SPM Article

A window function is a mathematical function that is zero valued outside some chosen interval [1] , [2] . For applications like filtering, detection, and estimation, the window functions take the form of limited time functions, which are in general real and even functions [3] , [4] , while for applications like beamforming and image processing, they are limited spatial functions. A spatial window can be a complex function for optimizing the beams in magnitude as well as in phase, as in the case of certain antenna arrays, where the phasor currents in the array are complex numbers [5].

A window function is a mathematical function that is zero valued outside some chosen interval [1] , [2] . For applications like filtering, detection, and estimation, the window functions take the form of limited time functions, which are in general real and even functions [3] , [4] , while for applications like beamforming and image processing, they are limited spatial functions. A spatial window can be a complex function for optimizing the beams in magnitude as well as in phase, as in the case of certain antenna arrays, where the phasor currents in the array are complex numbers [5].

The IEEE Signal Processing Society (SPS) is an international organization whose purpose is to advance and disseminate state-of-the-art scientific information and resources, educate the SP community, and provide a venue where people can interact and exchange ideas. To achieve its mission, the SPS relies heavily on volunteers working in the area of SP, governed by collaborative organizational practices in decision making that are transparent and fair. By bringing volunteers together, the SPS catalyzes advances in the field of SP in its pursuit of excellence.

For me and, probably, many readers, each issue of IEEE Signal Processing Magazine ( SPM ) is the opportunity and pleasure to learn something new in the area of signal and image processing. In addition to lecture notes, tips-and-tricks articles, special reports, and so on, which propose interesting and clever solutions to typical signal or image processing problems, the feature articles and special issue provide tutorial-like articles on various mature or fast-developing domains.

I am excited to start my service as the IEEE Signal Processing Society (SPS) president. I should note that I am the first SPS president directly elected by the SPS membership, due to the SPS Board of Governors (BOG) urging a stronger member voice in elections. This is a big honor for me and I would like to express my thanks to SPS members for their trust. I write this article to introduce myself, acknowledge key volunteers and staff for their service, outline the activities I will lead over the next two years, and invite your comments and suggestions.

The November 2021 IEEE Technical Activities Board meeting presentations articulated several warning signs and promising calls to action. A new, radical proposal to change the way IEEE elevates its Members to Fellow status may finally address the inclusion and equity issues that we discuss but have yet to address. The proposal is still in its infancy and was drafted by a committee chaired by our very own Jose Moura. It recognizes and documents what many of us have known anecdotally: the success rate of Fellow nominations coming from industry, government, and regions outside North America and Europe is abysmally low, despite the quality of the nominees.

Multiscale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (SCT) instruments are one of the most popular choices for 3D nondestructive characterization of materials at length scales ranging from the angstrom scale to the micron scale. These instruments typically have a source of radiation (such as electrons, X-rays, or neutrons) that interacts with the sample to be studied and a detector assembly to capture the result of this interaction (see Figure 1 ).

The Markov random field (MRF) is one of the most widely used models in image processing, constituting a prior model for addressing problems such as image segmentation, object detection, and reconstruction. What is not often appreciated is that the MRF owes its origin to the physics of solids, making it an ideal prior model for processing microscopic observations of materials. While both fields know of their respective interpretations of the MRF, each knows very little about the other’s version of it. Hence, both fields have “blind spots,” where some concepts readily appreciated by one field are completely obscured from the other. 

In an age when signal processing lies at the core of so many different technologies, nothing is more important than its contribution to health care. From improved cardiac patient support to enhanced magnetic resonance imaging (MRI) and advanced diagnostics, signal processing is helping physicians work more safely, efficiently, and accurately. Here is a look at three important research projects that are using signal processing to assist both patients and health-care providers.

With the year coming to a close, I couldn’t help but reflect on our experiences in 2020 and 2021. I began my term as president of the IEEE Signal Processing Society (SPS) roughly 65 days before we were told to work from home due to the COVID-19 pandemic. As I write this column 18 months later, I find myself, like many of you, still largely working remotely.