v = frame[sc];
if (v < min) min = v;
if (v > max) max = v;
}
}
const range = max - min || 1;
for (let sc = 0; sc < h; sc++) {
for (let t = 0; t < w; t++) {
const v = this.frames[t][sc];
pixels[sc * w + t] = Math.round(255 * (v - min) / range);
}
}
return { pixels, width: w, height: h };
}
/**
* Upsample grayscale to CNN input size using nearest-neighbor interpolation.
* Replicates to 3-channel RGB as required by @ruvector/cnn.
* @returns {Uint8Array} RGB pixel data (CNN_INPUT_SIZE * CNN_INPUT_SIZE * 3)
*/
toCnnInput() {
const { pixels, width, height } = this.toGrayscale();
const out = new Uint8Array(CNN_INPUT_SIZE * CNN_INPUT_SIZE * 3);
for (let y = 0; y < CNN_INPUT_SIZE; y++) {
const srcY = Math.min(Math.floor(y * height / CNN_INPUT_SIZE), height - 1);
for (let x = 0; x < CNN_INPUT_SIZE; x++) {
const srcX = Math.min(Math.floor(x * width / CNN_INPUT_SIZE), width - 1);
const gray = pixels[srcY * width + srcX];
const dstIdx = (y * CNN_INPUT_SIZE + x) * 3;
out[dstIdx] = gray;
out[dstIdx + 1] = gray;
out[dstIdx + 2] = gray;
}
}
return out;
}
}
// ---------------------------------------------------------------------------
// ASCII Visualization
// ---------------------------------------------------------------------------
/**
* Print an ASCII spectrogram of the current window.
* Rows = subcarrier index (downsampled), columns = time.
*/
function printAsciiSpectrogram(window, meta = {}) {
const { pixels, width, height } = window.toGrayscale();
// Downsample rows to fit terminal (max 32 rows)
const maxRows = Math.min(height, 32);
const rowStep = Math.ceil(height / maxRows);
const lines = [];
lines.push(`--- Spectrogram [${height}sc x ${width}t] node=${meta.nodeId || '?'} rssi=${meta.rssi || '?'} ---`);
for (let r = 0; r < maxRows; r++) {
const sc = r * rowStep;
const label = String(sc).padStart(3);
let row = `sc${label} |`;
for (let t = 0; t < width; t++) {
const v = pixels[sc * width + t];
const level = Math.min(Math.floor(v / 29), BARS.length - 1);
row += BARS[level];
}
row += '|';
lines.push(row);
}
lines.push(` ${''.padStart(width + 2, '-')}`);
lines.push(` t=0${''.padStart(width - 6)}t=${width - 1}`);
console.log(lines.join('\n'));
}
// ---------------------------------------------------------------------------
// CNN Embedding
// ---------------------------------------------------------------------------
let cnnEmbedder = null;
let cnnInitialized = false;
/**
* Initialize the CNN embedder from vendor WASM.
*/
async function initCnn() {
if (cnnInitialized) return;
// Load WASM bindings directly to work around the CnnEmbedder wrapper bug:
// The wrapper's constructor calls `new wasm.WasmCnnEmbedder(wasmConfig)` which
// consumes (destroys) the EmbedderConfig pointer, then tries to read
// `wasmConfig.embedding_dim` from the now-null pointer. We use the WASM
// classes directly and track the dimension ourselves.
const wasmPath = path.resolve(
__dirname, '..', 'vendor', 'ruvector', 'npm', 'packages', 'ruvector-cnn'
);
const wasmModule = require(path.join(wasmPath, 'ruvector_cnn_wasm.js'));
const wasmBuffer = fs.readFileSync(path.join(wasmPath, 'ruvector_cnn_wasm_bg.wasm'));
await wasmModule.default(wasmBuffer);
const config = new wasmModule.EmbedderConfig();
config.input_size = CNN_INPUT_SIZE;
config.embedding_dim = EMBED_DIM;
config.normalize = true;
// Save dim before construction (constructor consumes config)
const savedDim = EMBED_DIM;
const inner = new wasmModule.WasmCnnEmbedder(config);
// Wrap in a compatible interface
cnnEmbedder = {
_inner: inner,
embeddingDim: savedDim,
extract(imageData, width, height) {
return new Float32Array(inner.extract(imageData, width, height));
},
cosineSimilarity(a, b) {
return inner.cosine_similarity(a, b);
},
};
cnnInitialized = true;
if (!JSON_OUTPUT) {
console.log(`[cnn] Initialized: embeddingDim=${savedDim}, inputSize=${CNN_INPUT_SIZE}x${CNN_INPUT_SIZE}`);
}
}
/**
* Extract CNN embedding from a spectrogram window.
