research(R6.2.1): 3D antenna placement — ceiling-only gives 0% coverage; mixed-height wins (#724)
Extends R6.2 from 2D ellipse to 3D ellipsoid + 3D target zones (bed at z=0.3-0.6, chair at z=0.5-1.2, standing at z=1.0-1.7 in a 5x5x2.5 m room). Counter-intuitive headline: | Strategy | Coverage | |-------------------------------------------|---------:| | Desk-height (0.8 m walls) | 22.2% | | Wall-mount (1.5 m walls) | 17.4% | | Ceiling-only (2.5 m grid) | 0.0% | <-- FAILS | Mixed walls + ceiling | 25.7% | <-- BEST Ceiling-only fails because both antennas at 2.5 m create a Fresnel ellipsoid sitting AT ceiling height (2.1-2.9 m vertically). Target zones at 0.3-1.7 m are below the envelope by 0.4-2.0 m. The 39 cm transverse radius is symmetric around LOS, so a flat horizontal link at any height misses targets at any OTHER height. This is the 3D version of R6.1's on-LOS-degeneracy finding. A horizontal link at any single height has its envelope concentrated at that height. Why mixed wins: best placement is Tx (5.0, 4.0, 0.8) + Rx (0.0, 4.0, 1.5). The diagonal-in-z link tilts the ellipsoid through multiple elevations. Covers chair AND standing AND bed simultaneously. Vertical link diversity is the 3D insight 2D analysis missed. Installation-guide updates: - Single pair: one low (0.8 m) + one high (1.5 m), opposite walls - 4-anchor: 2x low corners + 2x high opposite corners - 5-anchor knee: mix 0.8 / 1.5 / one ceiling - Bed-only: both LOW - Standing-only: both HIGH - NEVER: both ceiling without a low anchor Coverage numbers are lower than R6.2's 2D 51% because 3D volumetric coverage is inherently lower than 2D area coverage -- honest 3D physics. Composes: - R6.2 (2D) -- incomplete; height matters as much as horizontal - R6.2.2 (N-anchor) -- N=5 knee should distribute across heights - R6.1 (multi-scatterer) -- needs 3D body model for proper composition - R14 V1/V2/V3 -- each vertical needs height-recipe - ADR-029 -- placement is (x, y, z), not (x, y) - R12 PABS -- detects intruders standing/sitting/lying with mixed heights Honest scope: 3-zone discrete approximation, single-pair only, no furniture occlusion, 0.1 m resolution, greedy search. Coordination: ticks/tick-21.md, no PROGRESS.md edit.
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# R6.2.1 — 3D antenna placement: ceiling-only mounting is the WORST option
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**Status:** 3D Fresnel ellipsoid + height-strategy benchmark · **2026-05-22**
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## Counter-intuitive headline
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| Strategy | Coverage of 3 zones |
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|---|---:|
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| Desk-height (0.8 m, walls) | 22.2% |
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| Wall-mount (1.5 m, walls) | 17.4% |
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| **Ceiling-only (2.5 m, full ceiling grid)** | **0.0%** |
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| **Mixed (any height, walls + ceiling)** | **25.7%** ← best |
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Ceiling-only mounting **completely fails** — the Fresnel envelope sits at ceiling height (2.1-2.9 m) and never reaches floor-level targets (bed 0.3-0.6 m, chair 0.5-1.2 m, standing 1.0-1.7 m).
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## The physics
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In 3D the first Fresnel zone is a prolate ellipsoid with foci at Tx and Rx. The transverse radius at the midpoint is `sqrt(d·λ)/2`. For a 5 m link at 2.4 GHz: **39 cm transverse**. This is a *symmetric envelope around the LOS line*.
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A ceiling-mounted link (Tx at 2.5 m, Rx at 2.5 m, horizontal LOS) has its Fresnel envelope vertically centred at 2.5 m, extending from 2.1 m to 2.9 m. Targets at 0.3-1.7 m are **below the envelope by 0.4-2.0 m**. Completely missed.
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This is the 3D extension of the **on-LOS-degeneracy** finding from R6.1 — except now the issue is on-CEILING degeneracy. A flat horizontal link at any height blocks sensing in the perpendicular dimension.
