If you want to turn your MATLAB prototype into a web app for signal processing, you’ll need a combination of technologies for different parts of the system.

For the backend, Python is an excellent choice because of libraries like NumPy, SciPy, and Matplotlib, which handle complex numbers, signal filtering, and plotting. Java also works, especially with libraries like JDSP or Apache Commons Math, though its DSP capabilities are more limited compared to Python. If performance is a concern, you could even use C++ or Rust compiled to WebAssembly for fast, browser-based computations.

To build the web app itself, you might consider frameworks like Spring Boot (Java) or FastAPI (Python) to handle the backend logic. These frameworks make it easy to expose your DSP functions as APIs.

On the frontend, you can use modern frameworks like React.js or Vue.js to create an interactive interface. Libraries like Plotly.js or D3.js can help you visualize signal data, making it easy for users to see results in real time.

In summary, a combination of Python for DSP, a web framework for APIs, and a JavaScript frontend for visualization would give you everything you need to build a powerful, web-based signal processing tool!