Selective Combining for Hybrid Cooperative Networks

In this study, we consider the selective combining in hybrid cooperative networks (SCHCNs scheme) with one source node, one destination node and $N$ relay nodes. In the SCHCN scheme, each relay first adaptively chooses between amplify-and-forward protocol and decode-and-forward protocol on a per frame basis by examining the error-detecting code result, and $N_c$ ($1\leq N_c \leq N$) relays will be selected to forward their received signals to the destination. We first develop a signal-to-noise ratio (SNR) threshold-based frame error rate (FER) approximation model. Then, the theoretical FER expressions for the SCHCN scheme are derived by utilizing the proposed SNR threshold-based FER approximation model. The analytical FER expressions are validated through simulation results.Comment: 27 pages, 8 figures, IET Communications, 201

Grasping Strategy in Space Robot Capturing Floating Target

AbstractWhen the space robot captures a floating target, contact impact occurs inevitably and frequently between the manipulator hand and the target, which seriously impacts the position and attitude of the robot and grasping security. “Dynamic grasping area” is introduced to describe the collision process of manipulator grasping target, and grasping area control equation is established. By analyzing the impact of grasping control parameters, base and target mass on the grasping process and combining the life experience, it is found that if the product of speed control parameter and dB adjustment parameter is close to but smaller than the minimum grasping speed, collision impact in the grasping process could be reduced greatly, and then an ideal grasping strategy is proposed. Simulation results indicate that during the same period, the strategy grasping is superior to the accelerating grasping, in that the amplitude of impact force is reduced to 20%, and the attitude control torque is reduced to 15%, and the impact on the robot is eliminated significantly. The results would have important academic value and engineering significance

Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Biobank Studies

Surrogate variables in electronic health records (EHR) and biobank data play an important role in biomedical studies due to the scarcity or absence of chart-reviewed gold standard labels. We develop a novel approach named SASH for {\bf S}urrogate-{\bf A}ssisted and data-{\bf S}hielding {\bf H}igh-dimensional integrative regression. It is a semi-supervised approach that efficiently leverages sizable unlabeled samples with error-prone EHR surrogate outcomes from multiple local sites, to improve the learning accuracy of the small gold-labeled data. {To facilitate stable and efficient knowledge extraction from the surrogates, our method first obtains a preliminary supervised estimator, and then uses it to assist training a regularized single index model (SIM) for the surrogates. Interestingly, through a chain of convex and properly penalized sparse regressions that approximate the SIM loss with bias-correction, our method avoids the local minima issue of the SIM training, and fully eliminates the impact of the preliminary estimator's large error. In addition, it protects individual-level information through summary-statistics-based data aggregation across the local sites, leveraging a similar idea of bias-corrected approximation for SIM.} Through simulation studies, we demonstrate that our method outperforms existing approaches on finite samples. Finally, we apply our method to develop a high dimensional genetic risk model for type II diabetes using large-scale data sets from UK and Mass General Brigham biobanks, where only a small fraction of subjects in one site has been labeled via chart reviewing

Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features

Due to label scarcity and covariate shift happening frequently in real-world studies, transfer learning has become an essential technique to train models generalizable to some target populations using existing labeled source data. Most existing transfer learning research has been focused on model estimation, while there is a paucity of literature on transfer inference for model accuracy despite its importance. We propose a novel $\mathbf{D}$oubly $\mathbf{R}$obust $\mathbf{A}$ugmented $\mathbf{M}$odel $\mathbf{A}$ccuracy $\mathbf{T}$ransfer $\mathbf{I}$nferen$\mathbf{C}$e (DRAMATIC) method for point and interval estimation of commonly used classification performance measures in an unlabeled target population using labeled source data. Specifically, DRAMATIC derives and evaluates the risk model for a binary response $Y$ against some low dimensional predictors $\mathbf{A}$ on the target population, leveraging $Y$ from source data only and high dimensional adjustment features $\mathbf{X}$ from both the source and target data. The proposed estimators are doubly robust in the sense that they are $n^{1/2}$ consistent when at least one model is correctly specified and certain model sparsity assumptions hold. Simulation results demonstrate that the point estimation have negligible bias and the confidence intervals derived by DRAMATIC attain satisfactory empirical coverage levels. We further illustrate the utility of our method to transfer the genetic risk prediction model and its accuracy evaluation for type II diabetes across two patient cohorts in Mass General Brigham (MGB) collected using different sampling mechanisms and at different time points

Performance Analysis of Hybrid Relay Selection in Cooperative Wireless Systems

The hybrid relay selection (HRS) scheme, which adaptively chooses amplify-and-forward (AF) and decode-and-forward (DF) protocols, is very effective to achieve robust performance in wireless networks. This paper analyzes the frame error rate (FER) of the HRS scheme in general cooperative wireless networks without and with utilizing error control coding at the source node. We first develop an improved signal-to-noise ratio (SNR) threshold-based FER approximation model. Then, we derive an analytical average FER expression as well as an asymptotic expression at high SNR for the HRS scheme and generalize to other relaying schemes. Simulation results are in excellent agreement with the theoretical analysis, which validates the derived FER expressions.Comment: IEEE Transactions on Communications, 201

On the Characterization of a Class of Fisher-Consistent Loss Functions and Its Application to Boosting

Accurate classification of categorical outcomes is essential in a wide range of applications. Due to computational issues with minimizing the empirical 0/1 loss, Fisher consistent losses have been proposed as viable proxies. However, even with smooth losses, direct minimization remains a daunting task. To approximate such a minimizer, various boosting algorithms have been suggested. For example, with exponential loss, the AdaBoost algorithm (Freund and Schapire, 1995) is widely used for two-class problems and has been extended to the multi-class setting (Zhu et al., 2009). Alternative loss functions, such as the logistic and the hinge losses, and their corresponding boosting algorithms have also been proposed (Zou et al., 2008; Wang, 2012). In this paper we demonstrate that a broad class of losses, including non-convex functions, achieve Fisher consistency, and in addition can be used for explicit estimation of the conditional class probabilities. Furthermore, we provide a generic boosting algorithm that is not loss-specific. Extensive simulation results suggest that the proposed boosting algorithms could outperform existing methods with properly chosen losses and bags of weak learners.Statistic