SPS Webinar

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. 

A new era of pervasive data generation is enabled by emerging sensing modalities and will pose new challenges to signal processing, data science, and robotics. For example, underwater robotic technology enables the development of advanced networks for underwater localization and mapping, and emerging aerial robotic technology enables the development of advanced networks for wide area localization and mapping. 

In the past decade, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data.

With cloud storage services, users can remotely store their data to the cloud and realize the data sharing with others. Remote data integrity auditing is proposed to guarantee the integrity of the data stored in the cloud. In some common cloud storage systems, such as the electronic health records system, the cloud file might contain some sensitive information that should not be exposed to others when the cloud file is shared.

In this webinar, we'll introduce FemtoPixel: A framework for lensless imaging with compressive ultrafast sensing. The FemtoPixel framework provides single-pixel imaging capabilities that are up to 50X faster compared to traditional single-pixel camera techniques. 

This webinar, “Toward Efficient and Flexible CNN-based Denoising in Photography,” begins with the design of denoising CNN (DnCNN) model by incorporating residual learning and batch normalization. 

This webinar will address the problem of identifying the structure of an undirected graph from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. 

This webinar, “Fast Detection of Transformed Data Leaks,” will discuss how the leak of sensitive data across secure network boundaries is becoming one of the most critical concerns for many industries in the move towards digitalization, building worldwide information portals, and the cloud. 

This webinar, “Direct Localization for Massive MIMO,” will discuss how Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution. 

This webinar, “Direct Localization for Massive MIMO,” will discuss how Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution.