Research Report on In-Depth Integration of LabVIEW and NI Hardware

Research Report on In-Depth Integration of LabVIEW and NI Hardware

1. Introduction

This report delves into the deep integration of National Instruments (NI)'s graphical system design platform, LabVIEW, with its various hardware types, including DAQ, PXI, CompactRIO, USRP, Vision, Motion Control, and more. The study covers driver usage, Real-Time (RT) and FPGA programming, application of specific toolkits, system performance optimization, system architecture design, troubleshooting, and comparison of different solutions. The report aims to provide comprehensive technical insights and practical guidance for experts in application areas such as test and measurement, industrial control, embedded systems, RF communication, and machine vision. The ultimate goal is to establish best practices, evaluate system performance, solve practical problems, and guide system design.

LabVIEW is renowned for its intuitive graphical programming language (G language), which significantly simplifies the development of complex systems. NI hardware offers a wide range of capabilities, from high-performance data acquisition to real-time control and software-defined radio. The deep combination of these two is key to building efficient, flexible, and reconfigurable engineering systems.

2. NI Platform Overview and Hardware Types

The core of the NI platform lies in the tight integration of LabVIEW software with a diverse hardware product line. This integration is achieved through unified drivers (such as NI-DAQmx) and configuration tools (such as Measurement & Automation Explorer - MAX), providing users with a seamless experience from low-level hardware control to high-level application development.

This study focuses on the following NI hardware types:

Data Acquisition (DAQ): Provides high-accuracy, multi-channel analog and digital signal acquisition and output capabilities. NI DAQ cards (such as PCIe-6353) are known for their high sampling rates, low noise, and synchronization accuracy, widely used in industrial automation, test and measurement, and scientific research. NI-STC3 timing and synchronization technology is crucial for achieving synchronized acquisition across multiple channels.

PXI/PXIe: A PC-based modular instrumentation platform offering high performance, high channel density, and precise synchronization capabilities. The PXIe platform (such as PXIe-4331, PXIe-4461) is particularly suitable for applications requiring high bandwidth and high-accuracy input/output, such as audio and vibration testing. The PXI platform achieves nanosecond-level synchronization across multiple devices via the RTSI bus.

CompactRIO (cRIO): A rugged, reconfigurable embedded system integrating a real-time processor, a reconfigurable FPGA, and interchangeable industrial I/O modules. cRIO is suitable for embedded control and monitoring applications requiring high reliability, determinism, and flexibility. The LabVIEW RIO architecture is its core, combining high-performance SoCs (such as Intel Atom) and powerful FPGAs (such as Xilinx Kintex-7).

USRP (Universal Software Radio Peripheral): A general-purpose software-defined radio peripheral that, combined with LabVIEW and the NI-USRP driver, can build flexible RF communication systems. The core of the USRP is an FPGA, used for high-speed signal processing tasks, suitable for RF communication teaching, cognitive radio research, and other fields.

Vision: NI Vision hardware (such as smart cameras, vision acquisition cards) combined with the LabVIEW Vision Development Module (VDM) provides powerful machine vision capabilities. VDM supports a rich set of image processing algorithms and achieves high-performance image processing through FPGA acceleration, suitable for industrial inspection and machine vision applications.

Motion Control: NI motion control hardware (such as motion control cards) combined with the LabVIEW Motion Control Module enables precise multi-axis motion control and trajectory planning. The NI SoftMotion module can maintain position, velocity, and trajectory synchronously across multiple axes, suitable for robotics, automated production lines, and more.

3. Exploring the Deep Integration of LabVIEW and NI Hardware

"Integration Depth" in this study is defined as a comprehensive understanding and application ranging from basic hardware integration to advanced programming techniques, system performance optimization, troubleshooting, and comparison of different solutions.

3.1 Driver Usage and Basic Integration

The basic integration of LabVIEW and NI hardware primarily relies on the drivers and configuration tools provided by NI.

NI-DAQmx: This is the core driver for NI data acquisition hardware, providing a unified API that simplifies access to various DAQ devices. In LabVIEW, the DAQmx function palette allows easy creation of virtual channels, configuration of timing and triggering, starting tasks, and reading/writing data. The MAX tool can be used to detect devices, configure sample clock sources, set analog input ranges, etc. Correct driver version compatibility with hardware and LabVIEW versions is crucial (e.g., DAQmx 21.0 supports LabVIEW 2021+).

MAX (Measurement & Automation Explorer): MAX is the central tool for configuring NI hardware. It allows users to identify and configure NI hardware, perform self-tests, and test I/O functionality. For network devices (such as CompactRIO, USRP), MAX is also used to configure network parameters and discover remote systems. The System Configuration API provides programmatic access to MAX functionality, facilitating automated configuration and deployment.

Signal Wiring Principles: Correct signal wiring is fundamental to ensuring data acquisition quality. For example, differential inputs should use twisted-pair shielded cables, and single-ended inputs require attention to common-mode voltage. Using a star grounding topology can prevent ground loops.

3.2 Advanced Programming Techniques: Real-Time, FPGA, and Specific Toolkits

LabVIEW provides powerful modules and toolkits that support real-time and FPGA programming, as well as application development in specific domains.

LabVIEW Real-Time (RT) Module: Suitable for applications requiring high determinism and low jitter. The RT module allows programs to run in a real-time operating system (RTOS), ensuring critical tasks are completed within a predefined time. Timed Loops are key to achieving strict time scheduling. RT systems are typically used with CompactRIO and PXI RT controllers. Separating critical and non-critical tasks is an important principle in RT system design.

LabVIEW FPGA Module: Allows users to graphically program FPGAs at the hardware level, enabling high precision, low latency, and massive parallel processing. FPGAs are suitable for motion control, high-speed data acquisition, complex signal processing, and mission-critical safety logic. With the LabVIEW FPGA module, time-consuming image processing or signal processing tasks can be offloaded to the FPGA, reducing processor load.

RT and FPGA Collaboration: In many high-performance applications, the RT and FPGA modules are used together. The FPGA handles low-level, high-speed, deterministic tasks, while the RT processor manages the overall control logic and communication with the host. DMA channels are crucial for high-speed data transfer between the FPGA and RT.

Specific Toolkits: *

Vision Development Module (VDM): Provides a rich library of image processing and machine vision algorithms, supporting 2D/3D processing. VDM integrates seamlessly with NI vision hardware and FPGAs, enabling high-performance machine vision applications.

Motion Control Module / SoftMotion: Offers advanced features such as multi-axis motion control, trajectory planning, electronic gearing, and electronic camming. NI SoftMotion can directly control drives, achieving precise synchronization.

Modulation Toolkit: Used for modulation, demodulation, and signal analysis in RF communication systems. Combined with NI USRP hardware, it can build software-defined radio applications.

Sound and Vibration Measurement Suite: Provides sound and vibration analysis functions, such as order analysis.

3.3 System Architecture Design Patterns

Proper system architecture design is essential for building scalable, maintainable, and high-performance LabVIEW systems.

Data Flow Design Patterns (DAQ): Choose appropriate data acquisition modes based on different signal characteristics and real-time requirements:

Single Point Read: Suitable for slow-changing signals, high latency, low CPU usage.

Finite Samples: Suitable for transient capture, moderate latency and CPU usage.

Continuous Acquisition: Suitable for real-time monitoring, low latency, high CPU usage.

CompactRIO Architecture: A typical CompactRIO application includes an RT processor, FPGA, and I/O modules. The RT processor runs control logic and HMI, while the FPGA handles high-speed I/O and critical logic. Data is transferred between the FPGA and RT via DMA or scan mode.

Machine Vision System Architecture: Typically includes image acquisition (camera), preprocessing, feature extraction, identification/measurement, and result output. High-performance systems often offload computationally intensive tasks like preprocessing or feature extraction to the FPGA.

Multitasking and Communication: In LabVIEW RT, a royalty-free kernel (VDK) is used to implement preemptive, thread-safe multitasking. Inter-Process Communication (IPC) mechanisms include Shared Variables, RT FIFOs, and Network Streams. RT FIFOs are suitable for deterministic data sharing within an RT system, Shared Variables are suitable for data publishing between multiple computers, and Network Streams are suitable for lossless network data transfer.

Modular Design: Keep code modular, use subVIs and project libraries to improve code reusability and maintainability.

Design Patterns: Consider using design patterns such as State Machines (JKI State Machine) or the Actor Framework to build complex, scalable applications. The Actor Framework is particularly suitable for concurrent and distributed system design.

3.4 Performance Optimization

Performance optimization is key to achieving high-performance NI systems, involving multiple levels of hardware, software, and programming techniques.

Hardware Optimization: *

Select appropriate hardware modules: Choose DAQ, PXI, cRIO, USRP modules based on requirements for acquisition rate, number of channels, accuracy, bandwidth, etc.

Camera selection and lighting: In machine vision, select high-resolution, high-frame-rate cameras and optimize light sources and lighting schemes.

PXIe platform advantages: Utilize the high bandwidth and low latency characteristics of PXIe.

FPGA acceleration: Offload computationally intensive tasks to the FPGA.

Software and Programming Optimization: *

LabVIEW Code Optimization: Write efficient code, reducing unnecessary computations and data copying.

Multithreading and Parallel Processing: LabVIEW natively supports multithreading, which can effectively process and display high-speed acquired data.

FPGA Performance Optimization: *

Pipelining: Break down a complex operation into multiple stages and execute them in parallel in the FPGA to increase clock frequency and throughput.

Parallel Loops: Execute multiple independent loops simultaneously in the FPGA.

Single-Cycle Timed Loops (SCTL): Implement high-performance, deterministic code, increase clock frequency, and shorten critical paths.

Resource Utilization Optimization: Effectively utilize FPGA resources (LUTs, FFs, DSPs, Block Memory), choose appropriate data types, and minimize front panel controls.

Timing Analysis and Optimization: Ensure FPGA designs meet timing requirements and increase clock frequency.

Data Flow Management: Use FIFO or DMA techniques to improve data flow efficiency. DMA control is crucial for high-speed data transfer between the FPGA and RT.

Real-Time System Optimization: Minimize jitter in RT systems, optimize task priorities, and avoid non-deterministic operations.

System-Level Optimization: *

Synchronization and Triggering: Precisely configure synchronization and triggering mechanisms for multiple channels and devices (e.g., PTP, RTSI, Action Command).

Data Flow Management and Storage: Design efficient data flow management and storage strategies to handle the large amount of data generated by high-speed acquisition.

Noise Immunity and Signal Integrity: Follow signal wiring principles, use shielded cables, and adopt appropriate grounding strategies.

3.5 Troubleshooting and Debugging

Effective troubleshooting and debugging techniques are essential for ensuring stable system operation.

General Troubleshooting: *

LabVIEW Crashes: Send crash reports, check for reproducibility, search the knowledge base, install patches, narrow down the scope, check for memory leaks, disable code sections.

Hardware Installation Issues: Check software and hardware compatibility, driver versions, installation order, Device Manager status, refresh MAX, reinstall drivers, manually associate drivers.

Network Device Recognition: Use the MAX tool, check software configuration, driver version, network connection, Ping test, manually add devices, check firewall, reinstall NI System Configuration.

LabVIEW Debugging Tools: *

Probe Tool: View real-time data on wires.

Breakpoint Tool: Halt program execution for single-stepping.

Execution Highlighting: Visualize data flow and execution paths.

Single Stepping: Execute code line by line.

Call List: View VI call relationships.

Disable Structure: Isolate problematic code sections.

Real-Time Trace Viewer: Analyze RT program performance, identify memory allocation, sleeps, and resource contention.

NI Hardware Specific Troubleshooting: *

NI-DAQmx: Check PCIe power supply, disable BIOS PCIe power management, measure signal source impedance, check input range, change clock source, check cable delay, refer to error codes.

NI USRP: Check software installation, hardware connection, IP configuration, USRP frequency band, TX/RX IQ Rate consistency, driver version.

NI CompactRIO: Understand the difference between Scan Mode and FPGA Mode, check module compatibility, NI Scan Engine status.

NI PXI: Check chassis, controller, and module compatibility, MAX configuration issues.

Toolkits and APIs: The System Configuration API and RT Management Toolkit provide programmatic access and configuration of NI hardware, aiding in automated fault diagnosis and system status monitoring.

3.6 Comparison of Different Solutions

When designing a system, it is necessary to compare different NI hardware platforms and software implementation schemes, weighing performance, cost, flexibility, and ease of use.

PXI vs. CompactRIO vs. DAQpad: *

PXI: Suitable for high-performance test and measurement, requiring high channel density, precise synchronization, and modularity. Relatively high cost.

CompactRIO: Suitable for rugged embedded control and monitoring, requiring real-time capability, FPGA flexibility, and standalone operation. Suitable for harsh environments.

DAQpad/USB DAQ: Suitable for portable or distributed data acquisition, lower cost, easy to connect. Performance and synchronization capabilities are typically lower than PXI and cRIO.

CompactRIO Scan Mode vs. FPGA Mode: *

Scan Mode: Easy to use, no FPGA programming required, suitable for synchronized I/O updates up to 1 kHz. Relatively lower determinism.

FPGA Mode: Provides the highest determinism and flexibility, suitable for high-speed control, custom logic, and parallel processing. Requires FPGA programming knowledge, longer development cycle.

LabVIEW Windows vs. RT System: *

LabVIEW Windows: Wide hardware compatibility, rich third-party libraries, easy HMI development. Does not have hard real-time capability.

LabVIEW RT: Provides hard real-time capability, suitable for applications with strict timing requirements. Tightly integrated hardware interfaces.

Software-Defined Radio Platform Comparison (NI USRP vs. Other SDR): Compare different SDR platforms based on frequency range, bandwidth, processing power, software support, and cost. NI USRP combined with LabVIEW provides a graphical development environment, lowering the barrier to SDR development.

Machine Vision Software Comparison (VDM vs. Vision Builder AI): *

VDM: Programming-based, high flexibility, suitable for complex algorithms and custom applications.

Vision Builder AI: Configuration-based, no programming required, suitable for standard inspection tasks.

When comparing solutions, a detailed cost-benefit analysis (ROI) is necessary, considering the total cost of ownership (TCO) including hardware, software, development, maintenance, and upgrades.

4. Deep Integration Practices in Application Domains

The deep integration of LabVIEW and NI hardware demonstrates powerful capabilities in multiple application domains.

Test and Measurement: *

High-Performance Data Acquisition: Utilize PXIe and high-speed DAQ modules for audio, vibration, and transient signal acquisition. LabVIEW provides data flow management and real-time analysis capabilities.

Automated Test Systems: Build modular test platforms based on PXI, implementing test sequences, instrument control, and data reporting through LabVIEW. The NI platform is widely used in end-of-line testing in automotive electronics manufacturing, requiring high test throughput and test integrity.

RF Testing: Combine NI USRP and modular RF instruments, using the LabVIEW Modulation Toolkit for testing standards such as WLAN (IEEE 802.11), DVB-T, GPS, WiMAX, significantly increasing test speed.

Industrial Control: *

Real-Time Control Systems: Use CompactRIO and LabVIEW RT/FPGA to implement high-determinism, low-latency closed-loop control, such as motion control and process control.

Distributed Control: Build distributed control systems using CompactRIO and network communication (such as EtherCAT). ZMC408CE and other EtherCAT motion controllers from Zmotion Technology also provide LabVIEW APIs.

Embedded Systems: *

CompactRIO Embedded Applications: Develop standalone embedded control and monitoring systems on the CompactRIO platform, suitable for harsh environments and remote deployment.

NI ELVIS III Teaching Platform: Integrates various instruments and FPGA functions for engineering teaching experiments, supporting multidisciplinary applications.

ZMC Industrial Platform: Based on the ADI Blackfin processor, supports LabVIEW programming, suitable for fixed and mobile devices, measurement instruments, and other embedded applications.

RF Communication: *

Software-Defined Radio (SDR): Utilize NI USRP and LabVIEW to build flexible SDR platforms for communication system prototyping, channel measurement, and cognitive radio research. Stanford University uses LabVIEW and USRP for RF communication teaching.

5G Millimeter-Wave Research: Use the NI platform for 5G millimeter-wave massive MIMO channel modeling and information acquisition research.

Machine Vision: *

Industrial Inspection: Use NI Vision hardware and VDM for defect detection, dimension measurement, and localization. Achieve high-speed online inspection through FPGA acceleration.

Image Processing Acceleration: Offload tasks such as image preprocessing and feature extraction to the FPGA to increase processing speed.

Multi-Camera Synchronization: Achieve precise synchronization of multiple cameras using PTP or Action Command for multi-angle monitoring or 3D reconstruction.

5. Achieving Research Objectives

This study, through a comprehensive exploration of the deep integration of LabVIEW and NI hardware, aims to achieve the following objectives:

Establish Best Practices: Summarize experiences from driver usage, programming techniques, architecture design, performance optimization, and troubleshooting to form best practice guidelines for different application scenarios. For example, in RT systems, place critical tasks in Timed Loops and use RT FIFOs for deterministic data sharing; in FPGAs, utilize pipelining and SCTL to improve performance.

Evaluate Performance: Clearly define performance metrics for different NI hardware platforms and programming schemes (sampling rate, bandwidth, latency, jitter, resource utilization) and discuss performance evaluation methods (such as benchmarking, Real-Time Trace Viewer). Understand the relationships between performance dimensions, such as throughput, timing, resource usage, and numerical precision.

Solve Specific Problems: Provide solutions and technical details based on LabVIEW and NI hardware for specific problems such as data flow management in high-speed data acquisition, synchronization in multi-axis motion control, real-time processing in machine vision, and signal processing in RF communication. For example, use DMA to solve high-speed data flow issues, use electronic gearing/camming to solve motion synchronization issues, and use FPGAs to solve image processing real-time issues.

Design Systems: Guide users in designing systems that meet specific performance, real-time, cost, and compatibility requirements based on an understanding of NI platform capabilities, design patterns, and optimization techniques. For example, choose RT or FPGA platforms based on real-time requirements, select appropriate buses and data transfer mechanisms based on data volume, and choose CPU or FPGA implementation based on algorithm complexity.

6. Considerations

In practical applications, it is necessary to comprehensively consider performance requirements, real-time needs, cost constraints, and existing system compatibility.

Performance and Real-Time: For applications requiring high determinism and low jitter, the CompactRIO or PXI RT platform combined with LabVIEW RT and FPGA is the preferred choice. It is necessary to carefully analyze the timing requirements of tasks and utilize Timed Loops, SCTL, DMA, and synchronization mechanisms to meet these requirements. The Real-Time Trace Viewer is an important tool for analyzing and optimizing RT system performance.

Cost Constraints: The cost of different NI hardware platforms varies significantly. It is necessary to choose the most cost-effective solution based on actual needs. For example, a simple DAQ application may only require a USB DAQ, while a complex test system may require PXI. Consider the total cost of ownership (TCO), including hardware, software, development, maintenance, and upgrade costs. Test component reuse mechanisms can effectively reduce long-term costs.

