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The potentials of using millimeter-wave (mmWave) frequency for future wireless cellular communication systems have motivated the study of large-scale antenna arrays for achieving highly directional beamforming. However, the conventional fully digital beamforming methods, which require one radio frequency (RF) chain per antenna element, are not viable for large-scale antenna arrays due to the high cost and high power consumption of RF chain components in high frequencies.

In recent years, we have seen the emergence of new compute-intensive and delay-critical mobile applications, such as virtual/augmented reality, online gaming, ultra-high-definition video streaming and autonomous driving. Multi-access edge computing (MEC) has become a key technology in 5G networks to shift computational tasks from resource-limited mobile devices to nearby servers placed at the edge of the network.

The first camera phone was sold in 2000, when taking pictures with your phone was an oddity, and sharing pictures online was unheard-of. Today, barely twenty years later, the smartphone is more camera than phone. This transformation was enabled by advances in computational photography — the science and engineering of making great images from small form factor, mobile cameras. 

Large-scale antenna arrays, also known as massive MIMO, are key enablers for 5G and beyond networks, which, however, bring tremendous pressures on hardware cost and energy consumption. 

Deep neural networks achieve state-of-the-art performance in many domains in signal processing. The main practice is getting pairs of examples, input, and its desired output, and then training a network to produce the same outputs with the goal that it will learn how to generalize also to new unseen data, which is indeed  the case in many scenarios.

In 2000, compact discs were the dominant mode of music distribution and the use of digital compressed audio files for playback using computers was just beginning. Most of the research on analyzing music was done with symbolic representations rather than audio signals. The presenter, Dr. George Tzanetakis, is fascinated by pattern recognition, computer vision, and speech recognition. 

Given the impossibility of travel during the COVID-19 crisis,  Computational Imaging TC is launching an SPS Webinar Series SPACE (Signal Processing And Computational imagE formation) as a regular bi-weekly online seminar series to reach out to the global computational imaging and signal processing community.

Given the impossibility of travel during the COVID-19 crisis,  Computational Imaging TC is launching an SPS Webinar Series SPACE (Signal Processing And Computational imagE formation) as a regular bi-weekly online seminar series to reach out to the global computational imaging and signal processing community.

Future networks must provide services to a range of applications and devices with competing and perhaps conflicting requirements while simultaneously allowing flexible deployment. Software Defined Networks (SDN) have a critical role to play in securing such networks against sophisticated security attacks, with its ability to manage dynamically security policies for monitoring and controlling traffic and enforcing them via virtualized network functions. 

Given the impossibility of travel during the COVID-19 crisis,  Computational Imaging TC is launching an SPS Webinar Series SPACE (Signal Processing And Computational imagE formation) as a regular bi-weekly online seminar series to reach out to the global computational imaging and signal processing community.

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