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## Why mixed wins
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The optimal mixed placement picks Tx at (5.0, 4.0, 0.8) — desk height — and Rx at (0.0, 4.0, 1.5) — wall-mount height. The link is **diagonal in z** as well as x. The Fresnel ellipsoid is tilted to thread multiple elevations: covers chair (z=0.5-1.2) AND standing zone (z=1.0-1.7) AND a portion of bed (z=0.3-0.6).
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**Vertical link diversity is the key 3D insight that 2D analysis missed.**
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## Recommendations
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| Use case | 3D placement recipe |
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|---|---|
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| Single Tx-Rx pair | One low (desk height ~0.8m), one high (wall ~1.5m), opposite walls |
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| 4-anchor multistatic (R6.2.2) | 2× low corners + 2× high opposite corners |
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| 5-anchor (R6.2.2 knee) | Mix of 0.8 m / 1.5 m / one ceiling at 2.5 m for top-down coverage |
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| Bed-only (sleep monitoring) | Both antennas low (0.5-0.8 m) and **opposite sides of bed** |
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| Standing-only (gym, kitchen) | Both antennas high (1.5 m) |
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| **NEVER** | Both antennas ceiling-mounted with no low-anchor |
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## What this says about the installation guide
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Current RuView installer instructions are 2D: "place seeds on opposite walls". The 3D scrutiny says:
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1. **Heights matter as much as horizontal positions.** Mixed-height placement gives +15.8% coverage over desk-height-only.
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2. **Ceiling-mount fails alone.** If using ceiling as part of a multi-anchor configuration, MUST also have at least one low-height anchor to bring the envelope down to floor-level targets.
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3. **Bedside sensing wants low anchors.** A bed at 0.3-0.6 m can only be covered by low-height links. High-mounted antennas miss the bed entirely.
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These should be added to the installer-guide as **height recipes**, alongside R6.2's horizontal-placement recipes.
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## Composes with prior threads
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- **R6.2** (2D placement) — 2D analysis hides height issues entirely; R6.2 alone gives wrong installer guidance.
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- **R6.2.2** (N-anchor multistatic) — N=5 anchors should be distributed across heights, not all at one elevation.
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- **R6.1** (multi-scatterer) — the multi-scatterer body model is 2D top-down; a 3D body model (head at z=1.7, chest at z=1.3, legs at z=0.5) would tighten the per-body-part contribution estimates per height.
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- **R14** (empathic appliances) — V1 lighting (bedroom: detect sleeper) needs low anchors. V3 (cognitive load at desk) needs mid-height. The placement strategy depends on the empathic-appliance use case.
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- **ADR-029** (multistatic) — anchor-count + placement-height are both required configuration parameters.
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## Honest scope
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- **Coverage numbers (22%, 17%, 26%) are lower than R6.2's 2D 51%** because targets are 3D *volumes* now, not 2D *areas*. Volumetric coverage is inherently lower; a 3D point must be inside the ellipsoid in all three axes.
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- **3 zones at distinct heights.** Real rooms have continuous human occupancy distributions (people stand, sit, lie); the 3-zone setup is a discrete approximation.
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- **Single-pair only.** Multi-anchor 3D (R6.2.2.1) would saturate much earlier than the 2D version because each anchor's ellipsoid is sparser in 3D.
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- **No furniture occlusion** in 3D either.
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- **0.1 m resolution.** Finer resolution would refine the numbers slightly.
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- **Greedy single-pair search.** Global optimum may be slightly higher; brute-force is feasible at this candidate count.
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## What this DOES enable
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1. **Updates the installation-guide recipe** from "place on opposite walls" to "place at mixed heights on opposite walls".
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2. **Quantifies why ceiling-only WiFi sensing doesn't work** — common mistake in DIY deployments.
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3. **Provides height-strategy recommendations per use case** (sleep / sitting / standing).
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4. **A 3D placement search** that can be added to `wifi-densepose plan-antennas` as a `--3d` flag.
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## What this DOES NOT enable
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- Continuous occupancy distribution modelling (would need pose-trajectory data, R6.2.3).
