Digital signal processing
The limited availability of Guitar notes datasets hinders the training of any artificial intelligence model in this field. TaptoTab dataset aims to fill this gap by providing a collection of notes recordings. This dataset is collected as part of an honours project at the Faculty of Computer and Information Sciences, Ain Shams University. The dataset is composed of audio data that has been self-collected, focusing on capturing a comprehensive range of guitar notes. The dataset consists of recordings of guitar notes played on each of the six strings, covering up to the 12th fret.
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This dataset comprises radar-acquired signals from 15 subjects walking on a treadmill, aimed at exploring methodologies for non-contact vital sign detection under conditions of significant body movement. Each subject participated in four experimental sessions, where radar data were collected using two Continuous Wave (CW) radars positioned to capture signals from the front and back of the subject. The data includes both raw and demodulated signals synchronized with ground-truth data obtained from a BioPac system.
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Ground Penetrating Radar (GPR) facilitates the detection and localisation of subsurface structural anomalies in critical transport infrastructure (e.g. tunnels), better informing targeted maintenance strategies. However, conventional fixed-directional systems suffer from limited coverage - especially of less-accessible structural aspects (e.g. crowns) - alongside unclear visual output of anomaly spatial profiles, both for physical and simulated datasets.
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This dataset is utilized for the research of blind identification of CPM signal modulation order. The signal parameters in the dataset range as follows: modulation index from 0.125 to 1, modulation order of 2, 4, 8, pulse types including REC, RC, SRC, TFM, and GMSK, and correlation lengths of 1 to 8. The signals are oversampled by a factor of 10, transmitted through an additive white Gaussian noise (AWGN) channel, and the signal-to-noise ratio (SNR) ranges from 0 to 30dB.
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Data set used in an IEEE TIM paper where the objectives of this paper are to test an open-source phase noise analyzer, the direct digital phase noise measurement bench developed by A. Holme (called AH analyzer in this paper), and compare it to a commercial phase noise analyzer, the 53100A.
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Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words.
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305 Views
Study on Sleep Positions using a Wearable Device
This device constantly collects data about acceleration in three directions (tri-axial) 50 times a second (50 Hz). It has several components: a microcontroller for gathering and sending data (ESP8266), a battery (lithium-ion), a sensor for measuring acceleration (ADLX345 accelerometer), and a protective case made of plastic. The device can also store data temporarily on a microSD card in case the wireless connection is lost.
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This radar raw datasets are collected with the FMCW radar system --- PARSAX in Delft University of Technology, the Netherlands. Two datasets are included, in which one is the echo scattered from a stationary industrial chimney and the other one is the echo of rain droplets. The radar echo of chimney was acquired in one FMCW signal sweep. And the echo of the rain droplets was measured over 512 sweeps, which can be used for both range profile and range-Doppler processing. In both radar datasets, the echoes of targets were contaminated by interferences.
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258 Views
The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).
All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.
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The dataset involves two sets of participants: a group of twenty skilled drivers aged between 40 and 68, each having a minimum of ten years of driving experience (class 1), and another group consisting of ten novice drivers aged between 18 and 46, who were currently undergoing driving lessons at a driving school (class 2).
The data was recorded using JINS MEME ES_R smart glasses by JINS, Inc. (Tokyo, Japan).
Each file consists of a signals from one sigle ride.
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64 Views