Real-Time Curve Plotting Implementation for Serial Port Sampled Data

During embedded system development or hardware device debugging, it's often necessary to acquire sampled data from sensors or devices through serial communication and plot these data in real-time curves for analysis. MATLAB provides robust serial communication and data visualization capabilities that can efficiently fulfill this requirement.

To implement real-time curve plotting for serial port sampled data, follow these key steps:

Serial Port Initialization and Configuration First, create a serial port object using MATLAB's `serial` or `serialport` function (for newer versions), configuring parameters including port name (e.g., COM3, COM4), baud rate, data bits, and stop bits. Critical implementation detail: Ensure configuration matches the device's serial settings using commands like `set(serialObj,'BaudRate',9600)` to avoid communication failures.

Data Reception and Parsing Continuously read data streams from the device using serial read functions such as `fread()` or `read()`. The data may be transmitted in ASCII or binary format - implement parsing logic based on the communication protocol. For example, use `fscanf(serialObj,'%f')` to extract numerical sampling values from ASCII data or bit-level operations for binary protocol decoding.

Real-Time Curve Plotting Utilize MATLAB's plotting functions like `plot()` or `animatedline()` for dynamic graph updates. Implement a while-loop structure that continuously reads data and refreshes the plot using `drawnow` command. The `animatedline` function is particularly effective for smoothly extending curves as new data arrives, optimizing performance with `addpoints` function for incremental plotting.

Optimization and Enhanced Features Improve real-time performance by setting appropriate buffer sizes using `InputBufferSize` property to prevent data overflow. Enhance functionality with dynamic axis adjustment through `axis auto` implementation, and add data logging features using `fwrite` or `save` functions for post-analysis. Consider implementing data filtering algorithms (e.g., moving average) for noise reduction in real-time display.

Following this methodology enables rapid development of a serial data monitoring and visualization tool, suitable for various real-time data observation scenarios such as sensor debugging, signal analysis, and industrial monitoring systems. The implementation leverages MATLAB's handle graphics system for efficient real-time plotting performance.