Parallel Evaluation of Quantum Algorithms for Computational Fluid Dynamics

The development and evaluation of quantum computing algorithms for computational fluid dynamics is described along with a detailed analysis of the parallel performance of a quantum computer simulator developed as part of the present work. The quantum computer simulator is used in the evaluation of the quantum algorithms on a conventional parallel computer, and is applied to quantum lattice-based algorithms as well as the Poisson equation. A key result is a demonstration of how the Poisson equation can be solved effeciently on a quantum computer, while its use within a larger algorithm representing a full CFD solver poses a number of signifi- cant challenges

Machine learning from coronas using parametrization of images

We were interested to develop an algorithm for detection of coronas of people in altered states of consciousness (two-classes problem). Such coronas are known to have rings (double coronas), special branch-like structure of streamers and/or curious spots. We used several approaches to parametrization of images and various machine learning algorithms. We compared results of computer algorithms with the human expert’s accuracy. Results show that computer algorithms can achieve the same or even better accuracy than that of human experts

Computer vision algorithms for 3D object recognition and orientation: a bibliometric study

This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few.This research was funded by the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games

This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear

Computing the canonical representation of constructible sets

Constructible sets are needed in many algorithms of Computer Algebra, particularly in the GröbnerCover and other algorithms for parametric polynomial systems. In this paper we review the canonical form ofconstructible sets and give algorithms for computing it.Peer ReviewedPostprint (author's final draft