Case Studies

Field-Proven Results

We don't list technologies — we demonstrate with metrics how we deliver results at hardware's theoretical limits. All data obtained on real hardware under repeatable conditions.

BSP & Yocto Optimization

Cold Boot Time Reduced by 90%

NXP i.MX8M Plus · Yocto Scarthgap 5.0

Problem

On an industrial platform, a standard Yocto distribution's 18.4-second cold boot time was causing unacceptable operational delays in the field. Every second was critical at system startup.

Architectural Approach

U-Boot Falcon Mode was implemented to bypass the full U-Boot boot process and load the kernel directly. Kernel compression was changed from zlib to LZ4. rootfs was migrated from ext4 to SquashFS + tmpfs overlay. Binary size was reduced by 65% using musl libc and BusyBox.

18.4s → 1.8s · −90%NXP i.MX8M Plus · Yocto

1.8s

Cold Boot Time

18.4s → 1.8s

0.21s

Kernel Decompress

Δ −88%

4.1 MB

Binary Size

Δ −65%

Operational Impact

The system becomes operational in 1.8 seconds from power-on to Qt interface ready. Critical startup latency was eliminated for field operations teams.

Edge AI & Computer Vision

127 fps Edge AI Inference Speed

NVIDIA Jetson Orin NX 16GB · TensorRT 8.6 · JetPack 6

Problem

In an autonomous UAV system, standard FP32 ONNX inference produced only 31 fps. This speed was insufficient for real-time object detection and tracking on SWaP-C constrained hardware.

Architectural Approach

INT8 quantization was applied to the YOLOv8-m model. Load time was optimized via TensorRT engine serialization. GPU pipeline overhead was minimized with CUDA graphs. Power consumption was reduced through DLA co-execution.

31 fps → 127 fps · +310%Jetson Orin NX · TensorRT

127 fps

Inference Speed

31 fps → 127 fps

8.4 W

Power Consumption

Δ −41%

15.1 fps/W

Efficiency Score

Δ +586%

Operational Impact

On SWaP-C constrained autonomous systems, the platform achieved performance capable of simultaneously detecting and tracking multiple objects. Power savings significantly extended battery life.

RTOS & Deterministic Systems

4.2 µs Worst-Case IRQ Latency

TI AM6442 · TI-RTOS 7.x

Problem

In a multi-axis motor control system, FreeRTOS achieved 38.7 µs worst-case interrupt latency, causing unacceptable jitter levels for precision servo synchronization.

Architectural Approach

Migrated to TI-RTOS HWI direct dispatch mechanism with interrupt priority elevated to priority=31. Tickless mode was activated, disabling timer coalescing. Task context switch time was reduced to 1.1 µs via zero-copy mailbox.

38.7µs → 4.2µs · σ 0.3µsTI AM6442 · TI-RTOS · Osiloskop

4.2 µs

Worst-Case IRQ

38.7 µs → 4.2 µs

0.3 µs

Jitter (σ)

Δ −94%

34%

CPU Load

71% → 34%

Operational Impact

Sub-microsecond synchronization guarantee was achieved, delivering deterministic motor control in industrial robotic networks. Performance was proven with oscilloscope-verified data.

Technical Deep Dive →

Predictive Maintenance & Anomaly Detection

Railway Anomaly Detection

Edge AI · Sensor Fusion · Real-Time Processing

Problem

Traditional periodic railway inspection methods could only be performed at fixed intervals, resulting in delayed detection of rail damage and anomalies. A continuous real-time monitoring system was needed.

Architectural Approach

A multi-modal anomaly detection architecture was designed through fusion of visual camera, acoustic sensor, and vibration data. TensorRT-optimized model performed real-time inference on edge devices, eliminating cloud dependency.

Real-Time

Continuous Monitoring

Periodic → Instant

Sensor Fusion

Multi-Modal Detection

Visual + Acoustic + Vibration

Edge Processing

Cloud Independent

Zero Latency

Operational Impact

Continuous and autonomous anomaly detection was achieved on critical railway infrastructure. Maintenance team reactive response times were shortened; accident risk was minimized.

Industrial Video Streaming & Remote Monitoring

Sub-200ms WebRTC Industrial Video

Low Latency · HW Accelerated · Multi-Sensor

Problem

High latency in video streams to remote monitoring operators severely slowed real-time decision-making. Traditional RTSP solutions produced 500ms+ delay.

Architectural Approach

A WebRTC-based peer-to-peer video streaming architecture was designed. Hardware-accelerated H.264/H.265 encoding was implemented via GStreamer pipelines. Secure low-latency communications via UDP transport layer.

500ms+ → <200msWebRTC · GStreamer · H.264

<200ms

Glass-to-Glass Latency

500ms+ → <200ms

<80ms

Encode Latency

HW Accelerated

Multi-Stream

Simultaneous Sensors

Video + Sensor + Telemetry

Operational Impact

Operators can now monitor field conditions in near real-time. Decision-making time was significantly reduced, increasing operational effectiveness.

Facing a similar engineering challenge? Sit down directly with our technical team.

Schedule a Discovery Meeting

* All performance data obtained in our own laboratory under repeatable conditions. Project-specific details are kept confidential under NDA. Methodology documentation available upon request.