Traditional liquid-resistive mounts are widely used in vehicle powertrain vibration isolation because of their favorable low-frequency damping characteristics. However, they usually suffer from pronounced high-frequency dynamic stiffening, which significantly degrades their vibration-isolation performance in the high-frequency range and remains a critical limitation in passive mount design. To address this problem, this study presents the systematic modeling, comparative analysis, and structural optimization of liquid-resistive mounts with different internal configurations. Three representative mount structures, namely the decoupler-membrane type, the conventional bell-plate type, and a novel bell-plate configuration, are first described in terms of their structural characteristics and working mechanisms. Based on the lumped-parameter method, mathematical models of the three mounts are established, and their low- and high-frequency dynamic characteristics are comparatively investigated. The vibration isolation performance of the mounts is further evaluated under various excitation conditions to clarify the influence of structural modifications on the dynamic response and transmitted force. In addition, sensitivity analysis is performed using the ISIGHT software platform (ISIGHT 5.6 Design Gateway) to identify the key parameters governing high-frequency performance. Subsequently, structural optimization is conducted using nonlinear programming under the quadratic Lagrangian algorithm and the Six Sigma method. The results indicate that the introduction of a bell plate has little influence on the low-frequency dynamic characteristics, while it effectively suppresses high-frequency hardening and improves high-frequency vibration isolation. Moreover, the Six Sigma optimization method achieves better performance improvement than the NLPQL approach, providing a useful reference for the design and optimization of passive liquid-resistive mounts.

Dual-comb ranging enables rapid, high-precision absolute distance measurements, but its performance is constrained by intrinsic phase noise, which induces temporal jitter and degrades pulse-to-pulse mutual coherence. Here, we propose a deep learning network Wavelet-Fused DenseNet (WFDNet) for hardware-free computational phase correction in dual-comb ranging. Through integration of complex wavelet decomposition and physics-guided feature encoding, the network, trained on model-generated data, can directly extract multi-scale time–frequency features to correct phase distortions and recover temporal coherence of the signals. Results from both simulated and experimental scenarios reveal that the approach can effectively suppress spectral noise and retrieve robust and unambiguous phases information, achieving high ranging accuracy with a standard deviation of 0.6 μm.

Human Activity Recognition (HAR) based on Channel State Information (CSI) has attracted considerable attention as a privacy-preserving sensing paradigm. However, CSI-based HAR faces several challenges, including environmental noise, long-range temporal dependencies, and the anisotropic structure of CSI tensors. To address these challenges, this paper presents Wi-CSNet, a lightweight CSI-oriented framework that integrates Discrete Wavelet Transform (DWT) preprocessing, asymmetric-stride convolutions, and a Cross-Scanning State Space Duality (CS-SSD) block derived from Mamba2. DWT preprocessing compresses temporal signals while preserving motion-related trends and reducing input dimensionality. Asymmetric-stride convolutions balance feature scales across heterogeneous CSI dimensions, while the lightweight CS-SSD module captures global dependencies with only a 0.63% parameter overhead. Extensive experiments demonstrate that Wi-CSNet achieves accuracies of 97.81% on HHI, 99.92% on UT-HAR, and 100% on NTU-HAR. These results confirm the effectiveness and robustness of Wi-CSNet for fine-grained CSI-based activity recognition in complex environments.

HfO₂-based ferroelectrics have extended a familiar CMOS high-κ dielectric into a materials platform for nonvolatile memory, low-power logic, and in-memory hardware. This review examines thickness engineering in hafnia, especially Hf_0.5Zr_0.5O₂ (HZO), through the state produced during fabrication, the voltage and time available for switching, and the signal retained during operation. Representative quantitative comparisons retain stack, anneal, electrical state, and endpoint definitions; they show that conditioning and drive voltage can change the apparent thickness trend substantially. Capacitor memory, FeFETs, tunnel junctions, negative-capacitance transistors, and computing devices are compared through architecture-specific read margins and failure criteria. The main unresolved challenge is to isolate thickness effects from coupled process and interface changes and establish reproducible, device-specific operating windows.

Intelligent in-vehicle voice systems face critical challenges in robustness, security, and semantic flexibility under complex acoustic conditions. To address these issues holistically, this paper proposes a novel multimodal and secure voice-control framework. The system integrates a hybrid dual-channel wake-up mechanism, combining a commercial English engine (Picovoice) with a custom lightweight ResNet-Lite model for Chinese, to achieve robust cross-lingual activation. For reliable identity authentication, an optimized ECAPA-TDNN model is introduced, enhanced with spectral augmentation, sliding window feature fusion, and an adaptive threshold mechanism. Furthermore, a two-tier fuzzy command matching algorithm operating at character and pinyin levels is designed to significantly improve tolerance to speech variations and ASR errors. Comprehensive experiments on a test set encompassing various Chinese dialects, English accents, and noise environments demonstrate that the proposed system achieves high performance across all components: the wake-up mechanism maintains commercial-grade reliability for English and provides a functional baseline for Chinese; the improved ECAPA-TDNN attains low equal error rates of 2.37% (quiet), 5.59% (background music), and 3.12% (high-speed noise), outperforming standard baselines and showing strong noise robustness against the state of the art; and the fuzzy matcher boosts command recognition accuracy to over 95.67% in quiet environments and above 92.7% under noise, substantially outperforming hard matching by approximately 30%. End-to-end tests confirm an overall interaction success rate of 93.7%. This work offers a practical, integrated solution for developing secure, robust, and flexible voice interfaces in intelligent vehicles.