Stroke classification is crucial for timely diagnosis and treatment, as it helps differentiate between hemorrhagic and ischemic strokes, which require distinct clinical interventions. This paper proposes a stroke classification method using multi-channel electroencephalography (EEG) data. Unlike single-channel data or simple multi-channel concatenation, our method processes EEG data as a channel matrix, significantly improving classification performance. We employ two complementary feature extraction techniques: discrete wavelet transform (DWT) and empirical mode decomposition (EMD). DWT extracts multi-scale wavelet coefficients from stroke-related frequency bands, while EMD decomposes EEG signals into intrinsic mode functions (IMFs), representing narrowband oscillation components. To enhance feature quality, we propose a hybrid selection method that integrates four metrics—information entropy, power spectral density (PSD) distance, statistical significance, and maximum information coefficient (MIC)—to comprehensively evaluate IMFs. This method accounts for both the intrinsic information content of EEG signals and the inter-class differences between hemorrhagic and ischemic stroke subjects. Furthermore, this paper designs a pyramid cascade convolutional neural network (PCCNN) model with multi-branch independent learning and hierarchical fusion. Each DWT and EMD feature is processed by an independent one-dimensional convolutional neural networks (1D-CNN) branch for targeted extraction. A pyramid fusion mechanism integrates branch outputs into a fused feature vector, enabling the feature interaction through a top-level fusion CNN. Experimental results demonstrate that the proposed method, which integrates channel matrix processing, high-quality DWT and EMD feature selection, and multi-branch feature fusion, significantly outperforms single-feature methods. The fusion feature achieves a classification accuracy of 99.48%, effectively distinguishing EEG data of hemorrhagic and ischemic stroke.

The proposed research deals with selection of particle swarm optimization (PSO) algorithm parameters for missile gliding trajectory optimization relying on Taguchi design of experiments, analysis of variance (ANOVA) and artificial neural networks (ANN). Population size, inertial weight and acceleration coefficients of PSO were chosen for the present study. The experiments have been designed as per Taguchi's design of experiments using L25 orthogonal array for selection of better PSO parameters. Missile gliding trajectory is optimized by discretizing angle of attack as control parameter, consequent conversion of optimal control problem to nonlinear programming problem (NLP) and finally solving the problem using PSO with optimized parameters to obtain optimum angle of attack and realization of maximum gliding range. Simulation results portrayed that the gliding range is maximized and missile glide distance is enhanced compared to earlier experiments. The efficiency of proposed approach was verified via different test scenarios.

In this paper, a user-following mobile robot which tracks and follows the user, offering stand-up assistance function is proposed. The proposed robot plays the role of a chair where the user can sit on, and offers a stand-up assistance function compensating the lack of muscle strength. In the proposed robot, a sensing method for buttocks recognition using a depth sensor is proposed. By measuring the distance from the user’s buttocks, the walking state is recognized and the tracking is performed at a fixed distance. As an approach to realize the tracking function, a human tracking method for mobile robots using PD control is constructed. According experimental study, usefulness of the proposed mobile robot with the function of user-following and stand-up assistance is confirmed. The user recognition method and the tracking method using PD control are confirmed effective. With the proposed robot system, improvement in welfare field can be expected.