Signal Processing

This set of datasets is the one used in the paper "Adaptive Parameter-Free STC Processing for Real-Time Logging While Drilling System".This dataset stores the original data and final results used for the experiments in the paper.

six shapefile

We present IsoNet, a large-scale multimodal dataset for audio-visual speaker isolation in reverberant environments using microphone arrays. The dataset comprises approximately 25,000 samples, each containing synchronized 4-channel spatial audio recordings and corresponding visual face localization data derived from the VoxCeleb2 corpus.

This WiFi_RFFI project implements a radio frequency fingerprint identification system for WiFi devices. The WiFi signals are captured from multiple routers using various receivers under different channel conditions, including flat fading and frequency-selective fading channels. The sampling rate is set to 20 Msps, with a bandwidth of 20 MHz. The corresponding processed signals are provided in the dataset. Further details about the datasets are available in the README document.

This dataset supports the manuscript submitted to IEEE Transactions 
on Geoscience and Remote Sensing. It includes the remote sensing data 
and processed results used for the experiments in the paper.
 

Early stage monitoring of Red Palm Weevil (RPW) activity in palms  is essential to prevent severe structural damage and economic losses in palm cultivation. Unlike conventional inspection methods, acoustic-based detection offers a non-invasive solution but is challenged by background noise and subtle signal variations.

Fault diagnosis of reciprocating compressors is crucial for ensuring their reliable and long-term operation. To address the accuracy limitations of conventional diagnostic methods that rely on single feature sets, this paper proposes a novel fault diagnosis method based on the fusion of geometric features from indicator diagrams. This method simultaneously extracts and integrates statistical features and computational geometry features from the indicator diagram, enabling information complementarity between the two types of features.

This dataset supports the research presented in the paper “Acoustic Indoor Localization Using Two Base Stations Based on TDoA and Relative Speed to Base Stations”， which proposes a novel acoustic indoor localization (AIL) strategy by fusing Time Difference of Arrival (TDoA) and Relative Speed to Base Stations (RStBS). The dataset enables the reproduction and further investigation of the proposed two-base-station localization system under non-line-of-sight (NLOS) conditions.

This data is some of the results generated in the paper

This Dataset consists of Wavelet Components created from the original PEM-43 Dataset.