Digital signal processing

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.

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.

PV Solar Power System Under Partial Shading Irradiance Conditions

Operation of a solar photovoltaic (PV) array connected to a variable DC source to plot the l-V and P-V characteristics under partial shading irradiance conditions.

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.