arXiv subjects

Xing Wang

Publications and source records attributed to Xing Wang.

240 records · Page 14Linked to original sources

arXiv2015

The study of the aging behavior on large area MCP-PMT

Aging experiments of a novel type of large area MCP-PMT made by JUNO collaboration were conducted.In these aging experiments, the multi-photoelectron spectrum and single photoelectron spectrum were measured daily,as well as the MCP resistance of the second PMT before and after the experiment. Two PMTs were aged successivelyfor cross check. The first PMT was aged for 52 days, while the other one was aged for 84 days. In order to study the mechanism of the aging process, the high voltage on the second PMT was increased to accelerate its aging process when the cumulative output of charge from its anode was about 4 C. From our study, it can be known that large area MCP-PMT aging had a strong relationship with the related MCPs. In accordance with the PMT aging curve,a PMT aging model was setup and a general aging formula was given.

physics.ins-det↗

arXiv2015

Approximate Message Passing-based Compressed Sensing Reconstruction with Generalized Elastic Net Prior

In this paper, we study the compressed sensing reconstruction problem with generalized elastic net prior (GENP), where a sparse signal is sampled via a noisy underdetermined linear observation system, and an additional initial estimation of the signal (the GENP) is available during the reconstruction. We first incorporate the GENP into the LASSO and the approximate message passing (AMP) frameworks, denoted by GENP-LASSO and GENP-AMP respectively. We then investigate the parameter selection, state evolution, and noise-sensitivity analysis of GENP-AMP. We show that, thanks to the GENP, there is no phase transition boundary in the proposed frameworks, i.e., the reconstruction error is bounded in the entire plane. The error is also smaller than those of the standard AMP and scalar denoising. A practical parameterless version of the GENP-AMP is also developed, which does not need to know the sparsity of the unknown signal and the variance of the GENP. Simulation results are presented to verify the efficiency of the proposed schemes.

cs.IT↗

arXiv2013

Twins:Device-free Object Tracking using Passive Tags

Without requiring objects to carry any transceiver, device-free based object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects such as intruders. However, existing device-free solutions mainly use sensors and active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a newly observed phenomenon called critical state caused by interference among passive tags. We contribute to both theory and practice of such phenomenon by presenting a new interference model that perfectly explains this phenomenon and using extensive experiments to validate it. We design a practical Twins based intrusion detection scheme and implement a real prototype with commercial off-the-shelf reader and tags. The results show that Twins is effective in detecting the moving object, with low location error of 0.75m in average.

cs.NI↗

arXiv2010

Encoded packet-Assisted Rescue Approach to Reliable Unicast in Wireless Networks

Recently, network coding technique has emerged as a promising approach that supports reliable transmission over wireless loss channels. In existing protocols where users have no interest in considering the encoded packets they had in coding or decoding operations, this rule is expensive and inefficient. This paper studies the impact of encoded packets in the reliable unicast network coding via some theoretical analysis. Using our approach, receivers do not only store the encoded packets they overheard, but also report these information to their neighbors, such that users enable to take account of encoded packets in their coding decisions as well as decoding operations. Moreover, we propose a redistribution algorithm to maximize the coding opportunities, which achieves better retransmission efficiency. Finally, theoretical analysis and simulation results for a wheel network illustrate the improvement in retransmissions efficiency due to the encoded packets.

cs.IT↗

arXiv2010

An Efficient Retransmission Based on Network Coding with Unicast Flows

Recently, network coding technique has emerged as a promising approach that supports reliable transmission over wireless loss channels. In existing protocols where users have no interest in considering the encoded packets they had in coding or decoding operations, this rule is expensive and inef-ficient. This paper studies the impact of encoded packets in the reliable unicast network coding via some theoretical analysis. Using our approach, receivers do not only store the encoded packets they overheard, but also report these information to their neighbors, such that users enable to take account of encoded packets in their coding decisions as well as decoding operations. Moreover, we propose a redistribution algorithm to maximize the coding opportunities, which achieves better retransmission efficiency. Finally, theoretical analysis and simulation results for a wheel network illustrate the improve-ment in retransmissions efficiency due to the encoded packets.

cs.IT↗

arXiv2009

Asymptotic distribution and sparsistency for l1-penalized parametric M-estimators with applications to linear SVM and logistic regression

Since its early use in least squares regression problems, the l1-penalization framework for variable selection has been employed in conjunction with a wide range of loss functions encompassing regression, classification and survival analysis. While a well developed theory exists for the l1-penalized least squares estimates, few results concern the behavior of l1-penalized estimates for general loss functions. In this paper, we derive two results concerning penalized estimates for a wide array of penalty and loss functions. Our first result characterizes the asymptotic distribution of penalized parametric M-estimators under mild conditions on the loss and penalty functions in the classical setting (fixed-p-large-n). Our second result explicits necessary and sufficient generalized irrepresentability (GI) conditions for l1-penalized parametric M-estimates to consistently select the components of a model (sparsistency) as well as their sign (sign consistency). In general, the GI conditions depend on the Hessian of the risk function at the true value of the unknown parameter. Under Gaussian predictors, we obtain a set of conditions under which the GI conditions can be re-expressed solely in terms of the second moment of the predictors. We apply our theory to contrast l1-penalized SVM and logistic regression classifiers and find conditions under which they have the same behavior in terms of their model selection consistency (sparsistency and sign consistency). Finally, we provide simulation evidence for the theory based on these classification examples.

math.ST↗