Signal Processing
Common Randomness (CR) can be considered as a resource in our future communication systems that will assist in various operations, such as cryptographic encryption in wireless communication, improving identification capacity for identification codes. In wireless communication, CR can be conveniently generated by reading the reciprocal channel properties between two wireless terminals, and by sending pilot signals to each other using the time division duplexing (TDD)-based half-duplexing method. In the channel probing stage, reciprocal channel characteristics are measured.
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This is the millimeter wave radar gesture recognition gesture dataset corresponding to paper <Gesture Recognition Using MIMO Radar Point Clouds with Targeted Signal Processing for Resource-Limited Platforms>. The file is stored in the. npy format of the numpy library in Python. It contains 4 gestures and 1 unrelated set of data, with a total of 5 categories. There are a total of 6000 pieces of data in each category, and each piece of raw data is packaged in a compressed package. The output screenshot of the script shows the details.
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The limited availability of Guitar notes datasets hinders the training of any artificial intelligence model in this field. TaptoTab dataset aims to fill this gap by providing a collection of notes recordings. This dataset is collected as part of an honours project at the Faculty of Computer and Information Sciences, Ain Shams University. The dataset is composed of audio data that has been self-collected, focusing on capturing a comprehensive range of guitar notes. The dataset consists of recordings of guitar notes played on each of the six strings, covering up to the 12th fret.
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**Abstract**
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In order to develop and analyse the performance of large-scale colored point cloud upsampling, we built a large-scale colored point cloud dataset for training and evaluating the upsampling network. This large-scale colored point cloud dataset consists of 121 original colored point clouds, 43 of which were scanned by us, while the other 78 were obtained from the SIAT-PCQD, Moving Picture Experts Group (MPEG) point cloud, and Greyc 3D colored mesh database. These point clouds cover six categories, including animals, plants, toys, sculptures, people and others.
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Brain-Computer Interface (BCI) technology facilitates a direct connection between the brain and external devices by interpreting neural signals. It is critical to have datasets that contain patient's native languages while developing BCI-based solutions for neurological disorders. However, present BCI research lacks appropriate language-specific datasets, particularly for languages such as Telugu, which is spoken by more than 90 million people in India.
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This code implements a novel family of generalized exponentiated gradient (EG) updates derived from an Alpha-Beta divergence regularization function. Collectively referred to as EGAB, the proposed updates belong to the category of multiplicative gradient algorithms for positive data and demonstrate considerable flexibility by controlling iteration behavior and performance through three hyperparameters: alpha, beta, and the learning rate eta. To enforce a unit l1 norm constraint for nonnegative weight vectors within generalized EGAB algorithms, we develop two slightly distinct approaches.
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In this research, a small-scale drone with the required magnetometer sensor technologies and novel intelligent automated techniques -- Maggy, was built to automate and ease the procedures of cleaning landmines and UXO/IDE. The dataset with an MP4-formated video demonstrates how a magnetometer-integrated autonomous drone can map the magnetic field of a landmine region effectively and efficiently using an intelligent application through near real-time data streaming.
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Ten marine mammal calls and one ocean noise. The ten marine mammals are the ‘striped dolphin,’ ‘spinner dolphin,’ ‘spotted dolphin,’ ‘Ross's seal,’ ‘bearded seal,’ ‘killer whale,’ ‘sperm whale,’ ‘white whale,’ and ‘white whale. ‘Ross's seal, bearded seal, killer whale, sperm whale, beluga whale, and pilot whale. ’, ‘pilot whale’ and ‘pilot whale’.' ‘sperm whale’, ‘beluga whale’, ‘pilot whale’ and ‘humpback whale’.We used the recording data of ten marine mammals from 1940 to 2000 provided by the Watkins Marine Mammal Sound Library.
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This graph illustrates the visualization trend of a subset of the dataset I have uploaded, which comprises 6500*9 data points. The dataset consists of nine columns representing underwater speed (UWS), underwater course (UWC), depth below the surface (DBS), rate of change in speed (RCS), rate of change in course (RCC), rate of change in depth (RCD), trend A and B of vibrational signals (TVS_A, TVS_B) and electromagnetic noise trend (TEN) recorded by the AUV.
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