PRODUCTIVE4.0 - AMBITIOUS PROJECT WITH A UNIQUE MAIN OBJECTIVE The main objective of Productive4.0 is to achieve improvement of digitising the European industry by electronics and ICT. Ultimately, the project aims at suitability for everyday application across all industrial sectors – up to TRL8. It addresses various industrial domains with one single approach of digitalisation. What makes the project unique is the holistic system approach of consistently focusing on the three main pillars: digital automation, supply chain networks and product lifecycle management, all of which interact and influence each other. This is part of the new concept of introducing seamless automation and network solutions as well as enhancing the transparency of data, their consistence and overall efficiency. Currently, such a complex project can only be realised in ECSEL. The consortium consists of 45% AENEAS, 30% ARTEMIS-IA, 25% EPOSS partners, thus bringing together all ECSEL communities. Representing over 100 partners from 19 EU and other associated countries, it is a European project, indeed. HANDS-ON SOLUTIONS FOR THE EUROPEAN DIGITAL INDUSTRY • Productive4.0 tackles technological and conceptual approaches in the field of Industry 4.0. The term comprises IIoT (Industrial Internet of Things), CPS (Cyber Physical Systems) and Automation. • The innovation project takes a step further towards hands-on solutions. In the process, practical reference implementations such as 3D printerfarms, customised production or self-learning robot systems will benefit in fields like service-oriented architecture (SOA), IOT components & infrastructures, process virtualisation or standardisation. These fields are addressed in the work packages WP1 through WP6. • In addition to furnishing the industry with tailor-made digital solutions, the Productive4.0 Framework will be provided. • Productive4.0 is a brain pool initiated to strengthen the international leadership of the European industry.

The overall objective of the RETINAL initiative of ULMA Embedded Solutions is to develop non-invasive and cost-effective tools with capacity for prevention and/or early detection of various diseases related to NCD complications, by analyzing the changes in the microvasculature of the retina.The basic framework of RETINAL is a comprehensive solution for ophthalmic imaging analysis and treatment composed of a simple, reliable and cost-effective color fundus digital image capture device and several specific software modules associated to the device for a number of specific clinical applications.The goal of this action is to demonstrate in the market, by independent peers, that RETINAL is the best available and cost-effective technology for the detection of diabetic retinopathy in DR Screening Programmes, and particularly valuable for its implementation at primary health centres, which makes screening more accessible to patients and therefore more effectiveThe overall objective of the RETINAL initiative of ULMA Embedded Solutions is to develop non-invasive and cost-effective tools with capacity for prevention and/or early detection of various diseases related to NCD complications, by analyzing the changes in the microvasculature of the retina.The basic framework of RETINAL is a comprehensive solution for ophthalmic imaging analysis and treatment composed of a simple, reliable and cost-effective color fundus digital image capture device and several specific software modules associated to the device for a number of specific clinical applications.The goal of this action is to demonstrate in the market, by independent peers, that RETINAL is the best available and cost-effective technology for the detection of diabetic retinopathy in DR Screening Programmes, and particularly valuable for its implementation at primary health centres, which makes screening more accessible to patients and therefore more effective

"The World Health Organization (WHO) in its “Global status report on noncommunicable diseases 2014” shows that the impact of noncommunicable diseases (NCDs), also known as chronic diseases, is steadily growing. NCDs are the leading cause of death globally (68% of all deaths in 2012). The 4 main types of NCDs are cardiovascular (CVD) diseases (like cerebrovascular accidents or strokes and heart attacks), cancers, chronic respiratory diseases and diabetes. Cardiovascular diseases account for most NCD deaths (17.5 million people annually), followed by cancers (8.2 million), respiratory diseases (4 million), and diabetes (1.5 million). This proposal addresses the prevention and early diagnosis of 2 types of NCDs, Cardiovascular (CVD) diseases and diabetes, by providing health professionals a tool for automatic quantification of parameters of the retina and automatic detection of lesions of the retina, with capacity for prevention and/or early detection of various diseases. ULMA Embedded Solutions (UES) has developed a non-invasive and cost-effective tool for Automatic evaluation of Retinal Imaging Biomarkers (ARIB), which can be used in public health interventions delivered through a primary health-care approach to strengthen early detection and timely treatment of various diseases. Evidence shows that such interventions are excellent economic investments because, if applied to patients early, can reduce the need for more expensive treatment. "

For the purpose of creating digitalisation and automation solutions Arrowhead Tools adresses engineering methodologies and suitable integrated tool chains. With the global aim of substantial reduction of the engineering costs for digitalisation/automation solutions. Thus the Arrowhead Tools vision is: - Engineering processes and tool chains for cost efficient developments of digitalization, connectivity and automation systems solutions in various fields of application For the further and wider commercialisation of automation and digitalisation services and products based on SOA, Arrowhead Framework and similar technologies there is a clear need for engineerings tools that integrates existing automation and digitalisation engineering procedures and tool with SOA based automation/digitalisation technology. For this purpose the Arrowhead Tool’s grand challenges are defined as: - Engineering costs reduction by 40-60% for a wide range of automation/digitalisation solutions. - Tools chains for digitalisation and automation engineering and management, adapted to: 1. existing automation and digitalisation engineering methodologies and tools 2. new IoT and SoS automation and digitalisation engineering and management tools 3. security management tools - Training material and kits for professional engineers The results will create impact on: - Automation and digitalisation solution market - Automation and engineering efficiency and the SSBS market - Automation and digitalisation security - Competence development on engineering of automation and digitalisation solution

Today only very light AI processing tasks are executed in ubiquitous IoT endpoint devices, where sensor data are generated and access to energy is usually constrained. However, this approach is not scalable and results in high penalties in terms of security, privacy, cost, energy consumption, and latency as data need to travel from endpoint devices to remote processing systems such as data centres. Inefficiencies are especially evident in energy consumption. To keep up pace with the exponentially growing amount of data (e.g., video) and allow more advanced, accurate, safe and timely interactions with the surrounding environment, next-generation endpoint devices will need to run AI algorithms (e.g., computer vision) and other compute intense tasks with very low latency (i.e., units of ms or less) and energy envelops (i.e., tens of mW or less). NimbleAI will harness the latest advances in microelectronics and integrated circuit technology to create an integral neuromorphic sensing-processing solution to efficiently run accurate and diverse computer vision algorithms in resource- and area-constrained chips destined to endpoint devices. Biology will be a major source of inspiration in NimbleAI, especially with a focus to reproduce adaptivity and experience-induced plasticity that allow biological structures to continuously become more efficient in processing dynamic visual stimuli. NimbleAI is expected to allow significant improvements compared to state-of-the-art (e.g., commercially available neuromorphic chips), and at least 100x improvement in energy efficiency and 50x shorter latency compared to state-of-the-practice (e.g., CPU/GPU/NPU/TPUs processing frame-based video). NimbleAI will also take a holistic approach for ensuring safety and security at different architecture levels, including silicon level.