Industrial Edge Intelligence Platform

VANTEDGE Signal. Compressed.
Intelligence. Delivered.

A proprietary four-stage signal processing pipeline that enables 8-bit edge hardware to transmit industrial sensor data with 32-bit fidelity — at 50% reduced bandwidth. No hardware upgrade required.

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50%
Bandwidth Reduction
4→2B
Per-Sample Payload
8→32
Bit Fidelity Bridge
0
Additional Hardware
The Problem

Industrial sensors are
choking your network

Modern industrial sensors output 32-bit floating-point data — four bytes per sample, every sample. At industrial sampling rates across hundreds of devices, this creates unsustainable bandwidth demand, especially on constrained fieldbus and wireless networks.

The standard solutions — averaging, decimation, fixed quantization — all require a tradeoff: less data, or less fidelity. VantEdge eliminates that tradeoff.

01 / BANDWIDTH
Float32 data is too heavy for the edge
4 bytes per sample — multiplied across hundreds of sensors at industrial sampling rates — saturates constrained networks and strains legacy fieldbus infrastructure.
02 / NOISE
Sensor noise is managed in the wrong place
Filtering and noise removal is typically done downstream — after noisy data has already consumed bandwidth and storage. The problem should be solved at the source.
03 / HARDWARE
8-bit edge hardware lacks signal intelligence
Resource-constrained microcontrollers can't run complex signal processing. Upgrading hardware is costly. The field needs a software-only solution that works within existing constraints.
The VantEdge Pipeline

Four stages.
One unified invention.

Each stage transforms the signal and passes its output to the next. The four stages are sequentially interdependent — producing emergent capabilities not present in any individual stage in isolation.

01
RLS Filter
Noise Removal
Recursive Least Squares adaptive filter removes sensor noise while preserving true signal dynamics
02
Horner Model
Derivative + Phase
Polynomial fit + O(N) Horner derivative produces virtual torque and mechanical cycle phase
03
RoPE Encoder
Temporal Encoding
Rotary Position Embedding encodes temporal position within the mechanical cycle — no timestamp needed
04
BitAug-RLT
Compression
Nearest-neighbor lookup in a pre-shared table compresses the embedding to a 2-byte payload
Stage 01 · RLS Adaptive Filter
Recursive Least Squares Refinement
Unlike fixed-window averaging, the RLS filter uses a Kalman-style covariance update with an exponential forgetting factor (λ = 0.97) to continuously adapt to non-stationary industrial signals. No step discontinuities. No fixed-delay artifacts. Just clean signal.
λ = 0.97 · Order 4 · Warm-up aware
Stage 02 · Horner Derivative Model
Virtual Torque — No Sensor Required
A polynomial is fit to a sliding window of filtered samples. Horner's method evaluates the first derivative at the most recent point in O(N) multiplications — producing a real-time torque estimate that eliminates the need for a dedicated torque transducer entirely.
Window = 16 · Degree 3 · O(N) evaluation
Stage 03 · RoPE Encoder
Timestamp-Free Temporal Encoding
Rotary Position Embedding — adapted from transformer language models and applied here for the first time to mechanical cycle phase encoding — rotates signal dimensions by frequency-dependent angles. The dot product of any two embeddings encodes their relative temporal separation without transmitting explicit timestamps.
Dim = 4 · Base = 10,000 · Phase-relative
Stage 04 · BitAug-RLT Compression
2-Byte Payload. Built-In Anomaly Detection.
A pre-shared Remote Lookup Table (built offline via K-Means clustering of representative embeddings) enables nearest-neighbor quantization to a 1-byte index + 1-byte residual delta. The delta channel is also your anomaly detector — a large delta means the signal has deviated from the learned model.
uint8 index + int8 delta · 50% compression
Technical Specifications

The numbers
speak for themselves

Bandwidth reduction 50% per sample
Input format float32 (4 bytes)
Output format 2 bytes (uint8 + int8)
Noise reduction 40–60% RMS improvement
Reconstruction MAE < 0.01 mm/s
Virtual torque range 40–55 N·m (no sensor)
RLS forgetting factor λ = 0.97
Platform compatibility 8-bit MCU · MQTT · Docker
Integration Litmus Edge · Databricks
IP status Patent Pending
Bandwidth Comparison · Per Sample
Standard float32 transmission 4 bytes
VantEdge compressed payload 2 bytes
# VantEdge pipeline output — per sample stage_1: RLS refined signal → float32 stage_2: virtual torque → 45.3 N·m stage_3: RoPE embedding → [0.82, -0.31, 0.55, 0.17] stage_4: compressed payload → [idx: 0xA4] [Δ: +3] TRANSMITTED: 2 bytes ← was 4 bytes ANOMALY SCORE: 0.12 ← nominal
Applications

Built for the
industrial edge

⚙️
Rotating Asset Monitoring
Bearings, fans, pumps, and motors. VantEdge extracts virtual torque from vibration data and detects developing faults through the anomaly channel — before failure occurs.
Predictive Maintenance
📡
Constrained Wireless Networks
Wireless sensor nodes with limited radio bandwidth or battery budgets. VantEdge's 50% payload reduction directly extends sensor network range and battery life.
IIoT Infrastructure
🔋
Battery Cell Manufacturing
High-density sensor arrays in EV battery production lines. Real-time signal quality at reduced data center ingestion cost — proven in one of the world's most demanding manufacturing environments.
EV / Energy
Bill Edenfield

IMG_0055.jpeg

Founder
Bill Edenfield
Founder & CTO · VantEdge Intelligence

Bill Edenfield spent years managing manufacturing production environments overseeing production systems where the quality and latency of sensor data had direct consequences on output quality and throughput.

The persistent challenge of managing noisy, bandwidth-heavy sensor streams in constrained industrial environments became the foundation of VantEdge Intelligence. Bill designed and built the entire four-stage pipeline — from mathematical concept to working proof-of-concept — and is currently validating the technology with leading industrial IoT platform partners.

VantEdge Intelligence is based in the Atlanta Metropolitan Area and is actively seeking partnerships with industrial automation OEMs, IIoT platform providers, and edge hardware manufacturers.

Founder & CTO Patent Pending Atlanta, GA IIoT · Edge AI
Get In Touch

Ready to see
VANTEDGE in action?

Whether you're an industrial automation OEM, an IIoT platform provider, or an engineering leader exploring edge optimization — we'd love to show you a live demonstration of the pipeline.

Or connect directly on LinkedIn  ·  VantEdge Company Page