Machine Learning
In nighttime driving scenes, due to insufficient and uneven lighting, and the scarcity of high-quality datasets, the miss rate of nighttime pedestrian detection (PD) is much higher than that of daytime. Vision-based distance detection (DD) has the advantages of low cost and good interpretability, but the existing methods have low precision, poor robustness, and the DD is mostly performed independently of PD.
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This dataset file is used for the study of imbalanced data and contains 6 imbalanced datasets
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Fifth Generation 5G cellular network users are increasing exponentially, where 5G coverage is a challenge for global telecommunications to provide end-users with maximum Quality of Experience (QoE). 5G technology New Radio (NR) is developed to address high bandwidth, low latency and massive connectivity requirements of enhanced Mobile Broadband (eMBB) compared to Fourth Generation (4G) Long-Term Evolution (LTE).
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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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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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This dataset includes real-world time-series statistics from network traffic on real commercial LTE networks in Greece. The purpose of this dataset is to capture the QoS/QoE of three COTS UEs interacting with three edge applications. Specifically, the following features are included: Throughput and Jitter for each UE-Application and Channel Quality Indicator (CQI) for each UE. The interactions were generated from a realistic network behavior in an office by developing multiple network traffic scenarios.
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The Berlin V2X dataset offers high-resolution GPS-located wireless measurements across diverse urban environments in the city of Berlin for both cellular and sidelink radio access technologies, acquired with up to 4 cars over 3 days. The data enables thus a variety of different ML studies towards vehicle-to-anything (V2X) communication.
The data includes information on:
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