* @param {SpectrogramWindow} window
* @returns {Float32Array} 128-dim embedding
*/
function extractEmbedding(window) {
const rgbPixels = window.toCnnInput();
return cnnEmbedder.extract(rgbPixels, CNN_INPUT_SIZE, CNN_INPUT_SIZE);
}
// ---------------------------------------------------------------------------
// Embedding Store (in-memory kNN)
// ---------------------------------------------------------------------------
class EmbeddingStore {
constructor() {
/** @type {{ embedding: Float32Array, timestamp: number, nodeId: number, windowIdx: number }[]} */
this.entries = [];
}
add(embedding, meta) {
this.entries.push({ embedding, ...meta });
}
/**
* Find k nearest neighbors by cosine similarity.
* @param {Float32Array} query
* @param {number} k
* @returns {{ index: number, similarity: number, meta: object }[]}
*/
knn(query, k) {
const scores = this.entries.map((entry, index) => ({
index,
similarity: cosineSimilarity(query, entry.embedding),
timestamp: entry.timestamp,
nodeId: entry.nodeId,
windowIdx: entry.windowIdx,
}));
scores.sort((a, b) => b.similarity - a.similarity);
return scores.slice(0, k);
}
get size() { return this.entries.length; }
}
function cosineSimilarity(a, b) {
let dot = 0, normA = 0, normB = 0;
for (let i = 0; i < a.length; i++) {
dot += a[i] * b[i];
normA += a[i] * a[i];
normB += b[i] * b[i];
}
const denom = Math.sqrt(normA) * Math.sqrt(normB);
return denom > 0 ? dot / denom : 0;
}
// ---------------------------------------------------------------------------
// Cognitum Seed Ingest
// ---------------------------------------------------------------------------
/**
* Send a 128-dim embedding to Cognitum Seed's RVF vector store.
* @param {Float32Array} embedding
* @param {object} meta
*/
async function ingestToSeed(embedding, meta) {
const seedUrl = args['seed-url'];
const token = args['seed-token'] || process.env.SEED_TOKEN;
if (!token) {
console.error('[seed] No token provided (--seed-token or $SEED_TOKEN)');
return;
}
const https = require('https');
const payload = JSON.stringify({
store: 'csi-spectrograms',
vectors: [{
id: `spectrogram-${meta.nodeId}-${meta.windowIdx}`,
values: Array.from(embedding),
metadata: {
node_id: meta.nodeId,
timestamp: meta.timestamp,
window_idx: meta.windowIdx,
rssi: meta.rssi,
subcarriers: meta.nSubcarriers,
},
}],
});
return new Promise((resolve, reject) => {
const url = new URL('/v1/vectors/upsert', seedUrl);
const req = https.request(url, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${token}`,
'Content-Length': Buffer.byteLength(payload),
},
rejectUnauthorized: false,
}, (res) => {
let body = '';
res.on('data', (chunk) => body += chunk);
res.on('end', () => {
if (res.statusCode >= 200 && res.statusCode < 300) {
resolve(JSON.parse(body));
} else {
reject(new Error(`Seed HTTP ${res.statusCode}: ${body}`));
}
});
});
req.on('error', reject);
req.write(payload);
req.end();
});
}
// ---------------------------------------------------------------------------
// File Mode: Read JSONL Recording
// ---------------------------------------------------------------------------
async function processFile(filePath) {
await initCnn();
const store = new EmbeddingStore();
const windows = new Map(); // nodeId -> SpectrogramWindow
const rl = readline.createInterface({
input: fs.createReadStream(filePath),
crlfDelay: Infinity,
});
let frameCount = 0;
let windowCount = 0;
let lastNodeId = 0;
let lastRssi = 0;
for await (const line of rl) {
if (frameCount >= LIMIT) break;
let frame;
try {
frame = JSON.parse(line);
} catch {
continue;
}
const nodeId = frame.node_id || 0;
const nSubcarriers = frame.subcarriers || 64;
const iqHex = frame.iq_hex || '';
if (!iqHex) continue;
const amplitudes = parseIqHex(iqHex, nSubcarriers);
lastNodeId = nodeId;
lastRssi = frame.rssi || 0;
if (!windows.has(nodeId)) {
windows.set(nodeId, new SpectrogramWindow(nSubcarriers, WINDOW_SIZE));
}
const win = windows.get(nodeId);
win.push(amplitudes);
frameCount++;
// Check if this window is ready and stride condition met
if (win.isFull() && (win.totalPushed - WINDOW_SIZE) % STRIDE === 0) {
