Signal Processing

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.
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The results are based on the measurements conducted on small drones and a bionic bird using a 60 GHz millimeter wave radar, analyzing their micro-Doppler characteristics in both time and frequency domain. The results are presented in .pkl format. The more detailed description of the data and how the authors processed it will be updated soon.
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VSR-QAD-3Video 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.
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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.
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These datasets are gathered from an array of four gas sensors to be used for the odor detection and recognition system. The smell inspector Kit IX-16 used to create the dataset. each of 4 sensor has 16 channels of readings. Odors of different 12 samples are taken from these six sensors
1- Natural Air
2- Fresh Onion
3- Fresh Garlic
4- Black Lemon
5- Tomato
6- Petrol
7- Gasoline
8- Coffee
9- Orange
10- Colonia Perfume
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These datasets are gathered from an array of six gas sensors to be used for the odor recognition system. The sensors those used to create the data set are; Df-NH3, MQ-136, MQ-135, MQ-8, MQ-4, and MQ-2.
odors of different 10 samples are taken from these six sensors
1- Natural Air
2- Fresh Onion
3- Fresh Garlic
4- Fresh Lemon
5- Tomato
6- Petrol
7- Gasoline
8- Coffee 1,2
9- Orange
10- Colonia Perfume
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The present contribution consists of two files. The first one (measurements_25GHz_11planes.mat) contains a series of measurements of the scattered electric field using two two-dimensional objects made of aluminum foil as targets. The dataset consists of S21 paramenter measurements, captured with a VNA at a frequency of 25 GHz. The field was sampled in 11 planes located at different distances from the plane containing the objects under test.
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The capabilities of the millimeter wave (mmWave) spectrum to fulfill the ultra high data rate demands of V2X (Vehicle-to-Everything) communications necessitates the need for accurate channel modeling to facilitate the efficient development of next-generation network and device design strategies. Ergo, this work describes the design of a novel fully autonomous robotic beam-steering platform, equipped with a custom broadband sliding correlator channel sounder, for 28GHz V2X propagation modeling activities on the NSF POWDER experimental testbed.
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Researchers have widely used commercial and alternative measurement systems in impedance-based structural health monitoring (SHM), where a piezoelectric transducer attached to the monitored structure is excited while its electrical impedance is measured. However, the literature lacks sufficient attention to the required number of cycles of the excitation signal, an issue frequently overlooked by researchers.
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As the field of human-computer interaction continues to evolve, there is a growing need for new methods of gesture recognition that can be used in a variety of applications, from gaming and entertainment to healthcare and robotics. While traditional methods of gesture recognition rely on cameras or other optical sensors, these systems can be limited by factors such as lighting conditions and occlusions.
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