No Coding Required: Create XR content effortlessly with a drag-and-drop interface. Industry-Specific Solutions: Suitable for manufacturing, logistics, energy, and various industries. Multi-Device Compatibility: Works on PCs, tablets, smartphones, and smart glasses (VisionX). Real-Time Collaboration: Multiple users can edit and share content simultaneously.

Original compact design and long-term production know-how. System reliability and overall competitiveness.

Thermostatic Temperature switches

The RFcore Radar series are FMCW type radar and were developed to detect/track small air targets. The major difference between three models is detection performance. RX32B is fundamental model. RX128B have four times larger radar than RX32B. RX192A have six times larger radar then RX32B. The number of the product name means the number of antenna elements. All have compact and lightweight physical architecture that concentrates all the components required for radar, such as antenna, RF components and radar signal processor, in a small single housing. Transport can be carried in the carrier box and can be easily carried by one person. For installation, you can use a commercially available camera tripod. The weight is about 7.5kg (RX32B) and 15.9kg (RX128B), and the lower connection part is compatible with the tripod connection for the camera. Power supply requires 12VDC (RX32B) and 24VDC power (RX128B and RX192A). When using a battery, it is possible to connect a 12V battery or 24V battery to radars. Usage time may vary depending on the battery capacity. RFCORE recommended the constant power supply. Transfer of Status and BIT information and simple control of the RX32B, RX128B and RX192A are possible with a web browser using the Ethernet connection for authorized users. Using the terminal program provided with the radar, you can view the radar control and detected target information in real time. All RX Radar series’ Power Amp and Core Chip were designed using RFcore's own products.

In collaboration with Hanwha Systems, we developed and optimized a Transformer-based AI model for ship and aircraft detection on embedded platforms. Targeting NVIDIA Jetson Orin AGX, the project utilized the MSAR (640×640) dataset along with large-scale training databases built from public sources. The RT-DETR model, currently under review, was optimized through hardware-friendly design, Int8 quantization, and specialized inference acceleration. As a result, the solution achieved up to 5x faster inference speed while maintaining accuracy, reaching 20% of NVIDIA TensorRT performance. This demonstrates the ability to deliver reliable, real-time detection capabilities in resource-constrained embedded environments, enabling next-generation defense and surveillance applications.

High productivity • The nodding head type spindle maximizes productivity and efficiency during multi-face and 5-axis simultaneous machining operations. High rigidity • Dual ball screws deliver improved stability and responsiveness for heavy-duty cutting. Easy chip disposal • The machines’ central chip removal system improves operational efficiencies.

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