Abstract
Object recognition represents the ability of a system to identify objects, humans or animals in images. Within this domain, this work presents a comparative analysis among different classification methods aiming at Tactode tile recognition. The covered methods include: (i) machine learning with HOG and SVM; (ii) deep learning with CNNs such as VGG16, VGG19, ResNet152, MobileNetV2, SSD and YOLOv4; (iii) matching of handcrafted features with SIFT, SURF, BRISK and ORB; and (iv) template matching. A dataset was created to train learning-based methods (i and ii), and with respect to the other methods (iii and iv), a template dataset was used. To evaluate the performance of the recognition methods, two test datasets were built: tactode_small and tactode_big, which consisted of 288 and 12,000 images, holding 2784 and 96,000 regions of interest for classification, respectively. SSD and YOLOv4 were the worst methods for their domain, whereas ResNet152 and MobileNetV2 showed that they were strong recognition methods. SURF, ORB and BRISK demonstrated great recognition performance, while SIFT was the worst of this type of method. The methods based on template matching attained reasonable recognition results, falling behind most other methods. The top three methods of this study were: VGG16 with an accuracy of 99.96% and 99.95% for tactode_small and tactode_big, respectively; VGG19 with an accuracy of 99.96% and 99.68% for the same datasets; and HOG and SVM, which reached an accuracy of 99.93% for tactode_small and 99.86% for tactode_big, while at the same time presenting average execution times of 0.323 s and 0.232 s on the respective datasets, being the fastest method overall. This work demonstrated that VGG16 was the best choice for this case study, since it minimised the misclassifications for both test datasets.

Abstract
In this work, we reviewed the most important achievements of INESC TEC related to the fabrication of long-period fiber gratings using the electric arc technique. We focused on the fabrication setup, the type of fiber used, and the effect of the fabrication parameters on the gratings' transmission spectra. The theory was presented, as well as a discussion on the mechanisms responsible for the formation of the gratings, supported by the measurement of the temperature reached by the fiber during an electric arc discharge.

Abstract
The Symposium of Electrical and Computer Engineering (ECE), integrated in the 2021 edition of FEUP’s Doctoral Congress of Engineering is an important showcase for PhD research in this broad and vibrant area, and an opportunity for doctoral students to interact, to improve communications skills, and to gather feedback on their research work.

Abstract
This paper discusses the improvement of product management activities in a fast-growing marketing automation software company. Recently, the company experienced an expansion of its international activities, a fast-growing turnover, and a significant enlargement of the software team. The new needs coming from this situation led the company to move the product development team from a traditional functional organization to a holocratic organization, where self-organized teams are entirely responsible for their product development activities. To design effective management practices and tools for this new organizational model, the company decided to analyze its business processes in-depth. At the first stage, high-level process mapping techniques were employed to comprehensively view the product management activities, their embedding organizational structure, and context. Then, detailed process mapping techniques were employed to build up a detailed visualization of the process workflows. Then, it was possible to conduct a root-cause analysis aiming at identifying the opportunities for improvement. These opportunities were then assessed and prioritized based on their expected impact upon business performance, namely productivity and time-to-market. The actions selected for implementation addressed three main issues: standardization of the planning and monitoring procedures; teams misalignments. Through a systematic process analysis and redesign, it was possible to set up a new set of management tools that cope with these issues, reinforce the workforce commitment and involvement, and ultimately improve business performance. © IEOM Society International.

Abstract
Design thinking refers to a creative approach to deal with complex problems in design contexts. Originally harnessed by designers, it is today within everyone's reach, to be learned and employed in their practices. To make it accessible and tangible to those who are not designers, a number of tools began to be developed and systematized. Among those, we highlight the group of card-based tools, which enable individuals to develop their creativity and to generate innovative design concepts. In order to explore these tools and to provide a scenario for creative ideas, we developed a card-based game-Cards GO-and conducted a workshop experiment to evaluate the applicability of the game to conceive other game concepts. We assessed the results of the workshop from three points of view: (1) that of the researchers, through direct observation; (2) that of the participants, by means of a questionnaire about their intrinsic motivation; (3) that of three experts in game design. Overall, Cards GO presented itself as a valuable tool for game design, creative thinking and collaboration. However, it was observed that the developed game concepts needed to be better detailed.

Abstract
The optimisation of forest fuels supply chain involves several entities actors, and particularities. To successfully manage these supply chains, efficient tools must be devised with the ability to deal with stakeholders dynamic interactions and to optimize the supply chain performance as a whole while being stable and robust, even in the presence of uncertainties. This work proposes a framework to coordinate different planning levels and event-based models to manage the forest-based supply chain. In particular, with the new methodology, the resilience and flexibility of the biomass supply chain is increased through a closed-loop system based on the system forecasts provided by a discrete-event model. The developed event-based predictive model will be described in detail, explaining its link with the remaining elements. The implemented models and their links within the proposed framework are presented in a case study in Finland and results are shown to illustrate the advantage of the proposed architecture.