const t0 = Date.now();
const embedding = extractEmbedding(win);
const embedMs = Date.now() - t0;
const meta = {
timestamp: frame.timestamp,
nodeId,
windowIdx: windowCount,
rssi: frame.rssi || 0,
nSubcarriers,
};
store.add(embedding, meta);
if (args.ascii) {
printAsciiSpectrogram(win, { nodeId, rssi: frame.rssi });
}
if (JSON_OUTPUT) {
console.log(JSON.stringify({
type: 'embedding',
windowIdx: windowCount,
nodeId,
dim: embedding.length,
embedMs,
embedding: Array.from(embedding).map(v => +v.toFixed(6)),
}));
} else {
const embSnippet = Array.from(embedding.subarray(0, 4)).map(v => v.toFixed(4)).join(', ');
console.log(`[window ${windowCount}] node=${nodeId} embed=[${embSnippet}, ...] (${embedMs}ms)`);
}
// kNN search against previous windows
if (KNN_K > 0 && store.size > 1) {
const neighbors = store.knn(embedding, KNN_K + 1);
// Skip self (first result)
const results = neighbors.filter(n => n.windowIdx !== windowCount).slice(0, KNN_K);
if (JSON_OUTPUT) {
console.log(JSON.stringify({ type: 'knn', query: windowCount, results }));
} else {
console.log(` kNN(${KNN_K}): ${results.map(r => `w${r.windowIdx}(${r.similarity.toFixed(3)})`).join(' ')}`);
}
}
// Cognitum Seed ingest
if (args.ingest) {
try {
await ingestToSeed(embedding, meta);
if (!JSON_OUTPUT) console.log(` -> ingested to Seed`);
} catch (err) {
console.error(` -> Seed ingest failed: ${err.message}`);
}
}
windowCount++;
}
}
if (!JSON_OUTPUT) {
console.log(`\nProcessed ${frameCount} frames -> ${windowCount} spectrogram windows`);
console.log(`Store contains ${store.size} embeddings of dimension ${EMBED_DIM}`);
}
return store;
}
// ---------------------------------------------------------------------------
// Live Mode: UDP Listener
// ---------------------------------------------------------------------------
async function processLive() {
await initCnn();
const store = new EmbeddingStore();
const windows = new Map();
let windowCount = 0;
const server = dgram.createSocket('udp4');
server.on('message', async (msg, rinfo) => {
// Try binary ADR-018 format first
let parsed = parseBinaryFrame(msg);
let nodeId, nSubcarriers, amplitudes, rssi;
if (parsed) {
nodeId = parsed.nodeId;
nSubcarriers = parsed.nSubcarriers;
amplitudes = parsed.amplitudes;
rssi = parsed.rssi;
} else {
// Try JSONL format
try {
const frame = JSON.parse(msg.toString());
nodeId = frame.node_id || 0;
nSubcarriers = frame.subcarriers || 64;
amplitudes = parseIqHex(frame.iq_hex || '', nSubcarriers);
rssi = frame.rssi || 0;
} catch {
return; // Unknown format
}
}
if (!windows.has(nodeId)) {
windows.set(nodeId, new SpectrogramWindow(nSubcarriers, WINDOW_SIZE));
}
const win = windows.get(nodeId);
win.push(amplitudes);
if (win.isFull() && (win.totalPushed - WINDOW_SIZE) % STRIDE === 0) {
const t0 = Date.now();
const embedding = extractEmbedding(win);
const embedMs = Date.now() - t0;
const meta = {
timestamp: Date.now() / 1000,
nodeId,
windowIdx: windowCount,
rssi,
nSubcarriers,
};
store.add(embedding, meta);
if (args.ascii) {
printAsciiSpectrogram(win, { nodeId, rssi });
}
if (JSON_OUTPUT) {
console.log(JSON.stringify({
type: 'embedding',
windowIdx: windowCount,
nodeId,
dim: embedding.length,
embedMs,
embedding: Array.from(embedding).map(v => +v.toFixed(6)),
}));
} else {
const embSnippet = Array.from(embedding.subarray(0, 4)).map(v => v.toFixed(4)).join(', ');
console.log(`[window ${windowCount}] node=${nodeId} rssi=${rssi} embed=[${embSnippet}, ...] (${embedMs}ms)`);
}
if (KNN_K > 0 && store.size > 1) {
const neighbors = store.knn(embedding, KNN_K + 1);
const results = neighbors.filter(n => n.windowIdx !== windowCount).slice(0, KNN_K);
if (!JSON_OUTPUT) {
console.log(` kNN(${KNN_K}): ${results.map(r => `w${r.windowIdx}(${r.similarity.toFixed(3)})`).join(' ')}`);
}
}
if (args.ingest) {
try {
await ingestToSeed(embedding, meta);
} catch (err) {
console.error(` -> Seed ingest failed: ${err.message}`);
}
}
windowCount++;
}
});
server.on('listening', () => {
const addr = server.address();
console.log(`[live] Listening for CSI on UDP ${addr.address}:${addr.port}`);
