Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection

Heading towards navigational autonomy in unmanned surface vehicles (USVs) in the maritime sector can fundamentally lead towards safer waters as well as reduced operating costs, while also providing a range of exciting new capabilities for oceanic research, exploration and monitoring. However, achieving such a goal is challenging. USV control systems must, safely and reliably, be able to adhere to the international regulations for preventing collisions at sea (COLREGs) in encounters with other vessels as they navigate to a given waypoint while being affected by realistic weather conditions, either during the day or at night. To deal with the multitude of possible scenarios, it is critical to have a virtual environment that is able to replicate the realistic operating conditions USVs will encounter, before they can be implemented in the real world. Such "digital twins" form the foundations upon which Deep Reinforcement Learning (DRL) and Computer Vision (CV) algorithms can be used to develop and guide USV control systems. In this paper we describe the novel development of a COLREG-compliant DRL-based collision avoidant navigational system with CV-based awareness in a realistic ocean simulation environment. The performance of the trained autonomous Agents resulting from this approach is evaluated in several successful navigations to set waypoints in both open sea and coastal encounters with other vessels. A binary executable version of the simulator with trained agents is available at https://github.com/aavek/Aeolus-OceanComment: 22 pages, last blank page, 17 figures, 1 table, color, high resolution figure

Comparative study of methodologies to compute the intrinsic Gilbert damping: interrelations, validity and physical consequences

Relaxation effects are of primary importance in the description of magnetic excitations, leading to a myriad of methods addressing the phenomenological damping parameters. In this work, we consider several well-established forms of calculating the intrinsic Gilbert damping within a unified theoretical framework, mapping out their connections and the approximations required to derive each formula. This scheme enables a direct comparison of the different methods on the same footing and a consistent evaluation of their range of validity. Most methods lead to very similar results for the bulk ferromagnets Fe, Co and Ni, due to the low spin-orbit interaction strength and the absence of the spin pumping mechanism. The effects of inhomogeneities, temperature and other sources of finite electronic lifetime are often accounted for by an empirical broadening of the electronic energy levels. We show that the contribution to the damping introduced by this broadening is additive, and so can be extracted by comparing the results of the calculations performed with and without spin-orbit interaction. Starting from simulated ferromagnetic resonance spectra based on the underlying electronic structure, we unambiguously demonstrate that the damping parameter obtained within the constant broadening approximation diverges for three-dimensional bulk magnets in the clean limit, while it remains finite for monolayers. Our work puts into perspective the several methods available to describe and compute the Gilbert damping, building a solid foundation for future investigations of magnetic relaxation effects in any kind of material.Comment: 16 pages, 5 figure

FAST TRANSFORMS BASED ON STRUCTURED MATRICES WITH APPLICATIONS TO THE FAST MULTIPOLE METHOD

The solution of many problems in engineering and science is enabled by the availability of a fast algorithm, a significant example being the fast Fourier transform, which computes the matrix-vector product for a $N \times N$ Fourier matrix in $O(N\log(N))$ time. Related fast algorithms have been devised since to evaluate matrix-vector products for other structured matrices such as matrices with Toeplitz, Hankel, Vandermonde, etc. structure. A recent fast algorithm that was developed is the fast multipole method (FMM). The original FMM evaluates all pair-wise interactions in large ensembles of $N$ particles in $O(p^2N)$ time, where $p$ is the number of terms in the truncated multipole/local expansions it uses. Analytical properties of translation operators that shift the center of a multipole or local expansion to another location and convert a multipole expansion into a local expansion are used. The original translation operators achieve the translation in $O(p^2)$ operations for a $p$ term expansion. Translation operations are among the most important and expensive steps in an FMM algorithm. The main focus of this dissertation is on developing fast accurate algorithms for the translation operators in the FMM for Coulombic potentials in two or three dimensions. We show that the matrices involved in the translation operators of the FMM for Coulombic potentials can be expressed as products of structured matrices. Some of these matrices have fast transform algorithms available, while for others we show how they can be constructed. A particular algorithm we develop is for fast computation of matrix vector products of the form $Px$, $P'x$, and $PP'x$, where $P$ is a Pascal matrix. When considering fast translation algorithms for the 3D FMM we decompose translations into an axial translation and a pair of rotations. We show how a fast axial translation can be performed. The bottleneck for achieving fast translation is presented by the lack of a fast rotation transform. A fast rotation algorithm is also important for many other applications, including quantum mechanics, geoscience, computer vision, etc, and fast rotation algorithms are being developed based on the properties of spherical harmonics. We follow an alternate path by showing that the rotation matrix $R$ can be factored in two different ways into the product of structured matrices. Both factorizations allow a fast matrix-vector product. Our algorithm efficiently computes the coefficients of spherical harmonic expansions on rotation. Numerical experiments confirm that the new $O(p\log p)$ translation operators for both the 2D and 3D FMM have the same accuracy as the original ones, achieve their asymptotic complexity for modest $p$, and significantly speed up the FMM algorithms in 2D and 3D. We hope that this thesis will also lead to promising future research in establishing fast translation for the FMM for other potentials, as well as applying the transforms in other applications such as in harmonic analysis on the sphere

