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+DEMO High-Performance Analog Meets AI
+===============================================================================
+
+Extracting data from high-performance, high-data-rate analog signal chains for AI
+model training and real-time inference presents significant challenges due to the
+complexity of interfaces, processing, and integration requirements. Analog Devices
+addresses these challenges by providing a comprehensive, open-source data extraction
+and integration software stack, which ensures seamless connectivity between advanced
+signal chains and high-performance compute platforms.
+
+
+Resources
+-------------------------------------------------------------------------------
+
+- HDL branch:       `adrv9009_qsfp_10G <https://github.com/analogdevicesinc/hdl/tree/adrv9009_qsfp_10G>`__
+- Linux branch:     `adr9009zu11eg_100MHZ_qsfp <https://github.com/analogdevicesinc/linux/tree/adr9009zu11eg_100MHZ_qsfp>`__
+- Corundum branch:  `corundum <https://github.com/ucsdsysnet/corundum.git>`__
+- PyADI-IIO branch: `jupiter_modulation <https://github.com/analogdevicesinc/pyadi-iio/tree/jupiter_modulation>`__
+
+Block diagram
+-------------------------------------------------------------------------------
+
+.. figure:: demo_block_diagram.svg
+   :align: center
+   :width: 900
+
+Demo description
+-------------------------------------------------------------------------------
+
+This demo illustrates an AI-based multi-channel RF modulation scheme recognition
+workflow for signal intelligence applications. Four AD-JUPITER-EBZ systems are used
+to generate RF signals with different modulation schemes across a total of eight
+channels. The signals are then digitized by two ADRV9009-ZU11EG SoMs, which stream
+the raw IQ data to a host PC via 10Gb Ethernet links. The AI model, derived from
+a MathWorks reference design, is deployed on the NVIDIA GPU hosted in the PC. The
+NVIDIA Holoscan AI infrastructure manages the efficient transfer of data from the
+network interfaces into GPU memory, where the AI model is executed. By combining
+ADI’s high-performance data extraction infrastructure with MathWorks development
+tools and NVIDIA deployment frameworks, the system enables efficient AI application
+development and real-time execution for advanced signal intelligence tasks.
+
+.. figure:: demo_description.svg
+   :align: center
+   :width: 600
+
+System Capabilities
+-------------------------------------------------------------------------------
+
+The system demonstrates an advanced, end-to-end data extraction and AI-based
+signal processing workflow designed for high-performance signal intelligence
+applications. It combines Analog Devices’ high-speed RF hardware and data
+infrastructure with third-party AI frameworks to deliver real-time modulation
+recognition and efficient AI model development.
+
+Key capabilities include:
+
+#. High-Performance Data Extraction
+
+   * Supports real-time acquisition of high-bandwidth RF data from multi-channel signal chains.
+   * Seamlessly bridges physical interfaces, FPGA-based logic, and low-level software drivers
+     to enable reliable data transfer from ADI RF front ends to edge processors.
+   * Flexible connectivity options, including Ethernet, PCIe, USB, and UART, allow integration
+     with a wide range of compute platforms.
+
+#. Real-Time AI Modulation Recognition
+
+   * Demonstrates multi-channel RF modulation scheme classification using AI models deployed on NVIDIA GPUs.
+   * The NVIDIA Holoscan AI infrastructure ensures efficient data movement between network interfaces and
+     GPU memory, supporting low-latency inference.
+
+#. Multi-Channel & Multi-Device Synchronization
+
+   * Incorporates multiple AD-JUPITER-EBZ boards and ADRV9009-ZU11EG SoMs to generate and
+     digitize RF signals across eight channels.
+   * Provides accurate clock distribution and synchronization through AD-SYNCHRONA14-EBZ,
+     ensuring deterministic latency and coherent signal processing across multiple systems.
+
+#. Seamless Data Integration Stack
+
+   * Enables flexible partitioning of data flow between edge and host compute devices, improving
+     scalability and system optimization.
+   * Utilizes an open-source ADI software stack that simplifies the setup of data collection
+     pipelines for AI model training and real-time inference.
+
+#. Integration with Industry-Standard AI Frameworks
+
+   * Compatible with MathWorks reference designs for AI model generation,
+     MATLAB-based workflows, NVIDIA Holoscan, and ROS2.
