Signal Processing
Precise modeling of dynamical systems can be crucial for engineering applications. Traditional analytical models often struggle when capturing real-world complexities due to challenges in system nonlinearity representation and model parameter determination. Data-driven models, such as deep neural networks (DNNs), offer better accuracy and generalization but require large quantities of high-quality data.
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The file “marine_data.mat” is the data from the marine experiment, including data from different navigation and positioning sensors. The file “lake_data.mat” is the data from the lake experiment, including data from different navigation and positioning sensors.
The meaning and explanation for each column in the file “lake_data.mat” is shown as below:
Acc_x is the x-axis acceleration of the surface vehicle.
Acc_y is the y-axis accelerationof the surface vehicle.
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s11 and s21:hows the ADS simulation of 2X-thru, where the S2P files of fixtures A and B are introduced to observe whether fixtures A and B are symmetric. It can be seen from the insertion loss S21 and return loss S11 of the 2X-thru simulation in Fig. 5(c) that the insertion losses of fixtures A and B almost coincide, but the return loss is not completely coincident because of the discontinuous impedance of the transmission line of fixtures A and B at the connection between the coaxial connector and the PCB. In Fig.
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This dataset contains EEG error-related potential signals elicited by humans while observing an AI agent play an atari-based maze game.
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Active noise control (ANC) aims at reducing a noise source at a listening point by destructive interference with a reversed phase noise emitted by one or more controlling devices. These data are from an active vibration control (AVC) system applied to a wall of the metal box of a cogeneration plant. It makes use of electro-dynamic shakers as controllers and accelerometers as error and reference signals. The algorithm employed for generating the cancelling signals is a single-reference, single-input multiple-output (MIMO) Filtered-X Normalized Least Mean Square (FxNLMS).
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The dataset contains the focus metrics values of a comprehensive synthetic underwater image dataset (https://data.mendeley.com/datasets/2mcwfc5dvs/1). The image dataset has 100 ground-truth images and 15,000 synthetic underwater images generated by considering a comprehensive set of effects of underwater environment. The current dataset focus on the focus metrics of these 15,100 images.
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Wild-SHARD presents a novel Human Activity Recognition (HAR) dataset collected in an uncontrolled, real-world (wild) environment to address the limitations of existing datasets, which often need more non-simulated data. Our dataset comprises a time series of Activities of Daily Living (ADLs) captured using multiple smartphone models such as Samsung Galaxy F62, Samsung Galaxy A30s, Poco X2, One Plus 9 Pro and many more. These devices enhance data variability and robustness with their varied sensor manufacturers.
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Hand contact data, reflecting the intricate behaviours of human hands during object operation, exhibits significant potential for analysing hand operation patterns to guide the design of hand-related sensors and robots, and predicting object properties. However, these potential applications are hindered by the constraints of low resolution and incomplete capture of the hand contact data.
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The prototype of the calibration is verified with a 12-bit SAR ADC manufactured in 28-nm standard CMOS process. It is based on non-binary weights differential SAR ADC with bottom-plate sampling. This data was captured using a logic analyzer. The data for fast Fourier transform (FFT) is an input 1 MHz sine wave at 50MS/s. The signal input amplitude is 15dbm. The sampling points are 131072. The MATLAB code includes both the original weight and the calibration weight.
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The synthetic data is generated loosely following the concepts developed by Skomedal and Deceglie (2020)
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