SPS Webinars

Graphs are generic models of signal structure that can help to learn in several practical problems. To learn from graph data, we need scalable architectures that can be trained on moderate dataset sizes and that can be implemented in a distributed manner. Drawing from graph signal processing, the webinar will define graph convolutions and use them to introduce graph neural networks (GNNs). 

With the current rollout of 5G, the focus of the research community is shifting towards the design of the next generation of mobile systems, e.g., 6G mobile networks. Non-orthogonal multiple access (NOMA) has been recognized as an essential enabling technology for the forthcoming 6G networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity...

Signal sampling and reconstruction is a fundamental engineering task at the heart of signal processing. The celebrated Shannon-Nyquist theorem guarantees perfect signal reconstruction from uniform samples, obtained at a rate twice the maximum frequency present in the signal. 

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.

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. 

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.