AI
"LaneVisionIITR: A Comprehensive High-Resolution Dataset for Lane Detection recorded at IIT Roorkee ", which is a newly built high-resolution dataset for developing Lane detection dataset for advanced driver assistance systems.
This folder consists of three files for each image:
1. The image captured in .jpg format.
2. Annotations (.json) having left and center line coordinates represented as “L” and “C” respectively.
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Passive missiles, such as short-range or within-visual-range air-to-air missiles (SRAAMs or WVRAAMs) and man-portable air defense systems (MANPADS), which do not emit radio frequencies (RF) and thus evade detection by an aircraft’s Radar Warning Receiver (RWR). Current passive missile detection systems, primarily relying on Midwave Infrared (MWIR) exhibit limitations regarding high false alarm rates and extensive processing requirements.
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Although Unmanned Aerial Vehicles (UAVs) have long been recognized as systems to effectively deliver advanced and innovative services, their practical application to Beyond Line-of-Sight (BLOS) use cases is still largely missing due to safety concerns and regulations. Indeed, BLOS applications for UAVs require a reliable infrastructure that is capable of ensuring ubiquitous connectivity, low latency, and high data rates.
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This dataset includes real-world Channel Quality Indicator (CQI) values from UEs connected to real commercial LTE networks in Greece. Channel Quality Indicator (CQI) is a metric posted by the UEs to the base station (BS). It is linked with the allocation of the UE’s modulation and coding schemes and ranges from 0 to 15 in values. This is from no to 64 QAM modulation, from zero to 0.93 code rate, from zero to 5.6 bits per symbol, from less than 1.25 to 20.31 SINR (dB) and from zero to 3840 Transport Block Size bits.
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