Coherent, super resolved radar beamforming using self-supervised learning

High resolution automotive radar sensors are required in order to meet the high bar of autonomous vehicles needs and regulations. However, current radar systems are limited in their angular resolution causing a technological gap. An industry and academic trend to improve angular resolution by increasing the number of physical channels, also increases system complexity, requires sensitive calibration processes, lowers robustness to hardware malfunctions and drives higher costs. We offer an alternative approach, named Radar signal Reconstruction using Self Supervision (R2-S2), which significantly improves the angular resolution of a given radar array without increasing the number of physical channels. R2-S2 is a family of algorithms which use a Deep Neural Network (DNN) with complex range-Doppler radar data as input and trained in a self-supervised method using a loss function which operates in multiple data representation spaces. Improvement of 4x in angular resolution was demonstrated using a real-world dataset collected in urban and highway environments during clear and rainy weather conditions.Comment: 28 pages 10 figure

Secondary Electron Cloaking with Broadband Plasmonic Resonant Absorbers

Scanning electron microscopy (SEM) is one of the most powerful tools for nanoscale inspection and imaging. It is broadly used for biomedicine, materials science, and nanotechnology, enabling spatial resolution beyond the optical diffraction limit. In SEM, a high-energy electron beam illuminates a specimen, and the emitted secondary electrons are routed to a positively biased, synchronized detector for image creation. Here, for the first time, we experimentally demonstrate a cloaking of metallic objects from a secondary electron image. We make a metallic disc with a diameter of 300 nm almost invisible to a secondary electron detector with <5 nm spatial resolution. The secondary electron cloaking is based on broadband optical radiation absorption in the near field. Our secondary electron images are in good agreement with full-wave numerical solution of Maxwell’s equations at optical frequencies, confirming the concept of secondary electron cloaking based on broadband optical radiation absorption

Secondary Electron Imaging of Light at the Nanoscale

The interaction of fast electrons with metal atoms may lead to optical excitations. This exciting phenomenon forms the basis for the most powerful inspection methods in nanotechnology, such as cathodoluminescence and electron–energy loss spectroscopy. However, direct nanoimaging of light based on electrons is yet to be introduced. Here, we experimentally demonstrate simultaneous excitation and nanoimaging of optical signals using unmodified scanning electron microscope. We use high-energy electron beam for plasmon excitation and rapidly image the optical near fields using the emitted secondary electrons. We analyze dipole nanoantennas coupled with channel nanoplasmonic waveguides and observe both surface plasmons and surface plasmon polaritons with spatial resolution of 25 nm. Our experimental results are confirmed by rigorous numerical calculations based on full-wave solution of Maxwell’s equations, showing high correlation between optical near fields and secondary electrons images. This demonstration of optical near-field mapping using direct electron imaging provides essential insights to the exciting relations between electrons plasmons and photons, paving the way toward secondary electron-based plasmon analysis at the nanoscale

Non-volatile memory accelerated geometric multi-scale resolution analysis

Dimensionality reduction algorithms are standard tools in a researcher’s toolbox. Dimensionality reduction algorithms are frequently used to augment downstream tasks such as machine learning, data science, and also are exploratory methods for understanding complex phenomena. For instance, dimensionality reduction is commonly used in Biology as well as Neuroscience to understand data collected from biological subjects. However, dimensionality reduction techniques are limited by the von-Neumann architectures that they execute on. Specifically, data intensive algorithms such as dimensionality reduction techniques often require fast, high capacity, persistent memory which historically hardware has been unable to provide at the same time. In this paper, we present a re-implementation of an existing dimensionality reduction technique called Geometric Multi-Scale Resolution Analysis (GMRA) which has been accelerated via novel persistent memory technology called Memory Centric Active Storage (MCAS). Our implementation uses a specialized version of MCAS called PyMM that provides native support for Python datatypes including NumPy arrays and PyTorch tensors. We compare our PyMM implementation against a DRAM implementation, and show that when data fits in DRAM, PyMM offers competitive runtimes. When data does not fit in DRAM, our PyMM implementation is still able to process the data.Accepted manuscrip

Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning

Abstract Widespread adaptation of autonomous, robotic systems relies greatly on safe and reliable operation, which in many cases is derived from the ability to maintain accurate and robust perception capabilities. Environmental and operational conditions as well as improper maintenance can produce calibration errors inhibiting sensor fusion and, consequently, degrading the perception performance and overall system usability. Traditionally, sensor calibration is performed in a controlled environment with one or more known targets. Such a procedure can only be carried out in between operations and is done manually; a tedious task if it must be conducted on a regular basis. This creates an acute need for online targetless methods, capable of yielding a set of geometric transformations based on perceived environmental features. However, the often-required redundancy in sensing modalities poses further challenges, as the features captured by each sensor and their distinctiveness may vary. We present a holistic approach to performing joint calibration of a camera–lidar–radar trio in a representative autonomous driving application. Leveraging prior knowledge and physical properties of these sensing modalities together with semantic information, we propose two targetless calibration methods within a cost minimization framework: the first via direct online optimization, and the second through self-supervised learning (SSL)

Improved and optimized drug repurposing for the SARS-CoV-2 pandemic.

The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on knowledge graphs, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently developed the Dr-COVID model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the Dr-COVID model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware-we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking