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APTIS Android Mobile Telemetry
Download APK
Laboratory Floor Mobile Telemetry

Aptis Android Client &
Optical Table HUD

Take your high-power laser diagnostics directly to the optical table. Download the Aptis Android APK to monitor real-time sub-pixel centroid offsets, D4σ beam widths, and 2D FFT health indexes wirelessly from your phone or tablet.

Download Android APK (v1.0.5) Source Code
PLATFORM Android 9.0+
STREAMING Low-Latency MJPEG
DEPLOYMENT Direct Sideload APK
aptis_mobile.apk
ARM64 / x86
Dx: +1.2px | Dy: -0.4px H_k: 0.994
Lab Wi-Fi Connected 30 FPS

Portable tablet & phone monitoring for laser control rooms.

Mobile Release

Download Aptis Android Client

Install the APK directly on your Android device to connect with your laboratory telemetry server.

Aptis Telemetry Client v1.0.5

Compatible with Android 9.0 and newer (Universal APK)

Download the binary APK directly to your mobile device or tablet. Point the app to your local server IP (e.g., `http://46.101.190.76:5000`) to begin live optical beam tracking.

Download APK (Release v1.0.5)

Sideloading Instructions

  1. Download: Tap the download button from your Android device browser.
  2. Allow Installation: If prompted by Android security, enable "Install from unknown sources" for your browser.
  3. Configure Server: Open Aptis Mobile, enter your lab server IP address and port `5000`.
  4. Monitor: Enjoy real-time touch-to-set target crosshairs and health scores right at the optical table.
Live Interface Preview

Interactive Operator HUD Simulator

Click anywhere on the Gaussian pulse beam profile below to relocate the tracking target center.

APTIS // MOBILE TELEMETRY HUD

Android Client Preview (HTML5 Canvas Simulation)

OPTIMAL LOCK
ALIGNMENT LOCKED (Optimal Deviation < 15px)
Click canvas to set target center
OFFSET (Dx, Dy) +0, 0 px
RADIAL DIST 0.0 px
D4σ WIDTH 142.4 px
HEALTH (H_k) 0.998
VPI Forbidden Radius: 27
Academic Foundation

Related Research & Publications

Peer-reviewed literature by Vlad Gaciu supporting machine learning beam classification, high-power laser diagnostics, and plasma analysis.

Machine Learning & Science

Real-time classification of petawatt laser beam profiles

Machine Learning: Science and Technology, 2026. Automated feature extraction and real-time classification architectures for high-power laser facilities.

View on Google Scholar
Laser Diagnostics

Classification of laser beam profiles using machine learning at the ELI-NP high power laser system

AIP Advances, 2024. Experimental application of machine learning classifiers for near-field and far-field beam quality characterization at ELI-NP.

View on Google Scholar
Plasma Physics

Computer vision-based image analysis to characterize turbulent strongly coupled dusty plasma

Univ. Politehnica Bucharest, Sci. Bull. A, 2025. Computer vision algorithms and contour segmentation for analyzing complex plasma flow dynamics.

View on Google Scholar
Explore Full Google Scholar Profile (Vlad Gaciu)
Open Source Core

Modular Software Architecture

Built with transparency and extensibility in mind for research laboratories worldwide.

Flask REST Backend

Lightweight Python server providing local JSON telemetry endpoints and MJPEG video streaming feeds.

OpenCV Processing

Grayscale image moments combined with Otsu adaptive thresholding for robust sub-pixel beam tracking.

2D FFT Health Index

Frequency domain analysis evaluating forbidden energy ratios to quantify beam stability ($H_k$).

Android Companion

Native mobile client allowing optical table operators to monitor alignment feeds wirelessly in real time.