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Structured Robust Covariance Estimation
Covariance matrices have found applications in many diverse areas. These include beamforming in array processing; portfolio analysis in finance; classification of data and the …
Learning with Limited Samples
Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are …
Video Coding
Video Coding is the second part of the two-part monograph Fundamentals of Source and Video Coding by Wiegand and Schwarz. This part describes the application of the techniques …
Operating Characteristics for Classical and Quantum Binary Hypothesis Testing
Binary decisions guide our everyday lives in situations both critical and trivial. The choices made by politicians and physicians may have consequential implications on a global or …
Compressed Sensing with Applications in Wireless Networks
Many natural signals possess only a few degrees of freedom. For instance, the occupied radio spectrum may be intermittently concentrated to only a few frequency bands of the system …
Sparse Sensing for Statistical Inference
Sensors are becoming increasingly omnipresent throughout society. These sensors generate a billion gigabytes of data every day. With the availability of immense computing power at …
Theory and Use of the EM Algorithm
Theory and Use of the EM Algorithm introduces the expectation-maximization (EM) algorithm and provides an intuitive and mathematically rigorous understanding of this method. It …
Model-Based Deep Learning
Signal processing traditionally relies on classical statistical modelling techniques. Such model-based methods utilise mathematical formulations that represent the underlying …
Markov Random Fields in Image Segmentation
Markov Random Fields in Image Segmentation introduces the fundamentals of Markovian modeling in image segmentation as well as providing a brief overview of recent advances in the …
Bivariate Markov Processes and Their Estimation
Bivariate Markov processes play a central role in the theory and applications of estimation, control, queuing, biomedical engineering, and reliability. Bivariate Markov Processes …
Linear Predictive Coding and the Internet Protocol
Linear prediction has long played an important role in speech processing, especially in the development during the late 1960s of the first low bit rate speech compression/coding …
Bilevel Methods for Image Reconstruction
Methods for image recovery and reconstruction aim to estimate a good-quality image from noisy, incomplete, or indirect measurements. Such methods are also known as computational …