The Raga Ornamentation Detection (ROD) dataset is a curated collection of Hindustani Classical Music (HCM) vocal performances for the study of ornamentations in IAM. It consists of 212 audio files recorded by two expert singers, totalling 4.08 hours of audio.
With the introduction of the low-altitude economy concept, the application of electric vertical takeoff and landing (eVTOL) aircraft has become more widespread, particularly in search and rescue missions. However, most of the existing path planning methods cannot effectively cope with dynamic environments and changes in destinations, which limits the ability of eVTOL drones to autonomously perform planning tasks in unknown dynamic environments.
This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds.
The LLM-RIMSA dataset, designed to advance 6G networks through ultra-massive connectivity and intelligent radio environments. The dataset is built around a novel framework that integrates large language models (LLMs) with a reconfigurable intelligent metasurface antenna (RIMSA) architecture. This integration addresses limitations in hardware efficiency, dynamic control, and scalability seen in existing RIS technologies.
Dataset used in an IOP publication under review in the journal Physics in Medicine & Biology.
This data set includes student responses and expert ratings from the test administrations for the Turkish History course. For each question, the correct answer is assigned a new label, while incorrect answers are labeled as “0”. For 15 questions, there are true-false scores and for 4 questions there are true-partially true-false scores.
The dataset consists of airport specific ground crew and allocation data for four major airports - Kempegowda International Airport (BLR), Rajiv Gandhi International Airport (HYD), Indira Gandhi International Airport (DEL), and Chhatrapati Shivaji Maharaj International Airport (BOM). The tasks, floors and gates, i.e, the tasks and their locations are factual data where as the allocation data is approximately close to realistic demand. The crew demand is synthetically generated.
The Cross-Domain Deception Dataset (CD3) contains frame-level features extracted from video data using OpenFace and OpenPose to support research in deception detection through facial expressions, facial action units, body and hand gestures, and gaze coordinates. Using a commercial off-the-shelf laptop and Microsoft Teams, we collected video data of 45 participants completing mock interviews where they answered questions related to biographical information, academic success, and well-being across two sessions.
This dataset contains IoT-based soil data collected from various districts of Bangladesh, aimed at assessing soil quality for crop recommendation purposes. The data includes key soil parameters, such as nitrogen, phosphorus, potassium levels, soil conductivity, pH, humidity, and temperature, measured over multiple time intervals. The dataset includes a Soil Quality Index (SQI) and fuzzy classification categories, allowing for an in-depth soil fertility analysis.
This study aims to create a robust hand grasp recognition system using surface electromyography (sEMG) data collected from four electrodes. The grasps to be utilized in this study include cylindrical grasp, spherical grasp, tripod grasp, lateral grasp, hook grasp, and pinch grasp. The proposed system seeks to address common challenges, such as electrode shift, inter-day difference, and individual difference, which have historically hindered the practicality and accuracy of sEMG-based systems.
