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With the development of human-computer interaction technology, the multi-modal, natural interaction will become the primary way of interaction between human and computer. A supporting technology is that the computer must understand and capture the behavior characteristic of human, thus motion capture is proposed. Through motion capture technology, the computer can understand human action, and the...
The study of social networks has become increasingly important in recent years. Previous implementations of multi-agent systems have observed a phenomenon called tolerance between agents through simulation studies, which is defined as an agent maintaining an unrewarding connection. This concept has also arisen in the social sciences through the study of networks. We aim to bridge this gap between...
Minimum mean square error (MMSE) signal detection is near-optimal for uplink multi-user large-scale MIMO systems with hundreds of antennas at the base station, but involves matrix inversion with high complexity. In this paper, we first prove that the filtering matrix of the MMSE algorithm in large-scale MIMO is symmetric positive definite, based on which we propose a low-complexity signal detection...
In this paper, we propose a multimodal multimedia retrieval model based on probabilistic Latent Semantic analysis (pLSA) to achieve multimodal retrieval. Firstly, We employ pLSA, to respectively simulate the generative processes of texts and images in the same documents. Then we employ the multivariate linear regression method to analyze the correlation between representations of texts and images...
A novel dynamic observer for linear descriptor systems is proposed in this paper. Sufficient conditions for the stability of the presented observer are established and expressed as linear matrix inequalities. Moreover, the proposed approach is extended to uncertain linear descriptor systems. The advantage of the proposed observer lies in the fact that it has a nonsingular structure, which is easy...
A reliable human brain atlas is critical for brain network analysis at macro-scale. Most studies employed existing anatomical brain atlases or randomly parcellated the whole brain into discrete regions. However, these anatomical atlases had a large variation in region sizes, and the random parcellation procedure was lack of explicit biological significance. In this study, we proposed a new brain parcellation...
A fast distributed approach is developed for the market clearing with large-scale demand response in electric power networks. In addition to conventional supply bids, demand offers from aggregators serving large numbers of residential smart appliances with different energy constraints are incorporated. Leveraging the Lagrangian relaxation based dual decomposition, the resulting optimization problem...
Recognizing the importance of smart grid data analytics, modern statistical learning tools are applied here to wholesale electricity market inference. Market clearing congestion patterns are uniquely modeled as rank-one components in the matrix of spatiotemporally correlated prices. Upon postulating a low-rank matrix factorization, kernels across pricing nodes and hours are systematically selected...
The paper presents a readily implementable approach for sensor fault detection, identification (SFD/I) and faulted sensor data reconstruction in complex systems based on self-organizing map neural networks (SOMNNs). Two operational regimes are considered, i.e. the steady operation and operation with transients. For steady operation, SOMNN based estimation error (EE) are used for SFD. EE contribution...
This paper presents a novel method for building textual feature defined on semantic distance and describes multi-model approach for Visual Concept Detection and Annotation(VCDA). Nowadays, the tags associated with images have been popularly used in the VCDA task, because they contain valuable information about image content that can hardly be described by low-level visual features. Traditionally the...
Canonical Polyadic (or CANDECOMP/PARAFAC, CP) decompositions are widely applied to analyze high order data, i.e. $N$-way tensors. Existing CP decomposition methods use alternating least square (ALS) iterations and hence need to compute the inverse of matrices and unfold tensors frequently, which are very time consuming for large-scale data and when is large. Fortunately, once at least one factor...
We present an accurate technique for efficiently estimating the gradient of time-varying responses at each time step. Using only one extra simulation, the sensitivities of a transient field response with respect to all the system parameters are evaluated regardless of their number at all time steps. A step function excitation is used to generate the adjoint fields. Our approach is validated through...
In transmit beamforming multi-antenna systems, the representative quantized channel direction information (CDI) may become outdated due to the feedback delay. Grassmannian predictive coding (GPC) has been shown as an effective method to overcome this problem. However, the existing GPC algorithm is still of practical concern as the quantization resolution may not be ensured at relatively low feedback...
In this paper, we propose a robust Grassmannian prediction algorithm to provide channel state information (CSI) at the base station for multiuser multiple-input multiple-output (MIMO) systems with delayed limited feedback. The underlying coherence among adjacent quantized CSIs is fully exploited to achieve robustness against the quantization error caused by the limited feedback. Rather than sticking...
Recently, we proposed an i-vector approach to acoustic sniffing for irrelevant variability normalization based acoustic model training in large vocabulary continuous speech recognition (LVCSR). Its effectiveness has been confirmed by experimental results on Switchboard- 1 conversational telephone speech transcription task. In this paper, we study several discriminative feature extraction approaches...
The paper presents two approaches for sensor fault detection and discrimination within a group, in real-time. In the first approach, the concept of y-indices is proposed through use of a transpose formulation of the data matrices traditionally used in Principal Component Analysis (PCA). The proposed formulation is introduced to measure the differences between sensor reading datasets in the ‘sensor...
In this paper, we propose a quantized predictive precoding scheme for spatial multiplexing multiple- input multiple-output (MIMO) systems with delayed feedback. A geodesic transmit subspace prediction algorithm is designed to predict the transmit subspace for the forthcoming transmission at the receiver. An infrequently updated codebook for transmit subspace quantization is developed to improve the...
To distinguish between a pair of transition faults, we need to find a test vector pair (LOC or LOS type)that produces different output responses for the two faults. By adding a few logic gates and one modeling flip-flop to the circuit under test (CUT), we create a diagnostic ATPG model usable by a conventional single stuck-at fault test pattern generator. Given a transition fault pair, this ATPG model...
Polarization filtering attracts extensive attention for its special property in solving problems which are unable to deal with in temporal or frequency domain. Conventional polarization filter can suppress interferences by seeking for the orthogonal vector of the interferences while it will inevitably bring in distortion to the target signal in its amplitude and phase; Null phase-shifting polarization...
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