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Neurons have a polarized structure, with dendrites and axons, and compartment-specific functions can be affected by the dwelling mitochondria. Recent studies have shown that the morphology of mitochondria is closely related to the functions of neurons and neurodegenerative diseases. However, the conventional mitochondria analysis workflow mainly relies on manual annotations and generic image-processing software. Moreover, even though there have been recent developments in automatic mitochondria analysis using deep learning, the application of existing methods in a daily analysis remains challenging because the performance of a pretrained deep learning model can vary depending on the target data, and there are always errors in inference time, requiring human proofreading. To address these issues, we introduce MitoVis, a novel visualization system for end-to-end data processing and an interactive analysis of the morphology of neuronal mitochondria. MitoVis introduces a novel active learning framework based on recent contrastive learning, which allows accurate fine-tuning of the neural network model. MitoVis also provides novel visual guides for interactive proofreading so that users can quickly identify and correct errors in the result with minimal effort. We demonstrate the usefulness and efficacy of the system via case studies conducted by neuroscientists. The results show that MitoVis achieved up to 13.3× faster total analysis time in the case study compared to the conventional manual analysis workflow.

To achieve accurate pose estimation for robots in industrial applications and services, this brief proposes an effective feature-based downward-facing monocular visual odometry technology that uses an affordable sensor system and a systematic optimization approach. To extract more effective features simply and efficiently from images of the ground, even for small mobile systems, the proposed visual odometry system is designed in a lightweight and cost-effective manner; we used an easily available LED, a single-channel time-of-flight (ToF) sensor, and a monocular camera. From the extracted features, the potentially irrelevant ones are removed in advance, using a masking algorithm and measured velocity. This enhances feature efficiency and reduces the computational burden. Finally, the optimal pose estimate is explicitly obtained by solving a nonconvex optimization problem, to make the best use of the features. The experiments’ results show that our proposed method improves feature tracking ability and pose estimation accuracy.

We analyzed the degradation mechanism by deteriorating the exciplex host‐based phosphorescent organic light‐emitting diodes (PhOLEDs) while repeatedly applying voltages in the range of several nA to mA. To identify the most significant degradation factors represented by bleaching, quenching, and charge imbalance, the degradation process was described from various perspectives by the capacitance (C)‐voltage (V) curves (CV), and magneto‐electroluminescence (MEL). Comparing the MEL of the non‐doped and the doped devices, the bleaching of the dopant was confirmed as degradation progressed. Since the highest occupied molecular orbital (HOMO) of the dopant aligns with that of the hole‐transporting layer, the injected holes are easily trapped. As a result, the trap‐induced Shockley‐Read Hall recombination became dominant. Thus, it was identified that the bleaching of the dopant itself acts as the major degradation factor.