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Many ask me what signal processing should be doing in the age of big data. My answer is clear: signal processing should continue to generate big ideas. Big ideas for big data.

Our discipline has always advanced ingenious methods and theories, irrespective of the size of the data: small or big. Many of these ideas permeate disciplines far and wide, ranging from imaging to video; speech processing to coding and communications, forensics, security, and privacy; and also social media, machine learning, and data science.

Industry involvement is a significant area of interest for the IEEE Signal Processing Society (SPS). It is also a frequent topic of conversation at the SPS Board of Governors meetings. According to the SPS’s website (https:// signalprocessingsociety.org), 42% of our 19,000 members are from industry. Yet the number of attendees from industry of our flagship conference, the IEEE International Conference on Acoustics, Speech, and Signal Processing, is much lower, based on my best estimate from the sessions I attended.

Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model the relationships between the pixels being predicted.

Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and deep learning have gained a lot of momentum in solving such imaging problems, often surpassing the performance provided by analytical approaches.

Visual perception is one of our most essential and fundamental abilities that enables us to make sense of what our eyes see and interpret the world that surrounds us. It allows us to function and, thus, our civilization to survive. No sensory loss is more debilitating than blindness as we are, above all, visual beings. Close your eyes for a moment after reading this sentence and try grabbing something in front of you, navigating your way in your environment, or just walking straight, reading a book, playing a game, or perhaps learning something new.

Prosthetic limbs have improved significantly over the past several years, and signal processing has played a key role in allowing these devices to operate more smoothly and precisely on command. Now, researchers are taking the next step forward by using signal processing approaches and methods to develop prosthetics that not only function reliably and efficiently but give wearers more natural control over artificial arms, hands, and legs.

Three new members-at-large will take their seats on the IEEE Signal Processing Society (SPS) Board of Governors (BoG) beginning 1 January 2018 and will serve until 31 December 2020. Nine candidates competed for the three member-at-large positions. The successful candidates represent a broad spectrum of the SPS. The successful candidates are: Shoji Makino, Athina P. Petropulu, Paris Smaragdis.

2018 class of DLsThe IEEE Signal Processing Society (SPS) has announced the 2018 Class of Distinguished Lecturers (DLs) for the term of 1 January 2018 to 31 Decem- ber 2019. In addition, a special Signal Processing Data Science DL has also been named to explicitly address the areas of signal processing and data sci- ence for the same term. The IEEE SPS DL Program provides a means for Chap- ters to have access to well-known educa- tors and authors in the fields of signal processing to lecture at Chapter meetings.

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