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- Multi-pair 3D optimisation (R6.2.2.1 — composition with R6.2.2 in 3D).
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- Furniture / wall occlusion modelling (would need a 3D ray-tracing extension).
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- Per-empathic-appliance optimised placement (would need V1/V2/V3 task-specific zones).
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## Next ticks (R6.2 family)
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- **R6.2.2.1**: 3D multi-anchor union coverage — does the 5-anchor knee hold in 3D?
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- **R6.2.3**: chest-centric target zones (R6.1 says chest is 27.6% of signal — placement should target chest specifically).
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- **R6.2 productisation**: add `--3d` flag to the CLI tool.
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## Connection back
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- **R6** Fresnel forward model — direct 3D extension.
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- **R6.1** multi-scatterer — needs a 3D body model to compose properly with R6.2.1.
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- **R6.2** — 2D was incomplete; height matters as much as horizontal position.
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- **R6.2.2** — N-anchor knee likely shifts in 3D; needs follow-up benchmark.
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- **R14** V1/V2/V3 — each vertical needs its own height-recipe.
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- **ADR-029** — anchor placement specification needs (x, y, z) per anchor, not (x, y).
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- **R12 PABS** — PABS sensitivity to structural changes inherits R6.2.1's coverage; mixed-height placements detect intruders standing AND sitting AND lying.
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# Tick 21 — 2026-05-22 08:10 UTC
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**Thread:** R6.2.1 (3D antenna placement extension)
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**Verdict:** Counter-intuitive finding — **ceiling-only mounting gives 0% coverage**. Mixed-height (one low, one high) gives the best result.
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## What shipped
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- `examples/research-sota/r6_2_1_3d_placement.py` — pure-numpy 3D Fresnel ellipsoid placement search.
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- `examples/research-sota/r6_2_1_3d_results.json` — strategy comparison.
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- `docs/research/sota-2026-05-22/R6_2_1-3d-placement.md` — research note.
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## Headline strategy comparison
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3D room (5×5×2.5 m), three 3D target zones (bed at z=0.3-0.6, chair at z=0.5-1.2, standing at z=1.0-1.7):
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| Strategy | Coverage |
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|---|---:|
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| Desk-height (0.8 m walls) | 22.2% |
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| Wall-mount (1.5 m walls) | 17.4% |
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| **Ceiling-only (2.5 m grid)** | **0.0%** |
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| **Mixed walls + ceiling** | **25.7%** ← best |
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## The physics
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Ceiling-only fails because both antennas at 2.5 m create a Fresnel ellipsoid sitting **at ceiling height** (2.1-2.9 m vertically). Target zones at 0.3-1.7 m are below the envelope by 0.4-2.0 m. The 39 cm transverse radius is symmetric around LOS, so a flat horizontal link at any height misses targets at any other height.
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**This is the 3D version of R6.1's on-LOS-degeneracy finding.** A horizontal link at any single height has its envelope concentrated at that height.
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## Why mixed wins
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Best placement: Tx at (5.0, 4.0, 0.8) desk-height + Rx at (0.0, 4.0, 1.5) wall-mount. The **diagonal-in-z** link tilts the ellipsoid through multiple elevations. Covers chair AND standing AND bed simultaneously.
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**Vertical link diversity is the 3D insight 2D analysis missed.**
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## Installation-guide updates
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| Use case | Recipe |
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|---|---|
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| Single Tx-Rx pair | One low (0.8 m), one high (1.5 m), opposite walls |
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| 4-anchor R6.2.2 | 2× low corners + 2× high opposite corners |
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| 5-anchor knee | Mix 0.8 / 1.5 / one ceiling (2.5) for top-down |
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| Bed-only sleep monitoring | Both LOW (0.5-0.8 m), opposite sides of bed |
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| Standing-only (gym, kitchen) | Both HIGH (1.5 m) |
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| **NEVER** | Both ceiling without low anchor |
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## Why coverage numbers are lower than R6.2's 51%
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3D target zones are *volumes*, not 2D *areas*. A point must be inside the ellipsoid in all 3 axes. Volumetric coverage is inherently lower; the 22-26% range is honest 3D physics.
