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Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists. This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales.
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This dataset was developed at the School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology as part of the ongoing activities at the Center for Energy and Geo-Processing (CeGP) at Georgia Tech and KFUPM. LANDMASS stands for “LArge North-Sea Dataset of Migrated Aggregated Seismic Structures”. This dataset was extracted from the North Sea F3 block under the Creative Commons license (CC BY-SA 3.0).
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Infrared imaging from aerial platforms can be used to detect landmines and minefields remotely and can save many lives. This dataset contains thermal images of buried and surface landmines. The images were recorded from a fixed camera for 24 hours with 15-minute intervals. DM-11 type anti-personnel landmines were used. This dataset is available for landmine detection research.
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This dataset lets the users to rapidally estimate electric generator size, mass and losses for given power requirement, speed and DC bus voltage.This is accomplished through the metamodeling of a normalized optimization based machine design framework. The method is applied to a permanent magnet ac machine. This work is developed using the paper Metamodeling of Rotating Electric Machinery published in IEEE Transactions on Energy Conversion.
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The Voltammetry-Based Sensing (VBS) methods are extremely interesting due to high specificity in several biochemical applications. Several considerations can be applied to use this method to measure different analytes, and implement efficient and optimized electronic measurement platform for point-of-care diagnostic, in wearable, portable, or IoT systems. The dataset contains the data presented in [1], which proves on real experimental data a method to define the optimized setup to develop efficient and electronic bio-sensing platforms.
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This dataset refers to the case study performed in the paper "A Real Options Market-Based Approach to Increase Penetration of Renewables", submitted to IEEE Transactions on Smart Grid. The file contains the Midcontinent ISO data used for the day-ahead prices, as well as the wind data from NREL's Wind Integration National Dataset Toolkit which was used to estimate the renewable productions in the case study.
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