Existing System Compatibility: It is necessary to evaluate the hardware interfaces, communication protocols, and software environment of existing systems and choose NI hardware and LabVIEW versions that can integrate seamlessly. LabVIEW provides various interface technologies for integrating with third-party systems, such as DLL calls, TCP/IP, and industrial bus protocols (such as CAN, EtherCAT). The System Configuration API and RT Management Toolkit help manage hybrid systems.

7. Future Trends and Outlook (Speculation)

Based on current technological developments and NI platform characteristics, some future trends can be speculated:

Deepening Integration of AI and Machine Learning: LabVIEW will further deepen its integration with AI and machine learning technologies. In addition to existing machine learning algorithms in the Vision Development Module, toolkits or hardware acceleration solutions (potentially utilizing FPGAs or GPUs) that make it easier to deploy and run third-party AI models (such as TensorFlow, PyTorch) in LabVIEW may emerge in the future. This will bring new breakthroughs in areas such as machine vision, predictive maintenance, and intelligent control.

Cloud Connectivity and Edge Computing: NI hardware systems will be more tightly integrated with cloud platforms for remote monitoring, data analysis, model training, and system management. Embedded platforms like CompactRIO will become important nodes for edge computing, performing local data preprocessing and real-time decision-making before uploading key information to the cloud.

More Powerful FPGAs and SoCs: With the advancement of semiconductor technology, NI hardware will integrate more powerful and resource-rich FPGAs and multi-core SoCs. This will make it possible to implement more complex algorithms, achieve higher data throughput, and run more parallel tasks on a single device.

Evolution of Software-Defined Instrumentation (SDI): The concept of software-defined instrumentation will further evolve, with instrument functionality increasingly defined and configured by software, and hardware becoming more generalized and flexible. LabVIEW, as the core software platform for SDI, will play a more important role.

Augmented Reality (AR) and Virtual Reality (VR) Integration: AR/VR technology may be used for visualization, remote operation, and training of LabVIEW systems. For example, viewing device status, sensor data, or control interfaces through AR glasses.

Enhanced Cybersecurity: As system interconnectivity increases, cybersecurity for embedded and real-time systems will become more important. The NI platform will continue to strengthen its capabilities in system security.

These speculations suggest that the integration of LabVIEW and NI hardware will continue to evolve towards intelligence, connectivity, high performance, and ease of use, providing engineers and scientists with more powerful tools to tackle complex engineering challenges.

8. Conclusion

This report has detailed the deep integration of LabVIEW and NI hardware at multiple levels. From basic driver usage and hardware configuration to advanced real-time and FPGA programming techniques, and further to system architecture design, performance optimization, and troubleshooting, LabVIEW provides a unified and efficient platform for leveraging the powerful capabilities of NI hardware. In a wide range of application areas such as test and measurement, industrial control, embedded systems, RF communication, and machine vision, this deep integration makes it possible to build high-performance, high-determinism, flexible, and reconfigurable systems.

By deeply understanding the LabVIEW programming paradigm, the characteristics of NI hardware, and the synergy between the two, users can establish best practices for specific applications, accurately evaluate system performance, effectively solve practical engineering problems, and design innovative systems that meet complex requirements. When considering system solutions, weighing performance, real-time capability, cost, and existing system compatibility is crucial. Future technological developments will further enhance the capabilities of LabVIEW and NI hardware, particularly in AI integration, cloud connectivity, and hardware performance.

9. Suggested Future Research Directions

Based on the findings of this study, it is recommended to further investigate the following directions:

Performance Comparison and Best Practices for Advanced Inter-Process Communication (IPC) Mechanisms in LabVIEW: Quantify in detail the performance differences of Network Streams, Shared Variables, RT FIFOs, etc., under varying data volumes, real-time requirements, and network conditions, and summarize their applicable scenarios and optimal configurations.

Complex LabVIEW System Design and Performance Evaluation Based on the Actor Framework: Deeply research the principles, design patterns, and implementation details of the Actor Framework, and evaluate its advantages and challenges in building large, concurrent, and scalable LabVIEW applications through practical case studies.

