Editorial Aims

The growth in all aspects of research in the last decade has led to a multitude of new publications and an exponential increase in published research. Finding a way through the excellent existing literature and keeping up to date has become a major time-consuming problem. Electronic publishing has given researchers instant access to more articles than ever before. But which articles are the essential ones that should be read to understand and keep abreast with developments of any topic? To address this problem Foundations and Trends® in Signal Processing publishes high-quality survey and tutorial monographs of the field.

Each issue of Foundations and Trends® in Signal Processing comprises a 50-100 page monograph written by research leaders in the field. Monographs that give tutorial coverage of subjects, research retrospectives as well as survey papers that offer state-of-the-art reviews fall within the scope of the journal.

Editorial Scope

Foundations and Trends® in Signal Processing publishes survey and tutorial articles on the following topics:

Adaptive signal processing
Audio signal processing
Biological and biomedical signal processing
Complexity in signal processing
Digital signal processing
Distributed and network signal processing
Image and video processing
Linear and nonlinear filtering
Multidimensional signal processing
Multimodal signal processing
Multirate signal processing
Multiresolution signal processing
Nonlinear signal processing
Randomized algorithms in signal processing
Sensor and multiple source signal processing, source separation
Signal decompositions, subband and transform methods, sparse representations
Signal processing for communications
Signal processing for security and forensic analysis, biometric signal processing
Signal quantization, sampling, analog-to-digital conversion, coding and compression
Signal reconstruction, digital-to-analog conversion, enhancement, decoding and inverse problems
Speech/audio/image/video compression
Speech and spoken language processing
Statistical/machine learning
Statistical signal processing
Classification and detection
Estimation and regression
Tree-structured methods