Soul Signature — References

Status: Research Specification (Pre-Implementation) Date: 2026-05-24 Author: ruv

1. Internal Architecture Decision Records

All ADRs are located at docs/adr/ADR-XXX-*.md in this repository.

ADR	Title	Relevance to soul signature
ADR-003	RVF Cognitive Containers for CSI Data	RVF container format used by soul signature
ADR-004	HNSW Vector Search for Signal Fingerprinting	HNSW index for person_track embedding search
ADR-005	SONA Self-Learning Pose Estimation	LoRA adaptation, EWC regularization, environment profiles
ADR-007	Post-Quantum Cryptography Secure Sensing	PQC cryptographic context; foundation for ADR-108/109
ADR-010	Witness Chains Audit Trail Integrity	Witness chain design; Ed25519 over frame bundles
ADR-014	SOTA Signal Processing Algorithms	RuvSense pipeline: conjugate multiplication, Hampel filter, spectrogram, BVP
ADR-021	Vital Sign Detection via rvdna Pipeline	Cardiac HR / respiratory extraction; bandpass filters; ADR-039 vitals packet
ADR-023	Trained DensePose Model with RuVector Pipeline	CsiToPoseTransformer backbone; MPJPE baseline 91.7 mm
ADR-024	Project AETHER — Contrastive CSI Embedding Model	Primary soul signature identity channel; 128-dim L2-normalized embedding; HNSW person_track index (>80% mAP target at 5 subjects)
ADR-027	Project MERIDIAN — Cross-Environment Domain Generalization	Environment-disentangled embeddings; HardwareNormalizer; multi-room portability
ADR-029	RuvSense Multistatic Sensing Mode	Multi-node mesh; 20 Hz DensePose; <30 mm jitter; person separation
ADR-030	RuvSense Persistent Field Model	Field normal modes; SVD eigenstructure; perturbation extraction; longitudinal drift; adversarial detection; cross-room continuity
ADR-039	ESP32-S3 Edge Intelligence Pipeline	Vitals packet wire format (magic `0xC511_0002`); HR/BR on-device extraction
ADR-075	MinCut Person Separation	ruvector-mincut for multi-person track assignment
ADR-079	Camera Ground-Truth Training	Paired camera + CSI training; skeletal proportions accuracy
ADR-082	Pose Tracker Confirmed Output Filter	Pose tracker output confidence filtering
ADR-100	Cog Packaging Specification	Ed25519 firmware signing; supply chain integrity
ADR-105	Federated CSI Training	Federated AETHER fine-tuning; secure aggregation
ADR-106	DP-SGD and Primitive Isolation	Differential privacy at training; biometric primitive isolation; (ε, δ)-DP budget
ADR-107	Cross-Installation Federation	Cross-installation secure aggregation; DH key exchange
ADR-108	Kyber Post-Quantum Key Exchange	Kyber-768 (NIST FIPS 203); hybrid X25519 + Kyber during migration
ADR-109	Dilithium PQC Signatures	Dilithium-3 (NIST FIPS 204); hybrid Ed25519 + Dilithium; cog signing
ADR-110	ESP32-C6 Firmware Extension	Wi-Fi 6 HE-LTF CSI (242 subcarriers); 802.15.4 time-sync; TWT; Ed25519 witness chain per-frame
ADR-113	Multistatic Placement Strategy	Node placement geometry; coverage analysis
ADR-115	Home Assistant Integration (HA-DISCO + HA-MIND)	Privacy mode; MQTT auto-discovery; semantic primitives layer under which soul signature operates

2. AETHER and Contrastive Embedding Foundations

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations (SimCLR). ICML 2020. arXiv:2002.05709.
Chen, T., Kornblith, S., Sohl-Dickstein, J., & Hinton, G. (2020). Big Self-Supervised Models are Strong Semi-Supervised Learners (SimCLR v2). NeurIPS 2020. arXiv:2006.10029.
Bardes, A., Ponce, J., & LeCun, Y. (2022). VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. ICLR 2022. arXiv:2105.04906.
Grill, J.-B., et al. (2020). Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (BYOL). NeurIPS 2020. arXiv:2006.07733.
Wang, T. & Isola, P. (2020). Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. ICML 2020. arXiv:2005.10242.

3. WiFi CSI Biometric Identification (Prior Art)

IdentiFi (2025): Self-supervised WiFi-based identity recognition in multi-user smart environments. Contrastive pretraining in the signal domain produces identity-discriminative embeddings without spatial labels. PMC:12115556.
WhoFi (2025): Transformer-based WiFi CSI encoding for person re-identification. 95.5% accuracy on NTU-Fi (18 subjects). Validates transformer backbones for CSI re-ID. arXiv:2507.12869.
Wi-PER81 (2025): Benchmark dataset of 162K wireless packets for WiFi-based person re-identification using Siamese networks. Nature Scientific Data, 2025. doi:10.1038/s41597-025-05804-0.
CAPC (Context-Aware Predictive Coding, 2024): CPC + Barlow Twins for WiFi sensing. 24.7% accuracy improvement on unseen environments. arXiv:2410.01825.
SSL for WiFi HAR Survey (2025): Comprehensive evaluation of SimCLR, VICReg, Barlow Twins, SimSiam on WiFi CSI. arXiv:2506.12052.

