The synthetic jet piezoelectric air pump is a potential miniature device for electronic cooling. In order to improve the performance of the device, a small-sized synthetic jet piezoelectric air pump is proposed in this work, which is mainly characterized by petal-shaped inlet channels. First, the structure and working principle of the piezoelectric vibrator and the proposed pump are analyzed. Then, three synthetic jet piezoelectric air pumps with different inlet channels are compared. These inlets are the direct channels, the diffuser/nozzle channels, and the petal-shaped channels, respectively. Furthermore, the performance of the synthetic jet piezoelectric air pump with the petal-shaped inlet channels is optimized by orthogonal tests. Finally, the simulation was used to investigate the heat dissipation capability of the synthetic jet piezoelectric pump. The experimental results show that among the three inlet channels, the petal-shaped channel can greatly improve the performance of the pump. The unoptimized pump with petal-shaped channels has a maximum flow rate of 1.8929 L/min at 100 V, 3.9 kHz. Additionally, the optimized pump with petal-shaped channels reaches a maximum flow rate of 3.0088 L/min at 100 V, 3.7 kHz, which is 58.95% higher than the unoptimized one. The proposed synthetic jet piezoelectric air pump greatly improves the output performance and has the advantages of simple structure, low cost, and easy integration. The convective heat transfer coefficient of the synthetic jet piezoelectric pump is 28.8 $\mathrm{W}/\left({\mathrm{m}}^2·°\mathrm{C}\right)$, which can prove that the device has a better heat dissipation capability.

In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver’s field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification.

Acoustic weighing is a promising method for non-contact mass measurement of tiny objects as it avoids contamination and contact losses. However, due to the highly nonlinear nature of the acoustic field, some parameters of the mechanism model of acoustic weighing cannot be accurately simulated, thereby reducing the accuracy of acoustic weighing. To improve the accuracy of acoustic weighing, we propose an acoustic weighing method based on oscillating signals and feature enhancement network. Firstly, to drive the object oscillation and collect oscillation data, an acoustic levitation-based data acquisition system is constructed. Then, to break the limitations of the mechanism model, a feature enhancement network named CNN-BiLSTM-SE is proposed, which directly establishes the correlation between oscillating signals and actual mass. Finally, these data are used to train and test the proposed network model, validating the effectiveness of the model. Experimental results show that the method achieves high accuracy in measuring object mass, following the actual measurements with remarkable consistency. In addition, our approach is also suitable for acoustic weighing of small and sensitive objects, opening up new perspective for the study and application of nonlinear acoustic systems.

With the rapid development of intelligent connected vehicles and autonomous driving, real-time and accurate road condition perception has become increasingly critical. Aiming at the limitations of traditional direct and indirect detection methods, this paper proposes an ultrasonic-based real-time detection system for road roughness. Most urban roads today feature asphalt pavements; therefore, this system focuses its research on asphalt pavements. Under the same pavement type (asphalt roads), there is a strong correlation between pavement roughness and the friction coefficient. By measuring the roughness of different pavements, the friction coefficient is estimated using the fuzzy processing method. Then the system through measuring ultrasonic echo amplitude and sensor–road distance, combined with software digital filtering, dual-parameter compensation (distance and temperature–humidity), probabilistic statistical analysis, and fuzzy inference, the mapping relationship among echo signals, road roughness and friction coefficient is established. The system mainly includes an ultrasonic transceiver module, a hardware signal conditioning module, and an MCU-based data processing, display and transmission module. Both simulated experiments and real asphalt pavement tests are carried out for verification. The results show that the system can effectively suppress noise, compensate distance attenuation and environmental interference, and achieve accurate real-time detection of road roughness, with a relative error less than 10% compared with the reference value. The proposed system can provide reliable data support for vehicle active safety systems and autonomous driving applications.

Small unmanned surface vehicles operating in confined, vegetation-rich, or ecologically sensitive waters require compact propulsion systems that minimize entanglement and unintended contact. Here, we present a 178 mm biomimetic surface robot propelled by bilateral flexible fins actuated by piezoelectric bimorphs. A differential high-voltage circuit drives each bimorph, while compliant fin structures transmit local bending into distributed deformation. We characterized static and dynamic fin kinematics, blocked thrust, and untethered swimming performance. Static fin-tip deflection increased nearly linearly with voltage, with similar spatial deformation profiles across the tested voltages. In dry-bench tests, fin-tip velocity reached a local maximum near 142 Hz. Under water-loaded conditions, both mean blocked thrust and path-averaged swimming speed were highest at 134 Hz, consistent with a shift in the preferred operating frequency following fluid loading and system integration. At 100 V under this condition, the robot produced a mean blocked thrust of approximately 15.3 mN and achieved a path-averaged speed of 43.68 ± 3.46 mm/s (mean ± SD, n = 5). At a differential peak voltage of 100 V and a drive frequency of 134 Hz, full-system battery-side input power was approximately 2.0 W; the blocked-thrust-to-power ratio was approximately 7.8 mN W⁻¹, and the system-level electrical cost of transport (COT) was approximately 17.2. These results show that local piezoelectric bending can be transmitted through compliant fins to generate measurable thrust and repeatable untethered motion, providing a blade-free propulsion approach for compact surface robots.