Journal: IEEE Signal Processing Magazine
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IEEE
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Publications1 - 10 of 18
- Lattice ReductionItem type: Journal Article
IEEE Signal Processing MagazineWuebben, Dirk; Seethaler, Dominik; Jalden, Joakim; et al. (2011) - Noncoherent ultra-wideband systemsItem type: Review Article
IEEE Signal Processing MagazineWitrisal, Klaus; Leus, Geert; Janssen, Gerald J.M.; et al. (2009) - Compressive Video SensingItem type: Journal Article
IEEE Signal Processing MagazineBaraniuk, Richard G.; Goldstein, Tom; Sankaranarayanan, Aswin C.; et al. (2017) - Performance analysis of multiprocessor DSPsItem type: Journal Article
IEEE Signal Processing MagazineThiele, Lothar; Wandeler, Ernesto; Chakraborty, Samarjit (2005) - The Importance of Space and Time for Signal Processing in Neuromorphic Agents: The Challenge of Developing Low-Power, Autonomous Agents That Interact with the EnvironmentItem type: Journal Article
IEEE Signal Processing MagazineIndiveri, Giacomo; Sandamirskaya, Yulia (2019) - Device-Free Radio Vision for Assisted Living: Leveraging wireless channel quality information for human sensingItem type: Journal Article
IEEE Signal Processing MagazineSavazzi, Stefano; Sigg, Stephan; Nicoli, Monica; et al. (2016) - Turning Images into 3-D ModelsItem type: Journal Article
IEEE Signal Processing MagazineRemondino, Fabio; El-Hakim, Sabry F.; Grün, Armin; et al. (2008) - Graph Signal Processing for Machine Learning: A Review and New PerspectivesItem type: Journal Article
IEEE Signal Processing MagazineDong, Xiaowen; Thanou, Dorina; Toni, Laura; et al. (2020)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 graphsItem type: Journal Article
IEEE Signal Processing MagazineLoeliger, Hans-Andrea (2004) - Event-Driven Sensing for Efficient Perception: Vision and audition algorithmsItem type: Journal Article
IEEE Signal Processing MagazineLiu, Shih-Chii; Rueckauer, Bodo; Ceolini, Enea; et al. (2019)
Publications1 - 10 of 18