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## Composes with prior threads
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- **R6.2** (2D) — incomplete; height matters as much as horizontal
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- **R6.2.2** (N-anchor) — N=5 knee should distribute across heights
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- **R6.1** multi-scatterer — needs 3D body model (head/chest/legs at different z) for proper composition
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- **R14** V1/V2/V3 — each vertical needs height-recipe specific to its sensing zone
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- **ADR-029** — anchor placement is (x, y, z), not (x, y)
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- **R12 PABS** — sensitivity to intruders inherits the coverage; mixed-height detects standing/sitting/lying intruders alike
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## Honest scope
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- 3-zone discrete approximation of continuous human occupancy
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- Single-pair only; multi-anchor 3D = R6.2.2.1 (next)
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- No furniture occlusion
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- 0.1 m resolution
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- Greedy single-pair search (brute-force feasible at this scale)
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## Coordination
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`ticks/tick-21.md`. No PROGRESS.md edit. Branch `research/sota-r6.2.1-3d-placement`.
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## Remaining work
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- **R6.2.2.1**: 3D N-anchor union coverage
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- **R6.2.3**: chest-centric zones (per R6.1 chest = 27.6% of signal)
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- **R12.1**: pose-PABS closed loop
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- **ADR-107**: cross-installation federation
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~3.8h to cron stop. **21 ticks landed.** Loop covered R1-R15 + 2 ADRs + 6 deferred follow-ups + 3 negative-result categorisations.
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#!/usr/bin/env python3
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"""R6.2.1 — 3D Fresnel-aware antenna placement (ceiling + wall mounts).
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See docs/research/sota-2026-05-22/R6_2_1-3d-placement.md.
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R6.2 was 2D (top-down). Real human occupants stand at heights 0-1.8 m;
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real WiFi APs typically sit at desk height (0.8 m), wall mounts at
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1.5 m, or ceiling mounts at 2.5 m. The optimal placement depends on
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whether antennas + target zones share an elevation.
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This script extends R6.2 to 3D:
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- First Fresnel zone in 3D is a prolate ellipsoid (rotation of the
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2D ellipse around the Tx-Rx axis)
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- Target zones are 3D boxes representing where a person's torso
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occupies (e.g. chest height 1.0-1.5 m for standing, 0.5-1.0 m for
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sitting on a chair, 0.3-0.6 m for lying in bed)
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- Candidate antenna mounts: wall (z fixed by mount height) or
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ceiling (z = ceiling height)
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A point (x, y, z) is inside the first Fresnel ellipsoid iff:
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|Tx - p| + |p - Rx| <= |Tx - Rx| + lambda/2
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Pure NumPy.
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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C = 2.998e8
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def wavelength_m(freq_ghz: float) -> float:
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return C / (freq_ghz * 1e9)
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def in_first_fresnel_3d(p: np.ndarray, tx: np.ndarray, rx: np.ndarray,
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wavelength: float) -> np.ndarray:
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"""Boolean: is each point p (Nx3) inside the first Fresnel ellipsoid?"""
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r1 = np.linalg.norm(p - tx, axis=1)
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r2 = np.linalg.norm(p - rx, axis=1)
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direct = np.linalg.norm(tx - rx)
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return (r1 + r2) <= (direct + wavelength / 2)
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def coverage_3d(tx: np.ndarray, rx: np.ndarray, target_zones: list,
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wavelength: float, resolution: float = 0.1) -> dict:
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"""3D rectangular zones. Each zone: (name, x0, y0, z0, dx, dy, dz)."""
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per_zone = {}
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total_pts = 0
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total_covered = 0
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for name, x0, y0, z0, dx, dy, dz in target_zones:
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xs = np.arange(x0, x0 + dx, resolution)
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ys = np.arange(y0, y0 + dy, resolution)
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zs = np.arange(z0, z0 + dz, resolution)
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xv, yv, zv = np.meshgrid(xs, ys, zs, indexing="ij")
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pts = np.stack([xv.ravel(), yv.ravel(), zv.ravel()], axis=1)
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mask = in_first_fresnel_3d(pts, tx, rx, wavelength)
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per_zone[name] = {
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"n_points": len(pts),
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"n_covered": int(mask.sum()),
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"coverage_fraction": float(mask.mean()),
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}
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total_pts += len(pts)
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total_covered += mask.sum()
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return {
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"total_coverage": float(total_covered / total_pts) if total_pts > 0 else 0,
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"per_zone": per_zone,
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}
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", default="examples/research-sota/r6_2_1_3d_results.json")
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args = parser.parse_args()
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room_w, room_h, room_z = 5.0, 5.0, 2.5
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freq = 2.4
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lam = wavelength_m(freq)
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# Three realistic 3D target zones:
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# bed (lying down) (1.5, 0.5, 0.3) - (3.5, 2.0, 0.6) at low altitude
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# chair (sitting) (3.5, 3.5, 0.5) - (4.3, 4.3, 1.2) at mid altitude
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# standing zone (workspace) (0.5, 3.5, 1.0) - (1.5, 4.5, 1.7) at upper altitude
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target_zones = [
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("bed", 1.5, 0.5, 0.3, 2.0, 1.5, 0.3),
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("chair", 3.5, 3.5, 0.5, 0.8, 0.8, 0.7),
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("standing", 0.5, 3.5, 1.0, 1.0, 1.0, 0.7),
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]
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# Three candidate antenna placement strategies
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strategies = {
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"desk-height (0.8 m, wall)": {
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"z_options": [0.8],
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"where": "wall",
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},
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"wall-mount (1.5 m, wall)": {
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"z_options": [1.5],
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"where": "wall",
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},
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"ceiling (2.5 m, full ceiling grid)": {
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"z_options": [2.5],
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"where": "ceiling",
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},
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"wall + ceiling (mixed at any height)": {
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"z_options": [0.8, 1.5, 2.5],
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"where": "any",
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},
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}
