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Outputs (26)

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation (2021)
Journal Article
Gibbs, J. A., Mcausland, L., Robles-Zazueta, C. A., Murchie, E. H., & Burgess, A. J. (2021). A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation. Frontiers in Plant Science, 12, Article 780180. https://doi.org/10.3389/fpls.2021.780180

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. Howev... Read More about A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation.

Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models. Scientific Reports, 11(1), Article 23279. https://doi.org/10.1038/s41598-021-02466-x

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is mu... Read More about Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models.

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification (2021)
Journal Article
Guo, Y., Jiao, L., Qu, R., Sun, Z., Wang, S., Wang, S., & Liu, F. (2022). Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5217818. https://doi.org/10.1109/TGRS.2021.3128908

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, whic... Read More about Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification.

Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation (2021)
Journal Article
Bowler, A. L., Pound, M. P., & Watson, N. J. (2021). Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation. Fermentation, 7(4), Article 253. https://doi.org/10.3390/fermentation7040253

Beer fermentation processes are traditionally monitored through sampling and off-line wort density measurements. In-line and on-line sensors would provide real-time data on the fermentation progress whilst minimising human involvement, enabling ident... Read More about Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation.

Self-Adaptive Particle Swarm Optimization-Based Echo State Network for Time Series Prediction (2021)
Journal Article
Xue, Y., Zhang, Q., & Neri, F. (2021). Self-Adaptive Particle Swarm Optimization-Based Echo State Network for Time Series Prediction. International Journal of Neural Systems, 31(12), Article 2150057. https://doi.org/10.1142/s012906572150057x

Echo state networks (ESNs), belonging to the family of recurrent neural networks (RNNs), are suitable for addressing complex nonlinear tasks due to their rich dynamic characteristics and easy implementation. The reservoir of the ESN is composed of a... Read More about Self-Adaptive Particle Swarm Optimization-Based Echo State Network for Time Series Prediction.

SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction (2021)
Journal Article
Le, H. L., Neri, F., & Triguero, I. (2022). SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction. Swarm and Evolutionary Computation, 69, Article 100991. https://doi.org/10.1016/j.swevo.2021.100991

Real-world optimisation problems pose domain specific challenges that often require an ad-hoc algorithmic design to be efficiently addressed. The present paper investigates the optimisation of a key stage in data mining, known as instance reduction,... Read More about SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction.

Locating Identities in Time: An Examination of the Formation and Impact of Temporality on Presentations of the Self Through Location-based Social Networks (2021)
Journal Article
Papangelis, K., Lykourentzou, I., Khan, V.-J., Chamberlain, A., Cao, T., Saker, M., & Lalone, N. (2021). Locating Identities in Time: An Examination of the Formation and Impact of Temporality on Presentations of the Self Through Location-based Social Networks. ACM Transactions on Social Computing, 4(3), 1-23. https://doi.org/10.1145/3473043

Studies of identity and location-based social networks (LBSN) have tended to focus on the performative aspects associated with marking one’s location. Yet, these studies often present this practice as being an a priori aspect of locative media. What... Read More about Locating Identities in Time: An Examination of the Formation and Impact of Temporality on Presentations of the Self Through Location-based Social Networks.

The Meaning in "the Mix": Using Ethnography to Inform the Design of Intelligent Tools in the Context of Music Production (2021)
Presentation / Conference Contribution
McGarry, G., Chamberlain, A., Crabtree, A., & Greenhalgh, C. (2021, September). The Meaning in "the Mix": Using Ethnography to Inform the Design of Intelligent Tools in the Context of Music Production. Presented at Audio Mostly 2021, Trento University, Italy

In this paper we report on two ethnographic studies of professional music producers at work in their respective studio settings, to underpin the design of intelligent tools and platforms in this domain. The studies are part of a body of work that exp... Read More about The Meaning in "the Mix": Using Ethnography to Inform the Design of Intelligent Tools in the Context of Music Production.

Convolutional feature extraction for process monitoring using ultrasonic sensors (2021)
Journal Article
Bowler, A., Pound, M., & Watson, N. (2021). Convolutional feature extraction for process monitoring using ultrasonic sensors. Computers and Chemical Engineering, 155, Article 107508. https://doi.org/10.1016/j.compchemeng.2021.107508

Ultrasonic sensors are a low-cost and in-line technique and can be combined with machine learning for industrial process monitoring. However, training accurate machine learning models for process monitoring using sensor data is dependant on the featu... Read More about Convolutional feature extraction for process monitoring using ultrasonic sensors.