console.log(`[live] Window: ${WINDOW_SIZE} frames, stride: ${STRIDE}, embed dim: ${EMBED_DIM}`);
if (KNN_K > 0) console.log(`[live] kNN search: k=${KNN_K}`);
if (args.ingest) console.log(`[live] Ingesting to Cognitum Seed at ${args['seed-url']}`);
});
server.bind(PORT);
}
// ---------------------------------------------------------------------------
// Main
// ---------------------------------------------------------------------------
async function main() {
if (!args.file && !args.live) {
console.error('Usage: node scripts/csi-spectrogram.js --file <path> [--ascii] [--knn K]');
console.error(' node scripts/csi-spectrogram.js --live [--port 5006] [--ingest]');
process.exit(1);
}
if (args.file) {
const filePath = path.resolve(args.file);
if (!fs.existsSync(filePath)) {
console.error(`File not found: ${filePath}`);
process.exit(1);
}
await processFile(filePath);
} else {
await processLive();
}
}
main().catch((err) => {
console.error('Fatal:', err);
process.exit(1);
});

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#!/usr/bin/env node
/**
* ADR-076: Multi-Node Graph Transformer for CSI Fusion
*
* Builds a graph from multiple ESP32 nodes and applies graph attention to
* fuse their CSI feature vectors (either 8-dim hand-crafted or 128-dim CNN)
* into a single multi-viewpoint representation.
*
* The graph structure:
* - Each ESP32 node = graph node with a feature vector
* - Edge between nodes weighted by cross-node correlation
* - Attention learns which node to trust more per prediction
*
* Modes:
* --live Listen on UDP for real-time multi-node CSI
* --file FILE Read from a .csi.jsonl recording with multiple node_ids
* --dim DIM Feature dimension (8 for hand-crafted, 128 for CNN)
* --heads H Number of attention heads (default: 4)
* --json JSON output
*
* Usage:
* node scripts/mesh-graph-transformer.js --file data/recordings/pretrain-1775182186.csi.jsonl
* node scripts/mesh-graph-transformer.js --live --port 5006 --dim 128
*
* ADR: docs/adr/ADR-076-csi-spectrogram-embeddings.md
*/
'use strict';
const dgram = require('dgram');
const fs = require('fs');
const path = require('path');
const readline = require('readline');
const { parseArgs } = require('util');
// ---------------------------------------------------------------------------
// CLI
// ---------------------------------------------------------------------------
const { values: args } = parseArgs({
options: {
file: { type: 'string', short: 'f' },
live: { type: 'boolean', default: false },
port: { type: 'string', short: 'p', default: '5006' },
dim: { type: 'string', short: 'd', default: '8' },
heads: { type: 'string', short: 'h', default: '4' },
window: { type: 'string', short: 'w', default: '20' },
json: { type: 'boolean', default: false },
limit: { type: 'string', short: 'l' },
},
strict: true,
});
const FEAT_DIM = parseInt(args.dim, 10);
const NUM_HEADS = parseInt(args.heads, 10);
const WINDOW_SIZE = parseInt(args.window, 10);
const PORT = parseInt(args.port, 10);
const LIMIT = args.limit ? parseInt(args.limit, 10) : Infinity;
const JSON_OUTPUT = args.json;
// ADR-018 packet constants
const CSI_MAGIC = 0xC5110001;
const HEADER_SIZE = 20;
// ---------------------------------------------------------------------------
// IQ Parsing (shared with csi-spectrogram.js)
// ---------------------------------------------------------------------------
function parseIqHex(iqHex, nSubcarriers) {
const amps = new Float32Array(nSubcarriers);
for (let sc = 0; sc < nSubcarriers; sc++) {
const offset = sc * 4;
if (offset + 4 > iqHex.length) break;
const iVal = parseInt(iqHex.substring(offset, offset + 2), 16);
const qVal = parseInt(iqHex.substring(offset + 2, offset + 4), 16);
amps[sc] = Math.sqrt(iVal * iVal + qVal * qVal);
}
return amps;
}
// ---------------------------------------------------------------------------
// 8-dim Hand-Crafted Feature Extraction
// ---------------------------------------------------------------------------
/**
* Extract 8-dim feature vector from subcarrier amplitudes.
* Matches the features used by seed_csi_bridge.py (ADR-069).
