Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions

Toward Co-Robotic Construction: Visual Site Monitoring & Hazard Detection to Ensure Worker Safety

Construction has remained the least automated and productive as well as the most hazardous industry. Moreover, it has been plagued by a significant lack of diversity in its workforce as well as aging laborers. To address these issues, co-robotic construction has emerged as a new paradigm of construction. The industry is gradually gearing up to embrace robotic solutions, and many construction robots with various degrees of autonomy are under development or in the early stage of deployment. Presenting a different horizon of construction—harmonious co-existence and co-work between workers and robots—co-robotic construction is expected to reform labor-intensive construction into the more productive, safer, and more inclusive industry. However, an in-depth understanding of the robots’ situational intelligence is still lacking, particularly conclusive logic and technologies to ensure workers’ safety nearby autonomous (or semi-) robots, which is fundamental in realizing the co-robotic construction. To fill the gap, this research established a comprehensive robotic hazard detection roadmap and developed core technologies to realize it, leveraging unmanned aerial vehicles, computer vision, and deep learning. In this dissertation, I describe how the developed technologies with a conclusive logic can pro-actively detect the robotics hazards taking various forms and scenarios in an unstructured and dynamic construction environment. The successful implementation of the robotic hazard detection roadmap in co-robotic construction allows for timely interventions such as pro-active robot control and worker feedback, which contributes to reducing robotic accidents. Eventually, this will make human-robot co-existence and collaboration safer, while also helping to build workers’ trust in robot co-workers. Finally, the ensured safety and trust between robots and workers would contribute to promoting construction enterprises to embrace robotic solutions, boosting construction reformation toward innovative co-robotic construction.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167981/1/daeho_1.pd

Proceedings of the Cardiff University Engineering Research Conference 2023

The conference was established for the first time in 2023 as part of a programme to sustain the research culture, environment, and dissemination activities of the School of Engineering at Cardiff University in the United Kingdom. The conference served as a platform to celebrate advancements in various engineering domains researched at our School, explore and discuss further advancements in the diverse fields that define contemporary engineering

The research on mechanical properties and compressive behavior of graphene foam with multi-scale model?

Computational simulation is an effective method to study the deformation mechanism and mechanical behaviour of graphene-based porous materials. However, due to limitations in computational methods and costs, existing research model deviate significantly from the real material in terms of the scale of structure. Therefore, building a highly accurate computational model and maintaining an appropriate cost is both necessary and challenging. This paper proposed a multi-scale modelling approach for finite element (FE) analysis based on the concept of structural hierarchy. The stochastic feature of the microstructure of porous materials are also considered. The simulation results of the regular structure model and the Voronoi tessellation model are compared to investigate the effect of regularity on the material properties. Despite some shortcomings, other microstructural features of porous graphene materials can be gradually introduced to improve the material model step by step. Thus the developed multiscale model has great potential to simulate the properties of materials with mesoscopic size structure such as graphene foam (GF)

Deep probabilistic methods for improved radar sensor modelling and pose estimation

Radar’s ability to sense under adverse conditions and at far-range makes it a valuable alternative to vision and lidar for mobile robotic applications. However, its complex, scene-dependent sensing process and significant noise artefacts makes working with radar challenging. Moving past classical rule-based approaches, which have dominated the literature to date, this thesis investigates deep and data-driven solutions across a range of tasks in robotics. Firstly, a deep approach is developed for mapping raw sensor measurements to a grid-map of occupancy probabilities, outperforming classical filtering approaches by a significant margin. A distribution over the occupancy state is captured, additionally allowing uncertainty in predictions to be identified and managed. The approach is trained entirely using partial labels generated automatically from lidar, without requiring manual labelling. Next, a deep model is proposed for generating stochastic radar measurements from simulated elevation maps. The model is trained by learning the forward and backward processes side-by-side, using a combination of adversarial and cyclical consistency constraints in combination with a partial alignment loss, using labels generated in lidar. By faithfully replicating the radar sensing process, new models can be trained for down-stream tasks, using labels that are readily available in simulation. In this case, segmentation models trained on simulated radar measurements, when deployed in the real world, are shown to approach the performance of a model trained entirely on real-world measurements. Finally, the potential of deep approaches applied to the radar odometry task are explored. A learnt feature space is combined with a classical correlative scan matching procedure and optimised for pose prediction, allowing the proposed method to outperform the previous state-of-the-art by a significant margin. Through a probabilistic consideration the uncertainty in the pose is also successfully characterised. Building upon this success, properties of the Fourier Transform are then utilised to separate the search for translation and angle. It is shown that this decoupled search results in a significant boost to run-time performance, allowing the approach to run in real-time on CPUs and embedded devices, whilst remaining competitive with other radar odometry methods proposed in the literature

Roadmap on measurement technologies for next generation structural health monitoring systems

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots

Roadmap on photonic metasurfaces

Funding: C.R. and U.L. acknowledge support through the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy via the Excellence Cluster 3D Matter Made to Order (EXC-2082/1, Grant No. 390761711). A.B.E. acknowledges support through the Cluster of Excellence PhoenixD (EXC 2122, Project ID No. 390833453). I.F.-C. and C.R. acknowledge support through the CRC Waves: Analysis and Numerics (SFB 1173, Grant No. 258734477. K.A. acknowledges funding from the Swiss National Science Foundation (Project No. PZ00P2_193221).Here we present a roadmap on Photonic metasurfaces. This document consists of a number of perspective articles on different applications, challenge areas or technologies underlying photonic metasurfaces. Each perspective will introduce the topic, present a state of the art as well as give an insight into the future direction of the subfield.Peer reviewe

Roadmap on measurement technologies for next generation structural health monitoring systems

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots