Outputs (492)

A Dual-spine Approach to Load Error Repair in a HEMS Sensor Network (2018)
Journal Article
Liu, X., Liu, Q., & Sun, M. (2018). A Dual-spine Approach to Load Error Repair in a HEMS Sensor Network. Computers, Materials & Continua, 57(2), 179-194. https://doi.org/10.32604/cmc.2018.04025

In a home energy management system (HEMS), appliances are becoming diversified and intelligent, so that certain simple maintenance work can be completed by appliances themselves. During the measurement, collection and transmission of electricity load... Read More about A Dual-spine Approach to Load Error Repair in a HEMS Sensor Network.

2-Dimensional Outline Shape Representation for Generative Design with Evolutionary Algorithms (2018)
Presentation / Conference Contribution
Lapok, P., Lawson, A., & Paechter, B. (2018, September). 2-Dimensional Outline Shape Representation for Generative Design with Evolutionary Algorithms. Presented at EngOpt 2018 International Conference on Engineering Optimization, Lisboa, Portugal

In this paper, we investigate the ability of genetic representation methods to describe two-dimensional outline shapes, in order to use them in a generative design system. A specific area of mechanical design focuses on planar mechanisms. These are a... Read More about 2-Dimensional Outline Shape Representation for Generative Design with Evolutionary Algorithms.

A tool for generating synthetic data (2018)
Presentation / Conference Contribution
Peng, T., & Telle, A. (2018). A tool for generating synthetic data. In DATA '18 Proceedings of the First International Conference on Data Science, E-learning and Information Systems. https://doi.org/10.1145/3279996.3280018

It is popular to use real-world data to evaluate data mining techniques. However, there are some disadvantages to use real-world data for such purposes. Firstly, real-world data in most domains is difficult to obtain for several reasons, such as budg... Read More about A tool for generating synthetic data.

Use of machine learning techniques to model wind damage to forests (2018)
Journal Article
Hart, E., Sim, K., Kamimura, K., Meredieu, C., Guyon, D., & Gardiner, B. (2019). Use of machine learning techniques to model wind damage to forests. Agricultural and forest meteorology, 265, 16-29. https://doi.org/10.1016/j.agrformet.2018.10.022

This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed i... Read More about Use of machine learning techniques to model wind damage to forests.

Athos - A Model Driven Approach to Describe and Solve Optimisation Problems (2019)
Presentation / Conference Contribution
Hoffman, B., Chalmers, K., Urquhart, N., & Guckert, M. (2019). Athos - A Model Driven Approach to Describe and Solve Optimisation Problems. . https://doi.org/10.1145/3300111.3300114

Implementing solutions for optimisation problems with general purpose high-level programming languages is a time consuming task that can only be carried out by professional software developers who typically are not domain experts. We address this pro... Read More about Athos - A Model Driven Approach to Describe and Solve Optimisation Problems.

Multi-reality games: an experience across the entire reality-virtuality continuum (2018)
Presentation / Conference Contribution
Casas, L., Ciccone, L., Çimen, G., Wiedemann, P., Fauconneau, M., Sumner, R. W., & Mitchell, K. (2018, December). Multi-reality games: an experience across the entire reality-virtuality continuum. Presented at the 16th ACM SIGGRAPH International Conference, Tokyo, Japan

Interactive play can take very different forms, from playing with physical board games to fully digital video games. In recent years, new video game paradigms were introduced to connect real-world objects to virtual game characters. However, even the... Read More about Multi-reality games: an experience across the entire reality-virtuality continuum.

GPU-accelerated depth codec for real-time, high-quality light field reconstruction (2018)
Journal Article
Koniaris, B., Kosek, M., Sinclair, D., & Mitchell, K. (2018). GPU-accelerated depth codec for real-time, high-quality light field reconstruction. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 1(1), 1-15. https://doi.org/10.1145/3203193

Pre-calculated depth information is essential for efficient light field video rendering, due to the prohibitive cost of depth estimation from color when real-time performance is desired. Standard state-of-the-art video codecs fail to satisfy such per... Read More about GPU-accelerated depth codec for real-time, high-quality light field reconstruction.

