SPM Article

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

SPM Article

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.
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.

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).

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.

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).

I love the last scene from the movie Planet of the Apes (1968), which revealed how the human race destroyed its beautiful planet only to be overtaken by intelligent apes. In that movie, humans were the victims of their own intelligence.
 

I just got back from the 2018 International Conference on Acoustics, Speech, and Signal Processing in Calgary, Alberta, Canada. One of the highlights for me as editor-in-chief (EIC) of IEEE Signal Processing Magazine (SPM) is SPM’s editorial board meeting.

By bringing research into the curriculum, this article explores new opportunities to refresh some classic signal processing courses. Since 2015, we in the Electrical and Electronic Engineering (EEE) Department of Imperial College London, United Kingdom, have explored the extent to which the level of student engagement and learning can be enhanced by inviting the students to perform signal processing exercises on their own physiological data.

Processing, storing, and communicating information that originates as an analog signal involves converting this information to bits. This conversion can be described by the combined effect of sampling and quantization, as shown in Figure 1. The digital representation is achieved by first sampling the analog signal to represent it by a set of discretetime samples and then quantizing these samples to a finite number of bits. 

Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with a focus on the most recent compressive sensing (CS)-enabled approaches.

Pages

SPS on Twitter

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar