592 research outputs found
3D Design Review Systems in Immersive Environments
Design reviews play a crucial role in the development process, ensuring the quality and effectiveness of designs in various industries. However, traditional design review methods face challenges in effectively understanding and communicating complex 3D models. Immersive technologies, particularly Head-Mounted Displays (HMDs), offer new opportunities to enhance the design review process. In this thesis, we investigate using immersive environments, specifically HMDs, for 3D design reviews. We begin with a systematic literature review to understand the current state of employing HMDs in industry for design reviews. As part of this review, we utilize a detailed taxonomy from the literature to categorize and analyze existing approaches. Additionally, we present four iterations of an immersive design review system developed during my industry experience. Two of these iterations are evaluated through case studies involving domain experts, including engineers, designers, and clients. A formal semi-structured focus group is conducted to gain further insights into traditional design review practices. The outcomes of these evaluations and the focus group discussions are thoroughly discussed. Based on the literature review and the focus group findings, we uncover a new challenge associated with using HMDs in immersive design reviewsâasynchronous and remote collaboration. Unlike traditional design reviews, where participants view the same section on a shared screen, HMDs allow independent exploration of areas of interest, leading to a shift from synchronous to asynchronous communication. Consequently, important feedback may be missed as the lead designer disconnects from the users' perspectives. To address this challenge, we collaborate with a domain expert to develop a prototype that utilizes heatmap visualization to display 3D gaze data distribution. This prototype enables lead designers to quickly identify areas of review and missed regions. The study incorporates the Design Critique approach and provides valuable insights into different heatmap visualization variants (top view projection, object-based, and volume-based). Furthermore, a list of well-defined requirements is outlined for future spatio-temporal visualization applications aimed at integrating into existing workflows. Overall, this thesis contributes to the understanding and improvement of immersive design review systems, particularly in the context of utilizing HMDs. It offers insights into the current state of employing HMDs for design reviews, utilizes a taxonomy from the literature to analyze existing approaches, highlights challenges associated with asynchronous collaboration, and proposes a prototype solution with heatmap visualization to address the identified challenge
Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design
Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data
LASSO â an observatorium for the dynamic selection, analysis and comparison of software
Mining software repositories at the scale of 'big code' (i.e., big data) is a challenging activity. As well as finding a suitable software corpus and making it programmatically accessible through an index or database, researchers and practitioners have to establish an efficient analysis infrastructure and precisely define the metrics and data extraction approaches to be applied. Moreover, for analysis results to be generalisable, these tasks have to be applied at a large enough scale to have statistical significance, and if they are to be repeatable, the artefacts need to be carefully maintained and curated over time. Today, however, a lot of this work is still performed by human beings on a case-by-case basis, with the level of effort involved often having a significant negative impact on the generalisability and repeatability of studies, and thus on their overall scientific value.
The general purpose, 'code mining' repositories and infrastructures that have emerged in recent years represent a significant step forward because they automate many software mining tasks at an ultra-large scale and allow researchers and practitioners to focus on defining the questions they would like to explore at an abstract level. However, they are currently limited to static analysis and data extraction techniques, and thus cannot support (i.e., help automate) any studies which involve the execution of software systems. This includes experimental validations of techniques and tools that hypothesise about the behaviour (i.e., semantics) of software, or data analysis and extraction techniques that aim to measure dynamic properties of software.
