87 research outputs found
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Body-centric modelling, identification, and acceleration tracking control of a quadrotor UAV
This paper presents the mathematical development of a body-centric nonlinear dynamic model of a quadrotor UAV that is suitable for the development of biologically
inspired navigation strategies. Analytical approximations are used to find an initial guess of the parameters of the nonlinear model, then parameter estimation methods are used to refine the model parameters using the data obtained from onboard sensors during flight. Due to the unstable nature of the quadrotor model, the identification process is performed with the system in closed-loop control of attitude angles. The obtained model parameters are validated using real unseen experimental data. Based on the identified model, a Linear-Quadratic (LQ) optimal tracker is designed to stabilize the quadrotor and facilitate its translational control by tracking body accelerations. The LQ tracker is
tested on an experimental quadrotor UAV and the obtained results are a further means to validate the quality of the estimated model. The unique formulation of the control problem in the body frame makes the controller better suited for bio-inspired navigation and guidance strategies than conventional attitude or position based control systems that can be found in the existing literature
Blind spots on western blots: Assessment of common problems in western blot figures and methods reporting with recommendations to improve them
Western blotting is a standard laboratory method used to detect proteins and assess their expression levels. Unfortunately, poor western blot image display practices and a lack of detailed methods reporting can limit a reader's ability to evaluate or reproduce western blot results. While several groups have studied the prevalence of image manipulation or provided recommendations for improving western blotting, data on the prevalence of common publication practices are scarce. We systematically examined 551 articles published in the top 25% of journals in neurosciences (n = 151) and cell biology (n = 400) that contained western blot images, focusing on practices that may omit important information. Our data show that most published western blots are cropped and blot source data are not made available to readers in the supplement. Publishing blots with visible molecular weight markers is rare, and many blots additionally lack molecular weight labels. Western blot methods sections often lack information on the amount of protein loaded on the gel, blocking steps, and antibody labeling protocol. Important antibody identifiers like company or supplier, catalog number, or RRID were omitted frequently for primary antibodies and regularly for secondary antibodies. We present detailed descriptions and visual examples to help scientists, peer reviewers, and editors to publish more informative western blot figures and methods. Additional resources include a toolbox to help scientists produce more reproducible western blot data, teaching slides in English and Spanish, and an antibody reporting template
SPARC 2022 book of abstracts
Welcome to the Book of Abstracts for the 2022 SPARC conference. Our conference is called âMoving Forwardsâ reflecting our re-emergence from the pandemic and our desire to reconnect our PGR community, in celebration of their research. PGRs have continued with their research endeavours despite many challenges, and their ongoing successes are underpinned by the support and guidance of dedicated supervisors and the Doctoral School Team. To recognise supervision excellence we will be awarding our annual Supervisor of the Year prizes, based on the wonderful nominations received from their PGR students.Once again, we have received a tremendous contribution from our postgraduate research community; with over 60 presenters, 12 Three-Minute Thesis finalists, and 20 poster presentations, the conference showcases our extraordinarily vibrant, inclusive, and resilient PGR community at Salford. This year there will be prizes to be won for âbest in conferenceâ presentations, in addition to the winners from each parallel session. Audience members too could be in for a treat, with judges handing out spot prizes for the best questions asked, so donât miss the opportunity to put your hand up. These abstracts provide a taster of the diverse and impactful research in progress and provide delegates with a reference point for networking and initiating critical debate. Take advantage of the hybrid format: in online sessions by posting a comment or by messaging an author to say âHelloâ, or by initiating break time discussions about the amazing research youâve seen if you are with us in person. Who knows what might result from your conversation? With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. As recent events have shown, researchers need to collaborate to meet global challenges. Interdisciplinary and international working is increasingly recognised and rewarded by all major research funders. We do hope, therefore, that you will take this opportunity to initiate interdisciplinary conversations with other researchers. A question or comment from a different perspective can shed new light on a project and could lead to exciting collaborations, and that is what SPARC is all about. SPARC is part of a programme of personal and professional development opportunities offered to all postgraduate researchers at Salford. More information about this programme is available on our website: Doctoral School | University of Salford. Registered Salford students can access full details on the Doctoral School hub: Doctoral School Hub - Home (sharepoint.com) You can follow us on Twitter @SalfordPGRs and please use the #SPARC2022 to share your conference experience.We particularly welcome taught students from our undergraduate and masterâs programmes as audience members. We hope you enjoy the presentations on offer and that they inspire you to pursue your own research career. If you would like more information about studying for a PhD here at the University of Salford, your lecturers can advise, or you can contact the relevant PGR Support Officer; their details can be found at Doctoral School | University of Salford. We wish you a rich and rewarding conference experience
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Application of Sliding-Mode Control to Knee-Joint Angle: Analysis, Computational Engineering in System Application
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Multiple model iterative learning control with application to upper limb stroke rehabilitation
Functional electrical stimulation (FES) is an upper limb stroke rehabilitation technology that can enable patients to recover their lost movement by assisting functional task training. Unfortunately, current FES controllers cannot simultaneously satisfy the competing demands of high accuracy, robustness to modelling error and minimal set-up/identification time that are needed for clinical or home deployment. To address this, an estimation-based multiple model switched iterative learning control framework is proposed, combining the most successful adaptive and learning properties of existing FES controllers. A practical design procedure guaranteeing robust performance is developed, and initial experimental results are then presented to confirm efficacy of the approach
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Towards optimizing the selection of neurons in affordable neural networks
This paper considers variations of a neuron pool selection method known as Affordable Neural Network
(AfNN). A saliency measure, based on the second derivative of the objective function is proposed to assess the ability of a trained AfNN to provide neuronal redundancy. The discrepancies between the various affordability variants are explained by correlating unique sub group selections with relevant saliency variations. Overall this study shows that the method in which neurons are selected from a pool is more relevant to how salient individual neurons are, than how often a particular neuron is used during training. The
findings herein are relevant to not only providing an analogy to brain function but, also, in optimizing the way a neural network using the affordability method is trained
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