Tailoring thermal radiation using low‐infrared‐emissivity materials has drawn significant attention for diverse applications, such as passive radiative heating and thermal camouflage. However, the ...previously reported low‐infrared‐emissivity materials have the bottleneck of lacking independent control over visible optical properties. Here, a novel visibly transparent and infrared reflective (VTIR) coating by exploiting a nano‐mesh patterning strategy with an oxide–metal–oxide tri‐layer structure is reported. The VTIR coating shows simultaneously high transmittance in the visible region (>80% at 550 nm) and low emissivity in the mid‐infrared region (< 20% in 7–14 µm). The VTIR coating not only achieves a radiative heating effect of 6.6 °C for indoor conditions but also enables a synergetic effect with photothermal materials to keep human body warm at freezing temperatures for outdoor conditions, which is 10–15 °C warmer than normal cotton and Mylar film. Moreover, it demonstrates an excellent thermal camouflage effect at various temperatures (34–250 °C) and good compatibility with visible camouflage on the same object, making it ideal for both daytime and nighttime cloaking. With its unique and versatile spectral features, this novel VTIR design has great potential to make a significant impact on personal heat management and counter‐surveillance applications.
A visibly transparent infrared reflective coating for personal thermal management and thermal camouflage is introduced using a tri‐layer nanophotonic structure. By transmitting visible light and reflecting infrared emission, the structure not only enables 6.6–15 °C passive heating for both indoor and outdoor conditions but also allows for integration of thermal and visible camouflage capabilities for both daytime and nighttime cloaking.
Abstract
The electrosynthesis of formate from CO
2
can mitigate environmental issues while providing an economically valuable product. Although stannic oxide is a good catalytic material for formate ...production, a metallic phase is formed under high reduction overpotentials, reducing its activity. Here, using a fluorine-doped tin oxide catalyst, a high Faradaic efficiency for formate (95% at 100 mA cm
−2
) and a maximum partial current density of 330 mA cm
−2
(at 400 mA cm
−2
) is achieved for the electroreduction of CO
2
. Furthermore, the formate selectivity (≈90%) is nearly constant over 7 days of operation at a current density of 100 mA cm
−2
.
In-situ/operando
spectroscopies reveal that the fluorine dopant plays a critical role in maintaining the high oxidation state of Sn, leading to enhanced durability at high current densities. First-principle calculation also suggests that the fluorine-doped tin oxide surface could provide a thermodynamically stable environment to form HCOO* intermediate than tin oxide surface. These findings suggest a simple and efficient approach for designing active and durable electrocatalysts for the electrosynthesis of formate from CO
2
.
Human behavior (e.g., the response to any incoming information) has very complex forms and is based on the response to consecutive external stimuli entering varied sensory receptors. Sensory ...adaptation is an elementary form of the sensory nervous system known to filter out irrelevant information for efficient information transfer from consecutive stimuli. As bioinspired neuromorphic electronic system is developed, the functionality of organs shall be emulated at a higher level than the cell. Because it is important for electronic devices to possess sensory adaptation in spiking neural networks, the authors demonstrate a dynamic, real‐time, photoadaptation process to optical irradiation when repeated light stimuli are presented to the artificial photoreceptor. The filtered electrical signal generated by the light and the adapting signal produces a specific range of postsynaptic states through the neurotransistor, demonstrating changes in the response according to the environment, as normally perceived by the human brain. This successfully demonstrates plausible biological sensory adaptation. Further, the ability of this circuit design to accommodate changes in the intensity of bright or dark light by adjusting the sensitivity of the artificial photoreceptor is demonstrated. Thus, the proposed artificial photoreceptor circuits have the potential to advance neuromorphic device technology by providing sensory adaptation capabilities.
Sol–gel‐derived neurotransistors integrated with perovskite‐based photodetectors are designed to serve as an artificial optoelectronic device array, which experimentally demonstrates the emulation of the dynamic adaptation process of the biological visual nervous system. The fundamental properties of biological adaptation, such as accuracy and desensitization behaviors, are characterized. These results enable post‐synaptic responses to be manipulated to obtain external‐environment‐dependent encoded images.
•An integrated two-stage diagnostic model for skin lesion boundary segmentation and multiple skin diseases classification is proposed.•Segmentation of skin lesion boundaries was performed using a ...full resolution convolutional network (FrCN) segmentator.•Four well-established classifiers are evaluated: Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201, to distinguish between different skin diseases.•The proposed work was trained and evaluated using three different datasets: two classes of ISIC 2016; three classes of ISIC 2017; and seven classes of ISIC 2018.•Proper rebalancing, segmentation, and augmentation of the datasets are investigated.
