Digital signal processing
An IEEE 802.15.4 backscatter communication dataset for Radio Frequency (RF) fingerprinting purposes.
It includes I/Q samples of transmitted frames from six carrier emitters, including two USRP B210 devices (labeled as c#) and four CC2538 chips (labeled as cc#), alongside ten backscatter tags (identified as tag#). The carrier emitters generate an unmodulated carrier signal, while the backscatter tags employ QPSK modulation within the 2.4 GHz frequency band, adhering to the IEEE 802.15.4 protocol standards.
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Inertial sensors are widely used in a variety of applications. A common task is orientation estimation. To tackle such a task, attitude and heading reference system algorithms are applied. Relying on the gyroscope readings, the accelerometer measurements are used to update the attitude angles, and magnetometer measurements are utilized to update the heading angle. In indoor environments, magnetometers suffer from interference that degrades their performance.
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Anomaly detection plays a crucial role in various domains, including but not limited to cybersecurity, space science, finance, and healthcare. However, the lack of standardized benchmark datasets hinders the comparative evaluation of anomaly detection algorithms. In this work, we address this gap by presenting a curated collection of preprocessed datasets for spacecraft anomalies sourced from multiple sources. These datasets cover a diverse range of anomalies and real-world scenarios for the spacecrafts.
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The Partial Discharge - Localisation Dataset, abbreviated: PD-Loc Dataset is an extensive collection of acoustic data specifically curated for the advancement of Partial Discharge (PD) localisation techniques within electrical machinery. Developed using a precision-engineered 32-sensor acoustic array, this dataset encompasses a wide array of signals, including chirps, white Gaussian noise, and PD signals.
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Noise recognition plays an essential role in human-computer interaction and various technological applications. However, identifying individual speakers remains a significant challenge, especially in diverse and acoustically challenging environments. This paper presents the Enhanced Multi-Layer Convolutional Neural Network (EML-CNN), a novel approach to improve automated speaker recognition from audio speech. The EML-CNN architecture features multiple convolutional layers and a dense block, finely tuned to extract unique voice signatures from English speech samples.
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It is suggested to use zero crossing detectors to build a high-precision power-factor meter. Low pass filters are suggested to stop this error source after the influence of input signal distortion is examined. Based on the measurement of voltage, current, and power factor, this system is also proposed as a new type of power standard meter.
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This dataset contains inertial measurements data (accelerometer, gyroscope and magnetometer), recorded as a part of DoorINet research. Our dataset was recorded using two types of IMUs: Memsense MS-IMU3025 [32] and Movella Xsens DOT [33]. The Memsense MS-IMU3025 was used to generate the ground-truth (GT) readings. This IMU has a gyroscope bias instability of 0.8°/h over the axis of interest Z and was recorded at 250Hz. The Movella Xsens DOT IMUs were used as units under test. It has a gyroscope bias instability of 10°/h over the axis of interest Z and was recorded at 120Hz.
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In today’s context, it is essential to develop technologies to help older patients with neurocognitive disorders communicate better with their caregivers. Research in Brain Computer Interface, especially in thought-to-text translation has been carried out in several languages like Chinese, Japanese and others. However, research of this nature has been hindered in India due to scarcity of datasets in vernacular languages, including Malayalam. Malayalam is a South Indian language, spoken primarily in the state of Kerala by bout 34 million people.
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