Signal Processing
This folder contains two csv files and one .py file. One csv file contains NIST ground PV plant data imported from https://pvdata.nist.gov/. This csv file has 902 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST ground PV plant data.
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Multi-modal Exercises Dataset is a multi- sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD).The MEx Dataset contains data from 25 people recorded with four sensors, 2 accelerometers, a pressure mat and a depth camera.
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This database contains the 166 Galvanic Skin Response (GSR) signal registers collected from the subjects participating in the first experiment (EXP 1) presented in:
R. Martinez, A. Salazar-Ramirez, A. Arruti, E. Irigoyen, J. I. Martin and J. Muguerza, "A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis," in IEEE Access, vol. 7, pp. 43730-43741, 2019. doi: 10.1109/ACCESS.2019.2908445
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WiFi measurements dataset for WiFi fingerprint indoor localization compiled on the first and ground floors of the Escuela Técnica Superior de Ingeniería Informática, in Seville, Spain. The facility has 24.000 m² approximately, although only accessible areas were compiled.
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Abstractــــ Wireless Sensor Networks (WSNs) typically have been consumed high energy in monitoring and radio communication trends. Furthermore, data diffusion modes in WSN typically generate errors such as noisy values, incorrect measurements or missing information, which minimize the standard of performance in such dynamic systems.
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Monitoring cell viability and proliferation in real-time provides a more comprehensive picture of the changes cells undergo during their lifecycle than can be achieved using traditional end-point assays. Our lab has developed a CMOS biosensor that monitors cell viability through high-resolution capacitance measurements of cell adhesion quality. The system consists of a 3 × 3 mm2 chip with an array of 16 sensors, on-chip digitization, and serial data output that can be interfaced with inexpensive off-the-shelf components.
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It is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems.
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This dataset includes gathering 18-month raw PV data at time intervals of about 200 µs (5 kHz sampling). A post-processing 365-day day-by-day downsampled version, converted to 10 ms intervals (100 Hz sampling), is also included. The end results are two databases: 1. The original, raw, data, including both fast (short circuit, 200 µs) and slow (sweep, 2.5-3.9 s) information for 18 months. These show intervals of missing points, but are provided to allow potential users to reproduce any new work. 2.
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This data set is composed by samples of load signature of electric devices acquired on a non-intrusively form. The test-bench was performed using four identical fluorescent lamps, four identical slots and four identical switches. Identical term means the same technical specifications (nominal voltage, power, isolation voltage, among others). The sensors are connected to the power supply in order to measure the electrical variations when appliances are turned on/off. We have 16 possible network configurations with 4 appliances, in which one, two, three or four appliances can be turned on.
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