4. WiFi Sensing SOTA (Pose, Vitals, Gait)

Geng, J., Huang, D., & De la Torre, F. (2022). DensePose From WiFi. CMU. arXiv:2301.00250.
Adib, F., Kabelac, Z., Katabi, D., & Miller, R.C. (2015). 3D Tracking via Body Radio Reflections (WiTrack). NSDI 2015.
Wang, J., Gao, X., Zhang, K., & Liu, X. (2019). Widar 3.0: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi. MobiSys 2019.
Zhao, M., Li, T., Abu Alsheikh, M., Tian, Y., Zhao, H., Torralba, A., & Katabi, D. (2018). Through-Wall Human Pose Estimation Using Radio Signals. CVPR 2018.
Zhao, M., Adib, F., & Katabi, D. (2016). Emotion Recognition Using Wireless Signals (EQ-Radio). MobiCom 2016. (HRV from WiFi; cardiac biometric baseline)
PerceptAlign (Chen et al., 2026): Geometry-conditioned cross-layout WiFi pose estimation. >60% cross-domain error reduction. Dataset: 21 subjects, 5 scenes, 18 actions. arXiv:2601.12252.
Person-in-WiFi 3D (Yan et al., 2024): Multi-person 3D pose from WiFi. 91.7 mm MPJPE (single-person). CVPR 2024.
DGSense (Zhou et al., 2025): Domain-invariant features for WiFi/mmWave/acoustic sensing. arXiv:2502.08155.
X-Fi (Chen & Yang, 2025): Modality-invariant foundation model for human sensing. 24.8% MPJPE improvement on MM-Fi. ICLR 2025. arXiv:2410.10167.
AM-FM (2026): First WiFi foundation model, pretrained on 9.2M CSI samples, 20 device types, 439 days. arXiv:2602.11200.
Ma, Y., Zhou, G., Wang, S., Zhao, H., & Jung, W. (2018). SignFi: Sign Language Recognition Using WiFi. ACM IMWUT. arXiv:1806.04583.

5. Training Datasets Referenced

MM-Fi (2022): Multi-Modal Non-Intrusive 4D Human Dataset — WiFi CSI, mmWave, LiDAR, RGB-D. 27 subjects, 40 actions, 5 environments, 320K samples. 56-subcarrier CSI, 17 COCO keypoints. [github.com/ybhbingo/MMFi_dataset]
Wi-Pose (2022): WiFi-based 3D pose estimation dataset. Used in ADR-015.
NTU-Fi (2022): 56 activities, WiFi CSI, 75 Hz sampling. Used for WhoFi evaluation.

6. Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep Learning with Differential Privacy. CCS 2016. [Moments Accountant; DP-SGD formulation used in ADR-106]
Mironov, I. (2017). Rényi Differential Privacy. CSF 2017. [Alternative DP accounting; referenced in ADR-106 as future enhancement]
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership Inference Attacks Against Machine Learning Models. IEEE S&P 2017. [Motivation for DP-SGD in ADR-106]

7. Cryptographic Standards

RFC 8032 (2017): Edwards-Curve Digital Signature Algorithm (EdDSA). [Ed25519; used in ADR-110 witness chain]
RFC 8439 (2018): ChaCha20 and Poly1305 for IETF Protocols. [At-rest encryption primitive specified in security.md §5]
RFC 9106 (2021): Argon2 Memory-Hard Function. [KDF for soul signature at-rest key derivation]
NIST FIPS 203 (2024): Module-Lattice-Based Key-Encapsulation Mechanism Standard (ML-KEM / Kyber). [ADR-108; post-quantum key exchange]
NIST FIPS 204 (2024): Module-Lattice-Based Digital Signature Standard (ML-DSA / Dilithium). [ADR-109; post-quantum signatures]
NIST SP 800-132 Draft (2024): Recommendation for Password-Based Key Derivation. [Argon2id parameter guidance]

8. Biometric Standards (for Standards Awareness)

The soul signature is not currently certified to any of these standards but the specification is designed with awareness of the relevant frameworks.

ISO/IEC 19794-1:2011: Biometric data interchange formats — Part 1: Framework. [Top-level; soul signature's node/edge schema follows the typed-attribute-record philosophy of this standard]
ISO/IEC 19794-2:2011: Biometric data interchange formats — Part 2: Finger minutiae data. [Structural analog for how the soul signature encodes per-channel discriminative features]
ISO/IEC 19794-4:2011: Biometric data interchange formats — Part 4: Finger image data. [Image-container analog; soul signature extends the concept to vector-valued multi-channel templates]
ISO/IEC 29794-1:2016: Biometric sample quality — Part 1: Framework. [Quality scoring framework; soul signature's per-node confidence field is conceptually analogous to ISO 29794 quality scores]
ISO/IEC 30107-3:2023: Biometric presentation attack detection — Part 3: Testing and reporting. [Presentation attack (anti-spoofing) framework; the adversarial.rs module is the soul signature's PAD implementation]

9. Reading List for RF Biometrics Newcomers

Ordered from most accessible to most technical.

Adib, F. (2017). Using Radio Reflections to See the World. MIT PhD thesis. [Most accessible introduction to using RF for human sensing; covers WiVi, WiTrack, EQ-Radio]
Ma, Y., et al. (2019). WiFi Sensing with Channel State Information: A Survey. ACM Computing Surveys. doi:10.1145/3310194. [Comprehensive survey of CSI-based sensing approaches through 2019]
Wang, X., et al. (2023). A Survey on WiFi Sensing: From Signal to Action. IEEE Internet of Things Journal. [Updated survey through 2023; covers contrastive learning approaches]
Chen, T., et al. (2020). A Simple Framework for Contrastive Learning (SimCLR). arXiv:2002.05709. [Best starting point for understanding the contrastive learning approach used in AETHER]
Geng, J., et al. (2022). DensePose From WiFi. arXiv:2301.00250. [Direct ancestor of this codebase; describes the cross-modal CSI → DensePose mapping]
Abadi, M., et al. (2016). Deep Learning with Differential Privacy. CCS 2016. [Essential reading before any deployment collecting biometric data at training time]