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.

CASE 01 / boot-optimizationBSP & 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

1.8sCold Boot Time18.4s → 1.8s

0.21sKernel DecompressΔ −88%

4.1 MBBinary SizeΔ −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.

Technical Deep Dive

CASE 02 / edge-ai-inferenceEdge 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 fpsInference Speed31 fps → 127 fps

8.4 WPower ConsumptionΔ −41%

15.1 fps/WEfficiency ScoreΔ +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.

Technical Deep Dive

CASE 03 / rtos-latencyRTOS & 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 µsWorst-Case IRQ38.7 µs → 4.2 µs

0.3 µsJitter (σ)Δ −94%

34%CPU Load71% → 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

CASE 04 / railway-inspectionSynthetic Data Generation & Computer Vision

Synthetic Training Data for Railway Anomaly Detection

Physically-correct compositing · 8 anomaly classes · automatic YOLO labeling

Problem

Training a model to detect obstacles on the track requires images of rocks/branches/sacks/animals lying on the rails. But these "black-swan" events are rare, dangerous, and expensive to capture in reality — you cannot deliberately place an obstacle in front of a train. The result: a scarcity of positive samples and severe class imbalance.

Architectural Approach

In frames selected from forward-camera video, the rail corridor is found with YOLOv8-seg; transparent anomaly assets generated via SDXL + BiRefNet are placed on the rail with physically-correct scale referenced to the standard gauge (1.435 m), plus shadow and color harmonization. Labels are derived automatically from the post-transform visible alpha; at the video level a fixed track-id + KLT anchor keep it frame-consistent.

8 sınıf · 1.435 m gauge · otomatik etiketSDXL · BiRefNet · YOLOv8-seg

8 classesAnomaly DiversityClass-balanced gen

1.435 mGauge-Based ScaleHand-est → Physically-correct

AutomaticYOLO LabelingManual → Zero-touch

Operational Impact

Critical obstacle scenarios — impossible to collect in the field — became available as synthetic, automatically labeled data. For autonomous railway safety (ODS) it targets the scarcity of positive samples and class imbalance in model training. (R&D — sim-to-real validation in progress.)

Technical Deep Dive

CASE 05 / webrtc-c2Industrial 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

<200msGlass-to-Glass Latency500ms+ → <200ms

<80msEncode LatencyHW Accelerated

Multi-StreamSimultaneous SensorsVideo + Sensor + Telemetry

Operational Impact

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

Technical Deep Dive

CASE 06 / ethercat-motionIndustrial Protocols & Motion Control

EtherCAT Master Architecture with a 3.8µs Cycle

NXP i.MX8M Plus · PREEMPT_RT · IgH EtherCAT Master

Problem

In a multi-axis servo topology, ~1ms of non-deterministic TCP/IP latency made coordinated motion impossible. Slave clocks drifted within a ±5µs band and position error kept accumulating.

Architectural Approach

The IgH EtherCAT master was moved to a dedicated core on PREEMPT_RT, isolated with SCHED_FIFO 99 + isolcpus. Distributed clock (DC) calibration was added to the start-up sequence; PDO maps were trimmed per axis and the CiA 402 profile standardized over CoE.

8µs → 3.8µs · DC <100nsi.MX8M Plus · PREEMPT_RT · IgH

3.8µsEtherCAT Cycle8µs → 3.8µs

<100nsSlave Sync Error±5µs → <100ns

0Missed Cycles / Hour1h full load

Operational Impact

A microsecond-class deterministic fieldbus was established on standard Ethernet PHY hardware. Hot-connect support allows line maintenance without stopping production; the architecture is ready for IEC 62443-compliant segmentation.

Technical Deep Dive

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

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

Schedule a Discovery Meeting