Signal Processing
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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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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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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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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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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The present numerical work combines gain switching and external optical feedback to generate pulses with a high level of coherence across a large array of semiconductor lasers. The influence of several parameters such as modulation frequency, feedback strength and frequency detuning is analyzed and this dataset gathers the temporal traces generated with our code, that is adapted from the Lang-Kobayashi model to take into account decayed coupling in an array of lasers.
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This dataset contain the pulse responses of the Sallen-Key bandpass filter circuit and the amplifier board circuit. The test excitation is a 10 us pulse signal with an amplitude of 5 V and a frequency of 5 kHZ that exhibits abundant frequency components. By observing the pulse response, the sampling frequency is set to 5 MHz and the number of sampling points for each sample is fixed at 1000 in Case 1. PSPICE is applied for circuit simulation to set up the circuit fault according to the range of fault component parameter values.
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