* refactor(train): hoist canonical PCK/OKS to un-gated metrics_core; fold test_metrics onto production (ADR-155 M1 §8)
ADR-155 §8 deferred item: test_metrics.rs reference kernels validated
production against their OWN reimplementation — a test that cannot catch a
canonical-impl bug (both could be wrong the same way).
- Extract canonical_torso_size / pck_canonical / oks_canonical / sigmas /
bounding_box_diagonal into a new NON-tch-gated `metrics_core` module, so
the single metric definition is reachable under
`cargo test --no-default-features` (the `metrics` module is tch-gated).
`metrics` re-exports every item → still exactly ONE implementation.
- Rewrite tests/test_metrics.rs to assert the PRODUCTION pck_canonical /
oks_canonical equal hand-computed fixtures (not a reimplementation):
canonical_pck_matches_hand_computed_fixture (corr=3/total=4/pck=0.75),
hip↔hip normalizer pin, zero-visible⇒0.0, OKS perfect⇒1.0, fake-Gold pin.
- Keep an INDEPENDENT raw-threshold reference kernel only as a differential
cross-check: test_kernel_agrees_with_canonical asserts it AGREES with
canonical where torso==1.0 (genuine cross-check, not duplication).
Grade: MEASURED. test_metrics 10→12 tests, 0 failed.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(sensing-server): relabel divergent live PCK/OKS so they're never conflated with canonical (ADR-155 M1 §2.1/§8 Goal C)
Goal C named training_api.rs:804 (torso-HEIGHT PCK). Auditing it surfaced
TWO findings the ADR-155 §1 table missed:
1. training_api.rs is an ORPHAN file — not declared `mod` in lib.rs OR main.rs,
so it does NOT compile into the crate. It does not drive the live server.
2. The REAL live `best_pck`/`best_oks` (main.rs training path → RVF metadata
JSON read by model_manager.rs) come from trainer.rs:
- `pck_at_threshold` = RAW-threshold PCK, NO torso normalization (the most
divergent kind), printed/serialized as bare "PCK@0.2".
- `oks_map` calls `oks_single(area=1.0)` = the EXACT fake-Gold pattern
ADR-155 §2.1 claimed closed elsewhere — still live here, inflating best_oks.
Resolution = RELABEL (torso/raw math is load-bearing on different data; the
pub fns can't be renamed without breaking API; sensing-server has no train/
ndarray dep). Honest unify is a tracked §8 backlog item.
- training_api.rs: `compute_pck` → `compute_pck_torso_height` + divergence doc;
val_pck/best_pck/val_oks struct fields documented as torso-HEIGHT proxies;
logs say `pck_torso_h@0.2`. Test torso_pck_is_labelled_distinctly_from_canonical.
- trainer.rs (LIVE): `pck_at_threshold` documented raw-unnormalized; `oks_map`
area=1.0 flagged fake-Gold; test pck_at_threshold_is_raw_unnormalized_not_canonical.
- main.rs: live print relabelled `pck_raw@0.2` / `oks_map(area=1.0 proxy)`.
No wire-format field renames (back-compat); no pub-API rename (no silent break).
Grade: MEASURED (relabel + divergence pinned). sensing-server 450→451 lib tests, 0 failed.
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(adr-155): mark §8 metric items RESOLVED + audit map + honest §1 under-count correction (M1b Goals A/D)
- §8.1: full PCK/OKS audit map (every def: file:line, basis, canonical/
legacy/distinct), the two §8 items marked RESOLVED with resolution+why.
- Honest finding: §1's "seven divergent metrics" was an UNDER-count —
sensing-server's LIVE trainer.rs has a raw-unnormalized PCK and an
area=1.0 fake-Gold OKS the table omitted, and the file §8 named
(training_api.rs) is orphaned dead code. §9 honest-limits updated.
- Goal D: metrics.rs *_v2 variants confirmed caller-less + deprecated;
noted for future cleanup, NOT deleted (public API, tch-gated).
- CHANGELOG [Unreleased] Fixed entry.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(ruvector): RaBitQ Pass-2 randomized rotation + topk bugfix (ADR-156 §8)
Implements the deferred "Multi-bit / Extended RaBitQ Pass 2" backlog item
from ADR-156 §8: a deterministic randomized orthogonal rotation applied
before sign-quantization, the published RaBitQ construction (Gao & Long,
SIGMOD 2024).