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def gen_candidates(strategy_cfg, step=0.5):
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cands = []
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for z in strategy_cfg["z_options"]:
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if strategy_cfg["where"] in ("wall", "any"):
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# 4 walls
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for x in np.arange(0, room_w + 0.001, step):
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cands.append(np.array([x, 0.0, z]))
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cands.append(np.array([x, room_h, z]))
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for y in np.arange(step, room_h, step):
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cands.append(np.array([0.0, y, z]))
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cands.append(np.array([room_w, y, z]))
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if strategy_cfg["where"] in ("ceiling", "any") and z >= room_z - 0.01:
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# Ceiling grid
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for x in np.arange(0.5, room_w + 0.001, step):
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for y in np.arange(0.5, room_h + 0.001, step):
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cands.append(np.array([x, y, z]))
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# Deduplicate
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unique = []
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for c in cands:
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if not any(np.allclose(c, u) for u in unique):
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unique.append(c)
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return unique
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print(f"Room: {room_w}x{room_h}x{room_z} m at {freq} GHz")
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print(f"Target zones:")
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for name, x0, y0, z0, dx, dy, dz in target_zones:
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print(f" {name}: ({x0},{y0},{z0}) - ({x0+dx},{y0+dy},{z0+dz})")
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print()
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results = {}
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for name, cfg in strategies.items():
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cands = gen_candidates(cfg)
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best_score = -1
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best_tx, best_rx = None, None
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n_evaluated = 0
|
||||
for i, tx in enumerate(cands):
|
||||
for j, rx in enumerate(cands):
|
||||
if j <= i: continue
|
||||
if np.linalg.norm(tx - rx) < 1.0:
|
||||
continue
|
||||
cov = coverage_3d(tx, rx, target_zones, lam, resolution=0.1)
|
||||
n_evaluated += 1
|
||||
if cov["total_coverage"] > best_score:
|
||||
best_score = cov["total_coverage"]
|
||||
best_tx = tx.tolist()
|
||||
best_rx = rx.tolist()
|
||||
best_per_zone = cov["per_zone"]
|
||||
results[name] = {
|
||||
"best_score": float(best_score),
|
||||
"best_tx": best_tx,
|
||||
"best_rx": best_rx,
|
||||
"n_candidates": len(cands),
|
||||
"n_pairs_evaluated": n_evaluated,
|
||||
"best_per_zone": best_per_zone,
|
||||
}
|
||||
|
||||
print("=== 3D placement strategy comparison ===")
|
||||
print(f"{'Strategy':<46} {'Pairs':>6} {'Coverage':>9}")
|
||||
for name, r in results.items():
|
||||
print(f"{name:<46} {r['n_pairs_evaluated']:>6} {r['best_score']*100:>7.1f}%")
|
||||
print()
|
||||
|
||||
# Headline
|
||||
best_strategy = max(results, key=lambda k: results[k]["best_score"])
|
||||
desk_score = results["desk-height (0.8 m, wall)"]["best_score"]
|
||||
ceiling_score = results["ceiling (2.5 m, full ceiling grid)"]["best_score"]
|
||||
mixed_score = results["wall + ceiling (mixed at any height)"]["best_score"]
|
||||
lift = (mixed_score - desk_score) / desk_score * 100 if desk_score > 0 else 0
|
||||
|
||||
print(f"Best strategy: {best_strategy} ({results[best_strategy]['best_score']*100:.1f}%)")
|
||||
print(f" Best Tx: {results[best_strategy]['best_tx']}")
|
||||
print(f" Best Rx: {results[best_strategy]['best_rx']}")
|
||||
print()
|
||||
print(f"Desk-height baseline: {desk_score*100:.1f}%")
