145 research outputs found
The Fifth Workshop on HPC Best Practices: File Systems and Archives
The workshop on High Performance Computing (HPC) Best Practices on File Systems and Archives was the fifth in a series sponsored jointly by the Department Of Energy (DOE) Office of Science and DOE National Nuclear Security Administration. The workshop gathered technical and management experts for operations of HPC file systems and archives from around the world. Attendees identified and discussed best practices in use at their facilities, and documented findings for the DOE and HPC community in this report
Spatially targeted nature-based solutions can mitigate climate change and nature loss but require a systems approach
Funding Information: This study was funded by the Royal Society for the Protection of Birds (RSPB) and Natural England (project code ECM 58632). The Breeding Bird Survey is a Partnership between the BTO, RSPB, and Joint Nature Conservation Committee (on behalf of Natural Resources Wales, Natural England, Council for Nature Conservation and Countryside, and NatureScot) and relies on volunteer surveyors. Simon Gillings provided tetrad-level predictions of relative abundance for wading birds. We are grateful to members of the RSPB steering group, who contributed to the development of our scenarios, and Profs. Tim Benton and Andrew Balmford who commented on an earlier version of this manuscript. Conceptualization, T.F. R.B.B. T.B.-L. G.M.B. W.J.P. and R.H.F.; methodology, T.F. T.B.-L. J.P.C. D.M. P.S. and R.H.F.; software, T.F.; formal analysis, T.F.; resources, D.M.; data curation, T.F.; writing – original draft, T.F.; writing – review & editing, R.B.B. T.B.-L. G.M.B. J.P.C. D.M. P.S. W.J.P. and R.H.F.; visualization, T.F.; supervision, W.J.P. The authors declare no competing interests. Publisher Copyright: © 2023 The AuthorsPeer reviewedPublisher PD
Myoelectric Control for Active Prostheses via Deep Neural Networks and Domain Adaptation
Recent advances in Biological Signal Processing (BSP) and Machine Learning (ML), in particular, Deep Neural Networks (DNNs), have paved the way for development of advanced Human-Machine Interface (HMI) systems for decoding human intent and controlling artificial limbs. Myoelectric control, as a subcategory of HMI sys- tems, deals with detecting, extracting, processing, and ultimately learning from Electromyogram (EMG) signals to command external devices, such as hand prostheses. In this context, hand gesture recognition/classification via Surface Electromyography (sEMG) signals has attracted a great deal of interest from many researchers. De- spite extensive progress in the field of myoelectric prosthesis, however, there are still limitations that should be addressed to achieve a more intuitive upper limb pros- thesis. Through this Ph.D. thesis, first, we perform a literature review on recent research works on pattern classification approaches for myoelectric control prosthesis to identify challenges and potential opportunities for improvement. Then, we aim to enhance the accuracy of myoelectric systems, which can be used for realizing an accu- rate and efficient HMI for myocontrol of neurorobotic systems. Beside improving the accuracy, decreasing the number of parameters in DNNs plays an important role in a Hand Gesture Recognition (HGR) system. More specifically, a key factor to achieve a more intuitive upper limb prosthesis is the feasibility of embedding DNN-based models into prostheses controllers. On the other hand, transformers are considered to be powerful DNN models that have revolutionized the Natural Language Processing (NLP) field and showed great potentials to dramatically improve different computer vision tasks. Therefore, we propose a Transformer-based neural network architecture to classify and recognize upper-limb hand gestures. Finally, another goal of this thesis is to design a modern DNN-based gesture detection model that relies on minimal training data while providing high accuracy. Although DNNs have shown superior accuracy compared to conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. We propose to solve this problem, by designing a framework which utilizes a combination of temporal convolutions and attention mechanisms
An Enhanced Goal-Oriented Decision-Making Model for Self-Adaptive Systems
The thesis proposes a generic, configurable and enhanced goal-oriented decision-making model for self-adaptive software systems. The model has been designed to include feedback control loops as first class entities in the adaptation process whereby the decision-making processes can assess the impact of a previously executed decision, so that better decisions can be made in the future. Furthermore, the model provides the ability to detect and resolve conflicts amongst dependant adaptation requirements. The realization of the decision-model is extremely generic, flexible and extensible. It allows different voting algorithms to be specified for choosing a winner requirement for clusters of flexible adaptation requirements. Moreover, the implementation also allows for the specification of a wide variety of reinforcement learning algorithms to assess the impact of a previously executed decision. The implementation has been developed as a plug-in for a generic Java-based adaptation framework. It was tested using two case studies namely a News Web Application and an IP Telephony System. The aim of the conducted experiments was to assess the impact of the model on the systems goals and to determine the impact of feedback control loops as first class entities in the decision-making process. Based on the obtained results, it can be concluded that the model does improve the overall customer satisfaction level compared to a non-adaptive system. Moreover, it will be concluded that incorporating feedback loops as first class entities yields better results as compared to a decision-making model based solely on policies or goals
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Developing a Framework for Stigmergic Human Collaboration with Technology Tools: Cases in Emergency Response
Information and Communications Technologies (ICTs), particularly social media and geographic information systems (GIS), have become a transformational force in emergency response. Social media enables ad hoc collaboration, providing timely, useful information dissemination and sharing, and helping to overcome limitations of time and place. Geographic information systems increase the level of situation awareness, serving geospatial data using interactive maps, animations, and computer generated imagery derived from sophisticated global remote sensing systems. Digital workspaces bring these technologies together and contribute to meeting ad hoc and formal emergency response challenges through their affordances of situation awareness and mass collaboration. Distributed ICTs that enable ad hoc emergency response via digital workspaces have arguably made traditional top-down system deployments less relevant in certain situations, including emergency response (Merrill, 2009; Heylighen, 2007a, b). Heylighen (2014, 2007a, b) theorizes that human cognitive stigmergy explains some self-organizing characteristics of ad hoc systems. Elliott (2007) identifies cognitive stigmergy as a factor in mass collaborations supported by digital workspaces. Stigmergy, a term from biology, refers to the phenomenon of self-organizing systems with agents that coordinate via perceived changes in the environment rather than direct communication. In the present research, ad hoc emergency response is examined through the lens of human cognitive stigmergy. The basic assertion is that ICTs and stigmergy together make possible highly effective ad hoc collaborations in circumstances where more typical collaborative methods break down. The research is organized into three essays: an in-depth analysis of the development and deployment of the Ushahidi emergency response software platform, a comparison of the emergency response ICTs used for emergency response during Hurricanes Katrina and Sandy, and a process model developed from the case studies and relevant academic literature is described
Electronic Imaging & the Visual Arts. EVA 2019 Florence
The Publication is following the yearly Editions of EVA FLORENCE. The State of Art is presented regarding the Application of Technologies (in particular of digital type) to Cultural Heritage. The more recent results of the Researches in the considered Area are presented. Information Technologies of interest for Culture Heritage are presented: multimedia systems, data-bases, data protection, access to digital content, Virtual Galleries. Particular reference is reserved to digital images (Electronic Imaging & the Visual Arts), regarding Cultural Institutions (Museums, Libraries, Palace - Monuments, Archaeological Sites). The International Conference includes the following Sessions: Strategic Issues; New Science and Culture Developments & Applications; New Technical Developments & Applications; Cultural Activities – Real and Virtual Galleries and Related Initiatives, Access to the Culture Information. One Workshop regards Innovation and Enterprise. The more recent results of the Researches at national and international level are reported in the Area of Technologies and Culture Heritage, also with experimental demonstrations of developed Activities
Greener Golf: An Ecological, Behavioral, and Communal Study of the University of Michigan Golf Courses
As one of the leading public universities in the world, the University of Michigan, owns
two 18-hole golf courses: Radrick Farms Golf Course (RFGC) and the University of Michigan
Golf Course, also known as the Blue Course. The land on which RFGC is situated has a long and
diverse history. Over 18,000 years ago, the area was covered by the Wisconsin glacier, the
recession of which left a unique till mix and geological features, including Fleming Creek and
deposits of sand and gravel. The presence of these resources led to the transformation of the
landscape into a gravel mine, which functioned through the 1920s. In the early 1930s, University
of Michigan alumnus Fredrick C. Matthaei, Sr., purchased the land from Cadillac Sand and Gravel,
along with additional acreage surrounding the mine, and began the process of restoring the gravel
pit by re-grading the area, planting alfalfa and red clover, and converting portions of the area to
farmland. Following its donation to the University in 1957, the land was converted into a
championship 18-hole golf course designed by world-renowned golf course architect Pete Dye.
