Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

https://www.cityscapes-dataset.com/wordpress/wp-content/papercite-data/pdf/cordts2016cityscapes.pdf

The Cityscapes Dataset for Semantic Urban Scene Understanding

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

/pdf/past-present-and-future-of-simultaneous-localization-and-dhljw0akg5.pdf

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Damascene copper electroplating for on-chip interconnections, a process that we conceived and developed in the early 1990s, makes it possible to fill submicron trenches and vias with copper without creating a void or a seam and has thus proven superior to other technologies of copper deposition. We discuss here the relationship of additives in the plating bath to superfilling, the phenomenon that results in superconformal coverage, and we present a numerical model which accounts for the experimentally observed profile evolution of the plated metal.

Damascene copper electroplating for chip interconnections

A strain sensor has been fabricated from a polymer nanocomposite with multiwalled carbon nanotube (MWNT) fillers. The piezoresistivity
of this nanocomposite strain sensor has been investigated based on an improved three-dimensional (3D) statistical resistor network
model incorporating the tunneling effect between the neighboring carbon nanotubes (CNTs), and a fiber reorientation model. The
numerical results agree very well with the experimental measurements. As compared with traditional strain gauges, much higher sensitivity
can be obtained in the nanocomposite sensors when the volume fraction of CNT is close to the percolation threshold. For a small
CNT volume fraction, weak nonlinear piezoresistivity is observed both experimentally and from numerical simulation. The tunneling
effect is considered to be the principal mechanism of the sensor under small strains.

Tunneling effect in a polymer/carbon nanotube nanocompositestrain sensor

Coral reef habitat structural complexity influences key ecological processes, ecosystem biodiversity, and resilience. Measuring structural complexity underwater is not trivial and researchers have been searching for accurate and cost-effective methods that can be applied across spatial extents for over 50 years. This study integrated a set of existing multi-view, image-processing algorithms, to accurately compute metrics of structural complexity (e.g., ratio of surface to planar area) underwater solely from images. This framework resulted in accurate, high-speed 3D habitat reconstructions at scales ranging from small corals to reef-scapes (10s km2). Structural complexity was accurately quantified from both contemporary and historical image datasets across three spatial scales: (i) branching coral colony (Acropora spp.); (ii) reef area (400 m2); and (iii) reef transect (2 km). At small scales, our method delivered models with <1 mm error over 90% of the surface area, while the accuracy at transect scale was 85.3% ± 6% (CI). Advantages are: no need for an a priori requirement for image size or resolution, no invasive techniques, cost-effectiveness, and utilization of existing imagery taken from off-the-shelf cameras (both monocular or stereo). This remote sensing method can be integrated to reef monitoring and improve our knowledge of key aspects of coral reef dynamics, from reef accretion to habitat provisioning and productivity, by measuring and up-scaling estimates of structural complexity.

https://www.mdpi.com/2072-4292/8/2/113/pdf

Quantifying multiscale habitat structural complexity: a cost-effective framework for underwater 3d modelling

Amorphous SiO2 (a-SiO2) thin films are widely used in integrated circuits (ICs) due to their excellent thermal stability and insulation properties In this paper, the thermal conductivity of a-SiO2 thin film was systematically investigated using non-equilibrium molecular dynamics (NEMD) simulations In addition to the size effect and the temperature effect for thermal conductivity of a-SiO2 thin films, the effect of defects induced thermal conductivity tuning was also examined It was found that the thermal conductivity of a-SiO2 thin films is insensitive to the temperature from −55 °C to 150 °C Nevertheless, in the range of the thickness in this work, the thermal conductivity of the crystalline SiO2 (c-SiO2) thin films conforms to the T−α with the exponent range from −012 to −037, and the thinner films are less sensitive to temperature Meanwhile, the thermal conductivity of a-SiO2 with thickness beyond 426 nm has no significant size effect, which is consistent with the experimental results Compared with c-SiO2 thin film, the thermal conductivity of a-SiO2 is less sensitive to defects Particularly, the effect of spherical void defects on the thermal conductivity of a-SiO2 is followed by Coherent Potential model, which is helpful for the design of low-K material based porous a-SiO2 thin film in microelectronics

/pdf/thermal-conductivity-of-amorphous-sio-2-thin-film-a-1blbpwnact.pdf

Thermal conductivity of amorphous SiO 2 thin film: A molecular dynamics study

The mosaicking of Unmanned Aerial Vehicle (UAV) imagery usually requires information from additional sensors, such as Global Position System (GPS) and Inertial Measurement Unit (IMU), to facilitate direct orientation, or 3D reconstruction approaches (e.g., structure-from-motion) to recover the camera poses. In this paper, we propose a novel mosaicking method for UAV imagery in which neither direct nor indirect orientation procedures are required. Inspired by the embedded deformation model, a widely used non-rigid mesh deformation model, we present a novel objective function for image mosaicking. Firstly, we construct a feature correspondence energy term that minimizes the sum of the squared distances between matched feature pairs to align the images geometrically. Secondly, we model a regularization term that constrains the image transformation parameters directly by keeping all transformations as rigid as possible to avoid global distortion in the final mosaic. Experimental results presented herein demonstrate that the accuracy of our method is twice as high as an existing (purely image-based) approach, with the associated benefits of significantly faster processing times and improved robustness with respect to reference image selection.

Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

This paper presents the development of a low-cost sensor platform for use in ground-based visual pose estimation and scene mapping tasks. We seek to develop a technical solution using low-cost vision hardware that allows us to accurately estimate robot position for SLAM tasks. We present results from the application of a vision based pose estimation technique to simultaneously determine camera poses and scene structure. The results are generated from a dataset gathered traversing a local road at the St Lucia Campus of the University of Queensland. We show the accuracy of the pose estimation over a 1.6km trajectory in relation to GPS ground truth.

Unaided stereo vision based pose estimation

The authors demonstrate that the micro magneto-rheological fluid device is designed by the method of outer magnetic coil to generate a mechanical loading less than 20 N. The results confirm that the method can obtain miniature device easily and control the magnetic field conveniently. The magnetic field, the channel gap, and the initial position of valve plug were optimized by finite element simulation and experimental test. Additionally, an impact force at the beginning can be eliminated by using a modeled synchronous linear current, which indicates that the micro-magnetorheological fluid damper has good soft-landing performance.

Hu He

Papers

Quantifying multiscale habitat structural complexity: a cost-effective framework for underwater 3d modelling

Thermal conductivity of amorphous SiO 2 thin film: A molecular dynamics study

Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

Unaided stereo vision based pose estimation

The soft-landing features of a micro-magnetorheological fluid damper