In the globalized academic landscape, university academic evaluation plays a crucial role. However, predicting university academic evaluation in time series prediction poses significant challenges due to its nonlinearity and nonstationarity. In this article, we propose a novel method to address the prediction problem in scenarios characterized by limited data volume. By leveraging the properties of Gaussian processes, our method constructs nonstationary kernels that are fully trainable. The time series data is decomposed into trend and seasonal components, which are then inputted into an independent Gaussian process model equipped with trainable parameters. The core of our method lies in the trend Gaussian process model, which consists of classical kernel functions and multilayer perceptron stacks. This combination enhances the model’s capacity to capture long-term dependencies and improves prediction accuracy. To evaluate the effectiveness of our novel method, experiments were conducted on both the M4 dataset and an academic indicator dataset. The experimental results demonstrate significant improvements in prediction accuracy and stability compared with traditional time series prediction methods. Additionally, comparative experiments were performed to contrast our method against other popular time series prediction methods, consistently affirming the superior predictive performance of our approach.

This article deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where the limited number of secondary data is available due to the heterogeneity of sea clutter. A class of linear discriminant analysis (LDA)-based matrix information geometry (MIG) detectors is proposed in the supervised scenario. As customary, Hermitian positive-definite (HPD) matrices are used to model the observational sample data, and the clutter covariance matrix of the received dataset is estimated as the geometric mean of the secondary HPD matrices. Given a set of training HPD matrices with class labels, which are elements of a higher dimensional HPD matrix manifold, the LDA manifold projection learns a mapping from the higher dimensional HPD matrix manifold to a lower dimensional one subject to maximum discrimination. In this study, the LDA manifold projection, with the cost function maximizing between-class distance while minimizing within-class distance, is formulated as an optimization problem in the Stiefel manifold. Four robust LDA-MIG detectors corresponding to different geometric measures are proposed. Numerical results based on both simulated radar clutter with interferences and real IPIX radar data show the advantage of the proposed LDA-MIG detectors against their counterparts without using LDA and the state-of-the-art maritime target detection methods in nonhomogeneous sea clutter.

In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A statistical model is established to analyze the asymptotic property of the estimator. In particular, we show the convergence rate as the sample size tends to infinity. We verify the convergence rate through simulated data and apply the estimated Weingarten map to curvature estimation and point cloud simplification to multiple real data sets.

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts as radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

In this letter, we propose a novel manifold-based algorithm to solve the constant envelope (CE) precoding problem with interference exploitation. For a given power budget, we design the precoded symbols subject to the CE constraints, such that the constructive effect of the multiuser interference is maximized. While the objective function for the original problem is not complex differentiable, we consider the smooth approximation of its real representation, and map it onto a Riemannian manifold. By using the Riemmanian conjugate gradient algorithm, a local minimizer can be efficiently found. The complexity of the algorithm is analytically derived in terms of floating-points operations (flops) per iteration. Simulations show that the proposed algorithm outperforms the conventional methods on both symbol error rate and computational complexity.

In this paper, we propose a novel interface between drivers and in-vehicle devices by using steady-state visual evoked potential (SSVEP) of brain signals. The SSVEP is detected by a canonical correlation analysis (CCA) classifier and applied to turn on and turn off in-vehicle devices. The proposed interface is built and tested online in a driving simulator by requiring drivers to use the interface to interact with the in-vehicle device while performing the primary driving tasks including lane keeping and obstacle avoidance. The pilot experimental results show the feasibility of the proposed interface.