Computer automated diagnosis of various skin lesions through medical dermoscopy images remains a challenging task.
In this work, we propose an integrated diagnostic framework that combines a skin lesion boundary segmentation stage and a multiple skin lesions classification stage. Firstly, we segment the skin lesion boundaries from the entire dermoscopy images using deep learning full resolution convolutional network (FrCN). Then, a convolutional neural network classifier (i.e., Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201) is applied on the segmented skin lesions for classification. The former stage is a critical prerequisite step for skin lesion diagnosis since it extracts prominent features of various types of skin lesions. A promising classifier is selected by testing well-established classification convolutional neural networks. The proposed integrated deep learning model has been evaluated using three independent datasets (i.e., International Skin Imaging Collaboration (ISIC) 2016, 2017, and 2018, which contain two, three, and seven types of skin lesions, respectively) with proper balancing, segmentation, and augmentation.
In the integrated diagnostic system, segmented lesions improve the classification performance of Inception-ResNet-v2 by 2.72% and 4.71% in terms of the F1-score for benign and malignant cases of the ISIC 2016 test dataset, respectively. The classifiers of Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201 exhibit their capability with overall weighted prediction accuracies of 77.04%, 79.95%, 81.79%, and 81.27% for two classes of ISIC 2016, 81.29%, 81.57%, 81.34%, and 73.44% for three classes of ISIC 2017, and 88.05%, 89.28%, 87.74%, and 88.70% for seven classes of ISIC 2018, respectively, demonstrating the superior performance of ResNet-50.
The proposed integrated diagnostic networks could be used to support and aid dermatologists for further improvement in skin cancer diagnosis.
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•A novel computer-aided diagnosis system based on deep learning techniques is proposed.•The proposed YOLO-based CAD system simultaneously handles both detection and classification of breast cancer ...masses.•YOLO-based CAD has a capability to handle most challenging cases of breast abnormalities.
Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework.
The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant.
Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%.
Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions.
Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of ...stiffness and spiking neural network (SNN)‐based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN‐based learning of ultrasound elastography images ed by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness‐encoding artificial tactile neuron and learning of spiking‐represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot‐assisted surgery with low power consumption, low latency, and yet high accuracy.
An artificial tactile neuron that encodes the stiffness of pressed materials into spike frequency evolution patterns is developed using an ovonic threshold switch and a piezoresistive sensor. The spiking‐represented stiffness of soft materials with varying stiffness in a combination of spiking neural network‐based learning enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%.
Perovskite has been actively studied for optoelectronic applications, such as photodetectors and light‐emitting diodes (LEDs), because of its excellent optoelectronic properties. However, ionic bonds ...of the perovskite structure are vulnerable to chemicals, which makes perovskite incompatible with photolithography processes that use polar solvents. Such incompatibility with photolithography hinders perovskite patterning and device integration. Here, an all‐solution based cesium lead halide perovskite (CsxPbyBrz) patterning method is introduced in which PbBr2 is patterned and then synthesized into CsxPbyBrz. Each step of the top‐down patterning process (e.g., developing, etching, and rinsing) is designed to be compatible with existing photolithography equipment. Structural, chemical, and optical analyses show that the PbBr2 pattern of (10 µm)2 squares is successfully transformed into CsPbBr3 and Cs4PbBr6 with excellent absorption and emission properties. High‐resolution photoconductor arrays and luminescent pattern arrays are fabricated with CsPbBr3 and Cs4PbBr6 on various substrates, including flexible plastic films, to demonstrate their potential applications in image sensors or displays. The research provides a fundamental understanding of the properties and growth of perovskite and promotes technological advancement by preventing degradation during the photolithography process, enabling the integration of perovskite arrays into image sensors and displays.
An all‐solution based cesium lead halide perovskite patterning process with adjustable phases and optical properties is reported. With the patterning process, the high‐resolution photodetector array and luminescent patterns are demonstrated. The perovskite patterning process enables the integration of perovskites into image sensors and displays.
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•A Fully integrated Computer-Aided Diagnosis (CAD) system based on deep learning is presented.•Deep model based YOLO is adopted to accurately detect the masses from the entire ...mammograms.•A newly deep model based on FrCN is utilized to segment the mass lesions pixel-to-pixel.•A deep CNN model is utilized to recognize the mass either as benign or malignant.
