15,974 research outputs found
Mobile Formation Coordination and Tracking Control for Multiple Non-holonomic Vehicles
This paper addresses forward motion control for trajectory tracking and
mobile formation coordination for a group of non-holonomic vehicles on SE(2).
Firstly, by constructing an intermediate attitude variable which involves
vehicles' position information and desired attitude, the translational and
rotational control inputs are designed in two stages to solve the trajectory
tracking problem. Secondly, the coordination relationships of relative
positions and headings are explored thoroughly for a group of non-holonomic
vehicles to maintain a mobile formation with rigid body motion constraints. We
prove that, except for the cases of parallel formation and translational
straight line formation, a mobile formation with strict rigid-body motion can
be achieved if and only if the ratios of linear speed to angular speed for each
individual vehicle are constants. Motion properties for mobile formation with
weak rigid-body motion are also demonstrated. Thereafter, based on the proposed
trajectory tracking approach, a distributed mobile formation control law is
designed under a directed tree graph. The performance of the proposed
controllers is validated by both numerical simulations and experiments
FEM simulation of laser shock processing on surface morphology and residual stress field of Ti-17 titanium alloy with different laser impact times
A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding
In this work, with combined belief propagation (BP), mean field (MF) and
expectation propagation (EP), an iterative receiver is designed for joint phase
noise (PN) estimation, equalization and decoding in a coded communication
system. The presence of the PN results in a nonlinear observation model.
Conventionally, the nonlinear model is directly linearized by using the
first-order Taylor approximation, e.g., in the state-of-the-art soft-input
extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to
handle the factor due to the nonlinear model, and a second-order Taylor
approximation is used to achieve Gaussian approximation to the MF messages,
which is crucial to the low-complexity implementation of the receiver with BP
and EP. It turns out that our approximation is more effective than the direct
linearization in the soft-in EKS with similar complexity, leading to
significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter
Identification of Novel Cancer-Related Genes with a Prognostic Role Using Gene Expression and Protein-Protein Interaction Network Data
Early cancer diagnosis and prognosis prediction are necessary for cancer patients. Effective identification of cancer-related genes and biomarkers and survival prediction for cancer patients would facilitate personalized treatment of cancer patients. This study aimed to investigate a method for integrating data regarding gene expression and protein-protein interaction networks to identify cancer-related prognostic genes via random walk with restart algorithm and survival analysis. Known cancer-related genes in protein-protein interaction networks were considered seed genes, and the random walk algorithm was used to identify candidate cancer-related genes. Thereafter, using the univariant Cox regression model, gene expression data were screened to identify survival-related genes. Furthermore, candidate genes and survival-related genes were screened to identify cancer-related prognostic genes. Finally, the effectiveness of the method was verified through gene function analysis and survival prediction. The results indicate that the cancer-related genes can be considered prognostic cancer biomarkers and provide a basis for cancer diagnosis
Turbo-Equalization Using Partial Gaussian Approximation
This paper deals with turbo-equalization for coded data transmission over
intersymbol interference (ISI) channels. We propose a message-passing algorithm
that uses the expectation-propagation rule to convert messages passed from the
demodulator-decoder to the equalizer and computes messages returned by the
equalizer by using a partial Gaussian approximation (PGA). Results from Monte
Carlo simulations show that this approach leads to a significant performance
improvement compared to state-of-the-art turbo-equalizers and allows for
trading performance with complexity. We exploit the specific structure of the
ISI channel model to significantly reduce the complexity of the PGA compared to
that considered in the initial paper proposing the method.Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letters on 8
March, 201
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