In this thesis a platform called LASSO (Large-Scale Software Observatorium) is introduced that overcomes this limitation by automating the collection of dynamic (i.e., execution-based) information about software alongside static information. It features a single, ultra-large scale corpus of executable software systems created by amalgamating existing Open Source software repositories and a dedicated DSL for defining abstract selection and analysis pipelines. Its key innovations are integrated capabilities for searching for selecting software systems based on their exhibited behaviour and an 'arena' that allows their responses to software tests to be compared in a purely data-driven way. We call the platform a 'software observatorium' since it is a place where the behaviour of large numbers of software systems can be observed, analysed and compared
Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications
L'abstract Ăš presente nell'allegato / the abstract is in the attachmen
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Information Technology Organisations
The studyâs main objective is to analyse the level of cloud computing adoption and usage during COVID-19 in Sri Lanka, especially in Information Technology (IT) organisations. Using senior IT employees, this study investigates what extent their organisation adopts with cloud computing, the level of cloud computing usage, current use of cloud service model, usage of cloud deployment model, preferred cloud service providers and reasons for adopting and not adopting cloud computing. The study also describes why cloud computing is a solution for new normal situations and the cloud-enabled services used during and after the COVID-19 pandemic. The finding suggests that 87.7% of the organisations currently use cloud-enabled services, whereas 12.3% do not and intend to adopt. Considering the benefits, cloud computing is the solution post COVID-19 pandemic to run the business way forward. Keywords: Cloud Computing, COVID-19, COVID-19 Pandemic, Cloud-enabled Services, Sri Lanka DOI: 10.7176/JIEA/13-2-02 Publication date:March 31st 202
Workplace-Based Learning: A Study in BIM-enabled Construction Projects
Building Information Modelling (BIM) is a fast-emerging technology that has promoted digital transformation in the construction project lifecycle through changing the ways in which people work. However, empirical studies show that professionals in the construction industry are still reluctant to adopt BIM in their construction projects due to a lack of skills and suitable learning approaches. Furthermore, embracing an appropriate learning approach is still challenging in built environment projects, which are generally complex, temporary, unique and uncertain due to their fragmented nature. To achieve more successful BIM-enabled construction projects, a flexible and relevant learning approach for the workplace needs to be determined. Consequently, resolving this issue requires identification of the key learning aspects that influence creation of a suitable learning approach. The aim of this doctoral study is to explore how workplace-based learning could be designed and implemented in BIM enabled-construction projects.
Learning that takes place in construction projects is predominantly determined by complex social practices. On the other hand, BIM â which professionals desire to adopt in construction projects â is interwoven with both interactions with humans and artefacts. To holistically investigate the learning in BIM-enabled construction projects, âConnectivismâ, a new learning approach for the digital age, is adopted in this study. This explains the complex learning that happens in the work environment through a combination of principles by understanding the unrelated unseen events (chaos), exploring the learning as a collective (network), investigating the position between order and disorder (complexity) and analysing unpredictable and uncontrollable learning that occurs due to non-linear interactions (self-organising). Understanding the continuous learning in both human and non-human activities through Connectivism has helped to identify the links between the key learning aspects in the workplace. Examining the identified learning aspects in a connected way has encouraged professionals to figure out the most suitable learning approach for their project team.
This study has been conducted in three phases: literature review, semi-structured interviews and a case study approach, in order to understand the learning that occurs in BIM-enabled construction projects. Semi-structured interviews were conducted with 20 professionals working in BIM-enabled construction projects. Two case studies were selected to analyse BIM-enabled construction projects in the ÂŁ30-60 million scale. Furthermore, six case studies within those selected projects were chosen for an in-depth investigation on the in-project learning. Data within the case studies were collected through project documents, semi-structured interviews and meeting observations. Nvivo was used to evaluate, interpret, explain and analyse the data collected from both semi-structured interviews and case studies.
The study reveals that BIM-enabled construction projects are largely involved with information that is digitally linked with federated 3D models and project participants. Investigation shows that learning in in these projects is continuous, networked and depends on participation in addition to knowledge accumulation and knowledge creation. âParticipationâ and âInterpretationâ as a combination have significant impacts on this complex learning that takes place in work environments. âParticipationâ at work shows how each individual wants to get involved, interpret and learn in each situation that they participate. On the other hand, the multidisciplinary nature of BIM-enabled construction projects confirms that project participants need to focus on interpretation to agree on a common meaning of artefacts and information. Therefore, âInterpretationâ is identified as a form of thinking that comprises planning, monitoring oneâs activities and problem-solving. Interpretation, which is enabled via thinking and sharing experience, helps to shape the decisions and solutions during Participation. To help construction projects in achieving a suitable learning approach which is vital for a success of a project, a model for learning in the workplace has been developed through merging the learning aspects that have been identified from chosen BIM-enabled construction projects.
