Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.

Fatigue driving is the main reason of traffic accidents. The electroencephalograph (EEG) signal can accurately reflect the changes of human physiological state, so EEG is considered as a reliable method to detect fatigue driving. In this letter, the Euclidean space data alignment approach is adopted to reduce individual differences. Then, to consider the spatial correlation between multiple channels EEG signals and further improve the accuracy, an efficient long short‐term memory (LSTM) network structure is designed to detect the fatigue state of drivers. The experimental result shows that the method improves the generalisation capability and achieves the performance with an average accuracy of 95.70% for three categories. Evaluation of the algorithm performance in comparison with other traditional methods indicates that the method has higher accuracy and lower standard deviation. Generally speaking, the proposed method is promising for the application of fatigue driving monitoring.

As the transmission rate of Wireless Body Area Network (WBAN) increases, ultrawideband (UWB) technology is increasingly being applied to WBAN. To effectively integrate UWB technology into WBAN transmission systems, it is essential to develop UWB antennas that meet the requirements of wide bandwidth, ease of fabrication, and compatibility with the human body. Based on the characteristic mode analysis (CMA) theory, this study proposed a honeycomb structure metasurface layout to significantly expand the antenna bandwidth. The implementation of a cross-slot and ring-shaped microstrip line series feed network introduced a 90∘ phase difference to a pair of vertically polarized degenerate characteristic modes. This excited the degenerate modes, achieving excellent far-field radiation. The dimensions of the designed antenna were 1.4λ0×1.4λ0×0.09λ0(where λ0 represents the wavelength corresponding to the center frequency of 7 GHz). Simulations showed that the antenna can stably operate within the 5.12–9.13 GHz frequency band, with absolute and relative bandwidths of 4.01 GHz and 57.14%, respectively. Within the passband, the maximum gain reached 10.43 dBi, with an average gain of approximately 9.4 dBi. The calculated specific absorption rate (SAR) of the antenna indicated that the average SAR was below 2 W/kg, complying with regulatory standards.

Wireless energy transfer system is an effective way to solve the visual prosthesis energy supply problems, theoretical modeling of the system is the prerequisite to do optimal energy transfer system design. On the basis of the ideal model of the wireless energy transfer system, according to visual prosthesis application condition, the system modeling is optimized. During the optimal modeling, taking planar spiral coils as the coupling devices between energy transmitter and receiver, the effect of the parasitic capacitance of the transfer coil is considered, and especially the concept of biological capacitance is proposed to consider the influence of biological tissue on the energy transfer efficiency, resulting in the optimal modeling’s more accuracy for the actual application. The simulation data of the optimal model in this paper is compared with that of the previous ideal model, the results show that under high frequency condition, the parasitic capacitance of inductance and biological capacitance considered in the optimal model could have great impact on the wireless energy transfer system. The further comparison with the experimental data verifies the validity and accuracy of the optimal model proposed in this paper. The optimal model proposed in this paper has a higher theoretical guiding significance for the wireless energy transfer system’s further research, and provide a more precise model reference for solving the power supply problem in visual prosthesis clinical application.

The degradation of indoor ultra-wideband (UWB) ranging accuracy caused by the non-line-of-sight (NLOS) condition has drawn considerable attention in the academic community. The existing solutions for generalized scenarios face challenges such as high cost, limited computational power, and lack of sufficient high-quality data, thereby hindering effective resolutions. Analyzing ranging errors caused by human body impact has been an effective approach for specific indoor scenarios. However, the accuracy of existing models and error correction schemes must be improved. After analyzing the error generation mechanisms of indoor UWB ranging, it was successfully identified that the two dominant error factors under human body impact include the human body shadowing error and the reference zero wall (RZW) reflection error. This paper proposes a particle filtering correction method based on Gaussian and generalized extreme value (GEV) distribution models and a direct compensation method. The experimental results show that the proposed Gauss-GEV error distribution model is more consistent with the measured values under line-of-sight (LOS) and human body shadowing conditions. The particle filtering correction scheme based on this model reduces the maximum mean error below 25 cm for human body shadowing error. A direct compensation scheme proposed to correct RZW reflection error also shows remarkable improvement in ranging accuracy. The human body impact model and error correction scheme for indoor UWB ranging proposed in this paper have great potential for indoor ranging applications.

To address the limitations of the single-modal electroencephalogram (EEG), such as its single physiological dimension, weak anti-interference ability, and inability to fully reflect emotional states, this paper proposes a gated multi-head cross-attention module (GMHCA) for multimodal fusion of EEG, electrooculography (EOG),and electrodermal activity (EDA). This attention module employs three independent and parallel attention computation units to assign independent attention weights to different feature subsets across modalities. Combined with a modality complementarity metric, the gating mechanism suppresses redundant heads and enhances the information transmission of key heads. Through multi-head concatenation, cross-modal interaction results from different perspectives are fused. For the backbone network, a multi-scale convolution and bidirectional long short-term memory network (MC-BiLSTM) is designed for feature extraction, tailored to the characteristics of each modality. Experiments show that this method, which primarily fuses eight-channel EEG with peripheral physiological signals, achieves an emotion recognition accuracy of 89.45%, a 7.68% improvement over single-modal EEG. In addition, in cross-subject experiments conducted on the SEED-IV dataset, the EEG+EOG modality achieved a classification accuracy of 92.73%. All were significantly better than the baseline method. This fully demonstrates the effectiveness of the innovative GMHCA module architecture and MC-BiLSTM feature extraction network proposed in this paper for multimodal fusion methods. Through the novel attention gating mechanism, higher recognition accuracy is achieved while significantly reducing the number of EEG channels, providing new ideas and approaches based on attention mechanisms and gated fusion for multimodal emotion recognition in resource-constrained environments.