Discrete-time signal processing
High-precision, high-resolution ultra-deep field astronomical observations are essential for detecting special celestial bodies and extremely rare astronomical events. Space astronomical telescopes can achieve this by employing the fine image stabilization system (FISS) to generate line-of-sight (LOS) dithering, enabling scientific instruments to obtain higher-resolution astronomical images through resampling and fusion algorithms. To meet the requirement for sub-pixel dithering control in the FISS of space telescopes, an adaptive control algorithm based on a single neuron is proposed.
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This graph illustrates the visualization trend of a subset of the dataset I have uploaded, which comprises 6500*9 data points. The dataset consists of nine columns representing underwater speed (UWS), underwater course (UWC), depth below the surface (DBS), rate of change in speed (RCS), rate of change in course (RCC), rate of change in depth (RCD), trend A and B of vibrational signals (TVS_A, TVS_B) and electromagnetic noise trend (TEN) recorded by the AUV.
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This dataset includes results of simulation and experiment for tuning of bumpless feedforward controller. The tuninig of FF and the simultaneous tuning of FF and DOB are selected as comparsion group. Their results are also included in this dataset. For each method, two parameters are chosen to be tuned in their inverse uniform model or inverse sub-model. The results of each iteration for every method and every trajectory are also included. In simuation file, there are five group of result. For each group, results of three methods are included.
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146 Views
This dataset comprises audio recordings of ultra-high-frequency ambient noise stored in the lossless waveform format (WAW). The recordings were sampled at a frequency sample rate of 2.048 MHz and then provided at a downsampled audio rate of 48 kHz for compatibility and practical usage. The total length of the dataset is 01:30:29, consisting of approximately 260 million data points. (2024-03-30)
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44 Views
The Partial Discharge - Localisation Dataset, abbreviated: PD-Loc Dataset is an extensive collection of acoustic data specifically curated for the advancement of Partial Discharge (PD) localisation techniques within electrical machinery. Developed using a precision-engineered 32-sensor acoustic array, this dataset encompasses a wide array of signals, including chirps, white Gaussian noise, and PD signals.
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1115 Views
The password for the zip files are in the articles which cite this dataset.
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200 Views
PV Solar Power System Under Partial Shading Irradiance Conditions
Operation of a solar photovoltaic (PV) array connected to a variable DC source to plot the l-V and P-V characteristics under partial shading irradiance conditions.
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675 Views
This research introduces the Open Seizure Database and Toolkit as a novel, publicly accessible resource designed to advance non-electroencephalogram seizure detection research. This paper highlights the scarcity of resources in the non-electroencephalogram domain and establishes the Open Seizure Database as the first openly accessible database containing multimodal sensor data from 49 participants in real-world, in-home environments.
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1787 Views
A recent study [1] alerts on the limitations of evaluating anomaly detection algorithms on popular time-series datasets such as Yahoo, Numenta, or NASA, among others. In particular, these datasets are noted to suffer from known flaws suchas trivial anomalies, unrealistic anomaly density, mislabeled ground truth, and run-to-failure bias. The TELCO dataset corresponds to twelve different time-series, with a temporal granularity of five minutes per sample, collected and manually labeled for a period of seven months between January 1 and July 31, 2021.
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866 Views
This dataset is made for traditional, machine learning, and deep neural-network-based virtual sensor development and evaluation.
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92 Views