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Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative filtering, video surveillance, and medical imaging to dimensionality reduction and adaptive filtering. Many modern high-dimensional data and interactions thereof can be modeled as lying approximately in a low-dimensional subspace or manifold, possibly with additional structures, and its proper exploitations lead to significant cost reduction in sensing, computation, and storage.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is termed subspace learning or subspace estimation. Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning (RSL) or robust PCA (RPCA).

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

In many signal processing applications, filtering is accomplished through linear time-invariant (LTI) systems described by linear constant-coefficient differential and difference equations since they are conveniently implemented using either analog or digital hardware [1]. An LTI system can be completely characterized in the time domain by its impulse response or in the frequency domain by its frequency response, which is the Fourier transform of the system’s impulse response.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The rapid advancement and proliferation of information and communication technologies in the past two decades significantly impacted how we do research. The research process has been digitalized and is increasingly relying on growing computing power and storage capacity to gather and process a constant production of data—our observations of systems and phenomena we would like to understand, control, and improve.

Section: Publications & Resources

Individual feature articles and special issues are two major mechanisms of full-length tutorial surveys of IEEE Signal Processing Magazine (SPM). Since the May 2016 feature article cluster by Jane Wang et al. on brain signal analytics, SPM’s current and past editors-in-chief and their teams have been exploring a different way to complement this existing structure—a feature article cluster (or mini special issue) that allows for a set of three to five solicited articles on a current topic, instead of just one (feature article) or ten to 11 (a special issue).

Section: Publications & Resources

In 1882, a banker in Sacramento, Calif., named Frank Miller developed an absolutely unbreakable encryption method. Nearly 140 years later, cryptographers have yet to come up with something better.

Section: Publications & Resources

The Nominations and Appointments Subcommittee for each Technical Committee is currently seeking nominations for new Members, as well as the Vice Chair position for some Technical Committees. Nominations for both positions should be submitted directly to each Technical Committee’s Nominations and Appointments Subcommittee.

Section: Publications & Resources

The Board of Governors of the IEEE Signal Processing Society elected one new Officer who will start her term on 1 January 2019.

Section: Publications & Resources

The IEEE Board of Directors has approved the 2019 IEEE Technical Field Awards. The complete list of recipients and their citations is online at www.ieee.org/awards. Please feel free to send your notes of congratulations and to publicize the recipients within your IEEE and professional affiliations.

Section: Publications & Resources

Will there be a cure for cancer in our lifetime? Opinions surrounding this question vary, but many people are inclined to think there will be. The unfortunate reality is all types of cancer are fatal if left untreated.