Ultrahigh-resolution full-color perovskite nanocrystal patterning for ultrathin skin-attachable displays.

High-definition red/green/blue (RGB) pixels and deformable form factors are essential for the next-generation advanced displays. Here, we present ultrahigh-resolution full-color perovskite nanocrystal (PeNC) patterning for ultrathin wearable displays. Double-layer transfer printing of the PeNC and organic charge transport layers is developed, which prevents internal cracking of the PeNC film during the transfer printing process. This results in RGB pixelated PeNC patterns of 2550 pixels per inch (PPI) and monochromic patterns of 33,000 line pairs per inch with 100% transfer yield. The perovskite light-emitting diodes (PeLEDs) with transfer-printed active layers exhibit outstanding electroluminescence characteristics with remarkable external quantum efficiencies (15.3, 14.8, and 2.5% for red, green, and blue, respectively), which are high compared to the printed PeLEDs reported to date. Furthermore, double-layer transfer printing enables the fabrication of ultrathin multicolor PeLEDs that can operate on curvilinear surfaces, including human skin, under various mechanical deformations. These results highlight that PeLEDs are promising for high-definition full-color wearable displays.

Deep Learning in Physiological Signal Data: A Survey.

Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.