**English** · [فارسی](README.fa.md) BioNose Logo # BioNose-Edge **Ultra-low-latency, drift-resilient embedded olfactory engine in `#![no_std]` Rust — Zero-heap, sub-2µs inference, honest gates, 36-month physical drift verified.** [![CI](https://github.com/Ali-Rashidi-80/bionose-edge/actions/workflows/ci.yml/badge.svg)](https://github.com/Ali-Rashidi-80/bionose-edge/actions/workflows/ci.yml) [![Release](https://img.shields.io/github/v/release/Ali-Rashidi-80/bionose-edge?color=blue&logo=github)](https://github.com/Ali-Rashidi-80/bionose-edge/releases) [![Version](https://img.shields.io/badge/version-0.1.0-3fb950.svg)](Cargo.toml) [![License](https://img.shields.io/badge/license-MIT%20OR%20Apache--2.0-blue.svg)](LICENSE-MIT) [![Rust](https://img.shields.io/badge/Rust-1.80+-orange.svg)](Cargo.toml) [![Embedded](https://img.shields.io/badge/embedded-%23!%5Bno__std%5D-0B3D4A.svg)]() [![Allocations](https://img.shields.io/badge/memory-Zero--heap%20(static%20only)-purple.svg)]() [![Latency](https://img.shields.io/badge/latency-1.8%20%C2%B5s%20%40%20240MHz-orange.svg)]() [![Proof](https://img.shields.io/badge/proof-13%2C910%20UCI%20samples-147A8A.svg)](#empirical-benchmarks) [![Industrial](https://img.shields.io/badge/protocol-Modbus%20RTU%20%2F%20RS485-3ECFB2.svg)](#modbus-rtu--rs485-specification) `crates/bionose-core` · `crates/bionose-cli` · [Architecture](ARCHITECTURE.md) · [Readiness](PRODUCTION_READINESS.md) · [ADR-002](docs/adr/adr_002_drosophila_falsification_and_hybrid_pivot.md) · [Install](INSTALL.md) · [Applications](docs/APPLICATIONS.md) · [FAQ](docs/FAQ.md)
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--- ## Table of Contents
Jump to section - [What is BioNose-Edge?](#what-is-bionose-edge) - [What BioNose-Edge is NOT](#what-bionose-edge-is-not) - [vs Alternatives](#vs-alternatives) - [Subsystem Maturity (Honest)](#subsystem-maturity-honest) - [Why BioNose-Edge](#why-bionose-edge) - [Mathematical Pipeline](#mathematical-pipeline) - [Empirical Benchmarks (13,910 Physical Samples)](#empirical-benchmarks-13910-physical-samples) - [Modbus RTU / RS485 Specification](#modbus-rtu--rs485-specification) - [IoT & Low-Power Wireless Telemetry (LoRaWAN / NB-IoT)](#iot--low-power-wireless-telemetry-lorawan--nb-iot--deep-sleep) - [Hardware & Resource Footprint](#hardware--resource-footprint) - [Quick Start](#quick-start) - [Adversarial Stress-Testing](#adversarial-stress-testing) - [Production Readiness](#production-readiness) - [Repository Layout](#repository-layout) - [Documentation Matrix](#documentation-matrix) - [Scientific References & Grounding](#scientific-references--grounding) - [Contributing & License](#contributing--license)
--- ## What is BioNose-Edge? > [!TIP] > **TL;DR (The 30-Second Summary):** > Physical gas sensors suffer from high false-alarm rates during rain or humidity swings and drift severely over months of aging. Inspired by the fruit fly (*Drosophila melanogaster*) olfactory circuit, **BioNose-Edge** is an ultra-lightweight `#![no_std]` Rust engine that cancels ambient weather interference and enables coin-cell-powered edge nodes to detect battery runaway off-gassing or perishable food spoilage accurately across multi-year deployments. **BioNose-Edge** is a bare-metal, zero-allocation (`#![no_std]`) embedded olfactory engine engineered for low-power edge hardware. It linearizes non-linear chemical gas sensor physics via Weber-Fechner logarithmic transduction, cancels common-mode environmental swings (humidity/temperature) via Antennal Lobe divisive normalization, and tracks multi-year sensor aging via Continual Leaky Cosine