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This dataset was created based on the paper 'Andras Hajdu, Gyorgy Terdik, Attila Tiba, and Henrietta Toman: A stochastic approach to handle knapsack problems in the creation of ensembles'.To summarize our experimental setup for UCI binary classification problems, we have considered base classifiers perceptron, decision tree, Levenberg-Marquardt feedforward neural network, random neural network, and discriminative restricted Boltzmann machine classifier for the 5 UCI datasets MAGIC Gamma Telescope, HIGGS, EEG EyeState, Musk (Version 2), and Spambase; datasets of large cardinalities were sele

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The zipped file includes a description of the combined household model example and a Simscape/MATLAB file for the model.

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The performance testing sample results for RPS = 110 (with the maximum TPS = ~ 350 achieved) for manuscript "Blockchain and IoT for Insurance: A Case Study and Cyberinfrastructure Solution on Fine-grained Transportation Insurance"

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"3_multi-media-of-figure5.zip" includes two short videos. Both of which shows the process of Inertial Generated Timing Sequence (IGTS) works.

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"3_multi-media-of-figure5.zip" includes two short videos. Both of which shows the process of Inertial Generated Timing Sequence (IGTS) works.

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Data and codes for journal paper "MmWave Vehicular Beam Training with Situational Awareness Using Machine Learning" submitted to IEEE Access.

The code assumes Python 3.

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이 그룹은 음향 장비 분야의 음향 증폭기 회로에 실리콘 트랜지스터를 사용할 때 오디오 관점에서 출력 신호의 음향 특성을 향상시키는 것을 목표로합니다.
음향 증폭기 회로에서 실리콘 트랜지스터를 사용할 때 사운드 출력은 차갑고 거칠고 선명하며 풍부하지 않은 것으로 알려져 있습니다. 따라서 오디오 애호가들은 여전히 ​​진공관을 사용하는 증폭기를 좋아합니다. 오디오 애호가는 소리를들을 수있는 능력 때문에 음질을 판단하는 뛰어난 능력을 가지고 있습니다.
그래서이 그룹은 실리콘 트랜지스터의 불충분 한 사운드 특성을 제거하고 튜브의 사운드로 개선하는 방법을 보여줍니다.
이 개선 방법을 적용하여이 부분을 만드는 과정을 논의하고 마지막으로 실제 부분으로 만들 것입니다.

한국어로 쓰여진 원본이 포함되어 있습니다.

여러 파일 중 가장 큰 개정 번호 만 사용하십시오.  
나머지는 비교를위한 데이터입니다.
 

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This material is associated with the PhD Thesis of Javier Olias (which is supervised by Sergio Cruces) and the article: 

EEG Signal Processing in MI-BCI Applications with Improved Covariance Matrix Estimators” by J.Olias, R. Martin-Clemente, M.A. Sarmiento-Vega and S. Cruces,
which was accepted in 2019 by IEEE Transactions on Neural Systems and Rehabilitation Engineering.



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The presented dataset has been used as a basis for CAO - a system for analysis of emoticons in Japanese online communication, developed by Ptaszynski et al. (2010). Emoticons are strings of symbols widely used in text-based online communication to convey user emotions. The database contains: 1) a predetermined raw emoticon database containing over ten thousand emoticon samples extracted from the Web, 2) emoticon parts automatically divided from raw emoticons into semantic areas representing “mouths” or “eyes”.

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HTTP and DNS sample traffic traces for experimenting of protocol reverse engineering methods.

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