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This dataset is utilized for adversarial camouflage generation. We collect vehicle datasets in the CARLA simulation environment under 16 weather conditions. These weather conditions are generated by combining four sun altitude angles (-90°, 10°, 45°, 90°) with four fog densities (0, 25, 50, 90). Within each weather scenario,  we randomly choose 16 locations for texture generation. Camera transformation values are randomly selected within specified intervals at each car location.

This dataset is in support of my research claim and presentation. Make sure you have read Caution. Make sure you have read LegalDisclosureStatement, as some later accuse of hiding.

 I. Research/ Presentation Title *:

(For Free download, pls. click on title)

* The submitted work will be used in my future presentations or future research paper, with same or different titles.

Videos uploaded on YouTube  are 

II.   Model

This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for various sleep disturbance parameters, from respiratory disturbances, to motion-based disturbances from posture shifts, leg restlessness and confusional arousals.The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool.The Wi-Fi CSI respiratory disturbance data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.

Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words.