BCI-FES therapy has been proved to be an effective way to help post-stroke patients restore motor function of paralyzed limbs. In the existing BCI-FES system, patients can only asynchronously receive feedback in the f...
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This paper proposes a recognition method of human actions in video by adding new features, the joint angle acceleration to the feature space. In this method, human body is described as three-dimensional skeletons. The...
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In this paper, we propose an algorithm for 12-leads ECG signals feature extraction by Uncorrelated Multilinear Principal Component Analysis(UMPCA). However, traditional algorithms usually base on 2-leads ECG signals a...
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Based on the quadruped robot, this paper mainly studies the two directions of the content. The first part mainly introduces the mechanical structure design and the construction of the control system of the quadruped r...
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In the recent years haptic interfaces became a reliable solution in order to solve problems which arise when humans interact with the environment. If in the research area of the haptic interaction between human and en...
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In the recent years haptic interfaces became a reliable solution in order to solve problems which arise when humans interact with the environment. If in the research area of the haptic interaction between human and environment there are important researches, a innovative approach for the interaction between the robot and the environment using haptic interfaces and virtual projection method is presented in this paper. In order to control this interaction we used the Virtual Projection Method where haptic control interfaces of impedance and admittance will be embedded. The obtained results, validated by simulations assure stability, stiffness, high maneuverability and adaptability for rescue walking robots in order to move in disaster, dangerous and hazardous areas.
Understanding the dynamics of human movements is key to issues of significant current interest such as behavioral prediction, recommendation, and control of epidemic spreading. We collect and analyze big data sets of ...
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Understanding the dynamics of human movements is key to issues of significant current interest such as behavioral prediction, recommendation, and control of epidemic spreading. We collect and analyze big data sets of human movements in both cyberspace (through browsing of websites) and physical space (through mobile towers) and find a superlinear scaling relation between the mean frequency of visit 〈f〉 and its fluctuation σ:σ∼〈f〉β with β≈1.2. The probability distribution of the visiting frequency is found to be a stretched exponential function. We develop a model incorporating two essential ingredients, preferential return and exploration, and show that these are necessary for generating the scaling relation extracted from real data. A striking finding is that human movements in cyberspace and physical space are strongly correlated, indicating a distinctive behavioral identifying characteristic and implying that the behaviors in one space can be used to predict those in the other.
In this paper, we present the theory of online sparse least squares support vector machine (OS-LSSVM) for prediction and propose a predictor with OS-LSSVM to detect sensor fault. The principle of the predictor and its...
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complex networks have, in recent years, brought many innovative impacts to large-scale systems. However, great challenges also come forth due to distinct complex situations and imperative requirements in human life no...
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This paper investigates controlling the commercialized Spykee mobile robot, using only brain electroencephalography (EEG) signals transmitted by the Emotiv Epoc Neuro Headset. The Spykee robot is equipped with a wirel...
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Regarding the non-negativity property of the magnitude spectrogram of speech signals, nonnegative matrix factorization (NMF) has obtained promising performance for speech separation by independently learning a diction...
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ISBN:
(纸本)9781479928941
Regarding the non-negativity property of the magnitude spectrogram of speech signals, nonnegative matrix factorization (NMF) has obtained promising performance for speech separation by independently learning a dictionary on the speech signals of each known speaker. However, traditional NM-F fails to represent the mixture signals accurately because the dictionaries for speakers are learned in the absence of mixture signals. In this paper, we propose a new transductive NMF algorithm (TNMF) to jointly learn a dictionary on both speech signals of each speaker and the mixture signals to be separated. Since TNMF learns a more descriptive dictionary by encoding the mixture signals than that learned by NMF, it significantly boosts the separation performance. Experiments results on a popular TIMIT dataset show that the proposed TNMF-based methods outperform traditional NMF-based methods for separating the monophonic mixtures of speech signals of known speakers.
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