A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital X-ray mammograms involving detection, segmentation, and classification of breast masses via deep learning methodologies.
In this work, to detect breast mass from entire mammograms, You-Only-Look-Once (YOLO), a regional deep learning approach, is used. To segment the mass, full resolution convolutional network (FrCN), a new deep network model, is proposed and utilized. Finally, a deep convolutional neural network (CNN) is used to recognize the mass and classify it as either benign or malignant. To evaluate the proposed integrated CAD system in terms of the accuracies of detection, segmentation, and classification, the publicly available and annotated INbreast database was utilized. The evaluation results of the proposed CAD system via four-fold cross-validation tests show that a mass detection accuracy of 98.96%, Matthews correlation coefficient (MCC) of 97.62%, and F1-score of 99.24% are achieved with the INbreast dataset. Moreover, the mass segmentation results via FrCN produced an overall accuracy of 92.97%, MCC of 85.93%, and Dice (F1-score) of 92.69% and Jaccard similarity coefficient metrics of 86.37%, respectively. The detected and segmented masses were classified via CNN and achieved an overall accuracy of 95.64%, AUC of 94.78%, MCC of 89.91%, and F1-score of 96.84%, respectively. Our results demonstrate that the proposed CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies. Our proposed CAD system could be used to assist radiologists in all stages of detection, segmentation, and classification of breast masses.
•An integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions from the entire digital X-ray mammograms.•Breast ...lesions are accurately detected via YOLO detector, with F1-scores of 99.28% for DDSM and 98.02% for INbreast.•The capability of the YOLO detector boosted the modified InceptionResNet-V2 classifier achieving promising diagnosis performance with overall accuracies of 97.50% for DDSM and 95.32% for INbreast.•The proposed deep learning CAD system is able to detect and classify breast lesions in a single mammogram in less than 0.025 s.
Deep learning detection and classification from medical imagery are key components for computer-aided diagnosis (CAD) systems to efficiently support physicians leading to an accurate diagnosis of breast lesions.
In this study, an integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions. First, a deep learning YOLO detector is adopted and evaluated for breast lesion detection from entire mammograms. Then, three deep learning classifiers, namely regular feedforward CNN, ResNet-50, and InceptionResNet-V2, are modified and evaluated for breast lesion classification. The proposed deep learning system is evaluated over 5-fold cross-validation tests using two different and widely used databases of digital X-ray mammograms: DDSM and INbreast.
The evaluation results of breast lesion detection show the capability of the YOLO detector to achieve overall detection accuracies of 99.17% and 97.27% and F1-scores of 99.28% and 98.02% for DDSM and INbreast datasets, respectively. Meanwhile, the YOLO detector could predict 71 frames per second (FPS) at the testing time for both DDSM and INbreast datasets. Using detected breast lesions, the classification models of CNN, ResNet-50, and InceptionResNet-V2 achieve promising average overall accuracies of 94.50%, 95.83%, and 97.50%, respectively, for the DDSM dataset and 88.74%, 92.55%, and 95.32%, respectively, for the INbreast dataset.
The capability of the YOLO detector boosted the classification models to achieve a promising breast lesion diagnostic performance. Such prediction results should help to develop a feasible CAD system for practical breast cancer diagnosis.
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The magnetic skyrmion is a topologically protected spin texture that has attracted much attention as a promising information carrier because of its distinct features of suitability for high‐density ...storage, low power consumption, and stability. One of the skyrmion devices proposed so far is the skyrmion racetrack memory, which is the skyrmion version of the domain‐wall racetrack memory. For application in devices, skyrmion racetrack memory requires electrical generation, deletion, and displacement of isolated skyrmions. Despite the progress in experimental demonstrations of skyrmion generation, deletion, and displacement, these three operations have yet to be realized in one device. Here, a route for generating and deleting isolated skyrmion‐bubbles through vertical current injection with an explanation of its microscopic origin is presented. By combining the proposed skyrmion‐bubble generation/deletion method with the spin–orbit‐torque‐driven skyrmion shift, a proof‐of‐concept experimental demonstration of the skyrmion racetrack memory operation in a three‐terminal device structure is provided.
A new route to generate and delete magnetic skyrmion‐bubbles electrically is presented using a vertical current path. The vertical current path concept is easy to integrate with a widely used spin–orbit torque device. Using the vertical current path with a spin–orbit‐torque device, core operations for a skyrmion racetrack memory device in a three‐terminal device are demonstrated.
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