Compressed Animated Light Fields with Real-time View-dependent Reconstruction (2018)
Journal Article
Koniaris, C., Kosek, M., Sinclair, D., & Mitchell, K. (2019). Compressed Animated Light Fields with Real-time View-dependent Reconstruction. IEEE Transactions on Visualization and Computer Graphics, 25(4), 1666-1680. https://doi.org/10.1109/tvcg.2018.2818156

We propose an end-to-end solution for presenting movie quality animated graphics to the user while still allowing the sense of presence afforded by free viewpoint head motion. By transforming offline rendered movie content into a novel immersive repr... Read More about Compressed Animated Light Fields with Real-time View-dependent Reconstruction.

From Faces to Outdoor Light Probes (2018)
Journal Article
Calian, D. A., Lalonde, J., Gotardo, P., Simon, T., Matthews, I., & Mitchell, K. (2018). From Faces to Outdoor Light Probes. Computer Graphics Forum, 37(2), 51-61. https://doi.org/10.1111/cgf.13341

Image‐based lighting has allowed the creation of photo‐realistic computer‐generated content. However, it requires the accurate capture of the illumination conditions, a task neither easy nor intuitive, especially to the average digital photography en... Read More about From Faces to Outdoor Light Probes.

Feature-preserving detailed 3D face reconstruction from a single image (2018)
Presentation / Conference Contribution
Li, Y., Ma, L., Fan, H., & Mitchell, K. (2018, December). Feature-preserving detailed 3D face reconstruction from a single image. Presented at the 15th ACM SIGGRAPH European Conference, London, United Kingdom

Dense 3D face reconstruction plays a fundamental role in visual media production involving digital actors. We improve upon high fidelity reconstruction from a single 2D photo with a reconstruction framework that is robust to large variations in expre... Read More about Feature-preserving detailed 3D face reconstruction from a single image.

Cross-modality interactive attention network for multispectral pedestrian detection (2018)
Journal Article
Zhang, L., Liu, Z., Zhang, S., Yang, X., Qiao, H., Huang, K., & Hussain, A. (2019). Cross-modality interactive attention network for multispectral pedestrian detection. Information Fusion, 50, 20-29. https://doi.org/10.1016/j.inffus.2018.09.015

Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To exploit the complementary nature and remedy contradictory appearance between... Read More about Cross-modality interactive attention network for multispectral pedestrian detection.

"So far back, I'm anonymous": Exploring Student Identity using Photovoice (2019)
Presentation / Conference Contribution
Meharg, D., Cairncross, S., & Varey, A. (2019). "So far back, I'm anonymous": Exploring Student Identity using Photovoice. In 2018 IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109/FIE.2018.8658441

This Research Full paper presents empirical work focused on the transition experiences of transfer students into computing degrees in Scotland and seeks to understand the complex formation of identity from the student perspective. Drawing on [3]'s wo... Read More about "So far back, I'm anonymous": Exploring Student Identity using Photovoice.

Non-intrusive load monitoring and its challenges in a NILM system framework (2019)
Journal Article
Liu, Q., Lu, M., Liu, X., & Linge, N. (2019). Non-intrusive load monitoring and its challenges in a NILM system framework. International Journal of High Performance Computing and Networking, 14(1), 102-111. https://doi.org/10.1504/IJHPCN.2019.099748

With the increasing of energy demand and electricity price, researchers gain more and more interest among the residential load monitoring. In order to feed back the individual appliance’s energy consumption instead of the whole-house energy consumpti... Read More about Non-intrusive load monitoring and its challenges in a NILM system framework.

