As a core component of intelligent transportation systems, vehicle localization technology enables accurate positioning, supporting comprehensive insights into traffic flow, vehicle status, and environmental changes. Current vehicle position localization technologies primarily rely on visual sensors and the Global Positioning System, while their performance can be affected by extreme weather conditions and signal stability. However, vehicle-generated sound, as a stable data source unaffected by environmental conditions and free from signal limitations, is often underutilized by existing studies. In this article, we construct a vehicle localization dataset based on sound signals and further propose a combined filtering strategy that integrates adaptive filtering with spectral subtraction filtering, dynamically adjusting the filter parameters to suppress time-correlated noise within the signal. We also remove broadband noise in the frequency domain while preserving high-frequency signal details, offering a significant advantage over existing methods in terms of signal-to-noise ratio improvement. The proposed dataset and filtering strategy are validated using the EfficientNet-1D Fusion model. Experimental results demonstrate that the proposed combined filtering method excels in recognition accuracy and computational efficiency.

Spoken language from older adults often deviates from written norms due to omission, disordered syntax, constituent errors, and redundancy, limiting the usefulness of automatic transcripts in downstream tasks.We present COAS2W, a Chinese spoken-to-written corpus of 10,004 utterances from older adults, each paired with a written version, finegrained error labels, and four-sentence context.Fine-tuned lightweight open-source models on COAS2W outperform larger closed-source models.Context ablation shows the value of multi-sentence input, and normalization improves performance on downstream translation tasks.COAS2W supports the development of inclusive, context-aware language technologies for older speakers.Our annotation convention, data, and code are publicly available at https://github.com/Springrx/COAS2W.

In recent years, multi-stream gaze estimation methods have become mainstream, which estimate gaze point by eye picture or combine with facial appearance, have achieved considerable accuracy. However, these methods based on a single camera fail to obtain accurate eye spatial position information. To address this issue, we propose a multi-stream gaze estimation model that incorporates spatial position information. We acquire eye spatial position information using a stereo camera and fuse eye image features with eye spatial position information using a ResNet network with a fused attention mechanism. Additionally, we perform calibration of eye image features using the computed eye spatial position information. Our model demonstrates superior performance on our experimental dataset.

This paper studies stabilizer design for position-controlled humanoid robots. Stabilizers are an essential part for position-controlled humanoids, whose primary objective is to adjust the control input sent to the robot to assist the tracking controller to better follow the planned reference trajectory. To achieve this goal, this paper develops a novel force-feedback based whole-body stabilizer that fully exploits the six-dimensional force measurement information and the whole-body dynamics to improve tracking performance. Relying on rigorous analysis of whole-body dynamics of position-controlled humanoids under unknown contact, the developed stabilizer leverages quadratic-programming based technique that allows cooperative consideration of both the center-of-mass tracking and contact force tracking. The effectiveness of the proposed stabilizer is demonstrated on the UBTECH Walker robot in the MuJoCo simulator. Simulation validations show a significant improvement in various scenarios as compared to commonly adopted stabilizers based on the zero-moment-point feedback and the linear inverted pendulum model.