Signal Processing
We introduce a novel dataset specifically designed for the evaluation of “alarm flood classification” (AFC) methods within process plants. The growing complexity of industrial systems and the heightened demands for operational safety and efficiency underscore the critical need for advanced diagnostic tools capable of handling alarm floods—situations where numerous alarms are triggered simultaneously.
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215 Views
This is an initiative developed by FIEC-ESPOL professors. Temperature and Speed Control Lab (TSC-LAB) is an open source hardware development.
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98873 Views
A wide range of wearable sensors exist on the market for continuous physiological health monitoring. The type and scope of health data that can be gathered is a function of the sensor modality. Blumio presents a dataset of synchronized data from a reference blood pressure device along with several wearable sensor types: PPG, applanation tonometry, and the Blumio millimeter-wave radar. Data collection was conducted under set protocol with subjects seated at rest. 115 study subjects were included (age range 20-67 years), resulting in over 19 hours of data acquired.
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3426 Views
This dataset of 7200 channels is generated at different locations in the room area of 30x15x4 m3, where the locations are separated by 0.25m in both horizontal and vertical directions. Each AP uses 10 dBm TX power and 2D BF. In the concurrent mmWave BT scenario, all APs are operating, while in the single mmWave BT scenario, we consider a single AP fixed on the center of the room’s ceiling
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212 Views
WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
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450 ViewsWITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
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304 Views
This code demonstrate the example use of FOPDT (First-Order-Plus-Dead-Time) model identification. The Algorithm used in "FOPDT_fun" is available in the reference:
S. Sharma and P. K. Padhy, "A Novel Iterative System Identification and Modeling Scheme with Simultaneous Time-Delay and Rational Parameter Estimation," in IEEE Access,
vol. 8, pp. 64918-64931, 2020, doi: 10.1109/ACCESS.2020.2985132.
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284 Views
This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.
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356 Views
Data are collected on a 5m×10msized test bed, which is set up at Kadir Has University,Istanbul. Wireless access points are located around the corners of the testbed and markers are placed at every 45 cm. RSSI measurements done on the grid shown in Figure are stored via NetSurveyor program running on a Lenovo IdeapadFLEX 4 laptop, which has an Intel Dual Band Wireless-AC8260 Wi-Fi adaptor.At each measurement point, RSSI data are collected for1 min with a sampling interval of 250 ms.
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907 Views
This dataset was used for OFDM Signal Real-Time Modulation Recognition Based on Deep Learning and Software-Defined Radio, which provides additional details and description of the dataset. We generate 6 modulated OFDM baseband signals with header modulation and payload modulation as BPSK+BPSK, BPSK+QPSK, BPSK+8PSK, QPSK+BPSK, QPSK+QPSK, QPSK+8PSK, respectively. The SNR range of each signal is from -10 dB to +20 dB at intervals of 2 dB. There are 4096 pieces of data generated for each signal type under a specific SNR and each piece of data has 1024 samples.
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3306 Views