Artificial Intelligence
Horticulture crop recommendation in saline soils
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1430 Views
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1821 Views
This data set contains two kinds of road perception information: image and acoustics. It covers four kinds of pavement: asphalt pavement, water pavement, gravel pavement and snow pavement. The image and audio files of the whole data set are too large, and this data set is part of it for researchers' reference. Please contact wangzhangu1@163.com if you need the whole data.
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142 Views
Telecom Italia: As part of the “Big Data Challenge”, consists of data about telecommunication activity in the city of Milan and in the province of Trentino. Data collectors divide Milan into 100×100 regions, and all traffic data statistics are based on regions. The total volume of SMS, call, and internet traffic sent and received by users in each area is collected every 10 minutes. We sample the internet traffic data during the period from 22:00 10/31/2013 to 22:50 12/19/2013 with a cell area size of 15×15.
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1848 Views
Web page addresses and e-mail addresses turn into links automatically
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27 Views
This project builds a length-versatile and noise-robust LoRa radio frequency fingerprint identification (RFFI) system. The LoRa signals are collected from 10 commercial-off-the-shelf LoRa devices, with the spreading factor (SF) set to 7, 8, 9, respectively. The packet preamble part and device labels are provided.
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1629 Views
This dataset is created by an experimental setup of a DC-PV -Battery-based grid-connected distributed generation system. This dataset is split into four parts such as irradiance, and temperature, which were measured by a meteorological station, and lastly, PV output current and voltage acquired by an inverter. Furthermore, we can have a chance to obtain the output PV power by multiplying current and voltage. The dataset has 288 elements for one day as a time series since the station obtains the data within five minutes. However, the whole dataset has three days of data with 864 elements.
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458 Views
Dataset for the paper ''Model Based Deep Learning for Low-Cost IMU Dead-Reckoning of Wheeled Mobile Robot''. Include KITTI result and self-built experimental platform result.
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236 Views
The datasets needed to train the model ((a)-(d) shown in the figure) are stored here.
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109 Views