Machine Learning
The dataset contains:
1. We conducted a A 24-hour recording of ADS-B signals at DAB on 1090 MHz with USRP B210 (8 MHz sample rate). In total, we got the signals from more than 130 aircraft.
2. An enhanced gr-adsb, in which each message's digital baseband (I/Q) signals and metadata (flight information) are recorded simultaneously. The output file path can be specified in the property panel of the ADS-B decoder submodule.
3. Our GnuRadio flow for signal reception.
4. Matlab code of the paper, wireless device identification using the zero-bias neural network.
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We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c
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This work contains data gathered by a series of sensors (PM 10, PM 2.5, temperature, relative humidity, and pressure) in the city of Turin in the north part of Italy (more precisely, at coordinates 45.041903N, 7.625850E). The data has been collected for a period of 5 months, from October 2018 to February 2019. The scope of the study was to address the calibration of low-cost particulate matter sensors and compare the readings against official measures provided by the Italian environmental agency (ARPA Piemonte).
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This dataset is for light field image augmentaion. The dataset contains 100 pairs of light field image, each of them consists of "original" and "modified". "Original" is light field image with only background, "modified" is light field image with exactly same background and an object on it.
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In this network, a network US-WGAN, which can generate ultrasonic guided wave signals, is proposed to solve the problem of lack of data sets for ultrasonic nondestructive testing based on deep neural networks. This network was trained on the pre-enhanced data set and US-WGAN-enhanced data set with 3000 epochs, and the ultrasound signals generated by US-WGAN are proved to be of high quality (peak signal to noise ratio score of 30 – 50 dB) and belong to the same distribution population as the original data set.
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