Machine Learning
Mizo or Lushai language is the official language of Mizoram, a state in the north-eastern part of India. It is an under-resourced language that falls under the Tibeto-Burman language family and is highly tonal in nature.
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Mizo or Lushai language is the official language of Mizoram, a state in the north-eastern part of India. It is an under-resourced language that falls under the Tibeto-Burman language family and is highly tonal in nature.
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Abstract—A radar signal multiparameter-based deinterleaving method is proposed in this work. Semantic information formed by the coupling of the pulse repetition interval (PRI),
radio frequency (RF), pulse width (PW), and pulse amplitude (PA) of a radar signal is used to deinterleave radar signals. A bidirectional gated recurrent unit (BGRU) is employed, and
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This dataset is created by an experimental setup of a DC-PV -Battery-based grid-connected distributed generation system. This dataset is split into four parts such as irradiance, and temperature, which were measured by a meteorological station, and lastly, PV output current and voltage acquired by an inverter. Furthermore, we can have a chance to obtain the output PV power by multiplying current and voltage. The dataset has 288 elements for one day as a time series since the station obtains the data within five minutes. However, the whole dataset has three days of data with 864 elements.
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AHT2D dataset is composed of Handwritten Arabic letters with diacritics. In this dataset, we have 28 letter classes according to the number of Arabic letters. Each class contains a multiple letter form. We have different letter images from different sources such as the internet, our writers, etc. The AHT2D dataset includes only isolated letters. In addition, this dataset contains different writing styles, orientations, colors, thicknesses, sizes, and backgrounds, which makes it a very large and rich dataset.
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This dataset was collected in our lab using Kinect to emphasize three points: (1) Larger number of human activities. (2) Each subject performed all actions in a continuous manner with no breaks or pauses. Therefore, the start and end positions of body for the same actions are different. (3) Each subject performed the same actions four times while imaged from four different views: front view, left and right side views, and top view.
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SWAN is a large-scale outdoor point cloud semantic segmentation, instance segmentation and object detection dataset. The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.
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SWAN is a Large-Scale Outdoor Point Cloud semantic segmentation dataset . The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.
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