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As we witness the fourth industrial revolution, several aspects of our daily lives will soon be impacted beyond recognition. The list includes health care, education, security, transportation, warfare, and entertainment.

Drilling is the riskiest activity in the oil-field exploration and development stage. Real-time measurements are needed to monitor drilling conditions to keep it in the safe operating envelope, guide the drilling system into the most productive zones, and provide information for further stages in the completion of the well. In this article, we describe digital communication systems for drilling, including data transmission and data compression. We begin by describing data transmission techniques used for two systems: mud-pulse telemetry (MPT) and electromagnetic (EM) telemetry.

Environmental monitoring is a topic of increasing interest, especially concerning the matter of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with innovative and operational tools are needed to monitor, mitigate, and prevent risks related to volcanic hazards.

For centuries, humans have been exploring the subsurface structure of planet Earth. Several Earth geophysical applications, such as mining, earthquake studies, and oil and gas exploration, have driven research that produced, over the years, ground-breaking theories and innovative technologies that image Earth’s subsurface. The pursuit is ongoing with an increasing desire to have higher-resolution subsurface models and images. Signal processing, data interpretation, and modeling have been the cornerstones of such innovations.

Spatial and immersive audio mimics real-world sound environments

In an era of ubiquitous video, audio is often relegated to a secondary role. Yet electronic audio in all of its various forms is now staging a strong comeback as listeners, increasingly dissatisfied with the output of highly compressed audio files and streams, seek higher sound quality on all types of fixed and mobile platforms.

In this column of IEEE Signal Processing Magazine, 39 IEEE Signal Processing Society (SPS) members are recognized as IEEE Fellows and award recipients are announced.

39 SPS members elevated to Fellow:
Each year, the IEEE Board of Directors confers the grade of Fellow on up to one-tenth of 1% of the voting Members. To qualify for consideration, an individual must have been a Member, normally for five years or more, and a Senior Member at the time for nomination to Fellow. The grade of Fellow recognizes unusual distinction in IEEE’s designated fields. 

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.

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