List of Key Challenges

The List of Key Challenges identifies cross-cutting issues that combine societal relevance with ICT fields of research and innovation. This resource provides a problem-oriented view, focusing on shared challenges and issues.

  • The key challenges defined that will be the focus of the HubIT stakeholder network activities.
  • Cross-cutting issues that combine societal relevance with ICT fields of research and innovation These resource provides a catalogue of such challenges relevant in ICT and SSH. Each of these key challenges will be expanded and emphasize shared concerns among stakeholders.
Automated mass manufacturing of datacom photonic
2018-11-29

Demonstrate automated manufacturing of optical transceivers with high transfer rates and competitive costs according to the interconnection distance.

Photonics integrated circuits based on Silicon Photonics
2018-11-29

Providing open access to photonics integrated circuits based on Silicon Photonics, going beyond multi-project wafers and offering generic solutions for a wide class of applications.

Ultra-Short Laser Systems
2018-11-29

Development of ultra-short pulse laser systems with pulse durations in the nanosecond regime down to the femtosecond regime and with average beam power levels of at least 1kW enabling fast materials processing with minimal heat impact on the work piece.

Connected Lighting
2018-11-29

Integrating lighting infrastructure with the Internet of Things and demonstrating new functionalities such as visible light communication for indoor positioning and broadband data communication.

Next generation free-form optics
2018-11-29

providing access to optics with free-form shapes and exceptional surface finish, exploiting new optical materials and/or combining and integrating diffractive/refractive/reflective optical components, to obtain improved performances and capabilities.

Tailored Laser Beams for Laser-based Manufacturing
2018-11-29

Methods and schemes of beam shaping providing the optimal energy delivery on the work piece with a high spatial and temporal resolution.

Photonics Manufacturing for medical devices
2018-11-29

To develop methods that provide the clinicians with photonics enabled tools to support assessing the effects of treatments of major diseases like cancer (excluding skin cancer), infectious, degenerative and cardiovascular disease and to transform low TRL technologies into robust medical devices answering to clinician needs.

Sensor-Based Optimization of Production Processes
2018-11-29

Prototyping, demonstration, optimization and validation in real industry settings of highly advanced smart broadband multimodal photonic sensing solutions operating in the spectral range from the ultraviolet to the far infrared, and intended for improving production process through the monitoring of relevant process and product parameters (e.g. physical, chemical, imaging, geometrical and environmental)

Photonics System on Chip/ System in Package for optical interconnect applications
2018-11-29

Development of advanced techniques for the intimate combination of photonic integrated circuit technology with other enabling circuits, devices and mother boards to realise major advances in the capability, performance and complexity of photonic system-on-chip and system- in-package components targeting photonic interconnect applications in the network, datacentre and consumer communication space.

Data quality in financial applications
2018-11-29

The prediction power of an algorithm is highly dependent on the quality of the data fed as input. Even in quality sources, biases can be hidden in the data. The time and effort required to gather and prepare an appropriate set of data should not be underestimated. For the nascent self-driving automotive industry, for instance, most of the effort is spent on labelling hours of videos. This need has led to the creation of an entire offshore industry for video labelling. In the financial industry, the reconciliation of the data from front to back is already problematic, and data referentials are often plagued with quality issues. Having a data-quality program in place is a prerequisite to any large-scale artificial-intelligence initiative.

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