Abstract
Most Convolution Neural Network (CNN) based object detectors, to date, have been optimized for accuracy and/or detection performance on datasets typically comprised of well exposed 8-bits/pixel/channel Standard Dynamic Range (SDR) images. A major existing challenge in this area is to accurately detect objects under extreme/difficult lighting conditions as SDR image trained detectors fail to accurately detect objects under such challenging lighting conditions. In this paper, we address this issue for the first time by introducing High Dynamic Range (HDR) imaging to object detection. HDR imagery can capture and process approximate to 13 orders of magnitude of scene dynamic range similar to the human eye. HDR trained models are therefore able to extract more salient features from extreme lighting conditions leading to more accurate detections. However, introducing HDR also presents multiple new challenges such as the complete absence of resources and previous literature on such an approach. Here, we introduce a methodology to generate a large scale annotated HDR dataset from any existing SDR dataset and validate the quality of the generated dataset via a robust evaluation technique. We also discuss the challenges of training and validating HDR trained models using existing detectors. Finally, we provide a methodology to create an out of distribution (OOD) HDR dataset to test and compare the performance of HDR and SDR trained detectors under difficult lighting condition. Results suggest that using the proposed methodology, HDR trained models are able to achieve 10 - 12% more accuracy compared to SDR trained models on real-world OOD dataset consisting of high-contrast images under extreme lighting conditions.

Abstract
FPGAs have emerged as hardware accelerators, and in the last decade, researchers have proposed new languages and frameworks to improve the efficiency when mapping computations to FPGAs. One of the main tasks when considering the mapping of software code to FPGAs is code restructuring. Code restructuring is of paramount importance to achieve efficient FPGA-based accelerators, and its automation continues to be a challenge. This paper describes our recent work on techniques to automatically restructure and annotate C code with directives optimized for HLS targeting FPGAs. The input of our approach consists of an unfolded dataflow graph (DFG), currently obtained by a trace of the program's execution, and restructured C code with HLS directives as output. Specifically, in this paper we propose algorithms to optimize the input DFGs and use isomorphic graph detection for exposing data-level parallelism. The experimental results show that our approach is able to generate efficient FPGA implementations, with significant speedups over the input unmodified source codes, and very competitive to implementations obtained by manual optimizations and by previous approaches. Furthermore, the experiments show that, using our approach, it is possible to extract data-parallelism in linear to quadratic time with respect to the number of nodes of the input DFG.

Abstract
This work addresses the energy-efficient job shop scheduling problem and transport resources with speed scalable machines and vehicles which is a recent extension of the classical job shop problem. In the environment under consideration, the speed with which machines process production operations and the speed with which vehicles transport jobs are also to be decided. Therefore, the scheduler can control both the completion times and the total energy consumption. We propose a mixed-integer linear programming model that can be efficiently solved to optimality for small-sized problem instances.

Abstract
The European Project Semester (EPS) offered by the Instituto Superior de Engenharia do Porto (ISEP) provides engineering, business and product design undergraduates with a project-based learning experience in a multicultural and multidisciplinary teamwork environment. This paper reports the research and development of a reconfigurable and ergonomic three-level desk, for people who live in small spaces, by a multicultural and multidisciplinary team of five students. The main objective of the project was to integrate ethics- and sustainability-driven practices in the design, simulation and test an ergonomic, transformable desk. The FREE desk proposal aims to create a comfortable and dynamic working environment for people while providing a transformable space for different daily activities. This goal was pursued by designing a reconfigurable product, a smart desk that offers the user three levels of adjustability: bench level, sitting desk level, and standing desk level. The desk includes a folding light-sensor lamp into the table top and an integrated battery, in order to create a proper working space. The selected materials have a low environmental impact. The solution comes with different options regarding the table top lifting mechanism. This paper describes the state-of-the-art research, the ethics, sustainability, and marketing analyses, the design and simulation of the FREE desk as well as the obtained results.

Abstract
This paper presents a dynamic model to improve the resilience of the distribution network during contingent events. In this model, when an event occurs, the system operator maximizes power supply by changing the network topology as well as utilizing the direct load control (DLC) program. The model is implemented on a modified IEEE 69-bus distribution system and includes three types of residential, commercial and industrial loads. First, numerous scenarios are generated based on weather forecasting, and then the problem is solved for high-probability scenarios. It is noteworthy that industrial loads are considered as vital loads and the priority of load supply is for industrial, residential and commercial loads, respectively. The final problem is formulated as mixed-integer linear programming (MILP) problem and solved by CPLEX solver in GAMS software. The effect of dynamic topology on load supply has been investigated. In addition, the impact of using the DLC program and electrical energy storage systems (EES) systems on load supply been studied in detail.

Abstract
Nowadays, independent older people stay alone for long periods, which increases the risk of being seriously damaged after a fall without the quick attendance of medical services. Several smart clothing equipment was created to detect these falls using sensors such as accelerometers and gyroscopes, allowing a short intervention to the victims. This work considers the Sisfall database, where 23 adults and 15 older people performed several daily living simulations. The signals registered by three sensors were used to train a Long Short-Term Memory network and a Bi-Long Short-Term Memory network to detect everyday activities and falls. Several experiments were performed, where the BiLSTM model outperforms the LSTM model with a mean accuracy of 99.21% on the testing set.