Sensors

This data is designed for covariance matrix completion to train the neural networks. 

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This study reports the properties and the sensing mechanism of Pd nanoparticles (PdNPs) decorated n+/n-/n+ double-junction silicon nanobelt (SNB) device as hydrogen (H2) gas sensor. The SNB devices are prepared via CMOS process. Plasma-enhanced atomic layer deposition (PEALD) is adopted for PdNPs deposition as sensing material on the Al2O3 dielectric of SNB devices.PEALD of PdNPs provides high conformity and fine control of the particle size. The PdNPs-decorated SNB devices working at room temperature are characterizedat H2 concentration ranging from 10 to 1000 ppm.

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This study reports the properties and the sensing mechanism of Pd nanoparticles (PdNPs) decorated n+/n-/n+ double-junction silicon nanobelt (SNB) device as hydrogen (H2) gas sensor. The SNB devices are prepared via CMOS process. Plasma-enhanced atomic layer deposition (PEALD) is adopted for PdNPs deposition as sensing material on the Al2O3 dielectric of SNB devices.PEALD of PdNPs provides high conformity and fine control of the particle size. The PdNPs-decorated SNB devices working at room temperature are characterizedat H2 concentration ranging from 10 to 1000 ppm.

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This is the continuous Chinese and English gesture data of 14 Chinese and 4 English languages, respectively “不”,“程”,“刀”,“工”,“古”,“今”,“力”,“刘”,“木”,“石”,“土”,“外”,“中”,“乙”,“can”,“NO”,“Who”,“yes”.

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A mobile sensor can be described as a kind of smart technology that can capture minor or major changes in an environment and can respond by performing a particular task. The scope of the dataset is for forensic purposes that will help segregate day-to-day activities from criminal actions. Smartphones supplied with sensors can be utilised for monitoring and recording simple daily activities such as walking, climbing stairs, eating and more. For the generation of this dataset, we have collected data for 13 classes of daily life activities, which has been done by a single individual.

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This peach tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of peach trees, including stress analysis and prediction. An orchard of peach trees is considered in the area of Thessaly, where 889 peach trees were recorded in a full crop season starting from Jul. 2021 to Sep. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos.

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In this paper, we design and present a testbed implementation and the resulting dataset for modulation recognition from real-world imperfect scans. We describe our efforts to build a testbed of heterogeneous spectrum sensors (low-cost RTL-SDR and mid-cost USRP) and a controlled transmitter in order to facilitate real-world data collection for modulation recognition from partial and biased scans.

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Traditional magnetic tracking approaches based on mathematical models and optimization algorithms are computationally intensive, depend on initial guesses, and do not guarantee convergence to a global optimum. Although fully-supervised data-driven deep learning can solve the above issues, the demand for a comprehensive dataset hampers its applicability in magnetic tracking. Thus, we propose an annular magnet pose estimation network (called AMagPoseNet) based on dual-domain few-shot learning from a prior mathematical model, which consists of two sub-networks: PoseNet and CaliNet.

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The Research Paper "Detection of Bicep Form Using Myoware and Machine Learning" based on the novel dataset has been recently accepted in September 2022 and is being published in SCOPUS Indexed SPRINGER Book Series “Lecture Notes in Networks and Systems”

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This dataset is in support of my research paper - Short Circuit Analysis of 666 Wh Li-Ion NMC

 Faults and datasets can be copied to submit in fire cause investigation reports or thesis. The simulation is run for 20 hours (72000 seconds) of simulation time for each fault of 100 faults. 

PrePrint : (Make sure you have read Caution.)

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