Application of Advanced Timing Analysis and Automated Optimization Tools in LabVIEW FPGA: Explore the application of Static Timing Analysis (STA) in LabVIEW FPGA development, and research how to utilize NI or third-party tools for setting timing constraints, analyzing timing violations, and performing automated timing optimization.

Integration Methods and Performance Evaluation of LabVIEW Systems with Third-Party AI/ML Frameworks: Research how to call or integrate AI/ML models trained using frameworks such as TensorFlow and PyTorch within LabVIEW, evaluate the performance overhead and real-time capability of different integration methods, and explore the possibility of accelerating AI inference using NI hardware (such as FPGAs).

Quantitative Model and Case Studies for Total Cost of Ownership (TCO) of NI Hardware Systems: Establish a more refined TCO evaluation model covering costs such as hardware procurement, software licensing, development labor, system integration, maintenance, calibration, and upgrades, and compare the TCO of different NI platforms or NI vs. third-party solutions through specific cases.

Cybersecurity Design and Implementation for LabVIEW Systems: Research best practices for implementing cybersecurity in embedded systems such as LabVIEW RT and CompactRIO, including authentication, data encryption, access control, and firmware security.

Implementing Advanced Applications and Troubleshooting for Specific Industrial Bus Protocols (such as EtherCAT, PROFINET) Using LabVIEW and NI Hardware: Deeply research LabVIEW's support for these industrial buses, discuss how to achieve high-speed data exchange, precise synchronization, and distributed control, and research troubleshooting methods for these buses.

These suggested future research directions will help to gain a more comprehensive and in-depth understanding of the deep integration technology of LabVIEW and NI hardware, providing a solid foundation for tackling more complex engineering challenges.

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Mathematical Formula Example:

The Nyquist theorem requires the sampling rate to be at least twice the highest frequency of the signal: In practical applications, to ensure accurate signal reconstruction, it is generally recommended that the sampling rate is much greater than the Nyquist frequency, for example, .

Table Example:

NI Hardware Platform	Primary Application Area	Real-Time Capability	FPGA Programmability	Modularity	Typical Cost
DAQ	Data Acquisition	Low/Medium	Supported by some modules	Medium	Low/Medium
PXI/PXIe	Test and Measurement	Medium/High	Supported by some modules	High	High
CompactRIO	Embedded Control	High	Yes	High	Medium/High
USRP	RF Communication	Medium/High	Yes	Medium	Medium/High
Vision	Machine Vision	Medium/High	Supported by some hardware	Medium	Medium/High
Motion	Motion Control	High	Supported by some hardware	Medium	Medium/High

Mermaid Diagram Example (Simplified CompactRIO System Architecture Flow):

graph TD A[Sensors/Actuators] --> B(I/O Modules); B --> C{FPGA}; C -->|High-Speed Data/Control| D(Real-Time Processor); D --> E[Host PC / HMI]; D -->|Network Communication| F[Other Systems]; C -->|Scan Mode Data| D; D -->|Configuration/Commands| C; E -->|Monitoring/Commands| D;

Diagram Legend:

Sensors/Actuators: Inputs/outputs to the physical world.

I/O Modules: Connect sensors/actuators to CompactRIO.

FPGA: Handles high-speed I/O, parallel logic, and deterministic tasks.

Real-Time Processor: Runs the RTOS, manages control logic, communication, and HMI.

Host PC / HMI: Used for development, deployment, monitoring, and human-machine interaction.

Other Systems: External systems communicating with CompactRIO via the network.

High-Speed Data/Control: High-speed data and control signals transferred between FPGA and RT via DMA, etc.

Scan Mode Data: I/O data transferred via the CompactRIO Scan Engine.

Configuration/Commands: Configuration or control commands sent from the RT processor to the FPGA.

Monitoring/Commands: Monitoring requests or control commands sent from the Host PC or HMI to the RT processor.