Multi-disciplinary Neural Networks and DSP Lab

Neural Networks, DSP, and Machine Learning have many applications such as event detection, behavior classification, audio signal processing, satellite communications, sensor array processing, digital image processing, control systems, biomedical signal processing, green energy and smart systems, etc. DSP is the backbone of wireless communication, machine learning, and smart grid. More than 2000 students has been trained since 1986. More than six Ph.D. and 160 MS student theses/projects have been presented.





Hardware: TMS320 DSP Boards, Xilinx FPGA Boards
Software:

Capability:


Research and Teaching Projects/Courses in Multi-disciplinary DSP Lab

Signal Processing, Neural Networks, and Machine Learning, Communication Systems

Control, Smart Grid and Power Systems, Green Energy, Smart Systems/Machine Learning

Transportation Systems

Undergraduate and Graduate DSP/Neural Networks/Machine Learning Courses

Past and Present IEEE Distinguished Lectures in Multi-disciplinary DSP Lab (Partial List)

Both Inspiration and Technical Lectures in DSP/Machine Learning, Communications, MATLAB Toolboxes

Past Workshops and Seminars in Multi-disciplinary DSP Lab (Partial List)

MATLAB Machine Learning, Xilinx FPGA, TI Digital Signal Processors