Discrete-time signal processing

Device fingerprinting is a technique for remote indirect identification or classification of a device of interest. This database is designated for device fingerprinting by current consumption; it includes current recordings for 22" computer displays from 40 devices - 20 Dell P2217H and 20 Dell E2214H. Two signals for each device were sampled independently and sequentially to provide independent train and test parts. Each sampled signal includes a 250-second recording at a 50kHz sampling frequency.

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The data involves 15 years of daily data of DJI, S&P, and IXIC, ranging from 01/01/2005 to 12/31/2020.

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The figure is the screenshot of experimental monitoring information during the whole experiment.

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The design and implementation of an anthropomorphic robotic hand control system for the Bioengineering and Neuroimaging Laboratory LNB of the ESPOL were elaborated. The myoelectric signals were obtained using a bioelectric data acquisition board (CYTON BOARD) using six channels out of 8 available, which had an amplitude of 200 [uV] at a sampling frequency of 250 [Hz]. 

 

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<p>The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.

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This dataset is used to design patent.  The system is basic , as can be seen from figure.

There is related dataset- Data: 255W Panel Connected 72Ah Li-Ion.

This new datasets are needed to show that I know how to make simulation and the dataset of 'Data: 255W Panel Connected 72Ah Li-Ion' is not fake. 

The boost converter, BMS is on existing designs.It is very simple for any graduate,degree holder or school students, so no paper is written for it.

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In this appendix, the tested implementation in Matlab of our 2D-TDOA localization algorithm is given for the easier repetition of the obtained results and the future hardware implementation, due to the complexity of the formulas (25)-(31).

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This dataset was used to quantify the effects of environmental change on SSTDR measurements from solar panels. We collect illuminance (Lux), temperature (deg F), and humidity (%) alongside SSTDR waveforms on a fault free string. Data is collected once per minute in January 2020, and twice per minute in August-September 2020. 

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This is a dataset is an example of a distribution of 20 correlated Bernoulli random variables.

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Three raw (i.e., In-Phase and Quadrature data with a software radio, and observation files) GNSS dataset were recorded using a LabSat Version 3 inside of the West Virginia University  greenhouse and two outside recordings were also made to provide a quality reference and comparison. The outdoor location had to be an ideal location for satellite signal reception  and  the  indoor  location  was  a  greenhouse  room  where satellite visibility was limited, susceptible to attenuation, occlusion and multipath.

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