The novel model for workplace-based learning is a combination of participation and interpretation which is linked through three learning modes: Alignment, Insight and Engagement. The combination of these learning modes has contributed to interpret the ideas while participating at work. Consequently, it enabled project participants to align on a common meaning in an informative collaborative environment. The proposed model of learning in the workplace presents a systematic approach for achieving suitable learning in BIM-enabled projects by connecting the key learning aspects at the project level. Furthermore, this can be also used to employ skilled people and promote common standards on skills expectations associated with BIM-enabled projects
Study and development of a reliable fiducials-based localization system for multicopter UAVs flying indoor
openThe recent evolution of technology in automation, agriculture, IoT, and aerospace fields
has created a growing demand for mobile robots capable of autonomous operation and
movement to accomplish various tasks. Aerial platforms are expected to play a central
role in the future due to their versatility and swift intervention capabilities. However,
the effective utilization of these platforms faces a significant challenge due to localization,
which is a vital aspect for their interaction with the surrounding environment.
While GNSS localization systems have established themselves as reliable solutions for
open-space scenarios, the same approach is not viable for indoor settings, where localization
remains an open problem as it is witnessed by the lack of extensive literature on
the topic.
In this thesis, we address this challenge by proposing a dependable solution for small
multi-rotor UAVs using a Visual Inertial Odometry localization system. Our KF-based
localization system reconstructs the pose by fusing data from onboard sensors. The primary
source of information stems from the recognition of AprilTags fiducial markers,
strategically placed in known positions to form a âmapâ.
Building upon prior research and thesis work conducted at our university, we extend
and enhance this system. We begin with a concise introduction, followed by a justification
of our chosen strategies based on the current state of the art. We provide an
overview of the key theoretical, mathematical, and technical aspects that support our
work. These concepts are fundamental to the design of innovative strategies that address
challenges such as data fusion from different AprilTag recognition and the elimination
of misleading measurements. To validate our algorithms and their implementation,
we conduct experimental tests using two distinct platforms by using localization
accuracy and computational complexity as performance indices to demonstrate the
practical viability of our proposed system.
By tackling the critical issue of indoor localization for aerial platforms, this thesis tries
to give some contribution to the advancement of robotics technology, opening avenues
for enhanced autonomy and efficiency across various domains.The recent evolution of technology in automation, agriculture, IoT, and aerospace fields
has created a growing demand for mobile robots capable of autonomous operation and
movement to accomplish various tasks. Aerial platforms are expected to play a central
role in the future due to their versatility and swift intervention capabilities. However,
the effective utilization of these platforms faces a significant challenge due to localization,
which is a vital aspect for their interaction with the surrounding environment.
While GNSS localization systems have established themselves as reliable solutions for
open-space scenarios, the same approach is not viable for indoor settings, where localization
remains an open problem as it is witnessed by the lack of extensive literature on
the topic.
In this thesis, we address this challenge by proposing a dependable solution for small
multi-rotor UAVs using a Visual Inertial Odometry localization system. Our KF-based
localization system reconstructs the pose by fusing data from onboard sensors. The primary
source of information stems from the recognition of AprilTags fiducial markers,
strategically placed in known positions to form a âmapâ.
Building upon prior research and thesis work conducted at our university, we extend
and enhance this system. We begin with a concise introduction, followed by a justification
of our chosen strategies based on the current state of the art. We provide an
overview of the key theoretical, mathematical, and technical aspects that support our
work. These concepts are fundamental to the design of innovative strategies that address
challenges such as data fusion from different AprilTag recognition and the elimination
of misleading measurements. To validate our algorithms and their implementation,
we conduct experimental tests using two distinct platforms by using localization
accuracy and computational complexity as performance indices to demonstrate the
practical viability of our proposed system.
By tackling the critical issue of indoor localization for aerial platforms, this thesis tries
to give some contribution to the advancement of robotics technology, opening avenues
for enhanced autonomy and efficiency across various domains
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