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https://github.com/ruvnet/RuView
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chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427)
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/) without any sibling under rust-port/ that warranted the extra level. Move the whole workspace up to v2/ to match v1/ (Python) at the same depth and shorten every cd / build command across the repo. git mv preserves history for all tracked files. 60 files updated for path references (CI workflows, ADRs, docs, scripts, READMEs, internal .claude-flow state). Two manual fixes for relative-cd paths in CLAUDE.md and ADR-043 that became wrong after the depth change (cd ../.. → cd ..). Validated: - cargo check --workspace --no-default-features → clean (after target/ nuke; the gitignored target/ was carried by the OS rename and had hard-coded old paths in build scripts) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (same totals as pre-rename) - ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm) After-merge follow-up: contributors should `rm -rf v2/target` once and let cargo regenerate from the new path.
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rUv
@@ -40,7 +40,7 @@ The WiFi-DensePose codebase is a real-time sensing system targeting 20 Hz output
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### FINDING PERF-R01: Tomography Weight Matrix -- O(L * nx * ny * nz) per Link [CRITICAL]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/tomography.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/tomography.rs`
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**Lines**: 345-383 (`compute_link_weights`)
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The `compute_link_weights` function iterates over every voxel in the grid for every link to compute Fresnel-zone intersection weights:
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@@ -76,7 +76,7 @@ for iz in 0..config.nz {
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### FINDING PERF-R02: Multistatic Fusion -- sin()/cos() per Subcarrier per Node [HIGH]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs`
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**Lines**: 287-298 (`attention_weighted_fusion`)
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```rust
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@@ -105,7 +105,7 @@ for (n, (&, &ph)) in amplitudes.iter().zip(phases.iter()).enumerate() {
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### FINDING PERF-R03: Pose Tracker find_track -- Linear Search [MEDIUM]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/pose_tracker.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/pose_tracker.rs`
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**Lines**: 546-553
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```rust
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@@ -124,7 +124,7 @@ pub fn find_track(&self, id: TrackId) -> Option<&PoseTrack> {
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### FINDING PERF-R04: Multistatic FusedSensingFrame -- Deep Clone of node_frames [HIGH]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs`
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**Line**: 222
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```rust
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@@ -150,7 +150,7 @@ Ok(FusedSensingFrame {
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### FINDING PERF-R05: Coherence Score -- Efficient but exp() in Hot Loop [LOW]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/coherence.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/coherence.rs`
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**Lines**: 224-252 (`coherence_score`)
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```rust
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@@ -174,7 +174,7 @@ for i in 0..n {
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### FINDING PERF-R06: Gesture DTW -- O(N * M) per Template [MEDIUM]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/gesture.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/gesture.rs`
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**Lines**: 288-328 (`dtw_distance`)
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The DTW implementation uses the Sakoe-Chiba band constraint (good), but allocates two full Vec<f64> per call:
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@@ -199,7 +199,7 @@ With T templates and band_width=5, complexity is O(T * N * band_width * feature_
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### FINDING PERF-R07: Field Model Covariance -- O(S^2) Memory [MEDIUM]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/field_model.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/field_model.rs`
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**Line**: 330 (`covariance_sum: Option<Array2<f64>>`)
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The full covariance matrix for SVD is S x S where S = number of subcarriers. With S=56, this is 56 * 56 * 8 = 25 KB -- reasonable. But the diagonal_fallback (lines 338-383) creates unnecessary intermediate allocations.
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@@ -212,7 +212,7 @@ The full covariance matrix for SVD is S x S where S = number of subcarriers. Wit
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### FINDING PERF-R08: Multiband Duplicate Frequency Check -- O(N^2) [LOW]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/multiband.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/multiband.rs`
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**Lines**: 126-135
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```rust
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@@ -235,7 +235,7 @@ for i in 0..self.frequencies.len() {
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### FINDING PERF-R09: Adversarial Detector -- Potential O(L^2) Consistency Check [MEDIUM]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/adversarial.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/adversarial.rs`
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**Lines**: 147+
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The multi-link consistency check compares energy ratios across all links. With L=12 links, the pairwise comparison (if implemented) would be O(L^2) = 144. Combined with the four independent checks (consistency, field model, temporal, energy), this runs on every frame.
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@@ -259,7 +259,7 @@ The multi-link consistency check compares energy ratios across all links. With L
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### FINDING PERF-NN01: Serial Batch Inference [CRITICAL]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/inference.rs`
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**File**: `v2/crates/wifi-densepose-nn/src/inference.rs`
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**Lines**: 334-336
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```rust
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@@ -283,7 +283,7 @@ pub fn infer_batch(&self, inputs: &[Tensor]) -> NnResult<Vec<Tensor>> {
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### FINDING PERF-NN02: Async Stats Update Spawns Tokio Task per Inference [HIGH]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/inference.rs`
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**File**: `v2/crates/wifi-densepose-nn/src/inference.rs`
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**Lines**: 311-315
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```rust
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@@ -307,7 +307,7 @@ tokio::spawn(async move {
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### FINDING PERF-NN03: Tensor Clone in run_single [MEDIUM]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/inference.rs`
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**File**: `v2/crates/wifi-densepose-nn/src/inference.rs`
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**Lines**: 122
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```rust
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@@ -326,7 +326,7 @@ fn run_single(&self, input: &Tensor) -> NnResult<Tensor> {
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### FINDING PERF-NN04: WiFiDensePosePipeline -- Two Sequential Inferences [MEDIUM]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/inference.rs`
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**File**: `v2/crates/wifi-densepose-nn/src/inference.rs`
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**Lines**: 389-413
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```rust
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@@ -634,7 +634,7 @@ uint32_t next = (s_ring.head + 1) & (EDGE_RING_SLOTS - 1);
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### FINDING PERF-XC01: Missing Parallelism in Multistatic Pipeline [HIGH]
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**File**: `rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/mod.rs`
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**File**: `v2/crates/wifi-densepose-signal/src/ruvsense/mod.rs`
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**Lines**: 183-232
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The `RuvSensePipeline` orchestrator processes stages sequentially. The multiband fusion and phase alignment stages for each node are independent and could run in parallel using Rayon:
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@@ -756,26 +756,26 @@ The following patterns were checked and found to be well-implemented:
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## Appendix A: File Paths Analyzed
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### Rust Signal Processing
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/mod.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/tomography.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/pose_tracker.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/field_model.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/gesture.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/coherence.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/coherence_gate.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/multiband.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/phase_align.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/adversarial.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/intention.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/longitudinal.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/cross_room.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/temporal_gesture.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/ruvsense/attractor_drift.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/mod.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/tomography.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/pose_tracker.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/field_model.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/gesture.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/coherence.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/coherence_gate.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/multiband.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/phase_align.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/adversarial.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/intention.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/longitudinal.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/cross_room.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/temporal_gesture.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-signal/src/ruvsense/attractor_drift.rs`
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### Rust Neural Network
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/inference.rs`
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- `/workspaces/ruview/rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/tensor.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-nn/src/inference.rs`
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- `/workspaces/ruview/v2/crates/wifi-densepose-nn/src/tensor.rs`
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### Python Pipeline
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- `/workspaces/ruview/v1/src/core/csi_processor.py`
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