This research presents results from evaluating a range of centroid functions for the nearest centroid classifier applied to several multivariate time series classification datasets. These are tested on seven of the largest benchmark datasets available. The centroid functions includes two proposed methods for generating centroids from time series: DTW-MP I and DTW-MP D and five state-of-art centroid functions: SoftDTW, DBA, PAM, Mean and SE. These proposed centroid algorithms use dynamic time warping to combine any number of time series together into one representative centroid. The input time series can be of different lengths and of multivariate data (n-dimensional). Centroids of time series have a wide range of uses but are often used as a nearest centroid classifier which is faster than a DTW-1NN classifier due to a much lower number of DTW comparisons being required to classify a new time series into existing classes. Therefore we utilize the task of nearest centroid classification in this research as a means to evaluate the performance of two proposed and five state-of-art centroid functions. For evaluation, six of the largest publicly available multivariate time series benchmark datasets currently available were used. These results show that the proposed DTW-MP I and DTW-MP D centroid algorithms perform comparatively with state-of-art time series centroid functions and these could have further uses in other time series applications.

Motion artifacts in structural and functional magnetic resonance imaging (MRI) pose a significant challenge for both clinical use and machine learning (ML)-based image analysis. Existing ML approaches for artifact correction require paired clean and corrupted datasets, which are difficult to acquire. We present py-simpace, an open-source, pip-installable MRI motion artifact simulation toolkit with native ML integration. py-simpace supports structural MRI and functional MRI (fMRI) simulation, offering configurable k-space and image-space motion, ghosting, Gibbs ringing, and physiological noise. It provides an end-to-end pipeline with a ready-to-use PyTorch Dataset interface for ML training. We describe the design of py-simpace v2.0, compare it with existing tools, and demonstrate its utility for robust artifact correction model development.

Several new models and formats for the digital transformation of the manufacturing industry appear because of the rapid integration of information technology and the real economy, as well as the increasingly obvious evolution trend of industrial digitalization, networking, and intelligence.Among them, digital twins have increasingly become a research hotspot in all sectors of the industry and have broad prospects.It maps physical objects in virtual space in a digital way and simulates their behavioral characteristics in real environments.It makes the gap between virtuality and reality disappear based on their closed-loop interaction.Digital twins are undoubtedly an important and strategic technology in response to familiar products, production, and services.It can also speculate some indicators that cannot be directly measured by machine learning through collecting the direct data of limited physical sensor indicators.This can realize an assessment of the current state, a diagnosis of past problems, and a prediction of future trends, and simulate possibilities to provide more comprehensive decision support.Driven by "Industry 4.0/5.0", the concept of "Digital Twins" is setting off a new wave of industrial simulation boom.Entity industrial objects or systems are fully digitized, and models including virtual objects, virtual processes, and virtual plant areas are established.These digital twins can be placed in virtual environments to analyze, simulate, verify, test, and adjust various situations such as product or process optimization, optimizing the operation of real objects or systems, and increasing their added value.Also, the expression of "virtual reality" for scene visualization and the new scene interaction mode will be more conducive to better visual effects and interactive operations in the digital world.The combination of digital twins and virtual reality is currently widely used as guidance instructions for operation and maintenance, early warning of error prevention, and information tips to improve the efficiency and accuracy of operators.Virtual reality brings users better vision and senses through smart hardware like glasses and helmets.However, there is still room for improvement in the technical level of smart hardware.And rational hardware configuration and cost-reduction can be realized gradually with the improvement of R & D. Now the focus in this field should be how to enhance user experience and enrich products.In "Integrating digital twins and deep learning for medical image analysis in the era of COVID-19", the authors introduce a digital-twin-based smart healthcare system integrated with medical devices to collect information regarding the current health condition, configuration, and maintenance history of the device/ machine/system.Furthermore, medical images, that is, X-rays, are analyzed by using a deep-learning model to detect the infection of COVID-19.The designed system is based on the cascade recurrent convolution neural network (RCNN) architecture.In this architecture, the detector stages are deeper and more sequentially selective against small and close false positives.This architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the other.At each stage, the bounding

This research demonstrates an evolutionary process of a biological eye in a computer model. This demonstrates using a model, how the biological eye could have evolved, starting from a sheet of light sensitive cells towards a concave pinhole camera eye without a lens. The advantages of each iterative improvement lead to increased chance of being selected for subsequent generations because improved eye clarity helps animals to detect prey or predators. The fitness of the evolving eyes are evaluated on a range of 5 varied datasets with a range of classification algorithm tasks. The developed simulated eye model demonstrates that the requirement to detect the direction of an approaching predator does produce a gradual evolutionary transition from a flat light sensitive surface to the pinhole camera eye which models how eyes evolved in biological creatures.

This research project developed a Virtual Reality (VR) training simulator for paramedic procedures. Currently needle cricothyroidotomy and chest drain are modelled, which could form part of a larger system for training paramedics with VR in various other procedures. The simulator incorporates a number of advanced VR technologies including Oculus Rift and haptic feedback. We have gained input and feedback from NHS paramedics and several related organisation to design the system and provide feedback and evaluation of the preliminary working prototype.

This research project developed a Virtual Reality (VR) training simulator for the CPR procedure. This is designed for use training school children. It can also form part of a larger system for training paramedics with VR. The simulator incorporates a number of advanced VR technologies including Oculus Rift and Leap motion. We have gained input from NHS paramedics and several related organisation to design the system and provide feedback and evaluation of the preliminary working prototype.

This research presents computational models to represent visual navigation mechanisms which guide pigeons (Columba Livia) during flight. A 3D graphics computer simulator was developed to model autonomous flight in virtual pigeons. The aim was to investigate the role of (i) visual landmarks (ii) flocking with other pigeons and (iii) image familiarity in pigeon navigation. A recursive processing algorithm enabled landmarks and other pigeons to be located, identified and counted. Image processing could form a feasible mechanism for autonomous visual navigation by identification of familiar route headings. This could be used in autonomous flying drones or flight simulators.

This research proposes the application of dynamic time warping (DTW) algorithm to analyse multivariate data from virtual reality training simulators, to assess the skill level of trainees. We present results of DTW algorithm applied to trajectory data from a virtual reality haptic training simulator for epidural needle insertion. The proposed application of DTW algorithm serves two purposes, to enable (i) two trajectories to be compared as a similarity measure and also enables (ii) two or more trajectories to be combined together to produce a typical or representative average trajectory using a novel hierarchical DTW process. Our experiments included 100 expert and 100 novice simulator recordings. The data consists of multivariate time series data-streams including multi-dimensional trajectories combined with force and pressure measurements. Our results show that our proposed application of DTW provides a useful time-independent method for (i) comparing two trajectories by providing a similarity measure and (ii) combining two or more trajectories into one, showing higher performance compared to conventional methods such as linear mean. These results demonstrate that DTW can be useful within virtual reality training simulators to provide a component in an automated scoring and assessment feedback system.