The main objective of this project is to provide a flexible system that can be independently deployed by public administration authorities to monitor public spaces, where special events are planned, or anticipated to occur, that are expected to lead to crowding aggregation.

The Master's Degree in Managing Digital Transformation in the Health Sector (ManagiDiTH) is an innovative initiative in the European educational panorama, the result of a project financed by the European Union and led by the School of Applied Digital Technologies (Iscte-Sintra), in collaboration with two other universities: the Laurea - University of Applied Sciences, in Finland, and the AUTH - Aristotle University of Thessaloniki, in Greece. This Master's degree aims to train future professionals, both in the technological and healthcare areas, with essential skills to face the challenges and opportunities that the digital era brings to the healthcare sector. Given the rapid acceleration of technological development, there is an urgent need to equip professionals with the necessary tools to navigate this constantly evolving scenario. With a multidisciplinary and innovative curriculum, the ManagiDiTH master's degree integrates three fundamental components: societal, digital and health. Furthermore, students can choose between two areas of specialization: Data Science or Interoperability. This flexibility allows each student to personalize their training according to their interests and professional aspirations. Thus, students have the opportunity to deepen their knowledge in areas such as artificial intelligence, virtual reality, automation, cybersecurity and other emerging technologies that are redefining the way healthcare services are provided and managed. The study program, consisting of three semesters with a total of 90 ECTS, provides a solid theoretical basis combined with practical experiences and real projects in the sector. Teaching is taught in English, following the most recent pedagogical guidelines related to Distance Learning, which allows the participation of students of different origins and nationalities. The highly specialized teaching staff, coming from diverse academic and professional backgrounds, constitutes an added value of this master's ...

This project aims to explore new research problems that can be solved by using image and video data analysis. This project will be developed in collaboration with the Lisbon City Council (CML) and addresses challenges that CML launched for the scientific community. The area topic to be addressed is computer vision for knowledge extraction based on aerial and street-level city imagery. We propose a new multi-spatial scale urban fabric dataset and a novel convolutional neural network solution for urban fabric classification tasks. Considering the challenges launched by CML and the already established collaboration between Iscte and CML, this project proposes to explore solutions to a set of urban classification problems by using video and image analysis. In this topic the goal is to identify features of the city shape, namely: i) to estimate the existence of unregistered greenspaces, as green rooftops (since CML is not currently aware of most green rooftops existing in Lisbon) and green back yards (not registered in the city council); ii) to estimate the height and deployment shape and area of Lisbon’s buildings, which will enable to develop an automatic and constantly updated tri-dimensional urban fabric map for Lisbon; iii) to estimate and automatically classify the conservation state of buildings’ facades; iv) among others. Such a methodology will be initially based on the set of aerial and street-level images obtained from CML (during the period of the scholarship). We are also considering exploiting existing city plans freely available at OpenStreetMaps to boost the convolutional neural network's training process, as well as to improve its inference performance. In a second phase, we will explore the use of other types of images such as from google street viewer and the ones directly collected by cameras in drones or city council vehicles (e.g., garbage trucks). For this second phase, we envision submitting a proposal to competitive funding and propose this topic...

The ECI4.0 project aims to develop and validate a prototype of multimodal platform for intelligent analysis of costumer behvior in comercial spaces using computer-vision, sensor fusion, and machinel learning techniques to enable an advancement in terms of the ambient intelligence for specialized retail spaces.

This operation aims to improve the service provided by the Polícia Judiciária (PJ) to citizens by automating the operational process of cataloguing photographic images and comparing them with the existing image bank with tens of thousands of images, implementing a prototype that uses the latest Artificial Intelligence (AI) approaches. In particular, an innovative solution will be created for the ballistics area, identified by the PJ as a priority area, which will allow to identify with a high rate of success and speed the correspondence between projectile and possible type of weapon used, reducing the effort and intervention time of specialized human resources, which will have an impact in the process of evidence production and consequently will make Portugal a fairer country. The solution to be developed at ISCTE consists in the implementation of a specific classification model, based on Siamese neural networks, which allows the identification of weapon models from images of the casings of deflagrated projectiles (ballistic classification system). The training and testing of the model will make use of a previously created image database containing a large number of images of deflated projectile casings.

The main objective of the project ?Quality Control on the Go? (QC OTG) is the development of a tool, integrated in the process of capture and ingest, which enables the analysis, in real time, of the quality of audio-visual content in three dimensions - video, audio and file container - in an automatic way and compliant with acquisition systems based on files and SDI . This project is promoted by the consortium formed by MOG Technologies (leader), IST, ISCTE-IUL and IPN. The new product to be developed under the scope of this project is targeted at the audio-visual and broadcast sectors (especially producers of audio-visual content), presenting a set of innovative features, of which we highlight: analysis, detection of artifacts of audio and video contents in various formats (already available or near completion, as for example Sony XAVC) and with different targets (UHDTB, HDTV, Internet, Mobile); Fully integration of the features of capture and ingest with the system of quality control of the video, audio, and file container, ensuring the operation proceeds at real time; Integration of a library with a wide range of modular functions that enable extraction and analysis of information of video and audio (semantic and syntactic); Higher efficiency of the ingest process by introducing a single step of quality control therefore reducing the necessary computational resources.Aiming at fulfilling the goals and innovative characteristics that the project proposes, the consortium has established a methodology that comprises research and industrial development activities, counting with the involvement of a total of 26 human resources of the consortium and with a total investment of 1.192.093,72 Euros (including the acquisition of equipment and expenses with activities of promotion and dissemination of the results).

Study and development of general planning tools for third generation UMTS/IMT 2000 systems.