The course Methods of Audio and Image Signal Processing in Control Systems provides a comprehensive modern foundation for working with digital audio and image signals, focusing on applications in industry, automation, measurement, and control systems. Students will become familiar with methods of signal acquisition, digitization, preprocessing, analysis, and interpretation, utilizing both classical digital signal processing techniques and artificial intelligence methods.
The curriculum covers audio signal analysis, including frequency analysis, synthesis, feature extraction, and speech recognition. Simultaneously, students are introduced to image signal processing, ranging from data preprocessing and geometric and brightness transformations to edge detection, segmentation, and object recognition in an industrial environment.
In the laboratories, students practically implement the discussed methods in the Python environment using the OpenCV library and MATLAB simulation software. They also test implementations on microcontrollers used in industrial technologies. Laboratory instruction includes work with virtual instrumentation as well as camera and acoustic measurement systems that correspond to real industrial applications, such as acoustic machine diagnostics, image-based quality control, process measurement and monitoring, and automated object recognition.
The course is designed for students specializing in control, industrial automation, robotics, measurement technology, acoustic diagnostics, and industrial and machine vision.