Signal Processing

Channel frequency response (CFR) dataset used for "In-situ Calibration of Antenna Arrays for Positioning with 5G Networks" paper. Since the CFR of the wireless channel, which is also known as the channel state information (CSI) in many cellular or Wi-Fi positioning literature, contains the angle and delay information of both the direct path and reflected paths, we believe the following research fields can benefit from this dataset:

1. direction-of-arrival (DOA) estimation and array calibration,

2. time-of-arrival (TOA) estimation,

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To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system.

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The experimental measurement setup is performed in an anechoic chamber to secure the LOS transmission at the Millimeter Wave and Terahertz Technologies Research Laboratories (MILTAL), Scientific and Technological Research Council of Turkey (TUBİTAK), Kocaeli. The dimensions of anechoic chamber are 7m × 4m × 3m. The dataset includes channel impulse response in between 240GHz and 300GHz for the transmitter-receiver distance of 20 cm, 30 cm, 40 cm, 50 cm, 60 cm, 70 cm, 80 cm, 90 cm and 100 cm and different misalignment degrees.

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In this dataset, we provided the raw analog-to-digital-converter (ADC) data of a 77GHz mmwave radar for the automotive object detection scenario. The overall dataset contains approximately 19800 frames of radar data as well as synchronized camera images and labels. For each radar frame, its raw data has 4 dimension: samples (fast time), chirps (slow time), transmitters, receivers. The experiment radar was assembled from the TI AWR 1843 board, with 2 horizontal transmit antennas and 4 receive antennas.

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Traces of Bluetooth sightings by groups of users carrying small devices (iMotes) for a number of days.

This data includes a number of traces of Bluetooth sightings by groups of users carrying small devices (iMotes) for a number of days - in office environments, conference environments, and city environments.

All versions of this dataset, oldest to newest: v. 2006-01-31,  v. 2006-09-15,  v. 2009-05-29.

network type: bluetooth

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In this dataset, we provided the raw analog-to-digital-converter (ADC) data of a 77GHz mmwave radar for the carry object detection scenario. The overall dataset contains approximately 3000 frames of radar data as well as synchronized camera images and labels. For each radar frame, its raw data has 4 dimensions: samples (fast time), chirps (slow time), transmitters, and receivers. The experiment radar was assembled from the TI cascaded-chip TIDEP-01012 board, with 12 transmit antennas and 16 receive antennas.

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Many algorithms for compressed sensing are studied. The common guarantee for the reconstruction algorithm is restricted isometry property (RIP), which is shown to only hold under ideal assumptions. However, in practice, more than one ideal condition is often violated and there is no RIP-based guarantee application. Based on this discrepancy, we propose a new oblique subspace thresholding pursuit (ObSTP) algorithm. It is guaranteed by the restricted biorthogonality property (RBOP) which requires no ideal assumptions.

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This data is designed for covariance matrix completion to train the neural networks. 

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Sign languages are natural, gestural languages that use visual channel to communicate. Deaf people develop them to overcome their inability to communicate orally. Sign language interpreters bridge the gap that deaf people face in society and provide them with an equal opportunity to thrive in all environments. However, Deaf people often struggle to communicate on a daily basis, especially in public service spaces such as hospitals, post offices, and municipal buildings.

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This is the continuous Chinese and English gesture data of 14 Chinese and 4 English languages, respectively “不”,“程”,“刀”,“工”,“古”,“今”,“力”,“刘”,“木”,“石”,“土”,“外”,“中”,“乙”,“can”,“NO”,“Who”,“yes”.

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