+   * Bridges data science workflows with embedded environments to enable
+     real-world dataset generation, model optimization, and deployment.
+
+#. End-to-End AI Development Ecosystem
+
+   * ADI’s AI Fusion tools within CodeFusion Studio™ enable model optimization, deployment,
+     and real-time performance analysis.
+   * Supports rapid development cycles by providing actionable insights and performance metrics for system tuning.
+
+Required Hardware
+-------------------------------------------------------------------------------
+
+The following hardware components are required to set up and run the multi-channel RF modulation recognition demo:
+
+.. list-table::
+   :widths: 15 30 5 15
+   :header-rows: 1
+
+   * - Component
+     - Role
+     - Quantity
+     - Notes
+   * - :dokuwiki:`Jupiter SDR <resources/eval/user-guides/jupiter-sdr>`
+     - Versatile 2 x RxTx software-defined-radio platform based on ADRV9002 and Xilinx Zynq UltraScale+ MPSoC.
+       Generates RF signals with configurable modulation schemes.
+     - 4
+     - Used to generate 8-channel RF input for AI recognition.
+   * - :dokuwiki:`ADRV9009-ZU11EG RF-SOM <resources/eval/user-guides/adrv9009-zu11eg>`
+     - RF System-on-Module with dual ADRV9009 wideband transceivers. Performs high-speed digitization and streaming of IQ data to the host.
+     - 2
+     - Provides synchronized multi-channel data acquisition.
+   * - :dokuwiki:`AD-SYNCHRONA14-EBZ <resources/eval/user-guides/ad-synchrona14-ebz>`
+     - Clock synchronization and distribution board based on AD9545 and HMC7044. Ensures accurate multi-channel phase alignment.
+     - 1
+     - Synchronizes all RF signal paths and data capture timing.
+   * - NVIDIA IGX Orin platform
+     - High-performance computing system with NVIDIA GPU acceleration. Runs Holoscan AI infrastructure and the AI modulation recognition model.
+     - 1
+     - Requires 10Gb Ethernet connectivity.
+   * - SMA Cables
+     - RF connection between the SDR transmit and receive channels.
+     - 8
+     - High-quality coaxial cables recommended for minimal signal loss.
+   * - 100G QSFP28 Active Optical Cable
+     - Provides high-speed data connection between the RF-SOM and the host compute platform.
+     - 1
+     - Supports low-latency, high-bandwidth Ethernet link.
+   * - Network switch with at least 4 PoE ports
+     - Provides Ethernet connectivity and power delivery to connected devices.
+     - 1
+     - Use a managed switch compatible with 10GbE interfaces.
+
+SD Card Configuration
+-------------------------------------------------------------------------------
+
+- For the Jupiter SDR platform, the boot files are generated using the Using Kuiper Image:
+
+ `Writing the Image to an SD Card <https://analogdevicesinc.github.io/adi-kuiper-gen/use-kuiper-image.html>`__
+
+- For the ADRV9009-ZU11EG, begin by checking out the HDL branch, then navigate to the **adrv2crr_fmc** directory.
+
+Run the following command to enable Corundum support and build the design: **make CORUNDUM=1**
+Once the build process is complete, generate the necessary boot files: boot.bin, device tree, and uImage by following
+the steps:
+
+- BOOT.BIN: `Build the boot image BOOT.BIN <https://analogdevicesinc.github.io/hdl/user_guide/build_boot_bin.html>`__
+- Devicetree: :dokuwiki:`Building the Zynq Linux kernel and devicetrees from source <resources/tools-software/linux-build/generic/zynq?s%5b%5d=devicetree>`
+
+Capture in Data Using Scopy2.0
+-------------------------------------------------------------------------------
+
+Captured RF Signal in Time Domain
+
+.. figure:: capture_time.jpg
+   :align: center
+   :width: 900
+
+Captured RF Signal in Frequency Domain
+
+.. figure:: capture_frequency.jpg
+   :align: center
+   :width: 900
+
+AI Modulation Detection Applications
+-------------------------------------------------------------------------------
+
+
+Software Configuration
+-----------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   software/index