Open-source

CIRDC

The IEEE Xplore database is vital in democratizing access to high-quality research datasets, fostering global collaboration, and promoting interdisciplinary studies. Insights from the IEEE Xplore database support applications in academic collaboration networks, predictive research trends, recommendation systems, and the evolution of scientific discourse. Our cirdc dataset extracts key information of all articles in the IEEE Xplore database using web data mining methods. Source codes and scripts for data collection are provided to promote transparency and reproducibility.

Categories:: Other

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Respiratory Sound Track Grand Challenge 2024: Respiratory Sound Compression and Event Detection for SPRSound Dataset

Respiratory diseases are major global killers, demanding early diagnosis for effective management. Digital stethoscopes offer promise, but face limitations in storage and transmission. A compressive sensing-based compression algorithm is needed to address these constraints. Meanwhile, fast-reconstruction CS algorithms are sought to balance speed and fidelity. Sound event detection algorithms are crucial for identifying abnormal lung sounds and augmenting diagnostic accuracy. Integrating these technologies can revolutionize respiratory disease management, enhancing patient outcomes.

Biophysiological Signals

Submitted On:

Fri, 03/01/2024 - 03:03

Last Updated On:

Fri, 05/31/2024 - 07:30

Respiratory Sound Track Grand Challenge 2023: Respiratory Sound Classification for SPRSound Dataset

Globally, respiratory diseases are the leading cause of death, making it essential to develop an automatic respiratory sounds software to speed up diagnosis and reduce physician workload. A recent line of attempts have been proposed to predict accurately, but they have yet been able to provide a satisfactory generalization performance. In this contest, we invited the community to develop more accurate and generalized respiratory sound algorithms. A starter code is provided to standardize the submissions and lower the barrier.

Biophysiological Signals

Submitted On:

Tue, 02/20/2024 - 01:21

Last Updated On:

Mon, 04/01/2024 - 08:51

A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN

This work contains data gathered by a series of sensors (PM 10, PM 2.5, temperature, relative humidity, and pressure) in the city of Turin in the north part of Italy (more precisely, at coordinates 45.041903N, 7.625850E). The data has been collected for a period of 5 months, from October 2018 to February 2019. The scope of the study was to address the calibration of low-cost particulate matter sensors and compare the readings against official measures provided by the Italian environmental agency (ARPA Piemonte).

Categories:: IoT
Machine Learning
Transportation
Sensors
Pollution

979 Views