Rotation construction: Fast Hadamard Transform + seeded ±1 sign flips
("HD" / randomized Hadamard), O(d log d) time and O(d) memory — a dense
d×d rotation is O(d²) and infeasible at the 65,535-d the wire format
provisions for. Pads to the next power of two; SplitMix64 seeds the sign
stream so index-time and query-time rotations are bit-identical.
API is additive and backward-compatible: Pass 1 (`from_embedding`) is
untouched; Pass 2 is opt-in via `Sketch::from_embedding_rotated` and
`SketchBank::with_rotation` (+ `insert_embedding` / `topk_embedding` /
`novelty_embedding` helpers that rotate consistently). Default behaviour
is unchanged.
While building the Pass-2 coverage harness, found and fixed a PRE-EXISTING
correctness bug in `SketchBank::topk`: the n>k heap path used
`BinaryHeap<Reverse<(d,id)>>` (a min-heap) but treated its peek as the
max, so it returned the k FARTHEST sketches as "nearest". The shipped unit
tests only exercised the n≤k fast path, so it went unnoticed. Fixed to a
plain max-heap; pinned by `topk_heap_path_returns_nearest` and
`tight_clusters_give_high_coverage_with_overfetch` (the latter measured
0.072 on the old code).
New tests (+17, 100→117 in the crate): rotation determinism/norm-preservation
(`rotation_is_deterministic_for_seed`, `rotation_preserves_norm`), Pass-2
shape-compatibility, `pass2_coverage_not_worse_than_pass1`, and a
deterministic coverage report.
MEASURED top-K coverage (anisotropic planted-cluster fixture, cosine ground
truth; dim=128 N=2048 K=8 64 clusters noise=0.35 128 queries):
candidate_k=K=8 : Pass1 36.13% -> Pass2 46.39% (both << 90% bar)
candidate_k=24 : Pass1 83.89% -> Pass2 91.60% (Pass2 clears 90%)
candidate_k=32 : Pass1/Pass2 100%
Honest result: rotation consistently helps (+10pp at strict K), but neither
pass clears the ADR-084 90% bar at candidate_k==K on this distribution.
Pass 2 reaches 90% only with ~3x over-fetch (the ADR-084 "candidate set"
deployment pattern). Multi-bit Pass 3 evaluated separately.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(ruvector): multi-bit Pass-3 experiment + ADR-156/084 measured results
Adds the multi-bit half of the ADR-156 §8 "Multi-bit / Extended RaBitQ"
item as a MEASURED experiment (coverage::measure_multibit): rotate, then
b-bit uniform scalar-quantize each coord, rank by L1 over codes — the
natural multi-bit generalization of hamming. Measures the bit/coverage
tradeoff the backlog item asked for.
MEASURED at the strict bar (candidate_k=K=8, anisotropic planted-cluster
fixture, cosine ground truth):
Pass1 (1-bit, no rot) 36.13% 16 B/vec
Pass2 (1-bit, rot) 46.39% 16 B/vec
Pass3 (rot, 2-bit) 54.39% 32 B/vec
Pass3 (rot, 3-bit) 66.70% 48 B/vec
Pass3 (rot, 4-bit) 74.22% 64 B/vec
Honest: multi-bit monotonically helps but even 4-bit (4x memory) reaches
only 74% at the strict bar — neither rotation nor <=4-bit multi-bit clears
the strict-K 90% bar on this distribution. The bar is met via over-fetch
(Pass2 @ candidate_k=24). Tests: multibit_tradeoff_report,
multibit_1bit_matches_pass2_approx (+ sanity that 1-bit ~= Pass-2).
Docs:
- ADR-156 §8 item #2 marked RESOLVED-PARTIAL; §5 #2 grade CLAIMED ->
MEASURED-on-our-hardware; new §10 with full measured tables, the topk
bugfix disclosure, and graded deferred sub-items.
- ADR-084: "Pass 2" section answering the rotation open-question with
measured numbers + the topk bug note.
- CHANGELOG [Unreleased]: Added (Pass-2 milestone) + Fixed (topk heap).
Co-Authored-By: claude-flow <ruv@ruv.net>
|
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|---|---|---|
| .. | ||
| benches | ||
| examples | ||
| src | ||
| tests | ||
| Cargo.toml | ||
| README.md | ||
README.md
wifi-densepose-sensing-server
Lightweight Axum server for real-time WiFi sensing with RuVector signal processing.
