Instituto Superior Técnico

About: Instituto Superior Técnico is a based out in . It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 10085 authors who have published 30226 publications receiving 667524 citations. The organization is also known as: IST & Instituto Superior Tecnico.

Deep eutectic solvents: overcoming 21st century challenges

Abstract: In the last couple of years, deep eutectic solvents (DESs) have been raising a lot of attention mainly due to their versatility and their easy and speedy preparation without the need of further purification. Moreover, the vast array of very different compounds that can be selected for their preparation has led to the full tailoring of their relevant properties as solvents. We herein leave our opinion on the current challenges and opportunities in this blossoming field, which are curiously aligned with the scope of today's major concerns, where DESs are called to make a valuable contribution to important areas such as clean water, clean energy, and biotechnology.

On frequency-domain equalization and diversity combining for broadband wireless communications

Abstract: This paper is concerned with the use of frequency-domain equalization (FDE) and space diversity within block transmission schemes for broadband wireless communications. The expected performance with both multicarrier (MC) and single-carrier (SC) modulations is emphasized, when a cyclic prefix, long enough to cope with the maximum relative channel delay, is appended to each transmitted block. A set of numerical results is presented and discussed, with the help of appropriate, analytical performance bounds which are conditional on a given channel realization. These bounds are used to explain the performance advantage of the SC/FDE option, the benefits of space diversity, and the impact of the criterion for computing the FDE parameters.

3DRegNet: A Deep Neural Network for 3D Point Registration

Abstract: We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results. The code is available.