Sensors

Sensors constitute a fundamental element of modern digital healthcare, acting as the primary interface for capturing physiological, environmental, and biomechanical data from patients and clinical settings [...]

Terrain segmentation performance directly affects the reliability of robotic environmental perception and decision making, yet most existing methods are built upon the assumptions of fixed sensing configurations and closed label sets. As a result, they struggle to meet real world outdoor requirements where modalities can be dynamically available and semantic classes continually expand. This paper systematically studies open-vocabulary terrain segmentation under arbitrary imaging modality combinations and proposes a unified foundation model-based framework named AIM-SEEM (SEEM for Arbitrary Imaging Modalities). Built upon Segment Everything Everywhere All at Once (SEEM), AIM-SEEM performs stable input side adaptation and controlled fusion of heterogeneous modalities, maximizing the reuse of pre-trained visual priors to accommodate different modality types and counts. Furthermore, to address the distribution shifts and the resulting vision–text alignment degradation caused by modality extension, a vision-guided text calibration mechanism is introduced to preserve open-vocabulary segmentation capability under multi-modality combination inputs. Experiments on two benchmarks under three evaluation settings, including full-modality, modality-agnostic, and open-vocabulary, show that AIM-SEEM consistently outperforms prior methods.

Integrating wav2vec 2.0 with Connectionist Temporal Classification (CTC) for automatic speech recognition (ASR) often involves a trade-off between capturing global semantic consistency and maintaining local feature discriminability. This study proposes DBA-wav2vec 2.0, an architecture designed to manage these modeling requirements by decoupling temporal modeling into parallel local and global streams at the encoder–decoder interface. Depthwise separable convolutions are utilized to capture local acoustic structures, while a self-attention path is retained for long-range dependencies. A task-aware gating mechanism is introduced to integrate these heterogeneous features. By adjusting fusion weights based on acoustic input characteristics, the gate facilitates the refinement of posterior probability distributions, leading to more distinct alignment points. Experimental results on AISHELL-1 and ST-CMDS datasets show relative Character Error Rate (CER) reductions of 6.4% and 7.4%, respectively, compared to a baseline wav2vec 2.0 model. Further evaluations under varying speaking rates demonstrate a 15.3% relative improvement in fast-speech scenarios, suggesting that structural adaptation at the decoding interface can enhance the robustness of CTC-based systems against temporal variations.