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We give an overview of wave scattering in open cavities in which the ray dynamics is chaotic. In the limit of short wavelengths certain properties emerge that are universal and do not depend on the details of the cavity. These universal features are described by random matrix theory. We discuss in particular results that characterize the transmission probabilities and transmission times of waves through...
Bistatic MIMO radar has become an active research area in recent years. In this paper, linear prediction combining with eigenvalue decomposition is applied to DOD and DOA estimation of bistatic MIMO radar. Firstly, according to signal model, the output signals of receivers are depicted by transceiver combing steering vector which is the kronecker product of receiving steering vector and transmitting...
According to the operation of the automaton transient impact, nonlinear, non-stationary signal, a method which is based on the time-frequency characteristics and PCA-SVM automaton fault diagnosis is proposed. Firstly, this paper uses statistical analysis and overall empirical mode decomposition method to construct high dimensional mixed domain initial feature vector from the characteristics of different...
The partially-conditioned Gaussian (PCG) density, a variant of the Gauss-Bingham density, quantifies the uncertainty of a state vector comprised of an attitude quaternion and other Euclidean states on their natural manifold, the unit hypercylinder. The conditioned Gaussian density is first developed by conditioning a Gaussian density on the unit hypersphere, and is an alternate representation of the...
Scientific and accurate cultivated quality grade results are an important guarantee for applications. However, the phenomenon of abnormal cultivated land quality is always occurred due to the error of investigation or computation, so exploring effective method to inspect abnormal data of the grade results is an important problem to be solved. Cultivated land quality grade results of Wuhan Hannan district...
Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model...
This paper introduces a subspace method for the estimation of an array covariance matrix. When the received signals are uncorrelated, it is shown that the array covariance matrices lie in a special subspace defined through all possible correlation vectors of the received signals and whose dimension is typically much smaller than the ambient dimension. Based on this observation, a subspace-based covariance...
Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios the information of interest resides in more irregular graph domains. The contribution in this paper is twofold. First, we propose several equivalent notions of weak stationarity for random graph signals,...
Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label...
Canonical correlation analysis(CCA) is a popular technique that works for finding the correlation between two sets of variables. However, CCA faces the problem of small sample size in dealing with high dimensional data. Several approaches have been proposed to overcome this issue, but the resulting transformation matrix fails to extract shared structures among data samples. In this paper, we propose...
It is a very important issue to discover data correlations in multi-label classification. A two-stage framework is presented to incorporate the supervised feature extraction and correlation exploration with the predictive modeling. Firstly, a low-dimensional feature mapping is obtained under the guidance of label information, and produces good feature extraction. Secondly, a predictive model is learnt...
The potential gains of multiple antennas in wireless systems can be limited by channel state information imperfections. In this context, this paper tackles the limited feedback in multiuser correlated multiple input single output (MU-MISO). We propose a framework to feedback the minimum number of bits with limited performance degradation. This framework is based on decor relating the channel state...
In this paper, we propose a data aware method to perform Quality of Experience (QoE) and/or Quality of Service (QoS) management. A data-aware QoE model is built based on Principle Component Analysis (PCA). Three main types of QoE-QoS management (optimal QoE management, optimal QoS management and QoE-QoS balance management) are discussed based on the proposed scheme. A QoE-QoS unified optimization...
Better understanding of boundary-layer wind field is fundamental for modeling of various wind-related phenomena, e.g., gas dispersal and energy generation. In this paper, we first record three groups of the near surface wind speed time series in different locations using a high-resolution 3D ultrasonic anemometer. Then, we map these experimental time series into complex networks and detect the corresponding...
The paper investigates a problem of increasing convergence rate for the process of adaptive weight vector adjustment in spatially reconfigurable antenna systems on the background of point-source interferences. It is shown that pre-processing with adjustment of adaptive antenna array element spacing gives the capability to reduce considerably eigenvalues spread of spatial correlation matrix and to...
Two estimation algorithms for the spreading sequence in DSSS signals are proposed. Some of previously proposed algorithms have some drawbacks such as large computational complexity, low estimation accuracy and poor accuracy by increasing the length of spreading sequence period. In this paper by using an initial estimate of chips, we can improve recursively the final decision with low computational...
Spectrum sensing is a key component in any cognitive radio network. Recently full-duplex communication, i.e., the ability to transmit and receive at the same time at the same frequency, has become feasible. Residual self interference is inevitable even after applying self interference cancellation techniques in radio frequency (RF) and baseband domains. In this paper, we study the performance of popular...
Parcellation of brain imaging data is desired for proper neurological interpretation in resting-state functional-magnetic resonance imaging (rs-fMRI) data. Some methods require specifying a number of parcels and using model selection to determine the number of parcels on a rs-fMRI dataset. However, this generalization does not fit with all subjects in a given dataset. A method has been proposed using...
Telemetry data, containing the data of multiple subsystems such as power system, implies the on-orbit operation status information of the satellite. We can obtain performance characteristics and fault symptom of the satellite subsystems through analyzing these data. Using classification algorithm we can provide normal data for anomaly detection and find the data from various subsystems which have...
In this paper, we propose a novel Multi-set Locality-Preserving Canonical Correlation Analysis (MLPCCA) for multi-view learning and fusion. The proposed MLPCCA captures the intrinsic structure of data while it learns the optimum basis for maximizing the correlation among different sets of data. To verify the effectiveness of the proposed technique, the proposed MLPC A has been applied in audio-based...
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