The CLAS (Cognitive Load, Affect and Stress) dataset was conceived as a freelyaccessible repository which is purposely developed to support research on the automated assessment of certain states of mind and the emotional condition of a person.
All data used for model evaluation and training are from the 16th Chinese National Graduate Mathematical Contest in Modeling (CNGMCM). The dataset is measured by HUAWEI TECHNOLOGIES CO., LTD, which includes engineering parameter data, map data and RSRP label data of multiple communities. And the dataset used in this letter is from the public dataset of CNGMCM, which is sponsored by the Ministry of Education's Degree and Graduate Education Development Center.
This dataset covers cellular communication signals in the SCF format. There is a total of 60000 signal instances, 36000 of them are reserved as training data and the rest is for the test. The SNR levels are between 1 dB and 15 dB.
In the fifth generation (5G) wireless communication, high-speed railway (HSR) communication is one of the most challenging scenarios. By adopting massive multi-input multi-output (mMIMO) technology in HSR communication, the design of the underlying communication system becomes more challenging. Some new channel characteristics must be studied, such as non-stationarity in space, time and frequency domains. In this paper, two models are established for the two states of HSR.
In the fifth generation (5G) wireless communication, high-speed railway (HSR) communication is one of the most challenging scenarios. By adopting massive multi-input multi-output (mMIMO) technology in HSR communication, the design of the underlying communication system becomes more challenging. Some new channel characteristics must be studied, such as non-stationarity in space, time and frequency domains. In this paper, two models are established for the two states of HSR at rest or in motion.
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. Comprising 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.
With the development of audio synthesis techniques, the most state-of-art synthesis methods based on Generative Adversarial Network(GAN) have been proposed. Whether the automatic speaker verification (ASV) systems are vulnerability to the GAN based synthesized audios is urgently needed to be verified. We present a publicly available set of GAN based synthesized audios generated by some open source schemes (WaveGAN,TifGAN,GANSynth,MelGAN), which allows researches to verify impact of the GAN-synthetic audio on security of ASV systems.
The experimental measurement setup is performed in an anechoic chamber to secure the LOS transmission at the Millimeter Wave and Terahertz Technologies Research Laboratories (MILTAL), Scientific and Technological Research Council of Turkey (TUBİTAK), Kocaeli. The dimensions of anechoic chamber are 7m × 4m × 3m. The dataset includes channel impulse response in between 240GHz and 300GHz for the transmitter-receiver distance of 20cm, 30cm, 40cm, 60cm, and 80cm.