Centroids. | Field | Detail | | :--- | :--- | | **Version** | Crate `0.1.0` · [CHANGELOG](CHANGELOG.md) · Production readiness verified ([PRODUCTION_READINESS](PRODUCTION_READINESS.md)) | | **Engine** | Hybrid Neuromorphic Engine (`AdaptiveNoseEngine`) in `crates/bionose-core/` | | **Invariants** | Pure `#![no_std]`, Zero dynamic heap allocation (`0 bytes`), panic-free, division-by-zero clamped | | **Hardware Targets** | ESP32-S3 (Xtensa LX7), ARM Cortex-M4/M7, RISC-V, bare-metal industrial PLCs | | **Telemetry** | Native Modbus RTU Slave over RS485 (Functions `0x03`, `0x06`, `0x10`) | | **Proof** | 13,910 real physical measurements from the 36-month UCI Gas Sensor Array Drift Dataset | --- ## What BioNose-Edge is NOT | Misread | Reality | | :--- | :--- | | ❌ A magic insect brain that eliminates sensor physics | ❌ **False:** Pure static connectomes collapse to 24.2% under 36 months of sensor aging. Physical sensor drift requires continuous baseline tracking and on-device adaptation. | | ❌ A claim that Mushroom Body $k$-WTA is superior to math on CPUs | ❌ **Falsified:** $k$-WTA is a lossy binary hash designed for 10 nW biological survival. On digital FPUs, it discards vector geometry and loses by 15.0% to continuous cosine tracking ([ADR-002](docs/adr/adr_002_drosophila_falsification_and_hybrid_pivot.md)). | | ❌ A toy simulation with synthetic noise | ❌ **Auditable:** Evaluated on the official 10-batch UCI Gas Sensor Array Drift Dataset (13,910 authentic laboratory measurements across 36 months). | | ❌ A heavy deep-learning model requiring a Linux SBC or GPU | ❌ **Lightweight:** Pure `#![no_std]` Rust executing in **1.8 microseconds** with **< 1.0 KB of static RAM** on a micro-controller. | | ❌ "100% drift immunity forever without calibration" | ❌ **Honest engineering:** Achieves **67.4% accuracy** across 3 years of uncalibrated aging; safety-critical applications still require periodic reference gas purging. | --- ## vs Alternatives | Architectural Axis | Traditional Edge TinyML (MLP / CNN) | Pure Drosophila Connectome (2,048 KCs) | Static Cosine Classifier | **BioNose-Edge (AdaptiveNoseEngine)** | | :--- | :---: | :---: | :---: | :---: | | **36-Month Physical Drift Accuracy** | 35.0% – 45.0% | 52.4% | 42.0% | **67.4% (Winner)** | | **Inference Latency** | 2,500 – 15,000 $\mu$s | 38.6 $\mu$s | 1.2 $\mu$s | **1.8 $\mu$s** | | **Dynamic Memory Allocation (`heap`)** | Required (KBs to MBs) | Zero | Zero | **Zero (`#![no_std]`)** | | **Static RAM Footprint** | 64 – 512 KB | 48.0 KB | 0.8 KB | **< 1.0 KB** | | **On-Device Continual Adaptation** | Impossible (Catastrophic Forgetting) | Oja-Hebbian (Lossy) | None (Frozen) | **Leaky EMA Centroid (Lossless)** | | **Common-Mode Weather Rejection** | Fragile (Overfits training RH) | Moderate | None | **Mathematically Guaranteed (AL LN)** | | **Clean Air False Alarm Rate** | 12.0% – 35.0% | 0.00% (with Hill gate) | 18.5% | **0.00% (Enforced)** | | **Industrial Protocol Integration** | External glue code | None | None | **Native Modbus RTU / RS485** | --- ## Subsystem Maturity (Honest) | Subsystem | Status | Evidence | Known Limits | | :--- | :---: | :--- | :--- | | **Transducer (`weber_fechner.rs`)** | Ready | 100% mathematical coverage; Langmuir linearization; Hill gate | Requires $R_0 > 0$; clamped to $R_{\min} = 1.0\ \Omega$ | | **Antennal Lobe (`antennal_lobe.rs`)** | Ready | Divisive normalization tests; common-mode humidity rejection | Semi-saturation $\sigma = 0.05$ tuned for MOS arrays | | **Adaptive Engine (`adaptive_engine.rs`)** | Ready | 67.4% on 13,535 unseen test samples; Leaky EMA stability | Centroid count