Signal & Image Processing and Machine Learning

Signal & Image Processing and Machine Learning photo
Signal processing is a broad engineering discipline that is concerned with extracting, manipulating, and storing information embedded in complex signals and images. Methods of signal processing include: data compression; analog-to-digital conversion; signal and image reconstruction/restoration; adaptive filtering; distributed sensing and processing; and automated pattern analysis. From the early days of the fast fourier transform (FFT) to today's ubiquitous MP3/JPEG/MPEG compression algorithms, signal processing has driven many of the products and devices that have benefited society. Examples include: 3D medical image scanners (algorithms for cardiac imaging aand multi-modality image registration) ; digital audio (.mp3 players and adaptive noise cancelation headphones); global positioning (GPS and location-aware cell-phones); intelligent automotive sensors (airbag sensors and collision warning systems); multimedia devices (PDA's and smart phones); and information forensics (Internet monitoring and automatic speaker identification). At the University of Michigan we view signal processing as a science in which new processing methods are mathematically derived and implemented using fundamental principles that allow prediction of the method's performance limitations and robustness. Signal processing research at UM is developing new models, methods and technologies that will continue to impact diagnostic and therapeutic medicine, radar imaging, sensor networking, image compression, communications and other areas.


Compressive Sampling and Sensing
Computational Imaging and Inverse Problems
Data Science
Machine Learning
Random matrix theory and applications
Signal Processing for Sensing and Sensor Networks
Source Coding and Compression
Statistical Estimation and Learning

Related Links

Michigan Institute for Data Science

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CSE Faculty

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Soltani Tehrani, Ali
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