Interactive Ray-Traced Area Lighting with Adaptive Polynomial Filtering (2016)
Presentation / Conference Contribution
Iglesias-Guitian, J. A., Moon, B., & Mitchell, K. (2016). Interactive Ray-Traced Area Lighting with Adaptive Polynomial Filtering. In Proceedings of the 13th European Conference on Visual Media Production (CVMP 2016)

Area lighting computation is a key component for synthesizing photo-realistic rendered images, and it simulates plausible soft shadows by considering geometric relationships between area lights and three-dimensional scenes, in some cases even account... Read More about Interactive Ray-Traced Area Lighting with Adaptive Polynomial Filtering.

Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models (2019)
Journal Article
Liu, Q., Kamoto, K. M., Liu, X., Sun, M., & Linge, N. (2019). Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models. IEEE Transactions on Consumer Electronics, 65(1), 1-1. https://doi.org/10.1109/tce.2019.2891160

Awareness of electric energy usage has both societal and economic benefits, which include reduced energy bills and stress on non-renewable energy sources. In recent years, there has been a surge in interest in the field of load monitoring, also refer... Read More about Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models.

The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers (2019)
Journal Article
Stapleton, G., Chapman, P., Rodgers, P., Touloumis, A., Blake, A., & Delaney, A. (2019). The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers. PLOS ONE, 14(3), https://doi.org/10.1371/journal.pone.0211234

This paper presents the first empirical investigation that compares Euler and linear diagrams when they are used to represent set cardinality. A common approach is to use area-proportional Euler diagrams but linear diagrams can exploit length-proport... Read More about The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers.

Simulating the actions of commuters using a multi-agent system (2019)
Journal Article
Urquhart, N., Powers, S., Wall, Z., Fonzone, A., Ge, J., & Polhill, G. (2019). Simulating the actions of commuters using a multi-agent system. Journal of Artificial Societies and Social Simulation, 22(2), https://doi.org/10.18564/jasss.4007

The activity of commuting to and from a place of work affects not only those travelling but also wider society through their contribution to congestion and pollution. It is desirable to have a means of simulating commuting in order to allow organisat... Read More about Simulating the actions of commuters using a multi-agent system.

An Agent Based Technique for Improving Multi-Stakeholder Optimisation Problems (2019)
Presentation / Conference Contribution
Urquhart, N., & Powers, S. T. (2019, June). An Agent Based Technique for Improving Multi-Stakeholder Optimisation Problems. Presented at PAAMS 2019: International Conference on Practical Applications of Agents and Multi-Agent Systems, Avila, Spain

We present an agent based framework for improving multi-stakeholder optimisation problems, which we define as optimisation problems where the solution is utilised by a number of stakeholders who have their own local preferences. We explore our ideas... Read More about An Agent Based Technique for Improving Multi-Stakeholder Optimisation Problems.

Enhanced Shadow Retargeting with Light-Source Estimation Using Flat Fresnel Lenses (2019)
Journal Article
Casas, L., Fauconneau, M., Kosek, M., Mclister, K., & Mitchell, K. (2019). Enhanced Shadow Retargeting with Light-Source Estimation Using Flat Fresnel Lenses. Computers, 8(2), Article 29. https://doi.org/10.3390/computers8020029

Shadow-retargeting maps depict the appearance of real shadows to virtual shadows given corresponding deformation of scene geometry, such that appearance is seamlessly maintained. By performing virtual shadow reconstruction from unoccluded real-shadow... Read More about Enhanced Shadow Retargeting with Light-Source Estimation Using Flat Fresnel Lenses.

Photo-Realistic Facial Details Synthesis from Single Image (2019)
Presentation / Conference Contribution
Chen, A., Chen, Z., Zhang, G., Zhang, Z., Mitchell, K., & Yu, J. (2019, October). Photo-Realistic Facial Details Synthesis from Single Image. Presented at 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea

We present a single-image 3D face synthesis technique that can handle challenging facial expressions while recovering fine geometric details. Our technique employs expression analysis for proxy face geometry generation and combines supervised and uns... Read More about Photo-Realistic Facial Details Synthesis from Single Image.