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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The sound part is built into many products.

It is used not only in audio systems, but also in a wide range of industries such as home theaters, broadcast amplifier systems, TVs, computers, AI speakers, and game consoles.

Even now, many companies are making efforts to improve the sound quality of the acoustic part.

In the future, high sound quality will be required in many industrial fields.

A wide range of industries will require high-quality technology.

 

Instructions: 

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The content in this zip file contains a video showing animations of all gaits presented in the figures of the paper as well as the source code for computing walking gaits from equilibria using numerical continuation methods.  Further information can be found in the README.txt file.  The content can also be downloaded for free at https://github.com/nr-codes/BipedalGaitGeneration.

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The dataset contains data collected on board games from the BoardGameGeek (BGG) website in Februay 2021. BGG is the largest online collection of board game data which consists of data on more than 100,000 total games (ranked and unranked). The voluntary online community contributes to the site with reviews, ratings, images, videos, session reports and live discussion forums on the expanding database of board games. This data set contains all ranked games as of the date of collection from the BGG database.

Instructions: 

The dataset is in .xlsx format and is readily accessible.

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1221 Views

Parallel fractional hot-deck imputation (P-FHDI) is a general-purpose, assumption-free tool for handling item nonresponse in big incomplete data by combining the theory of FHDI and parallel computing. FHDI cures multivariate missing data by filling each missing unit with multiple observed values (thus, hot-deck) without resorting to distributional assumptions. P-FHDI can tackle big incomplete data with millions of instances (big-n) or 10, 000 variables (big-p).

Instructions: 

This repository includes three types of data: incomplete data with massive instances (big-n data), incomplete data with many variables (big-p data), incomplete data with tremendous instances and high dimensionality (ultra data). The repository has synthetic data and practical data from various scientific domains. Overall, there exist seven big-n datasets, four big-p datasets, and ten ultra datasets. For instructions, see Readme files in the dataset folder for the step-by-step use of UP-FHDI with different types of incomplete datasets.

 

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Includes the performance and scaling result of MemXCT.

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