IEEE Signal Processing Magazine

The articles in this special section focus on nonconvex optimization for signal processing and machine learning. Optimization is now widely recognized as an indispensable tool in signal processing (SP) and machine learning (ML). Indeed, many of the advances in these fields rely crucially on the formulation of suitable optimization models and deployment of efficient numerical optimization algorithms. In the early 2000s, there was a heavy focus on the use of convex optimization techniques to tackle SP and ML applications.

We continue to live through a unique experience in history. Out of concern for each other, we have voluntarily participated in essentially shutting down economic activities across the globe. We have discovered the interdependencies and precariousness of our lives and livelihoods. We have learned who and what is essential or important and have simplified our lives. We have realized the virtue of patience and self-kindness as we navigate the tremendous challenges of working from home and balancing our work obligations and family needs.

The safety and success of autonomous vehicles (AVs) depend on their ability to accurately map and respond to their surroundings in real time. One of the most promising recent technologies for depth mapping is single-photon lidar (SPL), which measures the time of flight of individual photons. The long-range capabilities (kilometers), excellent depth resolution (centimeters), and use of low-power (eye-safe) laser sources renders this modality a strong candidate for use in AVs. 

The articles in this special section were focused on the current state of the art as well as emerging trends in the design, development, and deployment of sensing and perception technologies for autonomous and automated driving. Such technologies include camera, ultrasound, Global Navigation Satellite System-, lidar-, and radar-based platforms integrat ing signa l processing components to process the acquired data and extract information to be used for recognition, navigation, and situational awareness.

Reports on the technology of body worn cameras (BWMs) and discusses the threat to privacy that this passive data collection creates, along with opportunities to mitigate this risk. Furthermore, we argue that the use case of BWCs at work will stimulate the development of solutions that prevent the collection of data that could infringe upon the privacy of the wearer. Finally, we discuss the desirable properties of privacy-enhancing technologies (PETs) for BWCs.

Like many of you, I am still working remotely, due to COVID-19, while writing this editorial. As in the past two years, I was planning to give an update on the magazine from our editorial board meeting. However, since ICASSP was remote, we have not yet scheduled the board meeting. Instead, I have decided to talk about a topic of personal interest: connections between communications and sensing in the context of vehicular systems.

I am writing this column on the first official day of spring while “sheltering in place” in Northern California. In these uncertain times, we are all experiencing the anxiety that comes from an unpredictable situation that we do not control; the shock of seeing, perhaps for the first time, all of the shelves in grocery stores empty; and the stress of working, living, and sleeping in the same place.

Since the 1970s, various image and video coding techniques have been explored, and some of them have been included in the video coding standards issued by the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) Motion Pictures Expert Group (MPEG) and International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) Video Coding Experts Group (VCEG).

The current big data era routinely requires the processing of large-scale data on massive distributed computing clusters. In these applications, data sets are often so large that they cannot be housed in the memory and/or the disk of any one computer. Thus, the data and the processing are typically distributed across multiple nodes.

Batch training of machine learning models based on neural networks is well established, whereas, to date, streaming methods are largely based on linear models. To go beyond linear in the online setting, nonparametric methods are of interest due to their universality and ability to stably incorporate new information via convexity or Bayes's rule.