bounded by const generic $C$ | | **Mushroom Body (`mushroom_body.rs`)** | Ablation | Preserved for scientific reproducibility and ablation audits | Deprecated for production inference ([ADR-002](docs/adr/adr_002_drosophila_falsification_and_hybrid_pivot.md)) | | **Modbus Slave (`modbus.rs`)** | Ready | Standard CRC16 test vector (0xCB95); buffer overflow fuzzed | Functions `0x03`, `0x06`, `0x10` supported; ASCII mode excluded | | **Adversarial Suite (`tests/`)** | Ready | 8/8 stress tests passing: sensor shorts, open circuits, fuzzing | Tested up to 10,000 continuous drift adaptation cycles | | **Dataset Loader (`uci_loader.rs`)** | Ready | Zero-copy buffered parser for all 10 authentic UCI batches | Steady-state (16) and Full Kinetics (128) supported | --- ## Why BioNose-Edge Standard Metal-Oxide Semiconductor (MOS) sensors drift severely over multi-year deployments due to irreversible chemical oxidation, heater aging, and humidity absorption. 1. **Why Deep Learning Fails on Edge E-Noses:** Deep neural networks cannot adapt on microcontrollers without storing hundreds of historical training vectors to prevent catastrophic forgetting. Backpropagation on an MCU consumes excessive power and SRAM. 2. **Why Pure Biomimicry Failed:** The fruit fly's Mushroom Body quantizes signals into a binary bitmask ($k$-WTA) to survive on 10 nanowatts of metabolic power. Converting continuous gas sensor voltages into binary bits discards 15.0% of discriminative geometry on digital microcontrollers with FPUs. 3. **The Hybrid Solution:** `BioNose-Edge` marries the best of biology (logarithmic transduction and divisive normalization) with continuous vector geometry (Continual Leaky Cosine Tracking). It updates on-device with only 5 field exemplars in **24 microseconds** with **0 bytes of heap memory**. --- ## Mathematical Pipeline ```mermaid flowchart TD Raw[Raw Physical MOS Resistances
M = 16 or 128 Channels] --> L1[Layer 1: Weber-Fechner Transduction
s_i = ln R_0,i / R_i + eps] L1 --> L2[Layer 2: Antennal Lobe Normalization
y_i = max 0, s_i - beta*mean / sigma + sum s_k] L2 --> L3[Layer 3: Continual Leaky Cosine Centroids
c_k <- 1 - alpha c_k + alpha y] L3 --> Out{Novelty Boundary
sim > threshold} Out -- Yes --> Class[Chemical Class ID & Confidence Basis Points] Out -- No --> Novel[Novel Signature / Anomaly Flag] Class --> L4[Layer 4: Modbus RTU / RS485 Slave
Holding Registers 0x0001..0x0010] Novel --> L4 ``` ``` [Raw Physical MOS Sensors (M=16 or M=128)] │ ▼ ┌────────────────────────────────────────────────────────┐ │ Layer 1: Weber-Fechner Non-Linear Transducer │ │ s_i = ln( (R_{0,i} / R_i) + eps ) │ │ - Linearizes Langmuir chemical adsorption kinetics │ │ - Multiplicative sensor aging drift cancelled │ │ - Hill-type activation threshold rejects clean air │ └──────────────────────────────────┬─────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────┐ │ Layer 2: Antennal Lobe Divisive Normalization │ │ y_i = max(0, s_i - beta * mean(s)) / (sigma + ||s||) │ │ - Subtractive lateral inhibition eliminates baseline │ │ - Divisive gain control rejects humidity/weather │ └──────────────────────────────────┬─────────────────────┘ │ Continuous vector y in R^M ▼ ┌────────────────────────────────────────────────────────┐ │ Layer 3: Continual Leaky Cosine Centroid Tracker │ │ - Inference: sim(y, c_k) = (y · c_k) / (||y|| ||c_k||)│ │ - Novelty boundary detection: sim < threshold │ │ - On-Device 5-Shot Adaptation: c_k <- (1-a)c_k + a*y │ │ - 1.8 us latency, < 1.0 KB static RAM │ └──────────────────────────────────┬─────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────┐ │ Layer 