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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Tea chrysanthemums can provide many components that are beneficial to human health. However, the harvesting process is time-consuming and labor-intensive. In the future, tea chrysanthemums harvesting can be done by machines. The first step towards automated harvesting is the detection of tea chrysanthemums, which are highly dependent on the quantity and quality of datasets. In a natural environment, a strain of chrysanthemum can present multiple flower heads in different stages and sizes.
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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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The Advanced Metering Infrastructure is established in Electrical Drives Laboratory, School of Electrical and Electronics Engineering, SASTRA Deemed to be University, Thanjavur, Tamil Nadu,India. Further, the ARP spoofing attack emulation is deliberated between Smart Meter and Data Concentrator through the Ettercap tool in two different test beds by incorporating Modbus TCP/IP and MQTT.Then, the benign and malicious traffic patterns of two protocols are captured using Wireshark to form the dataset.
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This dataset is for the performance verification of sEMG-based intention recognition algorithm with upper-limb position effect.
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