IEEE Signal Processing Magazine

Journal: IEEE Signal Processing Magazine

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IEEE

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Publications1 - 10 of 18

Lattice Reduction
Wuebben, Dirk; Seethaler, Dominik; Jalden, Joakim; et al. (2011)
IEEE Signal Processing Magazine
Noncoherent ultra-wideband systems
Witrisal, Klaus; Leus, Geert; Janssen, Gerald J.M.; et al. (2009)
IEEE Signal Processing Magazine
Compressive Video Sensing
Baraniuk, Richard G.; Goldstein, Tom; Sankaranarayanan, Aswin C.; et al. (2017)
IEEE Signal Processing Magazine
Performance analysis of multiprocessor DSPs
Thiele, Lothar; Wandeler, Ernesto; Chakraborty, Samarjit (2005)
IEEE Signal Processing Magazine
The Importance of Space and Time for Signal Processing in Neuromorphic Agents: The Challenge of Developing Low-Power, Autonomous Agents That Interact with the Environment
Indiveri, Giacomo; Sandamirskaya, Yulia (2019)
IEEE Signal Processing Magazine
Device-Free Radio Vision for Assisted Living: Leveraging wireless channel quality information for human sensing
Savazzi, Stefano; Sigg, Stephan; Nicoli, Monica; et al. (2016)
IEEE Signal Processing Magazine
Turning Images into 3-D Models
Remondino, Fabio; El-Hakim, Sabry F.; Grün, Armin; et al. (2008)
IEEE Signal Processing Magazine
Graph Signal Processing for Machine Learning: A Review and New Perspectives
Dong, Xiaowen; Thanou, Dorina; Toni, Laura; et al. (2020)
IEEE Signal Processing Magazine
The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on the future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age. © 2020 IEEE.
An introduction to factor graphs
Loeliger, Hans-Andrea (2004)
IEEE Signal Processing Magazine
Event-Driven Sensing for Efficient Perception: Vision and audition algorithms
Liu, Shih-Chii; Rueckauer, Bodo; Ceolini, Enea; et al. (2019)
IEEE Signal Processing Magazine

Publications1 - 10 of 18

Journal: IEEE Signal Processing Magazine

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