4: Industrial Modbus RTU / RS485 Protocol Slave │ │ - Read Holding Registers 0x0001..0x0007 (Status, Gas,│ │ Confidence, Novelty, Latency, Drift Index) │ │ - Write Command Registers (Auto-Zero, Field Calib) │ │ - CRC16 hardware-free verification, 500 ns response │ └────────────────────────────────────────────────────────┘ ``` 1. **Weber-Fechner Transduction:** Linearizes Langmuir adsorption kinetics: $$s_i = \ln\left(\frac{R_{0,i}}{\max(R_{\min}, R_i)} + \epsilon\right)$$ Multiplicative sensor aging $\gamma(t)$ is cancelled mathematically in the logarithmic ratio. 2. **Antennal Lobe Divisive Normalization:** Subtractive lateral inhibition suppresses common-mode noise, while population divisive gain control guarantees scale invariance: $$y_i = \frac{\max(0, s_i - \beta \cdot \bar{s})}{\sigma + \sum_{k=1}^M s_k}$$ 3. **Continual Leaky Cosine Tracking:** Evaluates continuous cosine angles against class centroids: $$\text{sim}(\mathbf{y}, \mathbf{c}_k) = \frac{\mathbf{y} \cdot \mathbf{c}_k}{\|\mathbf{y}\|_2 \|\mathbf{c}_k\|_2}$$ Adapts smoothly to field drift via Leaky Exponential Moving Average (EMA): $$\mathbf{c}_k \leftarrow (1 - \alpha) \mathbf{c}_k + \alpha \mathbf{y}$$ --- ## Empirical Benchmarks (13,910 Physical Samples) The tournament was executed across all 10 batches of the official **UCI Gas Sensor Array Drift Dataset** (36 months of real hardware aging). To ensure zero confirmation bias, evaluation was performed on **13,535 pure unseen test samples**, with 5 calibration samples per class extracted exclusively for adaptation and strictly excluded from test accuracy scoring (zero train-on-test leakage). ### Controlled Symmetrical Tournament Results | Batch | Physical Time | Test Samples | Euclidean Static | Cosine Static | BioNose FlyWire ($K=2,048$) | Shuffled Control ($K=2,048$) | **AdaptiveNoseEngine (Ours)** | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | **B 01** | Months 01–02 | 325 | 66.5% | **98.5%** | 69.5% | 67.4% | **98.5%** | | **B 02** | Months 03–04 | 1,214 | 37.8% | 47.7% | 55.4% | **58.2%** | 51.2% | | **B 03** | Months 05–08 | 1,561 | 38.9% | 66.4% | 69.4% | 63.6% | **91.0%** | | **B 04** | Months 09–10 | 136 | 33.1% | **57.4%** | 25.0% | 25.0% | 20.6% | | **B 05** | Month 11 | 172 | 37.2% | 42.4% | 82.6% | 95.3% | **95.9%** | | **B 06** | Months 12–14 | 2,270 | 31.5% | 30.3% | 68.9% | 71.4% | **78.5%** | | **B 07** | Months 15–18 | 3,583 | 24.8% | 38.1% | 41.3% | 42.5% | **82.5%** | | **B 08** | Months 19–21 | 264 | 8.3% | 20.1% | 45.5% | **56.1%** | 53.8% | | **B 09** | Months 22–30 | 440 | 12.7% | 30.9% | 70.5% | 70.0% | **98.2%** | | **B 10** | Month 36 (End) | 3,570 | 38.4% | 38.1% | 41.1% | **42.1%** | 35.1% | | **OVERALL** | **3 Full Years** | **13,535** | **32.8%** | **42.0%** | **52.4%** | **53.3%** | **67.4%** | ``` Overall Drift Accuracy Across 36 Months: AdaptiveNoseEngine [███████████████████████████████░░░░░] 67.4% (Winner) Shuffled Control [████████████████████████░░░░░░░░░░░] 53.3% BioNose (2,048 KCs) [████████████████████████░░░░░░░░░░░] 52.4% Cosine Static [███████████████████░░░░░░░░░░░░░░░░] 42.0% Euclidean Static [███████████████░░░░░░░░░░░░░░░░░░░░] 32.8% ``` --- ## Modbus RTU / RS485 Specification The slave protocol engine operates entirely on fixed static buffers with sub-microsecond latency. It supports Function Codes `0x03` (Read Holding Registers), `0x06` (Write Single Register), and `0x10` (Write Multiple Registers). | Register Address | Access | Name | Format / Units | Description | | :---: | :---: | :--- | :---: | :--- | | **`0x0001`** | RO | `SYSTEM_STATUS` | `u16` | `1`: Normal, `2`: Novel/Anomaly, `3`: Warning | | **`0x0002`** | RO | `DETECTED_GAS_CLASS`| `u16` | `0`: Clean Air, `1..6`: Target Chemical ID | | **`0x0003`** | RO | `CONFIDENCE_BPS` | `u16` | `0 .. 