Signal Processing

The dataset consists of experimental data collected in an anechoic tank, with a specific setup involving single-source transmission and reception by a 6-element circular array with a radius of 0.046 meters. The transmitted signals include common wideband signals used in underwater positioning and communication, such as chirps, single-carrier QPSK, multi-tone signals, and OFDM signals. The transmitter and receiver are located at the same depth, and the receiving array rotates 360 degrees with 30-degree intervals.

This is the official Thaat and Raga Forest (TRF) Dataset

Please do cite our paper: Link to Paper

Dataset is also available here: Link to Dataset

Link to our repository: Github Repo

The "Thaat and Raga Forest (TRF) Dataset" represents

Video super-resolution (SR) has important real world applications such as enhancing viewing experiences of legacy low-resolution videos on high resolution display devices. However, there are no visual quality assessment (VQA) models specifically designed for evaluating SR videos while such models are crucially important both for advancing video SR algorithms and for viewing quality assurance. Therefore, we establish a super-resolution video quality assessment database （VSR-QAD） for implementing super-resolution video quality assessment.

The Dominica database is used to evaluate and compare the performance of sperm whale click detectors. The database consists of 3 hours of recordings of sperm whale echolocation clicks and 4 hours of sound recordings containing delphinid clicks and transients from different sound sources, but no sperm whale clicks.

Video super-resolution (SR) has important real world applications such as enhancing viewing experiences of legacy low-resolution videos on high resolution display devices. However, there are no visual quality assessment (VQA) models specifically designed for evaluating SR videos while such models are crucially important both for advancing video SR algorithms and for viewing quality assurance. Therefore, we establish a super-resolution video quality assessment database （VSR-QAD） for implementing super-resolution video quality assessment.

Video super-resolution (SR) has important real world applications such as enhancing viewing experiences of legacy low-resolution videos on high resolution display devices. However, there are no visual quality assessment (VQA) models specifically designed for evaluating SR videos while such models are crucially important both for advancing video SR algorithms and for viewing quality assurance. Therefore, we establish a super-resolution video quality assessment database （VSR-QAD） for implementing super-resolution video quality assessment.

This dataset contains RF (Radio Frequency) signals obtained from simulations, which model ultrasound propagation in cortical bone.

The simulations were designed to provide insights into the behaviour of ultrasound waves in cortical bone tissues, both in intact and pathological conditions. The dataset covers a wide range of parameters, including varying thickness (1-8 mm), porosity (1-20%), and frequency (1-8 MHz), allowing to explore the impact of these factors on ultrasound signal characteristics.

Video super-resolution (SR) has important real world applications such as enhancing viewing experiences of legacy low-resolution videos on high resolution display devices. However, there are no visual quality assessment (VQA) models specifically designed for evaluating SR videos while such models are crucially important both for advancing video SR algorithms and for viewing quality assurance. Therefore, we establish a super-resolution video quality assessment database （VSR-QAD） for implementing super-resolution video quality assessment.

Problems of neurodegenerative disorder patients can be solved by developing Brain-Computer Interface (BCI) based solutions. This requires datasets relevant to the languages spoken by patients. For example, Marathi, a prominent language spoken by over 83 million people in India, lacks BCI datasets based on the language for research purposes. To tackle this gap, we have created a dataset comprising Electroencephalograph (EEG) signal samples of selected common Marathi words.