467 research outputs found

    Pulse mode of operation : a new booster of TEG, improving power up to X2.7 : to better fit IoT requirements

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    Internet of Things (IoT) is becoming the new driver for semiconductor industry and the largest electronic market ever seen. The number of IoT nodes is already many times larger than the human population and is continuously growing. It is thus mandatory that IoT nodes become self-supplying with energy harvested from environment since periodic exchange of batteries in such a huge number of units (often located in inaccessible places e.g. industrial environment or elements of constructions) is impractical and soon will be simply impossible. Photovoltaic generators may easily harvest energy where light is available, but the IoT nodes often work in dark, hidden locations where the only available energy sources are heat losses. There, ThermoElectric Generators (TEGs) could be the best candidate, if not that if we speak of exploiting heat losses it often means very low temperature differences. This means conditions where TEGs power production drops down dramatically. In this paper we put forward a new idea of TEG's pulse operation that boosts the power production up to X2.7. This extends the domain of applicability of TEGs to lower temperature differences, where conventional TEGs are out of the game. Next, we show that the improvement X2.7 maintains also at larger temperature differences that presents obvious advantages

    Venus wind map at cloud top level with the MTR/THEMIS visible spectrometer. I. Instrumental performance and first results

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    Solar light gets scattered at cloud top level in Venus' atmosphere, in the visible range, which corresponds to the altitude of 67 km. We present Doppler velocity measurements performed with the high resolution spectrometer MTR of the Solar telescope THEMIS (Teide Observatory, Canary Island) on the sodium D2 solar line (5890 \AA). Observations lasted only 49 min because of cloudy weather. However, we could assess the instrumental velocity sensitivity, 31 m/s per pixel of 1 arcsec, and give a value of the amplitude of zonal wind at equator at 151 +/- 16 m/s.Comment: 17 pages, 12 figure

    Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes

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    General hardware architecture of an energy-harvested wireless sensor network node (EH-WSN) can be divided into power, sensing, computing and communication subsystems. Interrelation between these subsystems in combination with constrained energy supply makes design and implementation of EH-WSN a complex and challenging task. Separation of these subsystems into distinct hardware modules simplifies the design process and makes the architecture and software more generic, leading to more flexible solutions. From the other hand, tightly coupling these subsystems gives more room for optimizations at the price of increased complexity of the hardware and software. Additional engineering effort could be justified by a smaller, cheaper hardware, and more energy-efficient a wireless sensor node. The aim of this paper is to push further technical and economical boundaries related to EH-WSN by proposing a novel architecture which ÔÇô by tightly coupling software and hardware of power, computing, and communication subsystems ÔÇô allows the wireless sensor node to be powered by a thermoelectric generator working with about 1.5┬░C temperature difference while keeping the cost of all electronic components used to build such a node below 9 EUR (in volume)

    LC-MS based quantification of 2ÔÇÖ-ribosylated nucleosides Ar(p) and Gr(p) in tRNA

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    RNA nucleosides are often naturally modified into complex non-canonical structures with key biological functions. Here we report LC-MS quantification of the Ar(p) and Gr(p) 2'-ribosylated nucleosides in tRNA using deuterium labelled standards, and the first detection of Gr(p) in complex fungi

    Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

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    The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an L2-norm between the ground truth CT and the pCT. However, given that the ultimate purpose is often to use the pCT as an attenuation map (╬╝\mu-map) in Positron Emission Tomography Magnetic Resonance Imaging (PET/MRI), minimising the error between pCT and CT is not necessarily optimal. The main objective should be to predict a pCT that, when used as ╬╝\mu-map, reconstructs a pseudo PET (pPET) which is as close as possible to the gold standard PET. To this end, we propose a novel multi-hypothesis deep learning framework that generates pCTs by minimising a combination of the pixel-wise error between pCT and CT and a proposed metric-loss that itself is represented by a convolutional neural network (CNN) and aims to minimise subsequent PET residuals. The model is trained on a database of 400 paired MR/CT/PET image slices. Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69.68HU) compared to a baseline CNN (66.25HU), but lead to significant improvement in the PET reconstruction - 115a.u. compared to baseline 140a.u.Comment: Aceppted at SASHIMI201

    The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging

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    Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain imaging genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis. Here, we present the genetics Brain Imaging Data Structure extension, consisting of metadata files for human brain imaging data to which they are linked, and describe succinctly the genomic and transcriptomic data associated with them, which may be in different databases. This extension will facilitate identifying micro-scale molecular features that are linked to macro-scale imaging repositories, facilitating data aggregation across studies
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