Signal Processing
A Frequency-Dependent & Per-Port (FDPP) channel termination & renormalization method is presented as
a more accurate substitute for the traditional method which
uses frequency-independent, uniform impedance as the serialization/deserialization (SerDes) transmit (TX)/receive (RX)
termination impedances. Although the traditional method is
employed in practically all the existing high-speed interface
standards, the assumption of the constant, uniform termination
impedance at TX and RX is not exact and will lead to inaccuracy.
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The performances of ENRZ (Ensemble NRZ) under
the interferences of crosstalk, in conjunction with NRZ (NonReturn-to-Zero), PAM4 (Pulse Amplitude Modulation of 4-level),
and PAM3 are investigated. One-side and two-side crosstalk scenarios with varying levels of crosstalk are studied. The simulated
eye-diagram obtained with the four signaling techniques under
a range of crosstalk levels are compared and the reasons that
lead to the prominent advantages of ENRZ are analyzed. As
an alternative approach of validating the performances of the
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This dataset has been employed in the following articles:
https://ieeexplore.ieee.org/document/9682692
https://ieeexplore.ieee.org/document/9871051
https://content.iospress.com/articles/technology-and-health-care/thc202198
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The measurement and diagnosis of the severity of failures in rotating machines allow the execution of predictive maintenance actions on equipment. These actions make it possible to monitor the operating parameters of the machine and to perform the prediction of failures, thus avoiding production losses, severe damage to the equipment, and safeguarding the integrity of the equipment operators. This paper describes the construction of a dataset composed of vibration signals of a rotating machine.
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This paper introduces a compact Multiple input Multiple output (MIMO) antenna system for vehicular application in the sub-6GHz 5G systems that operates in the middle and high frequency bands from 1.71GHz to 5GHz. The proposed design consists of two symmetrical raised printed monopoles on Flame Retardant 4 (FR4) dielectric material with Electromagnetic Band Gap shape (EBG) to provide defected ground to improve bandwidth impedance and higher isolation across the operating frequency range.
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This dataset consists of the training and the evaluation datasets for the LiDAR-based maritime environment perception presented in our journal publication "Maritime Environment Perception based on Deep Learning." Within the datasets, LiDAR raw data are processed using Deep Neural Networks (DNN). In the training dataset, we introduce the method for generating training data in Gazebo simulation. In the evaluation datasets, we provide the real-world tests conducted by two research vessels, respectively.
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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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