skip to main content

Title: Neural network based 3D tracking with a graphene transparent focal stack imaging system

Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging.

; ; ; ; ; ; ; ; ; ; ; ;
Award ID(s):
Publication Date:
Journal Name:
Nature Communications
Nature Publishing Group
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. We consider several problems arising in the design process, including training a system to be robust to rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs, then sampling these to generate specialized training and test data. More generally, such languages can be used to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems such as autonomous cars and robots, whose environment at any point in time is ascene, a configuration of physical objects and agents. We design a domain-specific language,Scenic, for describingscenariosthat are distributions over scenes and the behaviors of their agents over time.Sceniccombines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided byScenic’s domain-specific syntax. Finally, we applyScenicin multiple case studies for training, testing, and debugging neural networks for perception bothmore »as standalone components and within the context of a full cyber-physical system.

    « less
  2. Abstract

    Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory,more »and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

    « less
  3. Abstract

    Computer-aided Design for Manufacturing (DFM) systems play an essential role in reducing the time taken for product development by providing manufacturability feedback to the designer before the manufacturing phase. Traditionally, DFM rules are hand-crafted and used to accelerate the engineering product design process by integrating manufacturability analysis during design. Recently, the feasibility of using a machine learning-based DFM tool in intelligently applying the DFM rules have been studied. These tools use a voxelized representation of the design and then use a 3D-Convolutional Neural Network (3D-CNN), to provide manufacturability feedback. Although these frameworks work effectively, there are some limitations to the voxelized representation of the design. In this paper, we introduce a new representation of the computer-aided design (CAD) model using orthogonal distance fields (ODF). We provide a GPU-accelerated algorithm to convert standard boundary representation (B-rep) CAD models into ODF representation. Using the ODF representation, we build a machine learning framework, similar to earlier approaches, to create a machine learning-based DFM system to provide manufacturability feedback. As proof of concept, we apply this framework to assess the manufacturability of drilled holes. The framework has an accuracy of more than 84% correctly classifying the manufacturable and non-manufacturable models using the newmore »representation.

    « less
  4. Abstract

    The reaction-diffusion system is naturally used in chemistry to represent substances reacting and diffusing over the spatial domain. Its solution illustrates the underlying process of a chemical reaction and displays diverse spatial patterns of the substances. Numerical methods like finite element method (FEM) are widely used to derive the approximate solution for the reaction-diffusion system. However, these methods require long computation time and huge computation resources when the system becomes complex. In this paper, we study the physics of a two-dimensional one-component reaction-diffusion system by using machine learning. An encoder-decoder based convolutional neural network (CNN) is designed and trained to directly predict the concentration distribution, bypassing the expensive FEM calculation process. Different simulation parameters, boundary conditions, geometry configurations and time are considered as the input features of the proposed learning model. In particular, the trained CNN model manages to learn the time-dependent behaviour of the reaction-diffusion system through the input time feature. Thus, the model is capable of providing concentration prediction at certain time directly with high test accuracy (mean relative error <3.04%) and 300 times faster than the traditional FEM. Our CNN-based learning model provides a rapid and accurate tool for predicting the concentration distribution of the reaction-diffusionmore »system.

    « less
  5. Abstract

    Taking advantage of the recent developments in machine learning, we propose an approach to automatic winter hydrometeor classification based on utilization of convolutional neural networks (CNNs). We describe the development, implementation, and evaluation of a method and tool for classification of snowflakes based on geometric characteristics and riming degree, respectively, obtained using CNNs from high-resolution images by a Multi-Angle Snowflake Camera (MASC). These networks are optimal for image classification of winter precipitation particles due to their high accuracy, computational efficiency, automatic feature extraction, and application versatility. They require little initial preparation, enable the use of smaller training sets through transfer learning techniques, come with large supporting communities and a wealth of resources available, and can be applied and operated by nonexperts. We illustrate both the ease of implementation and the usefulness of operation the CNN architecture offers as a tool for researchers and practitioners utilizing in situ optical observational devices. A training dataset containing 1450 MASC images is developed primarily from two storm events in December 2014 and February 2015 in Greeley, Colorado, by visual inspection of recognizable snowflake geometries. Defined geometric classes are aggregate, columnar crystal, planar crystal, small particle, and graupel. The CNN trained on this datasetmore »achieves a mean accuracy of 93.4% and displays excellent generalization (ability to classify new data). In addition, a separate training dataset is developed by sorting snowflakes into three classes and showcasing distinct degrees of riming. The CNN riming degree estimator yields promising initial results but would benefit from larger training sets.

    « less