Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.</jats:p

The dual-frequency zero-backward scattering realized in a hybrid metallo-dielectric nanoantenna

In this paper, we propose a hybrid metallo-dielectric core-shell nanorod for the Kerker-type effect at two different frequencies. The effect arises from the interference of the scattering waves of the nanorod, which are generated by the magnetic dipole moment (MD) of the high-index hollow particle and the electric dipole moment (ED) induced in both metallic and dielectric particles. Interestingly, we find that such kind of unidirectional radiation properties, (i.e., zero back scattering occurring at dual frequencies) can be sustained with a single nanorod, which usually being equivalent to a local electric dipole source. The effect of substrate is also considered to investigate the typical experimental realization for the dual-frequency unidirectionalities of the nanoantenna. Furthermore, the unidirectionality can be further improved by the design of one-dimensional array of the hybrid nanoantenna. Our results could provide an additional degree of freedom for light scattering manipulation, and widen the versatile applications in nanoantennas, optical sensor, light emitters, as well as photovoltaic devices

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning

Purpose: Hip fractures are a common cause of morbidity and mortality. Automatic identification and classification of hip fractures using deep learning may improve outcomes by reducing diagnostic errors and decreasing time to operation. Methods: Hip and pelvic radiographs from 1118 studies were reviewed and 3034 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous ORIF, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (DenseNet) was trained on a subset of the bounding box images, and its performance evaluated on a held out test set and by comparison on a 100-image subset to two groups of human observers: fellowship-trained radiologists and orthopaedists, and senior residents in emergency medicine, radiology, and orthopaedics. Results: The binary accuracy for fracture of our model was 93.8% (95% CI, 91.3-95.8%), with sensitivity of 92.7% (95% CI, 88.7-95.6%), and specificity 95.0% (95% CI, 91.5-97.3%). Multiclass classification accuracy was 90.4% (95% CI, 87.4-92.9%). When compared to human observers, our model achieved at least expert-level classification under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance. Conclusions: Our deep learning model identified and classified hip fractures with at least expert-level accuracy, and when used as an aid improved human performance, with aided resident performance approximating that of unaided fellowship-trained attendings.Comment: Presented at Orthopaedic Research Society, Austin, TX, Feb 2, 2019, currently in submission for publicatio

Optical Realization of Wave-Based Analog Computing with Metamaterials

Recently, the study of analog optical computing raised renewed interest due to its natural advantages of parallel, high speed and low energy consumption over conventional digital counterpart, particularly in applications of big data and high-throughput image processing. The emergence of metamaterials or metasurfaces in the last decades offered unprecedented opportunities to arbitrarily manipulate the light waves within subwavelength scale. Metamaterials and metasurfaces with freely controlled optical properties have accelerated the progress of wave-based analog computing and are emerging as a practical, easy-integration platform for optical analog computing. In this review, the recent progress of metamaterial-based spatial analog optical computing is briefly reviewed. We first survey the implementation of classical mathematical operations followed by two fundamental approaches (metasurface approach and Green&rsquo;s function approach). Then, we discuss recent developments based on different physical mechanisms and the classical optical simulating of quantum algorithms are investigated, which may lead to a new way for high-efficiency signal processing by exploiting quantum behaviors. The challenges and future opportunities in the booming research field are discussed

Chain substitution caused sub-fibril level differences in electromechanical structure and property of wild-type and oim/oim collagen fibers

10.1063/5.0016535Journal of Applied Physics128235111-23511

Poly(dimethylsilylene)diacetylenes Guided ZIF-based Heterostructures for Full Ku Band Electromagnetic Wave Absorption

Zeolitic imidazolate frameworks (ZIFs), a group of metal–organic frameworks (MOFs), hold promise as building blocks in electromagnetic (EM) wave absorption/shielding materials and devices. In this contribution, we propose a facile strategy to synthesis three dimensional ZIF-67-based hierarchical heterostructures through coordinated reacting a preceramic component, poly(dimethylsilylene)diacetylenes (PDSDA) with ZIF-67, following by carbonizing the PDSDA wrapped ZIF at high temperature. The introduction of PDSDA leads to a controllable generation of surface network containing branched carbon nano-tubes (CNTs) and regional distributed graphitic carbons, in addition to the nanostructures with well-defined size and mesoporous surface made by cobalt nanoparticles. The surface structures can be tailored through variations in pyrolysis temperatures, therefore providing a simple and robust route to form highly structural surface on ZIF-based nanostructures. The heterostructure of nanocomplex allows the existence of dielectric loss and magnetic loss, therefore, yielding a significant improvement on EM wave absorption with a minimum reflection coefficition (RCmin) of -50.9 dB at 17.0 GHz at a thickness of 1.9 mm and an effective absorption bandwidth (EAB) covering the Ku band (12.0 GHz to 18.0 GHz)