ERATO-VR: Utilizing Efficient Reads for ATomic Objects in 3D Networked Virtual Environments
EratoVR project aims to devise a Prof of Concept (PoC) implementation of a multiuser, interactive 3D NVE that will utilize a Distributed Shared Memory space with atomic guarantees for consistency and synchronization and provide experimental evidence for drawing conclusions on whether such theoretical structures may be viable in practical applications.
LightSense exploits the optical sensing capabilities of existing fibre, on-grid networks in Cyprus and employs smart algorithms for the holistic monitoring of the security and operation of these networks, and for timely detection and prevention of failures and malicious intrusions.
MARI-Sense employs smart management, spatial planning, and agile response measures to help preserve the natural environment, securing society, and ensuring economic growth. Its cognitive systems work in synergy with human operators to "make sense" of the maritime environment
Collaborate (https://projects.algolysis.com/collaborate/) (RPF/POST-DOC/0916/0090), proposes a novel atomic Distributed Storage System built on top of asynchronous message-passing, failure-prone, commodity devices, and providing tight consistency guarantees when the storage is accessed concurrnelty by different processes. Atomicity enables the most natural consistency guarantee as it provides the illusion of a centralised sequentially accessed storage. To enhance the practicality of our atomic DSS, Collaborate will develop and combine the following services: (i) Fragmentation, (ii) Reconfiguration, and (iii) Failure Prediction.
DriveNest (www.drivenest.com) is a crowd-monitoring platform of storage devices which are distributed anywhere around the globe. The objective is to collect reliability indicators from devices deployed 'in-the-wild' (not in controlled data center environments), and provide users with statistics and failure forecasting based on predictive analytics. Statistics across a global population of storage devices will enable end-users to purchase storage devices based on hard data, while failure prediction will assist end-users, system administrators, and backup software designers to proactively act upon imminent hardware failures.
SCHEDAR: Safeguarding the Cultural HEritage of Dance through Augmented Reality
SCHEDAR will provide novel solutions to the three key challenges of archiving, re-using and re-purposing, and ultimately disseminating ICH motion data. In addition, we will devise a comprehensive set of new guidelines, a framework and software tools for leveraging existing ICH motion databases. Data acquisition will be undertaken holistically; encompassing data related to the performance, the performer, the kind of the dance, the hidden/untold story, etc.
Algolysis is developing a low-cost, integrated software and hardware ecosystem to monitor and analyze the operational conditions of poultry farms. Through a highly synergistic system, composed of sensors, monitoring devices, data acquisition, and data analytics backoffice, we aim to offer invaluable tools and intelligence to farmers and authorities. In turn, they will be able to implement ideal conditions for the animals and optimize their production.