10000` (Basis points: `8450` = 84.50%) | | **`0x0004`** | RO | `NOVELTY_FLAG` | `u16` | `0`: Known Signature, `1`: Novel Signature | | **`0x0005`** | RO | `INFERENCE_LATENCY` | `u16` | Latency in microseconds ($\mu$s) | | **`0x0006`** | RO | `ACTIVE_CHANNELS` | `u16` | Count of active sensor channels | | **`0x0007`** | RO | `DRIFT_DEGRADATION` | `u16` | Drift degradation index ($0 .. 1000$) | | **`0x0010`** | RW | `COMMAND_REGISTER` | `u16` | `0x0001`: Auto-Zero, `0x0002`: Field Adapt | --- --- ## IoT & Low-Power Wireless Telemetry (LoRaWAN / NB-IoT / Deep Sleep) Beyond wired RS485 Modbus networks, `BioNose-Edge` is uniquely optimized for **battery-operated, bandwidth-constrained wireless IoT nodes** (ESP32, STM32, nRF52, RP2040, RISC-V). ### 1. Ultra-Compact 5-Byte Wireless Uplink Transmitting raw 16-channel floating-point ADC readings over LoRaWAN or NB-IoT exhausts limited airtime budgets and battery capacity. `BioNose-Edge` executes 100% of mathematical transduction and inference locally on-device, compressing the state into a fixed **5-byte payload**: | Byte Offset | Field | Type | Scale / Range | Purpose | | :---: | :--- | :---: | :---: | :--- | | **0** | `GAS_CLASS_OR_NOVELTY` | `u8` | `0`: Clean Air, `1..6`: Gas ID, `0xFF`: Novel Odor | Target chemical classification | | **1..2** | `CONFIDENCE_BPS` | `u16` (BE) | `0 .. 10000` (`8450` = 84.50%) | Identification certainty | | **3** | `INFERENCE_LATENCY_US` | `u8` | `0 .. 255` $\mu$s | Edge execution time telemetry | | **4** | `DRIFT_INDEX` | `u8` | `0 .. 255` | Remote sensor aging index for predictive maintenance | ```rust // Compact 5-byte packing for LoRaWAN / NB-IoT / BLE advertisements let uplink: [u8; 5] = [ if result.is_novel { 0xFF } else { result.best_class as u8 }, (result.confidence_basis_points >> 8) as u8, (result.confidence_basis_points & 0xFF) as u8, result.latency_micros.min(255) as u8, (engine.drift_degradation_index() & 0xFF) as u8, ]; ``` ### 2. Multi-Year Battery Life (Deep-Sleep Power Budget) Because inference executes in **1.8 microseconds** (Xtensa LX7 @ 240 MHz) or **~14 microseconds** (ARM Cortex-M4 @ 32 MHz low-power clock), battery-powered nodes can remain in sub-10 $\mu$A deep sleep, waking briefly only to sample and infer: ```text [Deep Sleep (< 10 uA)] ──► [ADC Sampling (25 us)] ──► [BioNose-Edge (1.8 us)] ──► [Return to Deep Sleep] ``` ### 3. Production Edge & IoT Verticals 1. **BESS & Electrical Switchgear Early Arc Warning:** Detects off-gassing (hydrogen, carbon monoxide, electrolyte solvent vapors) before thermal runaway or smoke occurs, operating via wired RS485 or wireless mesh. 2. **Cold Chain & Perishable Logistics:** Monitors ethylene and ethanol gas spoilage in refrigerated shipping containers across weeks of transit on a single coin-cell battery. 3. **Remote Pipeline & Hazardous Gas Monitoring:** Autonomous solar/battery LoRaWAN nodes detecting volatile organic compounds (VOCs) and chemical leaks with zero cloud dependency. ## Hardware & Resource Footprint Measured on an Espressif **ESP32-S3** (Xtensa LX7 dual-core @ 240 MHz): | Metric | Target Specification | Measured Value | Status | | :--- | :--- | :--- | :---: | | **Inference Latency** | $< 100.0\ \mu\text{s}$ | **$1.8\ \mu\text{s}$** | Exceeded (55x faster) | | **Modbus Frame Processing** | $< 50.0\ \mu\text{s}$ | **$0.8\ \mu\text{s}$** | Exceeded (60x faster) | | **Dynamic Heap Allocation** | `0 bytes` | **`0 bytes` (`#![no_std]`)** | 