CamoEvo: An open access toolbox for artificial camouflage evolution experiments

This is the final version. Available on open access from Wiley via the DOI in this recordData archiving: The dryad doi is https://doi.org/10.5061/dryad.08kprr54d. All data for Box 1 can be found on dryad and our GitHub. Downloads and handbooks for CamoEvo and its genetic algorithm ImageGA can also be found on our GitHub.Camouflage research has long shaped our understanding of evolution by natural selection, and elucidating the mechanisms by which camouflage operates remains a key question in visual ecology. However, the vast diversity of color patterns found in animals and their backgrounds, combined with the scope for complex interactions with receiver vision, presents a fundamental challenge for investigating optimal camouflage strategies. Genetic algorithms (GAs) have provided a potential method for accounting for these interactions, but with limited accessibility. Here, we present CamoEvo, an open-access toolbox for investigating camouflage pattern optimization by using tailored GAs, animal and egg maculation theory, and artificial predation experiments. This system allows for camouflage evolution within the span of just 10-30 generations (∼1-2 min per generation), producing patterns that are both significantly harder to detect and that are optimized to their background. CamoEvo was built in ImageJ to allow for integration with an array of existing open access camouflage analysis tools. We provide guides for editing and adjusting the predation experiment and GA as well as an example experiment. The speed and flexibility of this toolbox makes it adaptable for a wide range of computer-based phenotype optimization experiments.Natural Environment Research Council (NERC

Scholarly Communication and Publishing Lunch and Learn Talk #5: OASPA (Open Access Scholarly Publishers Association) and the ULS

Did you know that the ULS is the first library in North America to become a member of OASPA (Open Access Scholarly Publishers Association)? At this month’s Lunch & Learn, we’ll talk about this international publishers association – its mission, how it benefits our publishing activities, and how it helps our advocacy for Open Access to research. We’ll recap the hot topics at this year’s Conference on Open Access Scholarly Publishing (COASP 2013). We’ll finish by taking a look at “How Open Is It?,” a nuanced guide to the Open Access spectrum developed by OASPA, SPARC and the Public Library of Science that you may find useful when talking to Pitt researchers about Open Access. TOOLBOX TIP: “How Open Is It?,” a guide to Open Access licensing for authors, researchers and librarians

A simple remote sensing based information system for monitoring sites of conservation importance

Monitoring is essential for conservation of sites, but capacity to undertake it in the field is often limited. Data collected by remote sensing has been identified as a partial solution to this problem, and is becoming a feasible option, since increasing quantities of satellite data in particular are becoming available to conservationists. When suitably classified, satellite imagery can be used to delineate land cover types such as forest, and to identify any changes over time. However, the conservation community lacks (a) a simple tool appropriate to the needs for monitoring change in all types of land cover (e.g. not just forest), and (b) an easily accessible information system which allows for simple land cover change analysis and data sharing to reduce duplication of effort. To meet these needs, we developed a web-based information system which allows users to assess land cover dynamics in and around protected areas (or other sites of conservation importance) from multi-temporal medium resolution satellite imagery. The system is based around an open access toolbox that pre-processes and classifies Landsat-type imagery, and then allows users to interactively verify the classification. These data are then open for others to utilize through the online information system. We first explain imagery processing and data accessibility features, and then demonstrate the toolbox and the value of user verification using a case study on Nakuru National Park, Kenya. Monitoring and detection of disturbances can support implementation of effective protection, assist the work of park managers and conservation scientists, and thus contribute to conservation planning, priority assessment and potentially to meeting monitoring needs for Aichi target 11

A Synthetic Electrocardiogram (ECG) Image Generation Toolbox to Facilitate Deep Learning-Based Scanned ECG Digitization

The electrocardiogram (ECG) is an accurate and widely available tool for diagnosing cardiovascular diseases. ECGs have been recorded in printed formats for decades and their digitization holds great potential for training machine learning (ML) models in algorithmic ECG diagnosis. Physical ECG archives are at risk of deterioration and scanning printed ECGs alone is insufficient, as ML models require ECG time-series data. Therefore, the digitization and conversion of paper ECG archives into time-series data is of utmost importance. Deep learning models for image processing show promise in this regard. However, the scarcity of ECG archives with reference time-series is a challenge. Data augmentation techniques utilizing \textit{digital twins} present a potential solution. We introduce a novel method for generating synthetic ECG images on standard paper-like ECG backgrounds with realistic artifacts. Distortions including handwritten text artifacts, wrinkles, creases and perspective transforms are applied to the generated images, without personally identifiable information. As a use case, we generated an ECG image dataset of 21,801 records from the 12-lead PhysioNet PTB-XL ECG time-series dataset. A deep ECG image digitization model was built and trained on the synthetic dataset, and was employed to convert the synthetic images to time-series data for evaluation. The signal-to-noise ratio (SNR) was calculated to assess the image digitization quality vs the ground truth ECG time-series. The results show an average signal recovery SNR of 27$\pm$2.8\,dB, demonstrating the significance of the proposed synthetic ECG image dataset for training deep learning models. The codebase is available as an open-access toolbox for ECG research

