feat: complete vendor repos, add edge intelligence and WASM modules

- Add 154 missing vendor files (gitignore was filtering them)
  - vendor/midstream: 564 files (was 561)
  - vendor/sublinear-time-solver: 1190 files (was 1039)
- Add ESP32 edge processing (ADR-039): presence, vitals, fall detection
- Add WASM programmable sensing (ADR-040/041) with wasm3 runtime
- Add firmware CI workflow (.github/workflows/firmware-ci.yml)
- Add wifi-densepose-wasm-edge crate for edge WASM modules
- Update sensing server, provision.py, UI components

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

vendor/sublinear-time-solver/dist/core/solver.d.ts

@@ -0,0 +1,66 @@
+/**
+ * Core solver algorithms for asymmetric diagonally dominant systems
+ * Implements Neumann series, random walks, and push methods
+ */
+import { Matrix, Vector, SolverConfig, SolverResult, EstimationConfig, PageRankConfig, ProgressCallback } from './types.js';
+export declare class SublinearSolver {
+    private config;
+    private performanceMonitor;
+    private convergenceChecker;
+    private timeoutController?;
+    private wasmAccelerated;
+    private wasmModules;
+    constructor(config: SolverConfig);
+    private initializeWasm;
+    private validateConfig;
+    /**
+     * Solve ADD system Mx = b using specified method
+     */
+    solve(matrix: Matrix, vector: Vector, progressCallback?: ProgressCallback): Promise<SolverResult>;
+    /**
+     * Solve using Neumann series expansion
+     * x* = (I - D^(-1)R)^(-1) D^(-1) b = sum_{k=0}^∞ (D^(-1)R)^k D^(-1) b
+     */
+    private solveNeumann;
+    /**
+     * Compute off-diagonal matrix-vector multiplication: (M - D) * v
+     * This computes R*v where R = M - D (off-diagonal part of matrix)
+     */
+    private computeOffDiagonalMultiply;
+    /**
+     * Solve using random walk sampling
+     */
+    private solveRandomWalk;
+    /**
+     * Create transition matrix for random walks
+     */
+    private createTransitionMatrix;
+    /**
+     * Perform a single random walk
+     */
+    private performRandomWalk;
+    /**
+     * Solve using forward push method
+     */
+    private solveForwardPush;
+    /**
+     * Solve using backward push method
+     */
+    private solveBackwardPush;
+    /**
+     * Solve using bidirectional approach (combine forward and backward)
+     */
+    private solveBidirectional;
+    /**
+     * Estimate a single entry of the solution M^(-1)b
+     */
+    estimateEntry(matrix: Matrix, vector: Vector, config: EstimationConfig): Promise<{
+        estimate: number;
+        variance: number;
+        confidence: number;
+    }>;
+    /**
+     * Compute PageRank using the solver
+     */
+    computePageRank(adjacency: Matrix, config: PageRankConfig): Promise<Vector>;
+}