Discrete-time signal processing

The dataset has 150 three-second sampling motor current signals from each synthetically-prepared motors. There are five motors with respective fault condition - bearing axis deviation (F1), stator coil inter-turn short circuit (F2), rotor broken strip (F3), outer bearing ring damage (F4), and healthy (H). The motors are run under five coupling loads - 0, 25, 50, 75, and 100%. The sampling signals are collected and processed into frequency occurrence plots (FOPs). Each image has a label, for example F2_L50_130, where F2 is the fault condition, L50 is the coupling load condition.

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A VOR receiver based on Software-Defined Radio is presented. Experiments showed that the system indicated the radials of the VOR station of São José dos Campos with an average error rate of less than 1% and a standard deviation of less than 2.14% in relation to those calculated cartographically. The results suggest that low volume and weight SDR-based VOR receivers can be developed with processing on microcontrollers or FPGAs to equip drones that need to operate in aerodrome environments.

 

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Audio dataset for Household Multimodal Environment (HoME). It is a collection of audio samples from the Freesound.org collaborative database of Creative Commons Licensed sounds.

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This dataset includes  the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014. There are 4 columns in the file, which represent viewer ID, the current channel number, the next channel number, the date of the month, respectively. The first column, the ID code of a viewer, ranks in descent with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior. 

 

 

 

 

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This dataset includes  the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014.

There are 4 columns in the file, which represent viewer ID, the current channel number, th next channel number, the date of the month, respectively.

The first column, the ID code of a viewter, ranks with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger

the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior. 

 

 

 

 

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