Journal: Proceedings of the IEEE

Abbreviation

Proc. I.E.E.E.

Publisher

IEEE

Journal Volumes

ISSN

1558-2256
0018-9219

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Publications 1 - 10 of 53
  • Extending the Performance of Human Classifiers Using a Viewpoint Specific Approach
    Item type: Conference Paper
    Dibra, Endri; Maye, Jerome; Diamanti, Olga; et al. (2015)
    Proceedings of the IEEE ~ 2015 IEEE Winter Conference on Applications of Computer Vision (WACV 2015) : Waikoloa, Hawaii, USA, 5-9 January 2015: Volume 1
  • Magnetically Actuated Medical Robots: An in vivo Perspective
    Item type: Journal Article
    Nelson, Bradley; Gervasoni, Simone; Chiu, Philip W.Y.; et al. (2022)
    Proceedings of the IEEE
    The use of magnetic fields and field gradients to move magnetic material and devices within the human body has a surprisingly long history. Over the past two decades, there has been renewed interest in this area with the growth of magnetic medical microrobots. In this article, we focus on the state-of-the-art and future directions for magnetically actuated medical robots from an in vivo perspective. We initially review the history and relevant physics followed by a discussion on the limited in vivo research efforts that investigate magnetically guided devices. Our focus is on magnetically guided tethered probes, untethered devices (microrobots and nanorobots), and magnetic navigation systems that have been or could be utilized in vivo to provide increased control and safety for the physician and patient.
  • Growing Cells atop Microelectronic Chips
    Item type: Journal Article
    Hierlemann, Andreas; Frey, Urs; Hafizovic, Sadik; et al. (2011)
    Proceedings of the IEEE
  • Toward Causal Representation Learning
    Item type: Journal Article
    Schölkopf, Bernhard; Locatello, Francesco; Bauer, Stefan; et al. (2021)
    Proceedings of the IEEE
    The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.
  • Design Aspects for Wide-Area Monitoring and Control Systems
    Item type: Journal Article
    Zima, Marek; Larsson, Mats; Korba, Petr; et al. (2005)
    Proceedings of the IEEE
  • Interferometric Synthetic Aperture Radar (SAR) Missions Employing Formation Flying
    Item type: Journal Article
    Krieger, Gerhard; Hajnsek, Irena; Papathanassiou, Konstantinos P.; et al. (2010)
    Proceedings of the IEEE
  • Network Systems Engineering for Meeting the Energy and Environmental Dream
    Item type: Other Journal Item
    Andersson, Göran; Ilic, Marija D.; Madani, Vahid; et al. (2011)
    Proceedings of the IEEE
  • Multicarrier Energy Systems: Shaping Our Energy Future
    Item type: Journal Article
    O'Malley, Mark J.; Anwar, Muhammad Bashar; Heinen, Steve; et al. (2020)
    Proceedings of the IEEE
    Multicarrier energy systems (MCESs) are characterized by strong coordination in operation and planning across multiple energy vectors and/or sectors to deliver reliable, cost-effective energy services to end users/customers with minimal impact on the environment. They have efficiency and flexibility benefits and are deployed in large and small scales on the supply and demand sides and at the network level but are more complex to control and manage. In this article, MCESs are reviewed in the context of future low carbon energy systems based on electrification and very high variable renewable energy penetrations. Fully exploiting these systems requires some cost reductions, more sophisticated operations enabled by standardized communications and control capabilities detailed planning paradigms, and addressing their corresponding economic challenges. All these point toward the direction of analysis, markets, and technology research and development coupled with better policy and regulatory frameworks. One futuristic vision of a very low carbon energy system is proposed that illustrates potential pathways to an MCES-dominated energy future.
  • Error Characterization, Mitigation, and Recovery in Flash-Memory-Based Solid-State Drives
    Item type: Journal Article
    Cai, Yu; Ghose, Saugata; Haratsch, Erich F.; et al. (2017)
    Proceedings of the IEEE
  • The factor graph approach to model-based signal processing
    Item type: Journal Article
    Loeliger, Hans-Andrea; Dauwels, Justin; Hu, Junli; et al. (2007)
    Proceedings of the IEEE
Publications 1 - 10 of 53