From its beginning, environmental considerations have been a priority at the RFGC. In
2001, the management of RFGC committed to the Michigan Turfgrass Environmental Stewardship
Program (MTESP), initiating a series of strong sustainability objectives. Since 2001, RFGC has
received special recognition from the Washtenaw County Pollution Prevention Program, in
addition to becoming “one of only four courses in the state [of Michigan] with both MTESP and
Audubon Cooperative Sanctuary certifications.”1 Radrick Farms Golf Course is also the only club
in the state to become a Groundwater Guardian Green Site; in 2012, Washtenaw County presented
RFGC with the 2012 Washtenaw County Environmental Excellence Award for Water Quality
Protection, and in 2014, RFGC was recognized by the Department of Environmental Quality of
the State of Michigan as a Clean Corporate Citizen (C3), the first golf course in the state to receive
this recognition.
The Blue Course, is located near the iconic Michigan football stadium, south of Central
Campus. Prior to becoming a golf course, the area was used for farmland. In 1929, the Blue Course
was designed by Dr. Alister Mackenzie, now revered as one of the greatest golf architects. The
course officially opened in the spring of 1931 and immediately drew praise as one of the finest in
America. At the time of its opening, the Blue Course was only the fourth course to be located on
a college campus. In the mid-1990s, a multi-million dollar renovation was completed to restore
the prestige of the Blue Course to the ranks of Mackenzie's other classics. A new practice range
was added to assist Michigan's golf squads, as well as a number of practice greens and bunkers.
The popularity of golf carts necessitated large stretches of cart paths that partition landscaped
medians around the course.
The unique combination of such a highly regarded and historic golf campus with a strong
research university presented an opportunity to conduct a holistic exploration into the benefits that
golf courses offer to the ecological, social, economic, and cultural health of the communities that
contain them, as well as the opportunity to identify potential recommendations to enhance these
benefits. The project team utilized an exploration of current trends in the golf industry, specifically
the growing movement for integration of sustainability management techniques, in conjunction
with a broader multi-disciplinary focus to inform a working definition of sustainable golf. This
definition correlated with the three tenets of permaculture: care for the land, care for the people,
and the concept of fair share. The project team assessed the current state of the Blue Course and
RFGC in research designed around these three tenets. Specific research included an ecological
inventory and site analysis, community perception survey and a study of pre- and post-test cognitive function in golfers, and a high-level, qualitative analysis of economic implications.
Using the findings and results from this research, the project team provided recommendations
informed by the tenets of sustainable golf. The recommendations presented by the Greener Golf
Master’s Project Team highlight three approaches to pushing the boundaries of what it means to
be a sustainable golf course. The Greener Golf Master’s Project Team has broadly labeled these
three recommendations as engagement, accessibility, and innovation.
In addition to the recommendations provided, the Greener Golf Master’s Project Team
provided the design for a golf course and event space at RFGC that would provide multiple
beneficial functions; one of them being the creation of a “living laboratory” where innovations in
sustainable golf course management can be tested prior to implementation on the 18-hole golf
courses. The team has preliminarily recommended the site be named the Gateway Course due its
proximity to the entrance to RFGC as well as its mission to open a new door to how golf courses
can play a role in society in the future.
Appendix I is a project summary that includes further discussion of the team’s
recommendations. This summary is intended for those who wish to learn more about the project,
but cannot read the full report below. In addition, the project summary can be used in public
distribution for press and other media opportunities.Master of Science
Master of Landscape ArchitectureNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/111007/1/GreenerGolfWhitePaper_FINAL.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111007/2/GreenerGolf_GatewayDesignGuide_FINAL.pdfDescription of GreenerGolfWhitePaper_FINAL.pdf : Greener Golf DocumentDescription of GreenerGolf_GatewayDesignGuide_FINAL.pdf : Greener Golf Design Guid
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