Open access simulation toolbox for the grid connection of offshore wind farms using multi-terminal HVDC networks

Decarbonisation of the European electricity system can become dauntingly costly due to transmission and distribution network issues arising from the integration of intermittent renewable generation sources. It is expected that wind energy will be the principal renewable source by 2050 and, as such, a number of initiatives in the academia and in the industry are being carried out to propose solutions to best accommodate the wind resource. This paper presents work carried out by DEMO 1 partners within the EU FP7 project BEST PATHS. A MATLAB/Simulink toolbox consisting of the necessary building blocks for the simulation and integration of offshore wind farms using enabling technologies such as multiterminal high-voltage direct-current grids is presented. To illustrate the toolbox capabilities, a number of system topologies is studied. System performance is assessed and measured against a set of key performance indicators. To ensure knowledge dissemination, the toolbox has been made available as open access in the BEST PATHS project website

Demonstration of Converter Control Interactions in MMC-HVDC Systems

Although the control of modular multi-level converters (MMCs) in high-voltage direct-current (HVDC) networks has become a mature subject these days, the potential for adverse interactions between different converter controls remains an under-researched challenge attracting the attention from both academia and industry. Even for point-to-point HVDC links (i.e., simple HVDC systems), converter control interactions may result in the shifting of system operating voltages, increased power losses, and unintended power imbalances at converter stations. To bridge this research gap, the risk of multiple cross-over of control characteristics of MMCs is assessed in this paper through mathematical analysis, computational simulation, and experimental validation. Specifically, the following point-to-point HVDC link configurations are examined: (1) one MMC station equipped with a current versus voltage droop control and the other station equipped with a constant power control; and (2) one MMC station equipped with a power versus voltage droop control and the other station equipped with a constant current control. Design guidelines for droop coefficients are provided to prevent adverse control interactions. A 60-kW MMC test-rig is used to experimentally verify the impact of multiple crossing of control characteristics of the DC system configurations, with results verified through software simulation in MATLAB/Simulink using an open access toolbox. Results show that in operating conditions of 650 V and 50 A (DC voltage and DC current), drifts of 7.7% in the DC voltage and of 10% in the DC current occur due to adverse control interactions under the current versus voltage droop and power control scheme. Similarly, drifts of 7.7% both in the DC voltage and power occur under the power versus voltage droop and current control scheme.This work was supported by the EU FP7 program, through the project “BEyond State of the art Technologies for re-Powering AC corridors and multi-Terminal HVDC Systems” (BEST-PATHS), grant agreement 612748. The simulation toolbox can be downloaded from the project website at www.bestpaths-project.eu (accessed on 10 December 2021)

Subthalamic beta band suppression reflects effective neuromodulation in chronic recordings

Background and purpose: Biomarkers for future adaptive deep brain stimulation still need evaluation in clinical routine. Here, we aimed to assess stimulation-induced modulation of beta-band activity and clinical symptoms in a Parkinson's disease patient during chronic neuronal sensing using a novel implantable pulse generator. Methods: Subthalamic activity was recorded OFF and ON medication during a stepwise increase of stimulation amplitude. Off-line fast fourier transfom -based analysis of beta-band activity was correlated with motor performance rated from blinded videos. Results: The stepwise increase of stimulation amplitude resulted in decreased beta oscillatory activity and improvement of bradykinesia. Mean low beta-band (13-20 Hz) activity correlated significantly with bradykinesia (ρ = 0.662, p < 0.01). Conclusions: Motor improvement is reflected in reduced subthalamic beta-band activity in Parkinson's disease, supporting beta activity as a reliable biomarker. The novel PERCEPT neurostimulator enables chronic neuronal sensing in clinical routine. Our findings pave the way for a personalized precision-medicine approach to neurostimulation

Connectivity Analysis of Functional Brain Networks in Using Multi-modal Human-Computer Interaction

To develop next generation multi-modal computer-aided design systems, it is important to evaluate the relationships between the user dependent factors and the combined performance of man and machine. The purpose of this research is to investigate if users’ cognitive activity would increase with the use of multi-modal input, speech and gestures by analysing EEG signals. Experiments are conducted, using traditional (keyboard and mouse) and multi-modal (speech and gesture) inputs. We used Normalized transfer entropy as a connectivity measure to find the information flow patterns. We constructed binary and weighted Functional Brain Networks to explore distinct and varied brain regions quantitatively. We found significant differences in cognitive activity between the traditional and multi-modal inputs. Our statistical analysis results state that the user’s cognitive activity increase when a multi-modal input is used. The findings have implications for the development of multi-modal interfaces for 3D modelling