The charge transfer technique, which is capable of operating in charge accumulation-mode, is suitable for use in pH-imaging to detect small changes in potential. An advanced charge-transfer-type hydrogen ion image sensor consisting of 128 × 128 pixels with a 23-µm pixel pitch is fabricated. A new scanning system and high-performance drive buffer circuits are adopted to achieve high frame rates. For miniaturization of the sensor pixels, we developed an advanced new fabrication process. The pH sensitivity is 32.8 mV/pH when using standard pH solutions. Videos of the movement of hydrogen ions are clearly obtained with the 128 × 128 pixels display, and photo images are taken simultaneously with the videos of the movement of hydrogen ions. A frame rate of 58 frames per second is realized with this image sensor.
Charge-transfer-type pH sensors can be used to improve pH sensitivity with enhanced signal-to-noise ratio by applying the charge-accumulation technique. Theoretically, the pH sensitivity improves directly with the accumulated count. However, in a conventional sensor structure, a quasi-signal resulting from low charge transfer efficiency limits the accumulated count. In this paper, an effective solution to the problem of the quasi-signal and the novel sensor structure are investigated.
Spatial distribution of seismic intensity plays an important role in emergency response during and immediately after an earthquake. In this study, we propose a deep learning model to predict the seismic intensity based on only the observation records at the seismic stations in a surrounding area. The deep learning model is trained using the observation records at both the input and target stations, and no geological information is used. Once the model is developed, for example, using the data from a temporal seismic array, the model can spatially interpolate the seismic intensity from the sparse layout of the seismic stations. The model consists of long short-term memory cells, which are well-established neural network components for time series analysis. We used observed seismograms in 1996 through 2019 at the Kyoshin Network (K-NET) and Kiban–Kyoshin Network (KiK-net) stations located in the northeastern part of Japan. In our deep learning model, approximately 85% of validation data is successfully classified into seismic intensity scales, which is better than adopting either the maximum or weighted average of the input data. We also apply the deep learning model to earthquake early warning (EEW). The model can predict the seismic intensity accurately and provides a long warning time. We concluded that our approach is a possible future solution for increasing the accuracy of EEW.
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