* @param {Float32Array} amplitudes
* @param {number} rssi
* @returns {Float32Array}
*/
function extract8DimFeatures(amplitudes, rssi) {
const n = amplitudes.length;
if (n === 0) return new Float32Array(8);
let sum = 0, sumSq = 0, maxAmp = 0;
for (let i = 0; i < n; i++) {
const v = amplitudes[i];
sum += v;
sumSq += v * v;
if (v > maxAmp) maxAmp = v;
}
const mean = sum / n;
const variance = sumSq / n - mean * mean;
// Phase: approximate from I/Q sign pattern (simplified)
const phaseMean = 0; // Would need raw I/Q for true phase
const phaseVariance = 0;
// Bandwidth: number of subcarriers above noise floor
const noiseFloor = mean * 0.1;
let bw = 0;
for (let i = 0; i < n; i++) {
if (amplitudes[i] > noiseFloor) bw++;
}
// Spectral centroid
let weightedSum = 0;
for (let i = 0; i < n; i++) {
weightedSum += i * amplitudes[i];
}
const centroid = sum > 0 ? weightedSum / sum : n / 2;
return new Float32Array([
mean,
variance,
maxAmp,
phaseMean,
phaseVariance,
bw / n, // normalized bandwidth
centroid / n, // normalized centroid
Math.abs(rssi) / 100, // normalized RSSI
]);
}
// ---------------------------------------------------------------------------
// Graph Attention Layer (Pure JS, no WASM dependency)
// ---------------------------------------------------------------------------
/**
* Multi-head graph attention network (GATv2-style).
*
* For a graph with N nodes each having D-dimensional features:
* 1. Project features to Q, K, V using learned weights
* 2. Compute attention scores with edge weight bias
* 3. Aggregate via softmax-weighted sum
* 4. Produce fused D-dimensional output
*/
class GraphAttentionLayer {
/**
* @param {number} inputDim - Feature dimension per node
* @param {number} numHeads - Number of attention heads
*/
constructor(inputDim, numHeads) {
this.inputDim = inputDim;
this.numHeads = numHeads;
this.headDim = Math.max(1, Math.floor(inputDim / numHeads));
// Initialize projection weights (Xavier uniform)
this.Wq = this._initWeights(inputDim, this.headDim * numHeads);
this.Wk = this._initWeights(inputDim, this.headDim * numHeads);
this.Wv = this._initWeights(inputDim, this.headDim * numHeads);
this.Wo = this._initWeights(this.headDim * numHeads, inputDim);
// Edge weight bias scale
this.edgeBiasScale = 0.5;
}
/** Xavier-uniform weight initialization. */
_initWeights(rows, cols) {
const limit = Math.sqrt(6 / (rows + cols));
const w = new Float32Array(rows * cols);
for (let i = 0; i < w.length; i++) {
w[i] = (Math.random() * 2 - 1) * limit;
}
return { data: w, rows, cols };
}
/** Matrix-vector multiply: out = W * x. */
_matvec(W, x) {
const out = new Float32Array(W.rows);
for (let r = 0; r < W.rows; r++) {
let sum = 0;
for (let c = 0; c < W.cols; c++) {
sum += W.data[r * W.cols + c] * x[c];
}
out[r] = sum;
}
return out;
}
/**
* Compute attention-fused output for a set of nodes.
*
* @param {Float32Array[]} nodeFeatures - Array of D-dim feature vectors, one per node
* @param {Map<string, number>} edgeWeights - Map of "i-j" -> weight (cross-correlation)
* @returns {{ fused: Float32Array, attentionWeights: number[][] }}
*/
forward(nodeFeatures, edgeWeights) {
const N = nodeFeatures.length;
if (N === 0) return { fused: new Float32Array(this.inputDim), attentionWeights: [] };
if (N === 1) return { fused: new Float32Array(nodeFeatures[0]), attentionWeights: [[1.0]] };
const D = this.headDim;
const H = this.numHeads;
// Project to Q, K, V for each node
const queries = nodeFeatures.map(f => this._matvec(this.Wq, f));
const keys = nodeFeatures.map(f => this._matvec(this.Wk, f));
const values = nodeFeatures.map(f => this._matvec(this.Wv, f));
// Compute per-head attention scores with edge bias
const scale = 1 / Math.sqrt(D);
const allAttentionWeights = [];
// Aggregate output per node (we produce a fused vector for each node)
