Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul

Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Robust Mobile Visual Recognition System: From Bag of Visual Words to Deep Learning

With billions of images captured by mobile users everyday, automatically recognizing contents in such images has become a particularly important feature for various mobile apps, including augmented reality, product search, visual-based authentication etc. Traditionally, a client-server architecture is adopted such that the mobile client sends captured images/video frames to a cloud server, which runs a set of task-specific computer vision algorithms and sends back the recognition results. However, such scheme may cause problems related to user privacy, network stability/availability and device energy.In this dissertation, we investigate the problem of building a robust mobile visual recognition system that achieves high accuracy, low latency, low energy cost and privacy protection. Generally, we study two broad types of recognition methods: the bag of visual words (BOVW) based retrieval methods, which search the nearest neighbor image to a query image, and the state-of-the-art deep learning based methods, which recognize a given image using a trained deep neural network. The challenges of deploying BOVW based retrieval methods include: size of indexed image database, query latency, feature extraction efficiency and re-ranking performance. To address such challenges, we first proposed EMOD which enables efficient on-device image retrieval on a downloaded context-dependent partial image database. The efficiency is achieved by analyzing the BOVW processing pipeline and optimizing each module with algorithmic improvement.Recent deep learning based recognition approaches have been shown to greatly exceed the performance of traditional approaches. We identify several challenges of applying deep learning based recognition methods on mobile scenarios, namely energy efficiency and privacy protection for real-time visual processing, and mobile visual domain biases. Thus, we proposed two techniques to address them, (i) efficiently splitting the workload across heterogeneous computing resources, i.e., mobile devices and the cloud using our Moca framework, and (ii) using mobile visual domain adaptation as proposed in our collaborative edge-mediated platform DeepCham. Our extensive experiments on large-scale benchmark datasets and off-the-shelf mobile devices show our solutions provide better results than the state-of-the-art solutions

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

System Abstractions for Scalable Application Development at the Edge

Recent years have witnessed an explosive growth of Internet of Things (IoT) devices, which collect or generate huge amounts of data. Given diverse device capabilities and application requirements, data processing takes place across a range of settings, from on-device to a nearby edge server/cloud and remote cloud. Consequently, edge-cloud coordination has been studied extensively from the perspectives of job placement, scheduling and joint optimization. Typical approaches focus on performance optimization for individual applications. This often requires domain knowledge of the applications, but also leads to application-specific solutions. Application development and deployment over diverse scenarios thus incur repetitive manual efforts. There are two overarching challenges to provide system-level support for application development at the edge. First, there is inherent heterogeneity at the device hardware level. The execution settings may range from a small cluster as an edge cloud to on-device inference on embedded devices, differing in hardware capability and programming environments. Further, application performance requirements vary significantly, making it even more difficult to map different applications to already heterogeneous hardware. Second, there are trends towards incorporating edge and cloud and multi-modal data. Together, these add further dimensions to the design space and increase the complexity significantly. In this thesis, we propose a novel framework to simplify application development and deployment over a continuum of edge to cloud. Our framework provides key connections between different dimensions of design considerations, corresponding to the application abstraction, data abstraction and resource management abstraction respectively. First, our framework masks hardware heterogeneity with abstract resource types through containerization, and abstracts away the application processing pipelines into generic flow graphs. Further, our framework further supports a notion of degradable computing for application scenarios at the edge that are driven by multimodal sensory input. Next, as video analytics is the killer app of edge computing, we include a generic data management service between video query systems and a video store to organize video data at the edge. We propose a video data unit abstraction based on a notion of distance between objects in the video, quantifying the semantic similarity among video data. Last, considering concurrent application execution, our framework supports multi-application offloading with device-centric control, with a userspace scheduler service that wraps over the operating system scheduler

Application of Radial Distribution Functions to Diffraction and Imaging Data: Interfacial Structures, Amorphous, Disordered Materials

The central theme of this thesis is the application of radial and pair distribution function analysis to materials characterisation problems for nanotechnology. These concepts are introduced in Chapter 1, and the associated methods are described in Chapter 2. Chapter 3 details the first of the results which discusses the design and development of a software tool called ImageDataExtractor. This auto-extracts microscopy images and then analyses them to afford quantitative information regarding particles in a sample, such as shape, size and distribution. It realises an opportunity for data-mining the ubiquity of readily available images in the literature. Chapter 4 presents results of the development and execution of a novel experimental technique, called glancing-angle pair distribution function (gaPDF) analysis, applied to the structure of the working electrode in dye-sensitised solar cells (DSSCs). This structure was successfully observed, validating this novel method. The investigation also suggested preferred binding modes of the carboxylic acid anchoring groups present in this interfacial structure. Chapters 5 and 6 demonstrate the application of PDF analysis to synchrotron-based powder diffraction data of two material case studies: the rare earth phosphate glass (REPG) (Gd2O3)0.230(P2O5)0.770, and four Ru based photo-isomers. The closest R…R rare earth separation, which governs optical properties of REPGs, was determined to be 4.2(1) Å, aided by various statistical techniques. Analysis on four Ru-based photo-isomers confirmed: the existence of local structure in such compounds, their ability to be photo-isomerised in powder form, the theoretical models constructed using computational techniques, and the lack of heterogeneity in photo-isomerisation throughout a given light-induced sample. Chapter 7 concludes the work and offers a future outlook

Montana Kaimin, December 5, 1997

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/10194/thumbnail.jp

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments