Hierarchical Optimization of Network Resource for Heterogeneous Service in Cloud Scenarios

AbstractAugmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.

Download Full-text

Bilateral Teleoperation Performance Model for Network Resource Management

IEEE Access ◽

10.1109/access.2021.3059233 ◽

2021 ◽

Vol 9 ◽

pp. 29106-29117

Author(s):

Konstantinos Antonakoglou ◽

Maliheh Mahlouji ◽

Toktam Mahmoodi

Keyword(s):

Resource Management ◽

Performance Model ◽

Bilateral Teleoperation ◽

Network Resource ◽

Network Resource Management

Download Full-text

A unified resource and configurable model of the synapse proteome and its role in disease

Scientific Reports ◽

10.1038/s41598-021-88945-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Oksana Sorokina ◽

Colin Mclean ◽

Mike D. R. Croning ◽

Katharina F. Heil ◽

Emilia Wysocka ◽

...

Keyword(s):

Protein Interactions ◽

Synaptic Proteins ◽

Protein Protein Interactions ◽

Network Resource ◽

Unique Protein ◽

Co Morbidity ◽

Genes Encoding ◽

Protein Components ◽

Accessible Format ◽

Neuronal Disorders

AbstractGenes encoding synaptic proteins are highly associated with neuronal disorders many of which show clinical co-morbidity. We integrated 58 published synaptic proteomic datasets that describe over 8000 proteins and combined them with direct protein–protein interactions and functional metadata to build a network resource that reveals the shared and unique protein components that underpin multiple disorders. All the data are provided in a flexible and accessible format to encourage custom use.

Download Full-text