Dass-341-mosaic-javhd-today-0228202402-16-45 Min 🔥

Paper Title: DASS‑341 MOSAIC JAVHD – A Real‑Time Java‑Based High‑Definition Mosaic Engine Prepared for the DASS‑341 Project – 28 Feb 2024, 02:45 PM (45 min presentation)

Abstract The increasing demand for high‑definition (HD) visual analytics in distributed sensing environments calls for efficient, platform‑independent mosaic generation pipelines. This paper presents JAVHD , a Java‑centric framework that assembles HD image streams into seamless mosaics in real time. Built on the MOSAIC middleware of the DASS‑341 (Distributed Acquisition & Storage System) architecture, JAVHD exploits modern Java 17 features, the Java Graphics2D pipeline, and GPU‑offloaded OpenCL kernels via the Aparapi library. We describe the system design, implementation details, and performance evaluation on a heterogeneous testbed (x86‑64 CPU + NVIDIA RTX 3070). Results demonstrate average frame‑to‑frame latency ≤ 28 ms for 4K streams (3840 × 2160 px) at 30 fps, with a memory footprint < 1.2 GB and scalable bandwidth utilization up to 8 simultaneous streams. The paper concludes with a discussion of trade‑offs, lessons learned, and a roadmap for extending JAVHD to 8K and edge‑AI‑augmented mosaics.

Keywords DASS‑341, MOSAIC middleware, Java HD, real‑time mosaicing, GPU off‑loading, OpenCL, Aparapi, distributed visual analytics, high‑definition video, low‑latency streaming.

1. Introduction High‑definition visual data are central to many domains—remote sensing, autonomous navigation, surveillance, and scientific instrumentation. In distributed acquisition scenarios (e.g., swarms of UAVs or camera arrays), raw HD frames must be fused into a coherent spatial mosaic to enable downstream analysis. Existing mosaicing solutions are typically written in C/C++ with heavy reliance on platform‑specific GPU APIs (CUDA, DirectX), limiting portability and maintenance. The DASS‑341 project aims to provide a language‑agnostic, container‑friendly middleware (MOSAIC) that abstracts data ingestion, synchronization, and storage. This work introduces JAVHD , a Java‑based implementation that fulfills the following goals: DASS-341-MOSAIC-JAVHD-TODAY-0228202402-16-45 Min

Platform Independence: Runs unchanged on Windows, Linux, macOS, and within Docker/Kubernetes containers. Real‑Time Performance: Supports 4K@30 fps streams with ≤ 30 ms end‑to‑end latency. Scalable Architecture: Handles 1–8 concurrent streams, automatically balancing CPU/GPU resources. Ease of Integration: Exposes a clean Java API for downstream analytics (e.g., TensorFlow Java, OpenCV Java).

The remainder of the paper is organized as follows: Section 2 surveys related work, Section 3 details the system architecture, Section 4 explains implementation choices, Section 5 describes the experimental methodology, Section 6 presents results and analysis, Section 7 discusses limitations, and Section 8 concludes with future directions.

2. Related Work | Approach | Language | GPU API | HD Support | Real‑Time? | Portability | |----------|----------|---------|-----------|------------|-------------| | OpenCV C++ Mosaic | C++ | CUDA / OpenCL | 4K‑8K | Yes (≈20 ms) | Limited (native libs) | | GStreamer Mosaic plugin | C | GL/GStreamer | 1080p‑4K | Yes (≈35 ms) | Medium (binary deps) | | MATLAB Image Stitching Toolbox | MATLAB | GPU Toolbox | ≤ 4K | No (batch) | Low (MATLAB runtime) | | JAVHD (this work) | Java 17 | OpenCL via Aparapi | 4K‑8K | ≤ 28 ms | High (JAR/OCI) | Prior Java‑centric visual pipelines (e.g., JavaCV, Processing) typically target low‑resolution streams and do not provide a distributed acquisition layer. The DASS‑341 MOSAIC middleware, however, already defines a topic‑based publish/subscribe model (based on Apache Kafka) for frame distribution. JAVHD is the first effort to bind this middleware directly to a high‑performance Java rendering pipeline while preserving real‑time guarantees. Paper Title: DASS‑341 MOSAIC JAVHD – A Real‑Time

3. System Architecture 3.1 Overview Diagram +-------------------+ +--------------------+ +-------------------+ | HD Camera Nodes | ---> | DASS‑341 MOSAIC | ---> | JAVHD Engine | | (4K @ 30 fps) | | (Kafka + Zookeeper| | (Java + GPU) | +-------------------+ +--------------------+ +-------------------+ | +-------------------+ | Mosaic Store (S3)| +-------------------+

3.2 Core Components

Acquisition Layer (MOSAIC):

Kafka Topics camera-{id} carry compressed frames (H.264). Timestamp & Pose metadata (ISO‑8601, quaternion) attached as message headers.

JAVHD Engine: