Scribble supervision reduces annotation costs in multi-organ segmentation. However, its sparsity results in insufficient supervision for most regions and inadequate feature learning in hard areas (e.g., organ boundaries). These hard areas cause model confirmation bias and high epistemic uncertainty, which existing methods fail to address. To overcome these core challenges, we propose an epistemic-driven hardness-adaptive focusing framework. This framework establishes a self-improving loop: quantified epistemic uncertainty guides hard sample generation, while hard sample learning and feature alignment jointly reduce epistemic uncertainty. Specifically, we first propose a phase-adaptive hardness-aware loss function to quantify epistemic uncertainty and generate dynamic hardness maps during training. Based on these maps, we employ a distribution-divergence-aware copy-paste operation to create hard samples, which are progressively incorporated into learning to reduce epistemic uncertainty. Furthermore, we introduce feature distribution alignment to mitigate bias and epistemic uncertainty by aligning organ-specific hard regions with global features. Extensive experiments on multi-organ CT and ultrasound datasets demonstrate the competitiveness and effectiveness of our method. The framework's generalizability and robustness are further validated under cross-dataset and noise-corrupted scenarios. This work offers a practical solution for clinical applications where annotation efficiency is critical.

Ultra-wideband (UWB) is a prominent technology for wireless localization, mainly attributed to its superior ranging performance enabled by the large signal bandwidth. However, its bearing capability remains underdeveloped due to practical issues such as phase deviations, antenna coupling, and phase ambiguity. This paper presents a high-accuracy stereo UWB bearing scheme through network ambiguity resolution and online phase calibration. Specifically, we propose a sparse variational Gaussian process regression-based calibration technique to eliminate phase deviations and a range-assisted network solution to resolve phase ambiguities. Building on these techniques, we present an online angle estimation scheme that performs real-time phase calibration, ambiguity resolution, and calibration model updates, significantly reducing calibration complexity in large-scale networks. Real-world experiments on 4-element stereo UWB platforms achieve root mean square errors of 2.3$^{\circ }$and 1.1$^{\circ }$for azimuth and elevation angles, respectively. The success rate for ambiguity resolution exceeds 96%, a 20% improvement over existing methods.

Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design methods specifically addressing the challenges posed by the poor contrast, blurred edge details and noise of echocardiography. These characteristics pose challenges to the generation of high-quality pseudo-labels in semi-supervised segmentation based on Mean Teacher. Inspired by human reflection on erroneous practices, we devise an error reflection strategy for echocardiography semi-supervised segmentation architecture. The process triggers the model to reflect on inaccuracies in unlabeled image segmentation, thereby enhancing the robustness of pseudo-label generation. Specifically, the strategy is divided into two steps. The first step is called reconstruction reflection. The network is tasked with reconstructing authentic proxy images from the semantic masks of unlabeled images and their auxiliary sketches, while maximizing the structural similarity between the original inputs and the proxies. The second step is called guidance correction. Reconstruction error maps decouple unreliable segmentation regions. Then, reliable data that are more likely to occur near high-density areas are leveraged to guide the optimization of unreliable data potentially located around decision boundaries. Additionally, we introduce an effective data augmentation strategy, termed as multi-scale mixing up strategy, to minimize the empirical distribution gap between labeled and unlabeled images and perceive diverse scales of cardiac anatomical structures. Extensive experiments on a public echocardiography dataset CAMUS, and a private clinical echocardiography dataset demonstrate the competitiveness of the proposed method.

Ultra-wide band (UWB) systems are gaining popularity for multi-robot localization benefiting from their high-accuracy ranging capabilities. However, current UWB systems fall short in determining orientations and realizing pair-wise localization for neglecting bearing information. Given the importance of bearing capabilities, especially when vision-based methods fail, this paper proposes a high-accuracy 2-D bearing estimation method using stereo UWB arrays. We propose a novel phase error calibration method that effectively mitigates various phase imperfections. This array is designed with antenna spacing larger than half the wavelength to diminish antenna coupling and enhance bearing accuracy. As regards the phase ambiguity issue arising from large antenna spacing, a distributed range-assisted phase ambiguity determination method is developed. Our bearing estimation method exhibits low complexity and is well-suited for the deployment on mobile robots with limited computational resources. The performance of the proposed method is validated on the practical platforms under dynamic scenarios, yielding root mean squared errors (RMSEs) less than 4° and 3° for azimuth and elevation angle estimation, respectively.

Atrial septal defect (ASD) is one of the most common congenital heart diseases. The diagnosis of ASD via transthoracic echocardiography is subjective and time-consuming. The objective of this study was to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic static images using end-to-end convolutional neural networks. The proposed depthwise separable convolution model identifies ASDs with static color Doppler images in a standard view. Among the standard views, we selected two echocardiographic views, i.e., the subcostal sagittal view of the atrium septum and the low parasternal four-chamber view. The developed ASD detection system was validated using a training set consisting of 396 echocardiographic images corresponding to 198 cases. Additionally, an independent test dataset of 112 images corresponding to 56 cases was used, including 101 cases with ASDs and 153 cases with normal hearts. The average area under the receiver operating characteristic curve, recall, precision, specificity, F1-score, and accuracy of the proposed ASD detection model were 91.99, 80.00, 82.22, 87.50, 79.57, and 83.04, respectively. The proposed model can accurately and automatically identify ASD, providing a strong foundation for the intelligent diagnosis of congenital heart diseases.