Signal Processing
EEG consists of collecting information from brain activity in the form of electrical voltage. Epileptic Seizure prediction and detection is a major sought after research nowadays. This dataset contains data from 11 patients of whom seizures are observed in EEG for 2 patients.
The total duration of seizures is 170 seconds. The number of channels is 16 and data is collected at 256Hz sampling rate.
The final dataset files in .csv format contain 87040 rows x 17 columns,
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This dataset is related to dog activity and is sensor data.
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With the rapid deployment of indoor Wi-Fi networks, Channel State Information (CSI) has been used for device-free occupant activity recognition. However, various environmental factors interfere with the stable propagation of Wi-Fi signals indoors, which causes temporal variation of CSI data. In this study, we investigated temporal CSI variation in a real-world housing environment and its impact on learning-based occupant activity recognition.
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This is the dataset for the letter "NLOS Mitigation Using Residual-Weighted Least-Squares in Wireless Localization"
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README File for Dataset of IAR harmonic frequencies and their ratios
Title of submitted article:
“Ratio between discrete IAR frequencies from observations in the solar cycle 24,” by A.S. Potapov, A.V. Guglielmi, and B.I. Klain
Description of the processing method:
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# RSS data from smartwatch for Contact Tracing
This dataset was collected for the purpose to understand the proximity between any two smartwatches worn by human.
We used the Google's Wear OS based smartwatch, powered by a Qualcomm Snapdragon Wear 3100 processor, from Fossil sport to collect the data.
The smartwatch is powered by a Qualcomm Snapdragon Wear 3100 processor and has an internal memory of up to 1GB.
Two volunteers were required to wear the smartwatch on different hand and stand at a certain distance from each other.
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It is the key problem of machine condition monitoring to judge whether the rolling bearing has a fault or not and judge the fault location according to the noise signal. Aiming at this problem, a rolling bearing fault identification method is proposed based on Wavelet Frequency Band Subdivision (WFBS), Principal Component Analysis (PCA) and Multi-Level Clustering (MLC).
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This is the First Arabic voice Commands Dataset to provide personalized control of devices at smart homes for elder persons and persons with disabilities. The dataset contains 12 speakers, each saying 36 different phrases or words in Arabic language. The goal of this dataset is to use it in an Arabic smart home system to control home devices through voice. Participants were asked to say each phrase multiple times. The phrases to record were presented in a random order.
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