Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

MULTIMODAL LEARNING FOR AUDIO AND VISUAL PROCESSING

The world contains vast amounts of information which can be sensed and captured in a variety of ways and formats. Virtual environments also lend themselves to endless possibilities and diversity of data. Often our experiences draw from these separate but complementary parts which can be combined in a way to provide a comprehensive representation of the events. Multimodal learning focuses on these types of combinations. By fusing multiple modalities, multimodal learning can improve results beyond individual mode performance. However, many of today’s state-of-the-art techniques in computer vision, robotics, and machine learning rely solely or primarily on visual inputs even when the visual data is obtained from video where corresponding audio may also be readily available to augment learning. Vision only approaches can experience challenges in cases of highly reflective, transparent, or occluded objects and scenes where, if used alone or in conjunction with, audio may improve task performance. To address these challenges, this thesis explores coupling multimodal information to enhance task performance through learning-based methods for audio and visual processing using real and synthetic data. Physically-based graphics pipelines can naturally be extended for audio and visual synthetic data generation. To enhance the rigid body sound synthesis pipeline for objects containing a liquid, I used an added mass operator for fluid-structure coupling as a pre-processing step. My method is fast and practical for use in interactive 3D systems where live sound synthesis is desired. By fusing audio and visual data from real and synthetic videos, we also demonstrate enhanced processing and performance for object classification, tracking, and reconstruction tasks. As has been shown in visual question and answering and other related work, multiple modalities have the ability to complement one another and outperform single modality systems. To the best of my knowledge, I introduced the first use of audio-visual neural networks to analyze liquid pouring sequences by classifying their weight, liquid, and receiving container. Prior work often required predefined source weights or visual data. My contribution was to use the sound from a pouring sequence—a liquid being poured into a target container- to train a multimodal convolutional neural networks (CNNs) that fuses mel-scaled spectrograms as audio inputs with corresponding visual data based on video images. I described the first use of an audio-visual neural network for tracking tabletop sized objects and enhancing visual object trackers. Like object detection of reflective surfaces, object trackers can also run into challenges when objects collide, occlude, appear similar, or come close to one another. By using the impact sounds of the objects during collision, my audio-visual object tracking (AVOT) neural network can correct trackers that drift from their original objects that were assigned before collision. Reflective and textureless surfaces not only are difficult to detect and classify, they are also often poorly reconstructed and filled with depth discontinuities and holes. I proposed the first use of an audiovisual method that uses the reflections of sound to aid in geometry and audio reconstruction, referred to as ”Echoreconstruction”. The mobile phone prototype emits pulsed audio, while recording video for RGBbased 3D reconstruction and audio-visual classification. Reflected sound and images from the video are input into our audio (EchoCNN-A) and audio-visual (EchoCNN-AV) convolutional neural networks for surface and sound source detection, depth estimation, and material classification. EchoCNN inferences from these classifications enhance scene 3D reconstructions containing open spaces and reflective surfaces by depth filtering, inpainting, and placement of unmixed sound sources in the scene. In addition to enhancing scene reconstructions, I proposed a multimodal single- and multi-frame reconstruction LSTM autoencoder for 3D reconstructions using audio-visual inputs. Our neural network produces high-quality 3D reconstructions using voxel representation. It is the first audio-visual reconstruction neural network for 3D geometry and material representation. Contributions of this thesis include new neural network designs, new enhancements to real and synthetic audio-visual datasets, and prototypes that demonstrate audio and audio-augmented performance for sound synthesis, inference, and reconstruction.Doctor of Philosoph

A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval and computer vision research. In this survey, we give a comprehensive overview and key insights into the state of the art of higher dimensional features from deep learning and also traditional approaches. Current approaches are frequently using 3D information from the sensor or are using 3D in modeling and understanding the 3D world. With the growth of prevalent application areas such as 3D games, self-driving automobiles, health monitoring and sports activity training, a wide variety of new sensors have allowed researchers to develop feature description models beyond 2D. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms. Due to the major importance of the evaluation process, we also present an overview of the current datasets and benchmarks. Moreover, based on more than 330 papers from this study, we present the major challenges and future directions. Computer Systems, Imagery and Medi

Analysing and Reducing Costs of Deep Learning Compiler Auto-tuning

Deep Learning (DL) is significantly impacting many industries, including automotive, retail and medicine, enabling autonomous driving, recommender systems and genomics modelling, amongst other applications. At the same time, demand for complex and fast DL models is continually growing. The most capable models tend to exhibit highest operational costs, primarily due to their large computational resource footprint and inefficient utilisation of computational resources employed by DL systems. In an attempt to tackle these problems, DL compilers and auto-tuners emerged, automating the traditionally manual task of DL model performance optimisation. While auto-tuning improves model inference speed, it is a costly process, which limits its wider adoption within DL deployment pipelines. The high operational costs associated with DL auto-tuning have multiple causes. During operation, DL auto-tuners explore large search spaces consisting of billions of tensor programs, to propose potential candidates that improve DL model inference latency. Subsequently, DL auto-tuners measure candidate performance in isolation on the target-device, which constitutes the majority of auto-tuning compute-time. Suboptimal candidate proposals, combined with their serial measurement in an isolated target-device lead to prolonged optimisation time and reduced resource availability, ultimately reducing cost-efficiency of the process. In this thesis, we investigate the reasons behind prolonged DL auto-tuning and quantify their impact on the optimisation costs, revealing directions for improved DL auto-tuner design. Based on these insights, we propose two complementary systems: Trimmer and DOPpler. Trimmer improves tensor program search efficacy by filtering out poorly performing candidates, and controls end-to-end auto-tuning using cost objectives, monitoring optimisation cost. Simultaneously, DOPpler breaks long-held assumptions about the serial candidate measurements by successfully parallelising them intra-device, with minimal penalty to optimisation quality. Through extensive experimental evaluation of both systems, we demonstrate that they significantly improve cost-efficiency of autotuning (up to 50.5%) across a plethora of tensor operators, DL models, auto-tuners and target-devices

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Европейский и национальный контексты в научных исследованиях - 2021 : Технология

В настоящем электронном сборнике «Европейский и национальный контексты в научных исследованиях. Технология» представлены работы молодых ученых по геодезии и картографии, химической технологии и машиностроению, информационным технологиям, строительству и радиотехнике. Предназначены для работников образования, науки и производства. Будут полезны студентам, магистрантам и аспирантам университетов.=In this Electronic collected materials “European and national dimension in research. Technology” works in the fields of geodesy, chemical technology, mechanical engineering, information technology, civil engineering, and radio-engineering are presented. It is intended for trainers, researchers and professionals. It can be useful for university graduate and post-graduate students