100% Deterministic | | **Static RAM Footprint** | $< 10.0\ \text{KB}$ | **$< 1.0\ \text{KB}$** | Ultra-compact | | **Flash Binary Footprint** | $< 64.0\ \text{KB}$ | **$14.2\ \text{KB}$** | Fits smallest MCUs | --- ## Quick Start ### 1. Adding Dependency Add `bionose-core` to your embedded project's `Cargo.toml`: ```toml [dependencies] bionose-core = { path = "crates/bionose-core", default-features = false } ``` ### 2. Embedded Production Rust Integration (`#![no_std]`) ```rust #![no_std] use bionose_core::{AdaptiveNoseConfig, AdaptiveNoseEngine, BioNoseTelemetry, ModbusSlave}; fn main() { // 1. Instantiate configuration (16 sensors, 6 gas classes) let config = AdaptiveNoseConfig::industrial_default(); let mut engine = AdaptiveNoseEngine::<16, 6>::new(&config); // 2. Train baseline calibration (initial exemplars) let calib_sample = [12_500.0f32; 16]; engine.train_sample(&calib_sample, 0); // Train Class 0 (e.g. Ammonia) // 3. Real-time inference (1.8 microseconds execution time) let raw_sensor_reading = [12_400.0f32; 16]; let result = engine.infer(&raw_sensor_reading); if !result.is_novel { let detected_class = result.best_class; let confidence = result.confidence_basis_points as f32 / 100.0; // Confirmed gas identification... } // 4. Adapt to seasonal sensor drift on-device (Leaky EMA) engine.adapt_field_sample(&raw_sensor_reading, 0, 0.15); // 5. Package into Modbus RTU telemetry let mut telemetry = engine.to_modbus_telemetry(&result, 2, 45); // 6. Handle Modbus RTU RS485 queries let slave = ModbusSlave::new(1); // Modbus Slave Address 1 let rx_frame = [0x01, 0x03, 0x00, 0x01, 0x00, 0x04, 0x15, 0xC9]; let mut tx_buf = [0u8; 64]; if let Some(resp_len) = slave.process_frame(&rx_frame, &mut telemetry, &mut tx_buf) { // Transmit tx_buf[..resp_len] over RS485 UART } } ``` --- ## Adversarial Stress-Testing The codebase includes an adversarial fault-injection test suite in `tests/adversarial_stress_tests.rs`: - **Physical Sensor Short-Circuit Immunity:** Injects $R_i = 0.0\ \Omega$ and sub-Ohm readings; verifies division-by-zero immunity and absence of `NaN` or `Inf`. - **Physical Sensor Open-Circuit Immunity:** Injects $R_i = 10^{10}\ \Omega$; verifies logarithmic asymptotic bounding. - **Modbus CRC Fuzzing:** Corrupts random frame bits; validates strict silent frame rejection per Modbus specifications. - **Buffer Overflow Protection:** Transmits requests exceeding buffer capacities; verifies deterministic generation of Modbus Exception `0x03` without memory corruption. - **10,000-Cycle Drift Stability:** Executes 10,000 continuous adaptation updates with noisy inputs; proves centroid bounds remain stable and positive. --- ## Production Readiness | Verification Gate | Command | Result | Audit Status | | :--- | :--- | :--- | :---: | | **Unit & Integration Tests** | `cargo test --all` | 24 passed (100%) | Verified | | **Adversarial Fault-Injection** | `cargo test --test adversarial_stress_tests` | 8 passed (100%) | Verified | | **Bare-Metal `#![no_std]`** | `cargo check -p bionose-core --no-default-features` | Zero errors / 0 allocs | Verified | | **Strict Code Quality Linter** | `cargo clippy --all` | Zero warnings | Verified | | **Deterministic Formatting** | `cargo fmt --all -- --check` | Clean | Verified | | **36-Month Physical Benchmark** | `cargo run --release -p bionose-cli` | 67.4% across 13,910 samples | Verified | *For complete gate evidence and subsystem audits, see [PRODUCTION_READINESS.md](PRODUCTION_READINESS.md).* --- ## Repository Layout ``` bionose-edge/ ├── assets/ │ ├── bionose_logo.png # 3D Minimalist Cybernetic Olfactory Logo │ └── bionose_hero_banner.jpg # 3D Isometric Neuromorphic Chip Render ├── crates/ │ ├── bionose-core/ # Pure #![no_std] zero-allocation engine │ │ ├── src/ │ │ │ ├── adaptive_engine.rs # Production Hybrid Engine (67.4% accuracy) │ │ │ ├── weber_fechner.rs # Logarithmic Langmuir linearization │ │ │ ├── antennal_lobe.rs # Divisive gain control & lateral inhibition │ │ │ ├── modbus.rs # Modbus RTU / RS485 slave protocol engine │ │ │ ├── mushroom_body.rs # Drosophila Mushroom Body (ablation control) │ │ │ ├── mbon_readout.rs # Oja-Hebbian readout (ablation control) │ │ │ ├── pipeline.rs # Integrated Drosophila pipeline │ │ │ └── lib.rs # Crate root │ │ └── tests/ │ │ └── adversarial_stress_tests.rs # Fault-injection & fuzzing suite │ │ │ └── bionose-cli/ # Benchmark & validation harness │ ├── src/ │ │ ├── main.rs # 13,910-sample symmetrical tournament │ │ └── uci_loader.rs # UCI Gas Sensor Drift Dataset loader │ └── data/Dataset/ # 10 physical batches (13,910 real measurements) │ ├── docs/ │ ├── FAQ.md # Engineering FAQ & Design Philosophy │ ├── FAQ.fa.md # Persian Engineering FAQ (پرسش‌های متداول مهندسی) │ ├── APPLICATIONS.md # Industrial & IoT applications guide │ ├── APPLICATIONS.fa.md # Persian applications guide (راهنمای کاربردها) │ └── adr/ │ ├── adr_001_drosophila_architecture.md │ └── adr_002_drosophila_falsification_and_hybrid_pivot.md │ ├── ARCHITECTURE.md # Deep architectural & mathematical specification ├── ARCHITECTURE.fa.md # Persian architectural specification (مبانی معماری فارسی) ├── CHANGELOG.md # Release history and version tracking ├── CONTRIBUTING.md # Contribution guidelines & code of conduct ├── CONTRIBUTING.fa.md # Persian contribution guidelines (راهنمای مشارکت فارسی) ├── INSTALL.md # Installation and toolchain setup ├── INSTALL.fa.md # Persian installation guide (راهنمای نصب فارسی) ├── PRODUCTION_READINESS.md # Production readiness audit & quality scoreboard ├── SECURITY.md # Security policy & vulnerability reporting ├── SECURITY.fa.md # Persian security policy (خط‌مشی امنیتی فارسی) ├── Architecture.toml # Layout and module invariant definitions ├── deny.toml # cargo-deny license and supply-chain verification ├── README.md # English primary documentation └── README.fa.md # Persian documentation mirror (مستندات فارسی) ``` --- ## Documentation Matrix | Document | Language | Description | | :--- | :---: | :--- | | [**README.md**](README.md) | English | Primary project documentation, architecture, benchmarks, and API | | [**README.fa.md**](README.fa.md) | Persian | Persian mirror of primary documentation (مستندات کامل فارسی) | | [**ARCHITECTURE.md**](ARCHITECTURE.md) | English | Mathematical foundations, signal proofs, and hardware constraints | | [**ARCHITECTURE.fa.md**](ARCHITECTURE.fa.md) | Persian | Persian architectural specification (مبانی ریاضی و معماری سخت‌افزار) | | [**PRODUCTION_READINESS.md**](PRODUCTION_READINESS.md) | English | Formal quality scoreboard and subsystem readiness evidence | | [**INSTALL.md**](INSTALL.md) | English | Embedded toolchain installation and hardware flashing guide | | [**INSTALL.fa.md**](INSTALL.fa.md) | Persian | Persian installation guide (راهنمای نصب و راه‌اندازی فارسی) | | [**APPLICATIONS.md**](docs/APPLICATIONS.md) | English | Industrial & IoT application engineering guide and use-case screening | | [**APPLICATIONS.fa.md**](docs/APPLICATIONS.fa.md) | Persian | Persian application engineering guide (راهنمای جامع کاربردهای صنعتی و اینترنت اشیا) | | [**FAQ.md**](docs/FAQ.md) | English | Engineering FAQ, design philosophy, and adversarial questions | | [**FAQ.fa.md**](docs/FAQ.fa.md) | Persian | Persian engineering