The advent of new wireless technologies in recent years has created a tremendous increase in the demand for wireless connectivity. The estimated number of mobile device connections exceeds eight billion globally, more than the population of the world, and the growth trend is rapidly increasing as the developing world penetration rate is still in its infancy.

Speech coding is a field in which compression paradigms have not changed in the last 30 years. Speech signals are most commonly encoded with compression methods that have roots in linear predictive theory dating back to the early 1940s. 

If John von Neumann were designing a computer today, there’s no way he would build a thick wall between processing and memory. At least, that’s what computer engineer Naresh Shanbhag of the University of Illinois at Urbana-Champaign believes. 

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. 

In the coming years, cities are expected to deal with an increasing number and type of services for their citizens, all having to do with overarching goals such as sustainability, environment, quality of life, energy saving, just to name a few. As the population living in urban areas is expected to double by 2050, there is a general consensus that any new process will require more than just an incremental upgrading of the cities’ organization, infrastructure, and services provided to its citizens.

Signal processing is always at the heart of the technology that differentiates today’s generations from those of the past is reaffirmed once again by the special issue on Machine Learning for Cognition in Radio Communications and Radar in IEEE Journal of Selected Topics in Signal Processing, February 2018.

The November 2017 special issue of SPM on deep learning for visual understanding surveyed deep-learning solutions under reinforcement; weakly supervised and multimodal settings, investigated their robustness; and presented overviews of their applications in domain adaptation, hashing, semantic segmentation, metric learning, inverse problems in imaging, image-to-text generation, and picture-quality assessment.

With the rapid development of multimedia technologies and the popularization of social networks, social multimedia content is being delivered to more and more users with a high quality of experience (QoE). However, as a huge amount of social users have ever-increasing demands to share and exchange multimedia content with each other, the current social multimedia analytics and delivery need to deal with various attacks perpetrated by malicious users or through spam content.