Overview
wifi-densepose-sensing-server is the operational backend for WiFi-DensePose. It receives raw CSI
frames from ESP32 hardware over UDP, runs them through the RuVector-powered signal processing
pipeline, and broadcasts processed sensing updates to browser clients via WebSocket. A built-in
static file server hosts the sensing UI on the same port.
The crate ships both a library (wifi_densepose_sensing_server) exposing the training and inference
modules, and a binary (sensing-server) that starts the full server stack.
Integrates wifi-densepose-wifiscan for multi-BSSID WiFi scanning per ADR-022 Phase 3.
Features
- UDP CSI ingestion -- Receives ESP32 CSI frames on port 5005 and parses them into the internal
CsiFramerepresentation. - Vital sign detection -- Pure-Rust FFT-based breathing rate (0.1--0.5 Hz) and heart rate (0.67--2.0 Hz) estimation from CSI amplitude time series (ADR-021).
- RVF container -- Standalone binary container format for packaging model weights, metadata, and
configuration into a single
.rvffile with 64-byte aligned segments. - RVF pipeline -- Progressive model loading with streaming segment decoding.
- Graph Transformer -- Cross-attention bottleneck between antenna-space CSI features and the
COCO 17-keypoint body graph, followed by GCN message passing (ADR-023 Phase 2). Pure
std, no ML dependencies. - SONA adaptation -- LoRA + EWC++ online adaptation for environment drift without catastrophic forgetting (ADR-023 Phase 5).
- Contrastive CSI embeddings -- Self-supervised SimCLR-style pretraining with InfoNCE loss, projection head, fingerprint indexing, and cross-modal pose alignment (ADR-024).
- Sparse inference -- Activation profiling, sparse matrix-vector multiply, INT8/FP16 quantization, and a full sparse inference engine for edge deployment (ADR-023 Phase 6).
- Dataset pipeline -- Training dataset loading and batching.
- Multi-BSSID scanning -- Windows
netshintegration for BSSID discovery viawifi-densepose-wifiscan(ADR-022). - WebSocket broadcast -- Real-time sensing updates pushed to all connected clients at
ws://localhost:8765/ws/sensing. - Static file serving -- Hosts the sensing UI on port 8080 with CORS headers.
Modules
| Module | Description |
|---|---|
vital_signs |
Breathing and heart rate extraction via FFT spectral analysis |
rvf_container |
RVF binary format builder and reader |
rvf_pipeline |
Progressive model loading from RVF containers |
graph_transformer |
Graph Transformer + GCN for CSI-to-pose estimation |
trainer |
Training loop orchestration |
dataset |
Training data loading and batching |
sona |
LoRA adapters and EWC++ continual learning |
sparse_inference |
Neuron profiling, sparse matmul, INT8/FP16 quantization |
embedding |
Contrastive CSI embedding model and fingerprint index |
Quick Start
# Build the server
cargo build -p wifi-densepose-sensing-server
# Run with default settings (HTTP :8080, UDP :5005, WS :8765)
cargo run -p wifi-densepose-sensing-server
# Run with custom ports
cargo run -p wifi-densepose-sensing-server -- \
--http-port 9000 \
--udp-port 5005 \
--static-dir ./ui
Using as a library
use wifi_densepose_sensing_server::vital_signs::VitalSignDetector;
// Create a detector with 20 Hz sample rate
let mut detector = VitalSignDetector::new(20.0);
// Feed CSI amplitude samples
for amplitude in csi_amplitudes.iter() {
detector.push_sample(*amplitude);
}
// Extract vital signs
if let Some(vitals) = detector.detect() {
println!("Breathing: {:.1} BPM", vitals.breathing_rate_bpm);
println!("Heart rate: {:.0} BPM", vitals.heart_rate_bpm);
}
Architecture
ESP32 ──UDP:5005──> [ CSI Receiver ]
|
[ Signal Pipeline ]
(vital_signs, graph_transformer, sona)
|
[ WebSocket Broadcast ]
|
Browser <──WS:8765── [ Axum Server :8080 ] ──> Static UI files
Related Crates
| Crate | Role |
|---|---|
wifi-densepose-wifiscan |
Multi-BSSID WiFi scanning (ADR-022) |
wifi-densepose-core |
Shared types and traits |
wifi-densepose-signal |
CSI signal processing algorithms |
wifi-densepose-hardware |
ESP32 hardware interfaces |
wifi-densepose-wasm |
Browser WASM bindings for the sensing UI |
wifi-densepose-train |
Full training pipeline with ruvector |
wifi-densepose-mat |
Disaster detection module |
License
MIT OR Apache-2.0