Machine Learning
We collected data to train the ML module to determine the user’s device's location based on beacon frame characteristics and RSSI values from Wi-Fi APs. To collect the data, we defined a threshold distance of 7 feet as the maximum allowable distance between the user’s devices. We then collected two datasets: one with data collected while the two Raspberry Pis were within 7 feet or less of each other named ”authentic”, and another with data collected while the distance between the two Raspberry Pis was over 7 feet named ”unauthorized”.
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649 Views
Here is the most fresh dataset used for clustering in TOM.
It contains time series for 1659 GitHub repos of the metrics.
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284 Views
Sample data set
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42 stimulus pictures are presented separately on the screen in the same sequences for all participants, including landscapes, people, social scenes and composite pictures. The eye tracker records the participants' gaze data on the stimulus pictures. Based on the gaze fixation position and duration, the fixation map could be visualized. We applies a 2-d convolution with a gauss filter on the fixation maps to get the visual heatmaps. The participants consist of schizophrenic patients and healthy controls.
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This dataset provides the high-resolution remote senisng data regarding various coastline scenes.
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295 Views
Recently, a limited number of datasets that exist are used to detect errors in the printing process of the 3D printer. Limited datasets lead most researchers to dive into sensor data fault classification.
The dataset is captured and labelled before being fed to the DL model. The image dataset is captured in a time-lapse video mode with a 15-second duration for each printing process. Next, the time-lapse is used to extract around 50 images per video. In total, 2297 images containing four classes are collected.
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1870 Views
Most machine learning (ML) proposals in the Internet of Things (IoT) space are designed and evaluated on pre-processed datasets, where the data acquisition and cleaning steps are often considered a black box. Therefore, the data acquisition stage requires additional data cleaning/anomaly techniques, which translate to additional resources, energy, and storage.
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1484 Views
Personal assistive devices for rehabilitation will be in increasing demand during the coming decades due to demographic change, i.e., an aging society. Among the elderly population, difficulty in walking is the most common problem. Even though there are commercially available lower limb exoskeleton systems, the coordination between user and device still needs to be improved to achieve versatile personalized gait. To tackle this issue, an advanced EXOskeleton framework for Versatile personalized gaIt generation with a Seamless user-exo interface (called "EXOVIS") is proposed in this study.
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405 Views
This is a data set for Radio Frequency fingerprinting, which is a kind of identification of wireless devices based on their intrinsic physical features. The data set is composed by GSM bursts collected from 12 GSM mobile phones while transmitting. The samples have been collected using a Software Defined Radio with a sample rate at 20 MS/s. The content information has been removed from the bursts to remove the risk of bias due to content. The data set is in MATLAB format.
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982 Views
A reliable and extensive set of public WiFi fingerprinting radio map databases for researchers to implement, evaluate and compare Wi-Fi RSSI indoor localization schemes, the radio map database contains RSSI information from more than 400 APs collected during the conducted experiment on different days with the support of lab mates and volunteers.
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1322 Views