Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are inherently different for individuals (biometrics), and they can be customized to realize multi-length codes or passwords (for example, by performing different gestures).

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The data set collected using a self-designed electronic nose (e-nose) involved eight Chinese liquor types, which are LanJinJiu with 38% alcohol concentration (LJJ38), LanJinJiu with 48% alcohol concentration (LJJ48), DaoHuaXiang with 42% alcohol concentration (DHX), LuZhouLaoJiao with 38% alcohol concentration (LZLJ), MianZhuDaQu with 38% alcohol concentration (MZDQ), QingJiu with 38% alcohol concentration (QJ), ShiLiXiang (SLX) with 40% alcohol concentration and BianFengHu with 40% alcohol concentration (BFH).

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The name of the data set is changed to 'Electronic nose dataset for recogniton of eight liquor types'.

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Representative videos of dMRI data from the numerical tests of the paper: "Kernel Regression Imputation in Manifolds via Bi-Linear Modeling: The Dynamic-MRI Case," by K. Slavakis, G. N. Shetty, L. Cannelli, G. Scutari,  U. Nakarmi, and L. Ying. 

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This is the example dataset and source code of the paper "A Generalized Channel Dataset Generator for 5G New Radio Systems Based on Ray-Tracing" , and the data generator can be downloaded for free by researchers.

The datagenerator_raytrace3.0 is now support for mm-wave (28GHz+) channel state information.

The advantages of our dataset are as follows:

  • To deal with the large-scale dataset requirements, this letter models the channel according to 5G NR stan- dard published by 3GPP, and provides a 5G NR channel datasets generator based on CDL channel model.

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Opportunity++ is a precisely annotated dataset designed to support AI and machine learning research focused on the multimodal perception and learning of human activities (e.g. short actions, gestures, modes of locomotion, higher-level behavior).

Instructions: 

Complete documentation is provided in the readme.

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Detecting radioactive materials in mixtures is challenging due to low concentration, environmental factors, sensor noise, and others. This paper presents new results on nuclear material identification and mixing ratio estimation for mixtures of materials in which there are multiple isotopes present. Conventional and deep learning-based machine learning algorithms were compared. Both simulated and actual experimental data were used in the comparative studies.

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This is a data set about seven letter gestures

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