Large spatial extension of the zero-energy Yu-Shiba-Rusinov state in a magnetic field

Various promising qubit concepts have been put forward recently based on engineered superconductor (SC) subgap states like Andreev bound states, Majorana zero modes or the Yu-Shiba-Rusinov (Shiba) states. The coupling of these subgap states via a SC strongly depends on their spatial extension and is an essential next step for future quantum technologies. Here we investigate the spatial extension of a Shiba state in a semiconductor quantum dot coupled to a SC for the first time. With detailed transport measurements and numerical renormalization group calculations we find a remarkable more than 50 nm extension of the zero energy Shiba state, much larger than the one observed in very recent scanning tunneling microscopy (STM) measurements. Moreover, we demonstrate that its spatial extension increases substantially in magnetic field.Comment: 11 pages, 7 figure

Simulation of Magnetic and Electronic Properties of Nanostructures

In the first part of this thesis I utilize density functional methods to simulate a previously unreported kind of single-molecule magnets with spin-crossover effect, which consist of a single 5d transition metal magnetic center adsorbed on a graphene nanoflake. In the second part I apply DFT to explain the stability of the [Au14(PPh3)8](NO3)4 nanocluster. The third part is dedicated to method development for electron transport simulation in mesoscopic two-dimensional nanodevices

Animating Virtual Human for Virtual Batik Modeling

This research paper describes a development of animating virtual human for virtual batik modeling project. The objectives of this project are to animate the virtual human, to map the cloth with the virtual human body, to present the batik cloth, and to evaluate the application in terms of realism of virtual human look, realism of virtual human movement, realism of 3D scene, application suitability, application usability, fashion suitability and user acceptance. The final goal is to accomplish an animated virtual human for virtual batik modeling. There are 3 essential phases which research and analysis (data collection of modeling and animating technique), development (model and animate virtual human, map cloth to body and add a music) and evaluation (evaluation of realism of virtual human look, realism of virtual human movement, realism of props, application suitability, application usability, fashion suitability and user acceptance). The result for application usability is the highest percentage which 90%. Result show that this application is useful to the people. In conclusion, this project has met the objective, which the realism is achieved by used a suitable technique for modeling and animating

Detection of abnormal cardiac response patterns in cardiac tissue using deep learning

This study reports a method for the detection of mechanical signaling anomalies in cardiac tissue through the use of deep learning and the design of two anomaly detectors. In contrast to anomaly classifiers, anomaly detectors allow accurate identification of the time position of the anomaly. The first detector used a recurrent neural network (RNN) of long short-term memory (LSTM) type, while the second used an autoencoder. Mechanical contraction data present several challanges, including high presence of noise due to the biological variability in the contraction response, noise introduced by the data acquisition chain and a wide variety of anomalies. Therefore, we present a robust deep-learning-based anomaly detection framework that addresses these main issues, which are difficult to address with standard unsupervised learning techniques. For the time series recording, an experimental model was designed in which signals of cardiac mechanical contraction (right and left atria) of a CD-1 mouse could be acquired in an automatic organ bath, reproducing the physiological conditions. In order to train the anomaly detection models and validate their performance, a database of synthetic signals was designed (n = 800 signals), including a wide range of anomalous events observed in the experimental recordings. The detector based on the LSTM neural network was the most accurate. The performance of this detector was assessed by means of experimental mechanical recordings of cardiac tissue of the right and left atria.Peer ReviewedPostprint (author's final draft