Signal Processing and Machine Learning
Signal processing algorithms, architectures, and systems are at the heart of modern technologies that generate, transform, and interpret information across applications as diverse as communications, robotics and autonomous navigation, biotechnology and entertainment. The growth in signal processing capability from early simpler, model based, low bandwidth applications to this current wide scope of impact has been enabled by the past 50 years of dramatic advances in semiconductor technology which made faster computation and high density rapidly accessible memory increasingly more available and affordable. In the past ten years machine learning and deep learning has continued this progress using data driven methods which do not require explicit models. This focus area includes courses in theory, architectures, implementations, and specific applications.
Faculty-in-Charge: Dr. Tokunbo Ogunfunmi and Dr. Sally Wood
Program planning information for subareas in Signal Processing and Machine Learning
Each of the five subareas, described briefly below, has distinct core courses, although many subareas are closely related and programs will typically include some overlap. The programs for each subarea specify 6 units of required focus area courses and 14 units of subarea courses and electives. Recommendations for applied mathematics and breadth courses are also included. Consult with an academic advisor about these sample programs to match your specific interests and build on your prior academic coursework.
Machine learning extracts information from data based on supervised and unsupervised learning methods. This includes understanding image content, spoken language, printed language, and large data sets for wide ranging applications such as autonomous driving, natural language processing, and medical data analysis. The rapid growth in AI with significant advances across such a broad application space is built on architectures and implementations enabled by increasing availability of GPU and FPGA parallel processing and large scale rapidly accessible data storage.
Computer vision extracts content information from images, and it includes detection and identification of objects in an image, building three dimensional models of objects from image data, and interpreting scene information for both navigation and localization. Application areas include content searchable image data, augmented reality, robotics, and autonomous vehicles. Courses in this subarea cover both traditional model-based methods from image processing and current data-driven methods based on machine learning.
Image processing analyzes and improves images using both linear and nonlinear signal processing techniques to extract information from the image data. This includes image restoration, super-resolution, image reconstruction for medical imaging applications, image compression, mapping for high dynamic range, and image preprocessing, resizing, and enhancement.
Speech processing area deals with analysis, synthesis, coding of speech signals, the most common form of human verbal communication. This includes linear predictive coding, waveform coding, quantization, predictive coding, transform coding, hybrid coding and sub-band coding. Applications of speech coding in
various systems. International and proprietary standards for speech and audio coding. Real-Time DSP implementation of speech coders. Voice over internet protocol. Recent advances in speech and speaker recognition, biometrics, etc. Deep Learning applications in speech processing.
Theory and Methods
Signal processing theory and methods includes the foundational knowledge for this focus area. It includes courses in the basic theory of digital signal processing and filter design and the use of statistical methods for detection and estimation.