Signal Processing
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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Dataset containing real word data related to the informations receive on a downlink and an uplink channels. The information related to the data flowing on the channel are expressed by means of complex numbers. Some processing can be performed to convert the complex number into integer numbers in order to represent the channel as preferred. This dataset can be used to develop new compression and channel estimation algorithms.
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RLED contains 80,400 images and corresponding events, we utilized a photometer to continuously measure scene illumination and calculate the illumination value after attenuation at the event camera. The capture scenes included city (35.0%), suburbs (10.3%), town (14.5%), village (17.8%), and valley (22.4%). Half of the RLED frames are captured at a frame rate of 25 fps, and the other half at 10 fps. The exposure time is set to 1ms, 3ms, and 5ms based on the varying environmental illumination.
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156 Views
Abstract—In massive Internet of Things (IoT) deployments,
the efficient allocation of computing resources to IoT devices
while preserving devices’ data poses a significant challenge.
This paper proposes a new online probabilistic model to address
uncertainties in demand and resource allocation for IoT
networks, where the task computing of requesting devices is
addressed by serving devices. The model incorporates uncertainty
and formulates an optimization problem, concerning available
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135 Views
The integration of uncrewed aerial vehicles (UAVs)
with fifth-generation (5G) cellular networks has been a prominent
research focus in recent years and continues to attract significant
interest in the context of sixth-generation (6G) wireless networks.
UAVs can serve as aerial wireless platforms to provide on-demand
coverage, mobile edge computing, and enhanced sensing and
communication services. However, UAV-assisted networks present
new opportunities and challenges due to the inherent size, weight,
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154 Views
We developed a unique and valuable dataset specifically for advancing Brain-Computer Interface (BCI) systems by recording brain activity from a dedicated volunteer. The participant was asked to pronounce 100 carefully selected Malayalam words, along with their English translations, which were chosen for their relevance to astronauts during human space missions. The volunteer pronounced these words both vocally and subvocally, each word being repeated 50 times. Non-invasive Electroencephalography (EEG) sensors were employed to capture the brain activity associated with these tasks.
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635 Views
After loading the MAT file (as in MATLAB with the load function), there are three variables (matrices): amplitude_complex_T1, amplitude_complex_T2, and amplitude_complex_info.
The headers of the data are stored in the amplitude_complex_info variable. Each row (with a length of 48) represents the data header of a pulse echo, with each position in the row representing a specific meaning as shown in the table. Each row of the amplitude_complex_info variable corresponds to each row in the amplitude_complex_T1 and amplitude_complex_T2 variables.
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As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches.
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222 Views