13,896 research outputs found

    Online oxygen monitoring using integrated inkjet-printed sensors in a liver-on-a-chip system

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    The demand for real-time monitoring of cell functions and cell conditions has dramatically increased with the emergence of organ-on-a-chip (OOC) systems. However, the incorporation of co-cultures and microfluidic channels in OOC systems increases their biological complexity and therefore makes the analysis and monitoring of analytical parameters inside the device more difficult. In this work, we present an approach to integrate multiple sensors in an extremely thin, porous and delicate membrane inside a liver-on-a-chip device. Specifically, three electrochemical dissolved oxygen (DO) sensors were inkjet-printed along the microfluidic channel allowing local online monitoring of oxygen concentrations. This approach demonstrates the existence of an oxygen gradient up to 17.5% for rat hepatocytes and 32.5% for human hepatocytes along the bottom channel. Such gradients are considered crucial for the appearance of zonation of the liver. Inkjet printing (IJP) was the selected technology as it allows drop on demand material deposition compatible with delicate substrates, as used in this study, which cannot withstand temperatures higher than 130 °C. For the deposition of uniform gold and silver conductive inks on the porous membrane, a primer layer using SU-8 dielectric material was used to seal the porosity of the membrane at defined areas, with the aim of building a uniform sensor device. As a proof-of-concept, experiments with cell cultures of primary human and rat hepatocytes were performed, and oxygen consumption rate was stimulated with carbonyl-cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP), accelerating the basal respiration of 0.23 ± 0.07 nmol s-1/106 cells up to 5.95 ± 0.67 nmol s-1/106 cells s for rat cells and the basal respiration of 0.17 ± 0.10 nmol s-1/106 cells by up to 10.62 ± 1.15 nmol s-1/106 cells for human cells, with higher oxygen consumption of the cells seeded at the outflow zone. These results demonstrate that the approach of printing sensors inside an OOC has tremendous potential because IJP is a feasible technique for the integration of different sensors for evaluating metabolic activity of cells, and overcomes one of the major challenges still remaining on how to tap the full potential of OOC systems.Peer ReviewedPostprint (author's final draft

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    Index to NASA Tech Briefs, 1975

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    This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

    Research and Technology

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    Langley Research Center is engaged in the basic an applied research necessary for the advancement of aeronautics and space flight, generating advanced concepts for the accomplishment of related national goals, and provding research advice, technological support, and assistance to other NASA installations, other government agencies, and industry. Highlights of major accomplishments and applications are presented

    Sensing motion using spectral and spatial analysis of WLAN RSSI

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    In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis confirms our claim that ’signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move’. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of WLAN signal’s RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support

    Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

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    © 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

    Flat-plate solar array project. Volume 5: Process development

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    The goal of the Process Development Area, as part of the Flat-Plate Solar Array (FSA) Project, was to develop and demonstrate solar cell fabrication and module assembly process technologies required to meet the cost, lifetime, production capacity, and performance goals of the FSA Project. R&D efforts expended by Government, Industry, and Universities in developing processes capable of meeting the projects goals during volume production conditions are summarized. The cost goals allocated for processing were demonstrated by small volume quantities that were extrapolated by cost analysis to large volume production. To provide proper focus and coverage of the process development effort, four separate technology sections are discussed: surface preparation, junction formation, metallization, and module assembly

    Design and Development of Smart Brain-Machine-Brain Interface (SBMIBI) for Deep Brain Stimulation and Other Biomedical Applications

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    Machine collaboration with the biological body/brain by sending electrical information back and forth is one of the leading research areas in neuro-engineering during the twenty-first century. Hence, Brain-Machine-Brain Interface (BMBI) is a powerful tool for achieving such machine-brain/body collaboration. BMBI generally is a smart device (usually invasive) that can record, store, and analyze neural activities, and generate corresponding responses in the form of electrical pulses to stimulate specific brain regions. The Smart Brain-Machine-Brain-Interface (SBMBI) is a step forward with compared to the traditional BMBI by including smart functions, such as in-electrode local computing capabilities, and availability of cloud connectivity in the system to take the advantage of powerful cloud computation in decision making. In this dissertation work, we designed and developed an innovative form of Smart Brain-Machine-Brain Interface (SBMBI) and studied its feasibility in different biomedical applications. With respect to power management, the SBMBI is a semi-passive platform. The communication module is fully passive—powered by RF harvested energy; whereas, the signal processing core is battery-assisted. The efficiency of the implemented RF energy harvester was measured to be 0.005%. One of potential applications of SBMBI is to configure a Smart Deep-Brain-Stimulator (SDBS) based on the general SBMBI platform. The SDBS consists of brain-implantable smart electrodes and a wireless-connected external controller. The SDBS electrodes operate as completely autonomous electronic implants that are capable of sensing and recording neural activities in real time, performing local processing, and generating arbitrary waveforms for neuro-stimulation. A bidirectional, secure, fully-passive wireless communication backbone was designed and integrated into this smart electrode to maintain contact between the smart electrodes and the controller. The standard EPC-Global protocol has been modified and adopted as the communication protocol in this design. The proposed SDBS, by using a SBMBI platform, was demonstrated and tested through a hardware prototype. Additionally the SBMBI was employed to develop a low-power wireless ECG data acquisition device. This device captures cardiac pulses through a non-invasive magnetic resonance electrode, processes the signal and sends it to the backend computer through the SBMBI interface. Analysis was performed to verify the integrity of received ECG data
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