Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for  synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.

Last Updated On: 
Tue, 11/12/2019 - 10:38
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.

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Spreadsheet use for conversion of visible light lux measurements to irradiance.

Back up for manuscript: Calculation of Visible Light Irradiance from Lux Illuminance and Relative Spectral Illuminance

Instructions: 

References shown in spreadsheet tabs

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this dataset provides the data of the paper titled "Variable-Gain Servo Matching Method for Two-axis Servo Feed System based on Joint Time-dependent Setpoints Bandwidth", including data of figures 4, 5, 8,9,10 and 11.

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My name is Ethan Smith. I'm a professional Mobile App Developers working with India's top app development companies. We deliver top-notch Mobile application development solutions with exceptional design & We offer the best ecommerce Website Development Services. We at RV Technologies, provide customized Shopify development services in India and 20+ offshore countries including the US and Europe. standard coding.

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Catalogue of Government Affairs Documents of Shanghai Archives

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In this paper, the design and fabrication of Waveguide Triplexer (WT) model in 1GHz-5GHz frequency band width, with the capability of determination of three frequency bands (first band: 1700MHz to 2200MHZ, second band: 2600MHz to 3100MHz, and third band: 3500MHZ to 4000MHz) in Band Pass Filter (BPF) structure in the form of a separate branch for each of the triplexer branches have been simulated. The purpose of this investigation is diminution of return signal losses and improvement of signal isolation and, as well, diminution of signal time delay in multiband waveguide triplexer filters.

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A set of electrical measurements from a TiO2-based memristive device is presented. The data correspond to a  resistive switching device, with a Ag/ITO/TiO2/Ag MIM structure. The thickness of TiO2 is 50nm. 

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Data and simulation, accompanying the publication "Insect Inspired Self-Righting Fixed-Wing Drones".

 

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Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas.

Instructions: 

Neural Audio Fingerprint Dataset

(c) 2021 by Sungkyun Chang

https://github.com/mimbres/neural-audio-fp

 

This dataset includes all music sources, background noises and impulse-reponses (IR) samples that have been used in the work "Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning" (https://arxiv.org/abs/2010.11910). 

This data set was generated by processing several external data sets, such as the Free Music Archive (FMA), Audioset, Common voice, Aachen IR, OpenAIR, Vintage MIC and the internal data set from Cochelar.ai. See README.md for details.

Dataset-mini vs. Dataset-full: the only difference between these two datasets is the size of 'test-dummy-db'.  So you can first train and test with `Dataset-mini`. `Dataset-full` is for  testing in 100x larger scale.

 

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