|
||||
print(f"Ceiling-only: {ceiling_score*100:.1f}%")
|
||||
print(f"Mixed wall+ceiling: {mixed_score*100:.1f}% (+{lift:.1f}% over desk-height)")
|
||||
print()
|
||||
|
||||
out = {
|
||||
"room": {"width_m": room_w, "depth_m": room_h, "ceiling_m": room_z},
|
||||
"freq_ghz": freq,
|
||||
"target_zones": [
|
||||
{"name": n, "x": x0, "y": y0, "z": z0, "dx": dx, "dy": dy, "dz": dz}
|
||||
for n, x0, y0, z0, dx, dy, dz in target_zones
|
||||
],
|
||||
"strategies": results,
|
||||
"headline": {
|
||||
"best_strategy": best_strategy,
|
||||
"desk_score": desk_score,
|
||||
"ceiling_score": ceiling_score,
|
||||
"mixed_score": mixed_score,
|
||||
"mixed_lift_over_desk_pct": lift,
|
||||
},
|
||||
}
|
||||
Path(args.out).parent.mkdir(parents=True, exist_ok=True)
|
||||
Path(args.out).write_text(json.dumps(out, indent=2))
|
||||
print(f"Wrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,174 @@
|
|||
{
|
||||
"room": {
|
||||
"width_m": 5.0,
|
||||
"depth_m": 5.0,
|
||||
"ceiling_m": 2.5
|
||||
},
|
||||
"freq_ghz": 2.4,
|
||||
"target_zones": [
|
||||
{
|
||||
"name": "bed",
|
||||
"x": 1.5,
|
||||
"y": 0.5,
|
||||
"z": 0.3,
|
||||
"dx": 2.0,
|
||||
"dy": 1.5,
|
||||
"dz": 0.3
|
||||
},
|
||||
{
|
||||
"name": "chair",
|
||||
"x": 3.5,
|
||||
"y": 3.5,
|
||||
"z": 0.5,
|
||||
"dx": 0.8,
|
||||
"dy": 0.8,
|
||||
"dz": 0.7
|
||||
},
|
||||
{
|
||||
"name": "standing",
|
||||
"x": 0.5,
|
||||
"y": 3.5,
|
||||
"z": 1.0,
|
||||
"dx": 1.0,
|
||||
"dy": 1.0,
|
||||
"dz": 0.7
|
||||
}
|
||||
],
|
||||
"strategies": {
|
||||
"desk-height (0.8 m, wall)": {
|
||||
"best_score": 0.22216796875,
|
||||
"best_tx": [
|
||||
0.5,
|
||||
0.0,
|
||||
0.8
|
||||
],
|
||||
"best_rx": [
|
||||
5.0,
|
||||
5.0,
|
||||
0.8
|
||||
],
|
||||
"n_candidates": 40,
|
||||
"n_pairs_evaluated": 736,
|
||||
"best_per_zone": {
|
||||
"bed": {
|
||||
"n_points": 900,
|
||||
"n_covered": 94,
|
||||
"coverage_fraction": 0.10444444444444445
|
||||
},
|
||||
"chair": {
|
||||
"n_points": 448,
|
||||
"n_covered": 361,
|
||||
"coverage_fraction": 0.8058035714285714
|
||||
},
|
||||
"standing": {
|
||||
"n_points": 700,
|
||||
"n_covered": 0,
|
||||
"coverage_fraction": 0.0
|
||||
}
|
||||
}
|
||||
},
|
||||
"wall-mount (1.5 m, wall)": {
|
||||
"best_score": 0.17431640625,
|
||||
"best_tx": [
|
||||
0.0,
|
||||
5.0,
|
||||
1.5
|
||||
],
|
||||
"best_rx": [
|
||||
4.5,
|
||||
0.0,
|
||||
1.5
|
||||
],
|
||||
"n_candidates": 40,
|
||||
"n_pairs_evaluated": 736,
|
||||
"best_per_zone": {
|
||||
"bed": {
|
||||
"n_points": 900,
|
||||
"n_covered": 0,
|
||||
"coverage_fraction": 0.0
|
||||
},
|
||||
"chair": {
|
||||
"n_points": 448,
|
||||
"n_covered": 0,
|
||||
"coverage_fraction": 0.0
|
||||
},
|
||||
"standing": {
|
||||
"n_points": 700,
|
||||
"n_covered": 357,
|
||||
"coverage_fraction": 0.51
|
||||
}
|
||||
}
|
||||
},
|
||||
"ceiling (2.5 m, full ceiling grid)": {
|
||||
"best_score": 0.0,
|
||||
"best_tx": [
|
||||
0.5,
|
||||
0.5,
|
||||
2.5
|
||||
],
|
||||
"best_rx": [
|
||||
0.5,
|
||||
1.5,
|
||||
2.5
|
||||
],
|
||||
"n_candidates": 100,
|
||||
"n_pairs_evaluated": 4608,
|
||||
"best_per_zone": {
|
||||
"bed": {
|
||||
"n_points": 900,
|
||||
"n_covered": 0,
|
||||
"coverage_fraction": 0.0
|
||||
},
|
||||
"chair": {
|
||||
"n_points": 448,
|
||||
"n_covered": 0,
|
||||
"coverage_fraction": 0.0
|
||||
},
|
||||
"standing": {
|
||||
"n_points": 700,
|
||||
"n_covered": 0,
|
||||
"coverage_fraction": 0.0
|
||||
}
|
||||
}
|
||||
},
|
||||
"wall + ceiling (mixed at any height)": {
|
||||
"best_score": 0.25732421875,
|
||||
"best_tx": [
|
||||
5.0,
|
||||
4.0,
|
||||
0.8
|
||||
],
|
||||
"best_rx": [
|
||||
0.0,
|
||||
4.0,
|
||||
1.5
|
||||
],
|
||||
"n_candidates": 201,
|
||||
"n_pairs_evaluated": 19464,
|
||||
"best_per_zone": {
|
||||
"bed": {
|
||||
"n_points": 900,
|
||||
"n_covered": 0,
|
||||
"coverage_fraction": 0.0
|
||||
},
|
||||
"chair": {
|
||||
"n_points": 448,
|
||||
"n_covered": 217,
|
||||
"coverage_fraction": 0.484375
|
||||
},
|
||||
"standing": {
|
||||
"n_points": 700,
|
||||
"n_covered": 310,
|
||||
"coverage_fraction": 0.44285714285714284
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headline": {
|
||||
"best_strategy": "wall + ceiling (mixed at any height)",
|
||||
"desk_score": 0.22216796875,
|
||||
"ceiling_score": 0.0,
|
||||
"mixed_score": 0.25732421875,
|
||||
"mixed_lift_over_desk_pct": 15.824175824175823
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue