IEEE Signal Processing Magazine

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.

The field of machine learning has undergone radical transformations during the last decade. These transformations, which have been fueled by our ability to collect and generate tremendous volumes of training data and leverage massive amounts of low-cost computing power, have led to an explosion in research activity in the field by academic and industrial researchers.