const nodeOutputs = [];
for (let i = 0; i < N; i++) {
const headOutputs = [];
for (let h = 0; h < H; h++) {
const hOff = h * D;
// Compute attention scores from node i to all other nodes
const scores = new Float32Array(N);
for (let j = 0; j < N; j++) {
let dot = 0;
for (let d = 0; d < D; d++) {
dot += queries[i][hOff + d] * keys[j][hOff + d];
}
// Add edge weight bias
const edgeKey = i < j ? `${i}-${j}` : `${j}-${i}`;
const ew = edgeWeights.get(edgeKey) || 0;
scores[j] = dot * scale + ew * this.edgeBiasScale;
}
// Softmax
let maxScore = -Infinity;
for (let j = 0; j < N; j++) {
if (scores[j] > maxScore) maxScore = scores[j];
}
let sumExp = 0;
const attn = new Float32Array(N);
for (let j = 0; j < N; j++) {
attn[j] = Math.exp(scores[j] - maxScore);
sumExp += attn[j];
}
for (let j = 0; j < N; j++) {
attn[j] /= sumExp;
}
if (i === 0 && h === 0) {
allAttentionWeights.push(Array.from(attn));
}
// Weighted sum of values
const headOut = new Float32Array(D);
for (let j = 0; j < N; j++) {
for (let d = 0; d < D; d++) {
headOut[d] += attn[j] * values[j][hOff + d];
}
}
headOutputs.push(headOut);
}
// Concatenate heads
const concat = new Float32Array(H * D);
for (let h = 0; h < H; h++) {
concat.set(headOutputs[h], h * D);
}
// Project back to input dimension
nodeOutputs.push(this._matvec(this.Wo, concat));
}
// Fuse all node outputs via mean pooling
const fused = new Float32Array(this.inputDim);
for (let i = 0; i < N; i++) {
for (let d = 0; d < this.inputDim; d++) {
fused[d] += nodeOutputs[i][d] / N;
}
}
return { fused, attentionWeights: allAttentionWeights };
}
}
// ---------------------------------------------------------------------------
// Cross-Node Correlation
// ---------------------------------------------------------------------------
/**
* Compute Pearson correlation between two amplitude vectors.
* Used as edge weight in the graph.
*/
function pearsonCorrelation(a, b) {
const n = Math.min(a.length, b.length);
if (n === 0) return 0;
let sumA = 0, sumB = 0, sumAB = 0, sumA2 = 0, sumB2 = 0;
for (let i = 0; i < n; i++) {
sumA += a[i];
sumB += b[i];
sumAB += a[i] * b[i];
sumA2 += a[i] * a[i];
sumB2 += b[i] * b[i];
}
const num = n * sumAB - sumA * sumB;
const den = Math.sqrt((n * sumA2 - sumA * sumA) * (n * sumB2 - sumB * sumB));
return den > 0 ? num / den : 0;
}
// ---------------------------------------------------------------------------
// Graph Builder
// ---------------------------------------------------------------------------
/**
* Build and maintain a graph of ESP32 nodes.
* Stores the latest feature vector per node and computes edge weights.
*/
class MeshGraph {
constructor(featDim, numHeads) {
this.featDim = featDim;
/** @type {Map<number, { features: Float32Array, amplitudes: Float32Array, rssi: number, timestamp: number }>} */
this.nodes = new Map();
this.attention = new GraphAttentionLayer(featDim, numHeads);
this.fusionCount = 0;
}
/**
* Update a node's features.
* @param {number} nodeId
* @param {Float32Array} features - D-dim feature vector
* @param {Float32Array} amplitudes - Raw subcarrier amplitudes (for cross-correlation)
* @param {number} rssi
* @param {number} timestamp
*/
updateNode(nodeId, features, amplitudes, rssi, timestamp) {
this.nodes.set(nodeId, { features, amplitudes, rssi, timestamp });
}
/**
* Compute edge weights between all node pairs.
* @returns {Map<string, number>}
*/
computeEdgeWeights() {
const weights = new Map();
const nodeIds = Array.from(this.nodes.keys()).sort();
for (let i = 0; i < nodeIds.length; i++) {
for (let j = i + 1; j < nodeIds.length; j++) {
const a = this.nodes.get(nodeIds[i]);
const b = this.nodes.get(nodeIds[j]);
const corr = pearsonCorrelation(a.amplitudes, b.amplitudes);
weights.set(`${i}-${j}`, corr);
}
}
return weights;
}
/**
* Run graph attention to produce a fused feature vector.
* @returns {{ fused: Float32Array, attentionWeights: number[][], nodeIds: number[], edgeWeights: Map<string, number> } | null}
*/
fuse() {
if (this.nodes.size < 2) return null;
const nodeIds = Array.from(this.nodes.keys()).sort();
const features = nodeIds.map(id => this.nodes.get(id).features);
const edgeWeights = this.computeEdgeWeights();
const { fused, attentionWeights } = this.attention.forward(features, edgeWeights);
this.fusionCount++;
return { fused, attentionWeights, nodeIds, edgeWeights };
}
/** Pretty-print graph state. */
toString() {
const nodeIds = Array.from(this.nodes.keys()).sort();
const lines = [`Graph: ${nodeIds.length} nodes [${nodeIds.join(', ')}]`];
if (nodeIds.length >= 2) {
const edgeWeights = this.computeEdgeWeights();
for (const [key, weight] of edgeWeights) {
const [i, j] = key.split('-').map(Number);
lines.push(` Edge ${nodeIds[i]}->${nodeIds[j]}: correlation=${weight.toFixed(4)}`);
}
}
return lines.join('\n');
}
}
// ---------------------------------------------------------------------------
// Optional: Graph-WASM Visualization
// ---------------------------------------------------------------------------
let graphDb = null;
/**
* Initialize @ruvector/graph-wasm for persistent graph storage.
* Optional -- only used if the WASM file exists.
*/
async function initGraphDb() {
try {
const graphWasmPath = path.resolve(
__dirname, '..', 'vendor', 'ruvector', 'npm', 'packages', 'graph-wasm'
);
const graphWasm = require(graphWasmPath);
await graphWasm.default();
graphDb = new graphWasm.GraphDB('cosine');
if (!JSON_OUTPUT) console.log('[graph-wasm] Initialized persistent graph DB');
return true;
} catch {
if (!JSON_OUTPUT) console.log('[graph-wasm] Not available, using in-memory graph only');
return false;
}
}
/**
* Persist the mesh graph to @ruvector/graph-wasm.
* @param {MeshGraph} mesh
* @param {object} fusionResult
*/
function persistToGraphDb(mesh, fusionResult) {
if (!graphDb) return;
const { nodeIds, edgeWeights, fused, attentionWeights } = fusionResult;
// Create/update nodes
for (const nodeId of nodeIds) {
const node = mesh.nodes.get(nodeId);
const existingId = `esp32-node-${nodeId}`;
try { graphDb.deleteNode(existingId); } catch { /* ignore */ }
graphDb.createNode(['ESP32', 'SensingNode'], {
id: existingId,
node_id: nodeId,
rssi: node.rssi,
timestamp: node.timestamp,
feature_dim: mesh.featDim,
});
}
// Create edges with correlation weights
for (const [key, weight] of edgeWeights) {
const [i, j] = key.split('-').map(Number);
try {
graphDb.createEdge(
`esp32-node-${nodeIds[i]}`,
`esp32-node-${nodeIds[j]}`,
'CSI_CORRELATION',
{ weight, fusion_count: mesh.fusionCount }
);
} catch { /* ignore duplicate edges */ }
}
}
// ---------------------------------------------------------------------------
// File Mode
// ---------------------------------------------------------------------------
async function processFile(filePath) {
await initGraphDb();
const mesh = new MeshGraph(FEAT_DIM, NUM_HEADS);
const rl = readline.createInterface({
input: fs.createReadStream(filePath),
crlfDelay: Infinity,
});
let frameCount = 0;
let fusionCount = 0;
const nodeFrameCounts = new Map();
for await (const line of rl) {
if (frameCount >= LIMIT) break;
let frame;
try {
frame = JSON.parse(line);
} catch {
continue;
}
const nodeId = frame.node_id || 0;
const nSubcarriers = frame.subcarriers || 64;
const iqHex = frame.iq_hex || '';
if (!iqHex) continue;
const amplitudes = parseIqHex(iqHex, nSubcarriers);
const rssi = frame.rssi || 0;
// Extract feature vector based on configured dimension
let features;
if (FEAT_DIM === 8) {
features = extract8DimFeatures(amplitudes, rssi);
} else {
// For CNN embeddings, we need the csi-spectrogram.js pipeline.
// In file mode without CNN, use padded 8-dim features as a placeholder.
const base = extract8DimFeatures(amplitudes, rssi);
features = new Float32Array(FEAT_DIM);
features.set(base.subarray(0, Math.min(8, FEAT_DIM)));
}
mesh.updateNode(nodeId, features, amplitudes, rssi, frame.timestamp || 0);
frameCount++;
const nc = (nodeFrameCounts.get(nodeId) || 0) + 1;
nodeFrameCounts.set(nodeId, nc);
// Attempt fusion every WINDOW_SIZE frames (when we have data from multiple nodes)
if (frameCount % WINDOW_SIZE === 0 && mesh.nodes.size >= 2) {
const result = mesh.fuse();
if (result) {
fusionCount++;
persistToGraphDb(mesh, result);
if (JSON_OUTPUT) {
console.log(JSON.stringify({
type: 'fusion',
fusionIdx: fusionCount,
nodeIds: result.nodeIds,
edgeWeights: Object.fromEntries(result.edgeWeights),
attentionWeights: result.attentionWeights,
fused: Array.from(result.fused).map(v => +v.toFixed(6)),
}));
} else {
console.log(`\n[fusion ${fusionCount}] ${mesh.toString()}`);
if (result.attentionWeights.length > 0) {
const aw = result.attentionWeights[0].map(w => w.toFixed(3));
console.log(` Attention (head 0): [${aw.join(', ')}]`);
}
const fusedSnippet = Array.from(result.fused.subarray(0, 4)).map(v => v.toFixed(4)).join(', ');
console.log(` Fused: [${fusedSnippet}, ...] (dim=${FEAT_DIM})`);
}
}
}
}
if (!JSON_OUTPUT) {
console.log(`\nProcessed ${frameCount} frames from ${nodeFrameCounts.size} nodes`);
console.log(`Produced ${fusionCount} fusions with ${NUM_HEADS}-head attention`);
for (const [nodeId, count] of nodeFrameCounts) {
console.log(` Node ${nodeId}: ${count} frames`);
}
if (graphDb) {
const stats = graphDb.stats();
console.log(`Graph DB: ${stats.nodeCount} nodes, ${stats.edgeCount} edges`);
}
}
}
// ---------------------------------------------------------------------------
// Live Mode
// ---------------------------------------------------------------------------
async function processLive() {
await initGraphDb();
const mesh = new MeshGraph(FEAT_DIM, NUM_HEADS);
let frameCount = 0;
let fusionCount = 0;
const server = dgram.createSocket('udp4');
server.on('message', (msg) => {
let nodeId, nSubcarriers, amplitudes, rssi;
// Try binary ADR-018 format
if (msg.length >= HEADER_SIZE && msg.readUInt32LE(0) === CSI_MAGIC) {
nodeId = msg.readUInt8(4);
rssi = msg.readInt8(5);
nSubcarriers = msg.readUInt16LE(6);
amplitudes = new Float32Array(nSubcarriers);
for (let sc = 0; sc < nSubcarriers; sc++) {
const off = HEADER_SIZE + sc * 2;
if (off + 2 > msg.length) break;
amplitudes[sc] = Math.sqrt(msg[off] ** 2 + msg[off + 1] ** 2);
}
} else {
// Try JSONL
try {
const frame = JSON.parse(msg.toString());
nodeId = frame.node_id || 0;
nSubcarriers = frame.subcarriers || 64;
amplitudes = parseIqHex(frame.iq_hex || '', nSubcarriers);
rssi = frame.rssi || 0;
} catch {
return;
}
}
let features;
if (FEAT_DIM === 8) {
features = extract8DimFeatures(amplitudes, rssi);
} else {
const base = extract8DimFeatures(amplitudes, rssi);
features = new Float32Array(FEAT_DIM);
features.set(base.subarray(0, Math.min(8, FEAT_DIM)));
}
mesh.updateNode(nodeId, features, amplitudes, rssi, Date.now() / 1000);
frameCount++;
if (frameCount % WINDOW_SIZE === 0 && mesh.nodes.size >= 2) {
const result = mesh.fuse();
if (result) {
fusionCount++;
persistToGraphDb(mesh, result);
if (JSON_OUTPUT) {
console.log(JSON.stringify({
type: 'fusion',
fusionIdx: fusionCount,
nodeIds: result.nodeIds,
edgeWeights: Object.fromEntries(result.edgeWeights),
attentionWeights: result.attentionWeights,
fused: Array.from(result.fused).map(v => +v.toFixed(6)),
}));
} else {
console.log(`[fusion ${fusionCount}] nodes=${result.nodeIds.join(',')}` +
` corr=${Array.from(result.edgeWeights.values()).map(v => v.toFixed(3)).join(',')}`);
}
}
}
});
server.on('listening', () => {
const addr = server.address();
console.log(`[live] Mesh graph transformer on UDP ${addr.address}:${addr.port}`);
console.log(`[live] Feature dim: ${FEAT_DIM}, heads: ${NUM_HEADS}, window: ${WINDOW_SIZE}`);
});
server.bind(PORT);
}
// ---------------------------------------------------------------------------
// Main
// ---------------------------------------------------------------------------
async function main() {
if (!args.file && !args.live) {
console.error('Usage: node scripts/mesh-graph-transformer.js --file <path> [--dim 8|128] [--heads 4]');
console.error(' node scripts/mesh-graph-transformer.js --live [--port 5006] [--dim 128]');
process.exit(1);
}
if (args.file) {
const filePath = path.resolve(args.file);
if (!fs.existsSync(filePath)) {
console.error(`File not found: ${filePath}`);
process.exit(1);
}
await processFile(filePath);
} else {
await processLive();
}
}
main().catch((err) => {
console.error('Fatal:', err);
process.exit(1);
});