Direction of arrival (DOA) estimation from array observations in a noisy environment is discussed. The source amplitudes are assumed to be correlated zero-mean complex Gaussian distributed with unknown covariance matr...
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ISBN:
(纸本)9781538647523
Direction of arrival (DOA) estimation from array observations in a noisy environment is discussed. The source amplitudes are assumed to be correlated zero-mean complex Gaussian distributed with unknown covariance matrix. The DOAs and covariance parameters of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL). The performance of SBL is evaluated in terms of the fidelity of the reconstructed coherency matrix of the estimated plane waves.
For unsupervised person re-identification, traditional Laplacian regularisation based dictionary learning methods encounter the fixed weight problem and thus impair the match rate. To address this limitation, we devel...
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ISBN:
(纸本)9781538663011
For unsupervised person re-identification, traditional Laplacian regularisation based dictionary learning methods encounter the fixed weight problem and thus impair the match rate. To address this limitation, we develop a novel unsupervised dictionary learning approach to learn a discriminative representation. The proposed approach takes the efficiency of l(2) graph regularization with a closed-form solution into account. Our approaches achieve very promising results on the challenging VIPeR dataset.
Performance improvement of up to 1.2 dB in Q(2)-factor is experimentally demonstrated in single-carrier dual-polarization 2 x28-Gbaud 16QAM 6x100 km EDFA-amplified transmission with digital post-equalization enhanced ...
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ISBN:
(纸本)9781943580422
Performance improvement of up to 1.2 dB in Q(2)-factor is experimentally demonstrated in single-carrier dual-polarization 2 x28-Gbaud 16QAM 6x100 km EDFA-amplified transmission with digital post-equalization enhanced with an artificial neural network.
We experimentally demonstrate mode group crosstalk mitigation in IM/DD mode-multiplexed OFDM transmission using pilot assisted least square algorithm. Transmission performance is improved through mitigating the crosst...
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ISBN:
(纸本)9781943580422
We experimentally demonstrate mode group crosstalk mitigation in IM/DD mode-multiplexed OFDM transmission using pilot assisted least square algorithm. Transmission performance is improved through mitigating the crosstalk between the LP01 and LP11 mode groups.
We consider a multi-user multiple-input single-output downlink system that provides each user with a prespecified level of quality-of-service. The rate-splitting (RS) approach to this problem involves splitting the me...
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ISBN:
(纸本)9781538635124
We consider a multi-user multiple-input single-output downlink system that provides each user with a prespecified level of quality-of-service. The rate-splitting (RS) approach to this problem involves splitting the messages of each user into common and private portions that are transmitted in superposition and decoded sequentially. By adjusting the rates of each portion, the RS approach is able to mitigate the interference that conventional linear beamforming (CLB) schemes incur when users have channels that are closely aligned. However, the transmitter design problem for the RS approach can be quite challenging to solve. In this paper we develop a single-rate-splitting approach, in which RS is applied only to the user with the channel that is "most aligned" with the other channels. This approach greatly reduces the computational cost of RS designs, and admits an offset-based variant that provides robustness to channel estimation errors. Despite its simplifications, our simulation results indicate that the proposed approach retains most of the performance advantage of RS transmission over CLB.
In this paper a method for estimating mean opinion score for noisy images is presented. The focus is to create a robust method, which is not suited to the single image database. Algorithm is validated using three diff...
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ISBN:
(纸本)9781538669792
In this paper a method for estimating mean opinion score for noisy images is presented. The focus is to create a robust method, which is not suited to the single image database. Algorithm is validated using three different databases, which shows that it is more generic than other methods. Moreover, proposed solution works as non-reference image quality assessment algorithm, which means that only distorted image is used for evaluation. Results obtained with proposed method are compared with two other algorithms designed for non-reference IQA.
This paper investigates the impact of array switching patterns on the accuracy of parameter estimation of multipath components for a time division multiplexed (TDM) channel sounder. To measure fast time-varying channe...
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ISBN:
(纸本)9781538631805
This paper investigates the impact of array switching patterns on the accuracy of parameter estimation of multipath components for a time division multiplexed (TDM) channel sounder. To measure fast time-varying channels, the conventional uniform array switching pattern poses a fundamental limit of the number of antennas that a TDM channel sounder can utilize. We propose a method, which is based on the simulated annealing algorithm, to find non-uniform array switching patterns for realistic antenna arrays, so that we can extend the Doppler estimation range of the channel sounder by suppressing the high sidelobes in the spatio-temporal ambiguity function. Monte Carlo simulations demonstrate that the optimal switching sequence leads to significantly smaller root mean square errors of both direction of departure and Doppler. Results can be applied in both vehicle-to-vehicle and mobile millimeter wave MIMO channel measurements.
Insider attacks can result in significant costs to an organization. There is an urgent need for an automatic insider threat detector with good accuracy and low false alarms. In this work, we propose a graph based insi...
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ISBN:
(纸本)9780996452762
Insider attacks can result in significant costs to an organization. There is an urgent need for an automatic insider threat detector with good accuracy and low false alarms. In this work, we propose a graph based insider threat detector to identify potential insider attackers based on identifying not only self-anomalous behaviors of an employee but also anomalies relative to other employees with similar job roles. A machine learning approach is developed to first infer the correlation graph among the organization's employees. Then, a graph signalprocessing method is designed to identify the potential insiders with detection and false positive rates better than performing detection independently on each employee. Our approach demonstrates that the correlated behaviors of an organization's employees should be exploited for a better detection of suspicious behaviors.
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