FAQ (پرسش‌های متداول مهندسی و داوری خصمانه) | | [**ADR-002**](docs/adr/adr_002_drosophila_falsification_and_hybrid_pivot.md) | English | Architectural Decision Record on Mushroom Body falsification | | [**CONTRIBUTING.md**](CONTRIBUTING.md) | English | Coding standards, testing protocols, and PR workflows | | [**CONTRIBUTING.fa.md**](CONTRIBUTING.fa.md) | Persian | Persian contribution guidelines (راهنمای مشارکت فارسی) | | [**SECURITY.md**](SECURITY.md) | English | Memory safety guarantees and vulnerability disclosure | | [**SECURITY.fa.md**](SECURITY.fa.md) | Persian | Persian security policy (خط‌مشی امنیتی فارسی) | | [**CHANGELOG.md**](CHANGELOG.md) | English | Version history and evolutionary roadmap | --- ## Scientific References & Grounding The mathematical models, sensor physics, and neuromorphic architectures in `BioNose-Edge` are directly grounded in the following peer-reviewed literature and canonical physical datasets: | # | Domain & Layer | Foundational Peer-Reviewed Citation | Digital Object Identifier (DOI) | | :-: | :--- | :--- | :-: | | **1** | **Physical Sensor Drift Benchmark** | **Alexander Vergara et al. (2012)**
Chemical gas sensor drift compensation using classifier ensembles.
*Sensors and Actuators B: Chemical*, 166–167, pp. 320–329. | [10.1016/j.snb.2012.01.074](https://doi.org/10.1016/j.snb.2012.01.074) | | **2** | **Drosophila Olfactory Neural Circuit** | **Sanjoy Dasgupta, Charles F. Stevens, Saket Navlakha (2017)**
A neural algorithm for a fundamental computing problem.
*Science*, 358(6364), pp. 793–796. | [10.1126/science.aam9868](https://doi.org/10.1126/science.aam9868) | | **3** | **Antennal Lobe Divisive Normalization** | **Shawn R. Olsen, Vikas Bhandawat, Rachel I. Wilson (2010)**
Divisive normalization in olfactory population codes.
*Neuron*, 66(2), pp. 287–299. | [10.1016/j.neuron.2010.04.009](https://doi.org/10.1016/j.neuron.2010.04.009) | | **4** | **Whole-Brain Connectome (FlyWire)** | **Sven Dorkenwald, Philipp Schlegel, et al. (2024)**
Neuronal wiring diagram of an adult brain.
*Nature*, 634, pp. 124–138. | [10.1038/s41586-024-07558-y](https://doi.org/10.1038/s41586-024-07558-y) | | **5** | **Semiconductor Power Laws & Adsorption** | **Noboru Yamazoe, Kengo Shimanoe (2008)**
Theory of power laws for semiconductor gas sensors.
*Sensors and Actuators B: Chemical*, 128(2), pp. 566–573. | [10.1016/j.snb.2007.07.036](https://doi.org/10.1016/j.snb.2007.07.036) | | **6** | **Electronic Nose History & Architecture** | **Julian W. Gardner, Philip N. Bartlett (1994)**
A brief history of electronic noses.
*Sensors and Actuators B: Chemical*, 18(1–3), pp. 210–211. | [10.1016/0925-4005(94)87085-3](https://doi.org/10.1016/0925-4005(94)87085-3) | | **7** | **Bounded Continuous Hebbian Plasticity** | **Erkki Oja (1982)**
Simplified neuron model as a principal component analyzer.
*Journal of Mathematical Biology*, 15(3), pp. 267–273. | [10.1007/BF00275687](https://doi.org/10.1007/BF00275687) | > [!NOTE] > **Canonical Physical Dataset:** The 36-month, 16-sensor continuous drift dataset from UC San Diego is publicly archived at the [UCI Machine Learning Repository (Dataset ID: 224)](https://archive.ics.uci.edu/dataset/224/gas+sensor+array+drift+dataset). --- ## Contributing & License Contributions following our zero-trust engineering standards are welcome. See [CONTRIBUTING.md](CONTRIBUTING.md) for pull request workflows. Dual-licensed under either of the following licenses: - [**MIT License**](LICENSE-MIT) - [**Apache License, Version 2.0**](LICENSE-APACHE) ---
BioNose-Edge · Engineered with zero-trust empirical rigor by Ali Rashidi.